Industrial Waste
Management
-------
This Guide provides state-of-the-art tools and
practices to enable you to tailor hands-on
solutions to the industrial waste management
challenges you face.
WHAT'S AVAILABLE
• Quick reference to multimedia methods for handling and disposing of wastes
from all types of industries
• Answers to your technical questions about siting, design, monitoring, operation.
and closure of waste facilities
• Interactive, educational tools, including air and ground water risk assessment
models, fact sheets, and a facility siting tool.
• Best management practices, from risk assessment and public participation to
waste reduction, pollution prevention, and recycling
-------
;NOWLEDGEMENTS
The rdowing members of the Industrial Waste Focus Group and the Industrial Waste Steering Commiw are grateUy
acknowledged far al of their time and assistance in the development of this guidance document
Current Industrial Waste Focus
Group Members
Paul Bar*, The Dow Chemical
Company
Walter Carey. Nestle USA Inc and
New Miltord Farms
Rama Chaturvedi Bethlehem Steel
Corporation
H.C. Clark. Rice University
Barbara Dodds, League of Women
voters
Chuck Feerick. Exxon Mobil
Corporation
Stacey Ford. Exxon Mobil
Corporation
Robert Giraud OuPont Company
John Harney Citizens Round
Tabte/PURE
Kyle Isakower. American Petroleum
Institute
Richard Jarman, National Food
Processors Association
James Meiers, Cinergy Power
Generation Services
Scott Murto. General Motors and
American Foundry Society
James Roewer, Edison Electric
Institute
Edward Repa. Environmental
Industry Association
Tim Savior, International Paper
Amy Schaffer. Weyerhaeuser
Ed Skemofc, WMX Technologies. Inc
Michael Wach Western
Environmental Law Center
David Wens, University of South
Wabnms Medical Center
Pat Gwn Cherokee Nation of
Oklahoma
Past industrial Waste Focus
Group Members
Dora Cetofius. Sierra Club
Brian Forrestal. Laidlaw Waste
Systems
Jonathan Greenberg. Browning-
Ferris Industries
Michael Gregory, Arizona Toxics
Information and Sierra Club
Andrew Mites The Dexter
Corporation
Gary Robbins, Exxon Company
Kevin Sail. National Paint & Coatings
Association
Bruce SteJne. American Iron & Steel
Lisa Williams, Aluminum Association
Cuircnt Industrial Waste Steering
Committee Members
Keiiy Catalan Aaaocauon oi Slate
and Territorial Solid Waste
Management Officials
Marc Crooks, Washington State
Department ot Ecology
Cyndi Darling. Maine Department of
Environmental Protection
Jon DilDard Montana Department of
Environmental Qualty
Anne Dobbs. Texas Natural
Resources Conservation
Commission
Richard Hammond New York State
Department of Environmental
Conservation
Elizabeth Haven California State
Waste Resources Control Board
Jim Hul Missouri Department of
Natural Resources
Jim Knudson, Washington State
Department of Ecology
Chris McGuire, Florida Department
of Environmental Protection
Gene Mitchell Wisconsin
Department of Natural Resources
William Pounds, Pennsylvania
Department of Environmental
Protection
Bijan Sharafkhani Louisiana
Department of Environmental
Qualty
James Warner, Minnesota Pollution
Control Agency
ittustrial Waste Steering
Pamela um*. nianie
Environmental Protection
NormGumenik Arizona Department
of Environmental Qualty
Steve Jenkins, Alabama Department
of Environmental Management
Jim North Arizona Department of
Environmental Quality
-------
Industrial waste is generated by the production
of commercial goods, products, or services.
Examples include wastes from the production
of chemicals, iron and steel, and food goods.
-------
United States Office of Environmental EPA/600/R-96/084
Environmental Protection Information July, 2000
Agency Washington, DC 20460
<&EPA Guidance for
Data Quality Assessment
Practical Methods for
Data Analysis
EPA QA/G-9
QAOO UPDATE
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FOREWORD
This document is the 2000 (QAOO) version of the Guidance for Data Quality Assessment
which provides general guidance to organizations on assessing data quality criteria and
performance specifications for decision making. The Environmental Protection Agency (EPA) has
developed a process for performing Data Quality Assessment (DQA) Process for project
managers and planners to determine whether the type, quantity, and quality of data needed to
support Agency decisions has been achieved. This guidance is the culmination of experiences in
the design and statistical analyses of environmental data in different Program Offices at the EPA.
Many elements of prior guidance, statistics, and scientific planning have been incorporated into
this document.
This document is distinctly different from other guidance documents; it is not intended to
be read in a linear or continuous fashion. The intent of the document is for it to be used as a
"tool-box" of useful techniques in assessing the quality of data. The overall structure of the
document will enable the analyst to investigate many different problems using a systematic
methodology.
This document is one of a series of quality management guidance documents that the EPA
Quality Staff has prepared to assist users in implementing the Agency-wide Quality System. Other
related documents include:
EPA QA/G-4 Guidance for the Data Quality Objectives Process
EPA QA/G-4D DEFT Software for the Data Quality Objectives Process
EPA QA/G-4HW Guidance for the Data Quality Objectives Process for Hazardous
Waste Site Investigations
EPA QA/G-9D Data Quality Evaluation Statistical Toolbox (DataQUEST)
This document is intended to be a "living document" that will be updated periodically to
incorporate new topics and revisions or refinements to existing procedures. Comments received
on this 2000 version will be considered for inclusion in subsequent versions. Please send your
written comments on Guidance for Data Quality Assessment to:
Quality Staff (2811R)
Office of Environmental Information
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Phone: (202) 564-6830
Fax: (202)565-2441
E-mail: quality@epa.gov
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TABLE OF CONTENTS
Page
INTRODUCTION 0-1
0.1 PURPOSE AND OVERVIEW 0-1
0.2 DQA AND THE DATA LIFE CYCLE 0-2
0.3 THE 5 STEPS OF DQA 0-2
0.4 INTENDED AUDIENCE 0-4
0.5 ORGANIZATION 0-4
0.6 SUPPLEMENTAL SOURCES 0-4
STEP 1: REVIEW DQOs AND THE SAMPLING DESIGN 1-1
1.1 OVERVIEW AND ACTIVITIES 1-3
1.1.1 Review Study Objectives 1-4
1.1.2 Translate Objectives into Statistical Hypotheses 1-4
1.1.3 Develop Limits on Decision Errors 1-5
1.1.4 Review Sampling Design 1-7
1.2 DEVELOPING THE STATEMENT OF HYPOTHESES 1-9
1.3 DESIGNS FOR SAMPLING ENVIRONMENTAL MEDIA 1-11
1.3.1 Authoritative Sampling 1-11
1.3.2 Probability Sampling 1-13
1.3.2.1 Simple Random Sampling 1-13
1.3.2.2 Sequential Random Sampling 1-13
1.3.2.3 Systematic Samples 1-14
1.3.2.4 Stratified Samples 1-14
1.3.2.5 Compositing Physical Samples 1-15
1.3.2.6 Other Sampling Designs 1-15
STEP 2: CONDUCT A PRELIMINARY DATA REVIEW 2-1
2.1 OVERVIEW AND ACTIVITIES 2-3
2.1.1 Review Quality Assurance Reports 2-3
2.1.2 Calculate Basic Statistical Quantities 2-4
2.1.3 Graph the Data 2-4
2.2 STATISTICAL QUANTITIES 2-5
2.2.1 Measures of Relative Standing 2-5
2.2.2 Measures of Central Tendency 2-6
2.2.3 Measures of Dispersion 2-8
2.2.4 Measures of Association 2-8
2.2.4.1 Pearson's Correlation Coefficient 2-8
2.2.4.2 Spearman's Rank Correlation Coefficient 2-11
2.2.4.3 Serial Correlation Coefficient 2-11
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2.3 GRAPHICAL REPRESENTATIONS 2-13
2.3.1 Histogram/Frequency Plots 2-13
2.3.2 Stem-and-Leaf Plot 2-15
2.3.3 Box and Whisker Plot 2-17
2.3.4 Ranked Data Plot 2-17
2.3.5 Quantile Plot 2-21
2.3.6 Normal Probability Plot (Quantile-Quantile Plot) 2-22
2.3.7 Plots for Two or More Variables 2-26
2.3.7.1 Plots for Individual Data Points 2-26
2.3.7.2 Scatter Plot 2-27
2.3.7.3 Extensions of the Scatter Plot 2-27
2.3.7.4 Empirical Quantile-Quantile Plot 2-30
2.3.8 Plots for Temporal Data 2-30
2.3.8.1 Time Plot 2-32
2.3.8.2 Plot of the Autocorrelation Function (Correlogram) 2-33
2.3.8.3 Multiple Observations Per Time Period 2-35
2.3.9 Plots for Spatial Data 2-36
2.3.9.1 Posting Plots 2-37
2.3.9.2 Symbol Plots 2-37
2.3.9.3 Other Spatial Graphical Representations 2-39
2.4 Probability Distributions 2-39
2.4.1 The Normal Distribution 2-39
2.4.2 The t-Distribution 2-40
2.4.3 The Lognormal Distribution 2-40
2.4.4 Central Limit Theorem 2-41
STEP 3: SELECT THE STATISTICAL TEST 3-1
3.1 OVERVIEW AND ACTIVITIES 3-3
3.1.1 Select Statistical Hypothesis Test 3-3
3.1.2 Identify Assumptions Underlying the Statistical Test 3-3
3.2 TESTS OF HYPOTHESES ABOUT A SINGLE POPULATION 3-4
3.2.1 Tests for a Mean 3-4
3.2.1.1 The One-Sample t-Test 3-5
3.2.1.2 The Wilcoxon Signed Rank (One-Sample) Test 3-11
3.2.1.3 The Chen Test 3-15
3.2.2 Tests for a Proportion or Percentile 3-16
3.2.2.1 The One-Sample Proportion Test 3-18
3.2.3 Tests for a Median 3-18
3.2.4 Confidence Intervals 3-20
3.3 TESTS FOR COMPARING TWO POPULATIONS 3-21
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3.3.1 Comparing Two Means 3-22
3.3.1.1 Student's Two-Sample t-Test (Equal Variances) 3-23
3.3.1.2 Satterthwaite's Two-Sample t-Test (Unequal Variances) . . 3-23
3.3.2 Comparing Two Proportions or Percentiles 3-27
3.3.2.1 Two-Sample Test for Proportions 3-28
3.3.3 Nonparametric Comparisons of Two Population 3-31
3.3.3.1 The Wilcoxon Rank Sum Test 3-31
3.3.3.2 The Quantile Test 3-35
3.3.4 Comparing Two Medians 3-36
3.4 Tests for Comparing Several Populations 3-37
3.4.1 Tests for Comparing Several Means 3-37
3.4.1.1 Dunnett's Test 3-38
STEP 4: VERIFY THE ASSUMPTIONS OF THE STATISTICAL TEST 4-1
4.1 OVERVIEW AND ACTIVITIES 4-3
4.1.1 Determine Approach for Verifying Assumptions 4-3
4.1.2 Perform Tests of Assumptions 4-4
4.1.3 Determine Corrective Actions 4-5
4.2 TESTS FOR DISTRIBUTIONAL ASSUMPTIONS 4-5
4.2.1 Graphical Methods 4-7
4.2.2 Shapiro-Wilk Test for Normality (the W test) 4-8
4.2.3 Extensions of the Shapiro-Wilk Test (Filliben's Statistic) 4-8
4.2.4 Coefficient of Variation 4-8
4.2.5 Coefficient of Skewness/Coefficient of Kurtosis Tests 4-9
4.2.6 Range Tests 4-10
4.2.7 Goodness-of-Fit Tests 4-12
4.2.8 Recommendations 4-13
4.3 TESTS FOR TRENDS 4-13
4.3.1 Introduction 4-13
4.3.2 Regression-Based Methods for Estimating and Testing for Trends .4-14
4.3.2.1 Estimating a Trend Using the Slope of the Regression Line 4-14
4.3.2.2 Testing for Trends Using Regression Methods 4-15
4.3.3 General Trend Estimation Methods 4-16
4.3.3.1 Sen's Slope Estimator 4-16
4.3.3.2 Seasonal Kendall Slope Estimator 4-16
4.3.4 Hypothesis Tests for Detecting Trends 4-16
4.3.4.1 One Observation per Time Period for
One Sampling Location 4-16
4.3.4.2 Multiple Observations per Time Period
for One Sampling Location 4-19
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4.3.4.3 Multiple Sampling Locations with Multiple Observations .4-20
4.3.4.4 One Observation for One Station with Multiple Seasons .4-22
4.3.5 A Discussion on Tests for Trends 4-23
4.3.6 Testing for Trends in Sequences of Data 4-24
4.4 OUTLIERS 4-24
4.4.1 Background 4-24
4.4.2 Selection of a Statistical Test 4-27
4.4.3 Extreme Value Test (Dixon's Test) 4-27
4.4.4 Discordance Test 4-29
4.4.5 Rosner's Test 4-30
4.4.6 Walsh's Test 4-32
4.4.7 Multivariate Outliers 4-32
4.5 TESTS FOR DISPERSIONS 4-33
4.5.1 Confidence Intervals for a Single Variance 4-33
4.5.2 The F-Test for the Equality of Two Variances 4-33
4.5.3 Bartlett's Test for the Equality of Two or More Variances 4-33
4.5.4 Levene's Test for the Equality of Two or More Variances 4-35
4.6 TRANSFORMATIONS 4-39
4.6.1 Types of Data Transformations 4-39
4.6.2 Reasons for Transforming Data 4-41
4.7 VALUES BELOW DETECTION LIMITS 4-42
4.7.1 Less than 15% Nondetects - Substitution Methods 4-43
4.7.2 Between 15-50% Nondetects 4-43
4.7.2.1 Cohen's Method 4-43
4.7.2.2 Trimmed Mean 4-45
4.7.2.3 Winsorized Mean and Standard Deviation 4-45
4.7.2.4 Atchison's Method 4-46
4.7.2.5 Selecting Between Atchison's Method or Cohen's Method .4-49
4.7.3 Greater than 5-% Nondetects - Test of Proportions 4-50
4.7.4 Recommendations 4-50
4.8 INDEPENDENCE 4-51
STEP 5: DRAW CONCLUSIONS FROM THE DATA 5-1
5.1 OVERVIEW AND ACTIVITIES 5-3
5.1.1 Perform the Statistical Hypothesis Test 5-3
5.1.2 Draw Study Conclusions 5-3
5.1.3 Evaluate Performance of the Sampling Design 5-5
5.2 INTERPRETING AND COMMUNICATING THE TEST RESULTS 5-6
5.2.1 Interpretation of p-Values 5-7
5.2.2 "Accepting" vs. "Failing to Reject" the Null Hypothesis 5-7
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5.2.3 Statistical Significance vs. Practical Significance
5.2.4 Impact of Bias on Test Results
5.2.5 Quantity vs. Quality of Data
5.2.6 "Proof of Safety" vs. "Proof of Hazard"
APPENDIX A: STATISTICAL TABLES A - 1
APPENDIX B: REFERENCES B - 2
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INTRODUCTION
0.1 PURPOSE AND OVERVIEW
Data Quality Assessment (DQA) is the scientific and statistical evaluation of data to
determine if data obtained from environmental data operations are of the right type, quality, and
quantity to support their intended use. This guidance demonstrates how to use DQA in
evaluating environmental data sets and illustrates how to apply some graphical and statistical tools
for performing DQA. The guidance focuses primarily on using DQA in environmental decision
making; however, the tools presented for preliminary data review and verifying statistical
assumptions are useful whenever environmental data are used, regardless of whether the data are
used for decision making.
DQA is built on a fundamental premise: data quality, as a concept, is meaningful only
when it relates to the intended use of the data. Data quality does not exist in a vacuum; one must
know in what context a data set is to be used in order to establish a relevant yardstick for judging
whether or not the data set is adequate. By using the DQA, one can answer two fundamental
questions:
1. Can the decision (or estimate) be made with the desired confidence, given the quality of
the data set?
2. How well can the sampling design be expected to perform over a wide range of possible
outcomes? If the same sampling design strategy is used again for a similar study, would
the data be expected to support the same intended use with the desired level of
confidence, particularly if the measurement results turned out to be higher or lower than
those observed in the current study?
The first question addresses the data user's immediate needs. For example, if the data
provide evidence strongly in favor of one course of action over another, then the decision maker
can proceed knowing that the decision will be supported by unambiguous data. If, however, the
data do not show sufficiently strong evidence to favor one alternative, then the data analysis alerts
the decision maker to this uncertainly. The decision maker now is in a position to make an
informed choice about how to proceed (such as collect more or different data before making the
decision, or proceed with the decision despite the relatively high, but acceptable, probability of
drawing an erroneous conclusion).
The second question addresses the data user's potential future needs. For example, if
investigators decide to use a certain sampling design at a different location from where the design
was first used, they should determine how well the design can be expected to perform given that
the outcomes and environmental conditions of this sampling event will be different from those of
the original event. Because environmental conditions will vary from one location or time to
another, the adequacy of the sampling design approach should be evaluated over a broad range of
possible outcomes and conditions.
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0.2 DQA AND THE DATA LIFE CYCLE
The data life cycle (depicted in Figure 0-1) comprises three steps: planning,
implementation, and assessment. During the planning phase, the Data Quality Objectives (DQO)
Process (or some other systematic planning procedure) is used to define quantitative and
qualitative criteria for determining when, where, and how many samples (measurements) to
collect and a desired level of confidence. This information, along with the sampling methods,
analytical procedures, and appropriate quality assurance (QA) and quality control (QC)
procedures, are documented in the QA Project Plan. Data are then collected following the QA
Project Plan specifications. DQA completes the data life cycle by providing the assessment
needed to determine if the planning objectives were achieved. During the assessment phase, the
data are validated and verified to ensure that the sampling and analysis protocols specified in the
QA Project Plan were followed, and that the measurement systems performed in accordance with
the criteria specified in the QA Project Plan. DQA then proceeds using the validated data set to
determine if the quality of the data is satisfactory.
PLANNING
Data Quality Objectives Process
Quality Assurance Project Plan Development
,
IMPLEMENTATION
Field Data Collection and Associated
Quality Assurance / Quality Control Activities
1
1
ASSESSMENT
Data Validation/Verification
Data Quality Assessment
/
1
1
I
I
/
t
t
QUALITY ASSURANCE ASSESSMENT
/_,.„, / /QC/Performance /
' Routine Data //,_,.. /
/ / Evaluation Data /
• INPUTS •
DATA VALIDATION/VERIFICATION
• Verify measurement performance
• Verify measurement procedures and
reporting requirements
^ OUTPUT
/ VALIDATED/VERIFIED DATA /
| INPUT
DATA QUALITY ASSESSMENT
• Review DQOs and design
• Select statistical test
• Verify assumptions
• Draw conclusions
• OUTPUT
1 CONCLUSIONS DRAWN FROM DATA /
/
Figure 0-1. DQA in the Context of the Data Life Cycle
0.3 THE 5 STEPS OF THE DQA
The DQA involves five steps that begin with a review of the planning documentation and
end with an answer to the question posed during the planning phase of the study. These steps
roughly parallel the actions of an environmental statistician when analyzing a set of data. The five
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steps, which are described in detail in the remaining chapters of this guidance, are briefly
summarized as follows:
1. Review the Data Quality Objectives (DQOs) and Sampling Design: Review the DQO
outputs to assure that they are still applicable. If DQOs have not been developed, specify
DQOs before evaluating the data (e.g., for environmental decisions, define the statistical
hypothesis and specify tolerable limits on decision errors; for estimation problems, define
an acceptable confidence or probability interval width). Review the sampling design and
data collection documentation for consistency with the DQOs.
2. Conduct a Preliminary Data Review: Review QA reports, calculate basic statistics, and
generate graphs of the data. Use this information to learn about the structure of the data
and identify patterns, relationships, or potential anomalies.
3. Select the Statistical Test: Select the most appropriate procedure for summarizing and
analyzing the data, based on the review of the DQOs, the sampling design, and the
preliminary data review. Identify the key underlying assumptions that must hold for the
statistical procedures to be valid.
4. Verify the Assumptions of the Statistical Test: Evaluate whether the underlying
assumptions hold, or whether departures are acceptable, given the actual data and other
information about the study.
5. Draw Conclusions from the Data: Perform the calculations required for the statistical
test and document the inferences drawn as a result of these calculations. If the design is to
be used again, evaluate the performance of the sampling design.
These five steps are presented in a linear sequence, but the DQA is by its very nature iterative.
For example, if the preliminary data review reveals patterns or anomalies in the data set that are
inconsistent with the DQOs, then some aspects of the study planning may have to be reconsidered
in Step 1. Likewise, if the underlying assumptions of the statistical test are not supported by the
data, then previous steps of the DQA may have to be revisited. The strength of the DQA is that it
is designed to promote an understanding of how well the data satisfy their intended use by
progressing in a logical and efficient manner.
Nevertheless, it should be emphasized that the DQA cannot absolutely prove that one has
or has not achieved the DQOs set forth during the planning phase of a study. This situation
occurs because a decision maker can never know the true value of the item of interest. Data
collection only provides the investigators with an estimate of this, not its true value. Further,
because analytical methods are not perfect, they too can only provide an estimate of the true value
of an environmental sample. Because investigators make a decision based on estimated and not
true values, they run the risk of making a wrong decision (decision error) about the item of
interest.
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0.4 INTENDED AUDIENCE
This guidance is written for a broad audience of potential data users, data analysts, and
data generators. Data users (such as project managers, risk assessors, or principal investigators
who are responsible for making decisions or producing estimates regarding environmental
characteristics based on environmental data) should find this guidance useful for understanding
and directing the technical work of others who produce and analyze data. Data analysts (such as
quality assurance specialists, or any technical professional who is responsible for evaluating the
quality of environmental data) should find this guidance to be a convenient compendium of basic
assessment tools. Data generators (such as analytical chemists, field sampling specialists, or
technical support staff responsible for collecting and analyzing environmental samples and
reporting the resulting data values) should find this guidance useful for understanding how their
work will be used and for providing a foundation for improving the efficiency and effectiveness of
the data generation process.
0.5 ORGANIZATION
This guidance presents background information and statistical tools for performing DQA.
Each chapter corresponds to a step in the DQA and begins with an overview of the activities to be
performed for that step. Following the overviews in Chapters 1, 2, 3, and 4, specific graphical or
statistical tools are described and step-by-step procedures are provided along with examples.
0.6 SUPPLEMENTAL SOURCES
Many of the graphical and statistical tools presented in this guidance are also implemented
in a user-friendly, personal computer software program called Data Quality Evaluation Statistical
Tools (DataQUEST) (G-9D) (EPA, 1996). DataQUEST simplifies the implementation of DQA
by automating many of the recommended statistical tools. DataQUEST runs on most IBM-
compatible personal computers using the DOS operating system; see the DataQUEST User's
Guide for complete information on the minimum computer requirements.
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CHAPTER 1
STEP 1: REVIEW DQOs AND THE SAMPLING DESIGN
THE DATA QUALITY ASSESSMENT PROCESS
Review DQOs and Sampling Design
Conduct Preliminary Data Review
Select the Statistical Test
Verify the Assumptions
Draw Conclusions From the Data
REVIEW DQOs AND SAMPLING DESIGN
Purpose
Review the DQO outputs, the sampling design, and
any data collection documentation for consistency. If
DQOs have not been developed, define the statistical
hypothesis and specify tolerable limits on decision errors.
Activities
• Review Study Objectives
• Translate Objectives into Statistical Hypothesis
• Develop Limits on Decision Errors
• Review Sampling Design
Tools
• Statements of hypotheses
• Sampling design concepts
Step 1: Review DQOs and Sampling Design
Review the objectives of the study.
P If DQOs have not been developed, review Section 1.1.1 and define these objectives.
P If DQOs were developed, review the outputs from the DQO Process.
Translate the data user's objectives into a statement of the primary statistical hypothesis.
P If DQOs have not been developed, review Sections 1.1.2 and 1.2, and Box 1-1,
then develop a statement of the hypothesis based on the data user's objectives.
P If DQOs were developed, translate them into a statement of the primary hypothesis.
Translate the data user's objectives into limits on Type I or Type II decision errors.
P If DQOs have not been developed, review Section 1.1.3 and document the data
user's tolerable limits on decision errors.
P If DQOs were developed, confirm the limits on decision errors.
Review the sampling design and note any special features or potential problems.
P Review the sampling design for any deviations (Sections 1.1.4 and 1.3).
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List of Boxes
Page
Box 1-1: Example Applying the DQO Process Retrospectively 1-8
List of Tables
Page
Table 1-1. Choosing a Parameter of Interest 1-6
Table 1-2. Commonly Used Statements of Statistical Hypotheses 1-12
List of Figures
Page
Figure 1-1. The Data Quality Objectives Process 1-3
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CHAPTER 1
STEP 1: REVIEW DQOs AND THE SAMPLING DESIGN
1.1 OVERVIEW AND ACTIVITIES
DQA begins by reviewing the key outputs from the planning phase of the data life cycle:
the Data Quality Objectives (DQOs), the Quality Assurance (QA) Project Plan, and any
associated documents. The DQOs provide the context for understanding the purpose of the data
collection effort and establish the qualitative and quantitative criteria for assessing the quality of
the data set for the intended use. The sampling design (documented in the QA Project Plan)
provides important information about how to interpret the data. By studying the sampling design,
the analyst can gain an understanding of the assumptions under which the design was developed,
as well as the relationship between these assumptions and the DQOs. By reviewing the methods
by which the samples were collected, measured, and reported, the analyst prepares for the
preliminary data review and subsequent steps of DQA.
Careful planning improves the
representativeness and overall quality of a sampling
design, the effectiveness and efficiency with which
the sampling and analysis plan is implemented, and
the usefulness of subsequent DQA efforts. Given
the benefits of planning, the Agency has developed
the DQO Process which is a logical, systematic
planning procedure based on the scientific method.
The DQO Process emphasizes the planning and
development of a sampling design to collect the
right type, quality, and quantity of data needed to
support the decision. Using both the DQO Process
and the DQA will help to ensure that the decisions
are supported by data of adequate quality; the
DQO Process does so prospectively and the DQA
does so retrospectively.
When DQOs have not been developed
during the planning phase of the study, it is
necessary to develop statements of the data user's
objectives prior to conducting DQA. The primary
purpose of stating the data user's objectives prior
to analyzing the data is to establish appropriate
criteria for evaluating the quality of the data with
respect to their intended use. Analysts who are not
familiar with the DQO Process should refer to the
Guidance for the Data Quality Objectives Process
(QA/G-4) (1994), a book on statistical decision
Step 1. State the Problem
Define the problem; identify the planning team;
examine budget, schedule.
Step 2. Identify the Decision
State decision; identify study question; define
alternative actions.
Step 3. Identify the Inputs to the Decision
Identify information needed for the decision (information
sources, basis for Action Level, sampling/analysis method).
Step 4. Define the Boundaries of the Study
Specify sample characteristics; define
spatial/temporal limits, units of decision making.
Step 5. Develop a Decision Rule
Define statistical parameter (mean, median); specify Action
Level; develop logic for action.
Step 6. Specify Tolerable Limits on Decision Errors
Set acceptable limits for decision errors relative to
consequences (health effects, costs).
Step 7. Optimize the Design for Obtaining Data
Select resource-effective sampling and analysis plan that
meets the performance criteria.
Figure 1-1. The Data Quality Objectives
Process
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making using tests of hypothesis, or consult a statistician. The seven steps of the DQO Process
are illustrated in Figure 1.1.
The remainder of this chapter addresses recommended activities for performing this step
of DQA and technical considerations that support these activities. The remainder of this section
describes the recommended activities, the first three of which will differ depending on whether
DQOs have already been developed for the study. Section 1.2 describes how to select the null
and alternative hypothesis and Section 1.3 presents a brief overview of different types of sampling
designs.
1.1.1 Review Study Objectives
In this activity, the objectives of the study are reviewed to provide context for analyzing
the data. If a planning process has been implemented before the data are collected, then this step
reduces to reviewing the documentation on the study objectives. If no planning process was used,
the data user should:
• Develop a concise definition of the problem (DQO Process Step 1) and the decision (DQO
Process Step 2) for which the data were collected. This should provide the fundamental
reason for collecting the environmental data and identify all potential actions that could
result from the data analysis.
Identify if any essential information is missing (DQO Process Step 3). If so, either collect
the missing information before proceeding, or select a different approach to resolving the
decision.
Specify the scale of decision making (any subpopulations of interest) and any boundaries
on the study (DQO Process Step 4) based on the sampling design. The scale of decision
making is the smallest area or time period to which the decision will apply. The sampling
design and implementation may restrict how small or how large this scale of decision
making can be.
1.1.2 Translate Objectives into Statistical Hypotheses
In this activity, the data user's objectives are used to develop a precise statement of the
primary1 hypotheses to be tested using environmental data. A statement of the primary statistical
hypotheses includes a null hypothesis, which is a "baseline condition" that is presumed to be true
in the absence of strong evidence to the contrary, and an alternative hypothesis, which bears the
burden of proof. In other words, the baseline condition will be retained unless the alternative
Throughout this document, the term "primary hypotheses" refers to the statistical hypotheses that correspond to the
data user's decision. Other statistical hypotheses can be formulated to formally test the assumptions that underlie the specific
calculations used to test the primary hypotheses. See Chapter 3 for examples of assumptions underlying primary hypotheses
and Chapter 4 for examples of how to test these underlying assumptions.
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condition (the alternative hypothesis) is thought to be true due to the preponderance of evidence.
In general, such hypotheses consist of the following elements:
a population parameter of interest, which describes the feature of the environment that the
data user is investigating;
• a numerical value to which the parameter will be compared, such as a regulatory or risk-
based threshold or a similar parameter from another place (e.g., comparison to a reference
site) or time (e.g., comparison to a prior time); and
• the relation (such as "is equal to" or "is greater than") that specifies precisely how the
parameter will be compared to the numerical value.
To help the analyst decide what parameter value should be investigated, Table 1-1 compares the
merits of the mean, upper proportion (percentile), and mean. If DQOs were developed, the
statement of hypotheses already should be documented in the outputs of Step 6 of the DQO
Process. If DQOs have not been developed, then the analyst should consult with the data user to
develop hypotheses that address the data user's concerns. Section 1.2 describes in detail how to
develop the statement of hypotheses and includes a list of common encountered hypotheses for
environmental decisions.
1.1.3 Develop Limits on Decision Errors
The goal of this activity is to develop numerical probability limits that express the data
user's tolerance for committing false rejection (Type I) or false acceptance (Type II) decision
errors as a result of uncertainty in the data. A false rejection error occurs when the null
hypothesis is rejected when it is true. A false acceptance decision error occurs when the null
hypothesis is not rejected when it is false. These are the statistical definitions of false rejection
and false acceptance decision errors. Other commonly used phrases include "level of significance"
which is equal to the Type I Error (false rejection) and "complement of power" equal to the Type
II Error (false acceptance). If tolerable decision error rates were not established prior to data
collection, then the data user should:
Specify the gray region where the consequences of a false acceptance decision error are
relatively minor (DQO Process Step 6). The gray region is bounded on one side by the
threshold value and on the other side by that parameter value where the consequences of
making a false acceptance decision error begin to be significant. Establish this boundary
by evaluating the consequences of not rejecting the null hypothesis when it is false and
then place the edge of the gray region where these consequences are severe enough to set
a limit on the magnitude of this false acceptance decision error. The gray region is the
area between this parameter value and the threshold value.
The width of the gray region represents one important aspect of the decision maker's
concern for decision errors. A more narrow gray region implies a desire to detect
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Table 1-1. Choosing a Parameter of Interest
Parameter
Points to Consider
Mean
1. Easy to calculate and estimate a confidence interval.
2. Useful when the standard has been based on consideration of health effects or long-term average exposure.
3. Useful when the data have little variation from sample to sample or season to season.
4. If the data have a large coefficient of variation (greater than about 1.5) testing the mean can require more samples than for testing
an upper percentile in order to provide the same protection to human health and the environment.
5. Can have high false rejection rates with small sample sizes and highly skewed data, i.e., when the contamination levels are
generally low with only occasional short periods of high contamination.
6. Not as powerful for testing attainment when there is a large proportion of less-than-detection-limit values.
7. Is adversely affected by outliers or errors in a few data values.
Upper Proportion
(Percentile)
1. Requiring that an upper percentile be less than a standard can limit the occurrence of samples with high concentrations, depending
on the selected percentile.
2. Unaffected by less-than-detection-limit values, as long as the detection limit is less than the cleanup standard.
3. If the health effects of the contaminant are acute, extreme concentrations are of concern and are best tested by ensuring that a
large portion of the measurements are below a standard.
4. The proportion of the samples that must be below the standard must be chosen.
5. For highly variable or skewed data, can provide similar protection of human health and the environment with a smaller size than
when testing the mean.
6. Is relatively unaffected by a small number of outliers.
Median
1. Has benefits over the mean because it is not as heavily influenced by outliers and highly variable data, and can be used with a
large number of less-than-detection-limit values.
2. Has many of the positive features of the mean, in particular its usefulness of evaluating standards based on health effects and
long-term average exposure.
3. For positively skewed data, the median is lower than the mean and therefore testing the median provides less protection for human
health and the environment than testing the mean.
4. Retains some negative features of the mean in that testing the median will not limit the occurrence of extreme values.
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conclusively the condition when the true parameter value is close to the threshold value
("close" relative to the variability in the data).
Specify tolerable limits on the probability of committing false rejection and false
acceptance decision errors (DQO Process Step 6) that reflect the decision maker's
tolerable limits for making an incorrect decision. Select a possible value of the parameter;
then, choose a probability limit based on an evaluation of the seriousness of the potential
consequences of making the decision error if the true parameter value is located at that
point. At a minimum, the decision maker should specify a false rejection decision error
limit at the threshold value (a), and a false acceptance decision error limit at the other
edge of the gray region (P).
An example of the gray region and limits on the probability of committing both false rejection and
false acceptance decision errors are contained in Box 1-1.
If DQOs were developed for the study, the tolerable limits on decision errors will already
have been developed. These values can be transferred directly as outputs for this activity. In this
case, the action level is the threshold value; the false rejection error rate at the action level is the
Type I error rate or a; and the false acceptance error rate at the other bound of the gray region is
the Type II error rate or p.
1.1.4 Review Sampling Design
The goal of this activity is to familiarize the analyst with the main features of the sampling
design that was used to generate the environmental data. The overall type of sampling design and
the manner in which samples were collected or measurements were taken will place conditions
and constraints on how the data must be used and interpreted. Section 1.3 provides additional
information about several different types of sampling designs that are commonly used in
environmental studies.
Review the sampling design documentation with the data user's objectives in mind. Look
for design features that support or contradict those objectives. For example, if the data user is
interested in making a decision about the mean level of contamination in an effluent stream over
time, then composite samples may be an appropriate sampling approach. On the other hand, if the
data user is looking for hot spots of contamination at a hazardous waste site, compositing should
only be used with caution, to avoid "averaging away" hot spots. Also, look for potential
problems in the implementation of the sampling design. For example, verify that each point in
space (or time) had an equal probability of being selected for a simple random sampling design.
Small deviations from a sampling plan may have minimal effect on the conclusions drawn from the
data set. Significant or substantial deviations should be flagged and their potential effect carefully
considered throughout the entire DQA.
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Box 1-1: Example Applying the DQO Process Retrospectively
A waste incineration company was concerned that waste fly ash could contain hazardous levels of
cadmium and should be disposed of in a RCRA landfill. As a result, eight composite samples each
consisting of eight grab samples were taken from each load of waste. The TCLP leachate from these
samples were then analyzed using a method specified in 40 CFR, Pt. 261, App. II. DQOs were not
developed for this problem; therefore, study objectives (Sections 1.1.1 through 1.1.3) should be
developed before the data are analyzed.
1.1.1 Review Study Objectives
P Develop a concise definition of the problem - The problem is defined above.
P Identify if any essential information is missing - It does not appear than any essential information is
missing.
P Specify the scale of decision making - Each waste load is sampled separately and decisions need to
be made for each load. Therefore, the scale of decision making is an individual load.
1.1.2 Translate Objectives into Statistical Hypotheses
Since composite samples were taken, the parameter of interest is the mean cadmium concentration. The
RCRA regulatory standard for cadmium in TCLP leachate is 1.0 mg/L. Therefore, the two hypotheses
are "mean cadmium > 1.0 mg/L" and "mean cadmium < 1.0 mg/L."
There are two possible decision errors 1) to decide the waste is hazardous ("mean > 1.0") when it truly is
not ("mean < 1.0"), and 2) to decide the waste is not hazardous ("mean < 1.0") when it truly is ("mean >
1.0"). The risk of deciding the fly ash is not hazardous when it truly is hazardous is more severe since
potential consequences of this decision error include risk to human health and the environment.
Therefore, this error will be labeled the false rejection error and the other error will be the false
acceptance error. As a result of this decision, the null hypothesis will be that the waste is hazardous
("mean cadmium > 1.0 mg/L") and the alternative hypothesis will be that the waste is not hazardous
("mean cadmium < 1.0 mg/L"). (See Section 1.2 for more information on developing the null and
alternative hypotheses.)
1.1.3 Develop Limits on Decision Errors
1
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1.2 DEVELOPING THE STATEMENT OF HYPOTHESES
The full statement of the statistical hypotheses has two major parts: the null hypothesis
(H0) and the alternative hypothesis (HA). In both parts, a population parameter is compared to
either a fixed value (for a one-sample test) or another population parameter (for a two-sample
test). The population parameter is a quantitative characteristic of the population that the data
user wants to estimate using the data. In other words, the parameter describes that feature of the
population that the data user will evaluate when making the decision. Table 1-1 describes several
common statistical parameters.
If the data user is interested in drawing inferences about only one population, then the null
and alternative hypotheses will be stated in terms that relate the true value of the parameter to
some fixed threshold value. A common example of this one-sample problem in environmental
studies is when pollutant levels in an effluent stream are compared to a regulatory limit. If the
data user is interested in comparing two populations, then the null and alternative hypotheses will
be stated in terms that compare the true value of one population parameter to the corresponding
true parameter value of the other population. A common example of this two-sample problem in
environmental studies is when a potentially contaminated waste site is being compared to a
reference area using samples collected from the respective areas. In this situation, the hypotheses
often will be stated in terms of the difference between the two parameters.
The decision on what should constitute the null hypothesis and what should be the
alternative is sometimes difficult to ascertain. In many cases, this problem does not arise because
the null and alternative hypotheses are determined by specific regulation. However, when the null
hypothesis is not specified by regulation, it is necessary to make this determination. The test of
hypothesis procedure prescribes that the null hypothesis is only rejected in favor of the alternative,
provided there is overwhelming evidence from the data that the null hypothesis is false. In other
words, the null hypothesis is considered to be true unless the data show conclusively that this is
not so. Therefore, it is sometimes useful to choose the null and alternative hypotheses in light of
the consequences of possibly making an incorrect decision between the null and alternative
hypotheses. The true condition that occurs with the more severe decision error (not what would
be decided in error based on the data) should be defined as the null hypothesis. For example,
consider the two decision errors: "decide a company does not comply with environmental
regulations when it truly does" and "decide a company does comply with environmental
regulations when it truly does not." If the first decision error is considered the more severe
decision error, then the true condition of this error, "the company does comply with the
regulations" should be defined as the null hypothesis. If the second decision error is considered
the more severe decision error, then the true condition of this error, "the company does not
comply with the regulations" should be defined as the null hypothesis.
An alternative method for defining the null hypothesis is based on historical information.
If a large amount of information exists suggesting that one hypothesis is extremely likely, then this
hypothesis should be defined as the alternative hypothesis. In this case, a large amount of data
may not be necessary to provide overwhelming evidence that the other (null) hypothesis is false.
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For example, if the waste from an incinerator was previously hazardous and the waste process has
not changed, it may be more cost-effective to define the alternative hypothesis as "the waste is
hazardous" and the null hypothesis as "the waste is not hazardous."
Consider a data user who wants to know whether the true mean concentration (|i) of
atrazine in ground water at a hazardous waste site is greater than a fixed threshold value C. If the
data user presumes from prior information that the true mean concentration is at least C due
possibly to some contamination incident, then the data must provide compelling evidence to reject
that presumption, and the hypotheses can be stated as follows:
Narrative Statement of Hypotheses
Statement of Hypotheses Using Standard
Notation
Null Hypothesis (Baseline Condition):
The true mean concentration of atrazine in
ground
water is greater than or equal to the threshold
value C; versus
H
versus
Alternative Hypothesis:
The true mean concentration of atrazine in
ground water is less than the threshold value
C.
H
A
On the other hand, if the data user presumes from prior information that the true mean
concentration is less than C due possibly to the fact that the ground water has not been
contaminated in the past, then the data must provide compelling evidence to reject that
presumption, and the hypotheses can be stated as follows:
Narrative Statement of Hypotheses
Statement of Hypotheses Using Standard
Notation
Null Hypothesis (Baseline Condition):
The true mean concentration of atrazine in
ground
water is less than or equal to the threshold
value C; versus
H0: |i < C;
versus
Alternative Hypothesis:
The true mean concentration of atrazine in
ground water is greater than the threshold
value C.
HA:
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In stating the primary hypotheses, it is convenient to use standard statistical notation, as
shown throughout this document. However, the logic underlying the hypothesis always
corresponds to the decision of interest to the data user.
Table 1-2 summarizes common environmental decisions and the corresponding
hypotheses. In Table 1-2, the parameter is denoted using the symbol "@," and the difference
between two parameters is denoted using "Oj - @2" where @j represents the parameter of the first
population and @2 represents the parameter of the second population. The use of "@" is to avoid
using the terms "population mean" or "population median" repeatedly because the structure of the
hypothesis test remains the same regardless of the population parameter. The fixed threshold
value is denoted "C," and the difference between two parameters is denoted "50" (often the null
hypothesis is defined such that 50= 0).
For the first problem in Table 1-2, only estimates of @ that exceed C can cast doubt on the
null hypothesis. This is called a one-tailed hypothesis test, because only parameter estimates on
one side of the threshold value can lead to rejection of the null hypothesis. The second, fourth,
and fifth rows of Table 1-2 are also examples of one-tailed hypothesis tests. The third and sixth
rows of Table 1-2 are examples of two-tailed tests, because estimates falling both below and
above the null-hypothesis parameter value can lead to rejection of the null hypothesis. Most
hypotheses connected with environmental monitoring are one-tailed because high pollutant levels
can harm humans or ecosystems.
1.3 DESIGNS FOR SAMPLING ENVIRONMENTAL MEDIA
Sampling designs provide the basis for how a set of samples may be analyzed. Different
sampling designs require different analysis techniques and different assessment procedures. There
are two primary types of sampling designs: authoritative (judgment) sampling and probability
sampling. This section describes some of the most common sampling designs.
1.3.1 Authoritative Sampling
With authoritative (judgment) sampling, an expert having knowledge of the site (or
process) designates where and when samples are to be taken. This type of sampling should only
be considered when the objectives of the investigation are not of a statistical nature, for example,
when the objective of a study is to identify specific locations of leaks, or when the study is
focused solely on the sampling locations themselves. Generally, conclusions drawn from
authoritative samples apply only to the individual samples and aggregation may result in severe
bias and lead to highly erroneous conclusions. Judgmental sampling also precludes the use of the
sample for any purpose other than the original one. Thus if the data may be used in further
studies (e.g., for an estimate of variability in a later study), a probabilistic design should be used.
When the study objectives involve estimation or decision making, some form of
probability sampling should be selected. As described below, this does not preclude use of the
expert's knowledge of the site or process in designing a probability-based sampling plan; however,
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Table 1-2. Commonly Used Statements of Statistical Hypotheses
Type of Decision
Null Hypothesis
Alternative
Hypothesis
Compare environmental conditions to a fixed
threshold value, such as a regulatory standard or
acceptable risk level; presume that the true
condition is less than the threshold value.
H0: 0 < C
HA: 0>C
Compare environmental conditions to a fixed
threshold value; presume that the true condition is
greater than the threshold value.
H0: 0 > C
HA: 0 60
(HA: 0j - 02 > 0)
Compare environmental conditions associated
with two different populations to a fixed threshold
value (60) such as a regulatory standard or
acceptable risk level; presume that the true
condition is greater than the threshold value. If it
is presumed that conditions associated with the
two populations are the same, the threshold value
isO.
H0: 0: - 02 > 60
(Ho: 0: - 02 > 0)
HA: 0j - 02 < 60
(HA: 01-02<0)
Compare environmental conditions associated
with two different populations to a fixed threshold
value (80) such as a regulatory standard or
acceptable risk level; presume that the true
condition is equal to the threshold value. If it is
presumed that conditions associated with the two
populations are the same, the threshold value is 0.
H0: 0: - 02 = 80
(Ho: 0j - 02 = 0)
HA: e^e^do
(HA: 0j - 02 * 0)
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valid statistical inferences require that the plan incorporate some form of randomization in
choosing the sampling locations or sampling times. For example, to determine maximum SO2
emission from a boiler, the sampling plan would reasonably focus, or put most of the weight on,
periods of maximum or near-maximum boiler operation. Similarly, if a residential lot is being
evaluated for contamination, then the sampling plan can take into consideration prior knowledge
of contaminated areas, by weighting such areas more heavily in the sample selection and data
analysis.
1.3.2 Probability Sampling
Probability samples are samples in which every member of the target population (i.e.,
every potential sampling unit) has a known probability of being included in the sample.
Probability samples can be of various types, but in some way, they all make use of randomization,
which allows valid probability statements to be made about the quality of estimates or hypothesis
tests that are derived from the resultant data.
One common misconception of probability sampling procedures is that these procedures
preclude the use of important prior information. Indeed, just the opposite is true. An efficient
sampling design is one that uses all available prior information to stratify the region and set
appropriate probabilities of selection. Another common misconception is that using a probability
sampling design means allowing the possibility that the sample points will not be distributed
appropriately across the region. However, if there is no prior information regarding the areas
most likely to be contaminated, a grid sampling scheme (a type of stratified design) is usually
recommended to ensure that the sampling points are dispersed across the region.
1.3.2.1 Simple Random Sampling
The simplest type of probability sample is the simple random sample where every possible
sampling unit in the target population has an equal chance of being selected. Simple random
samples, like the other samples, can be either samples in time and/or space and are often
appropriate at an early stage of an investigation in which little is known about systematic variation
within the site or process. All of the sampling units should have equal volume or mass, and
ideally be of the same shape if applicable. With a simple random sample, the term "random"
should not be interpreted to mean haphazard; rather, it has the explicit meaning of equiprobable
selection. Simple random samples are generally developed through use of a random number table
or through computer generation of pseudo-random numbers.
1.3.2.2 Sequential Random Sampling
Usually, simple random samples have a fixed sample size, but some alternative approaches
are available, such as sequential random sampling, where the sample sizes are not fixed a priori.
Rather, a statistical test is performed after each specimen's analysis (or after some minimum
number have been analyzed). This strategy could be applicable when sampling and/or analysis is
quite expensive, when information concerning sampling and/or measurement variability is lacking,
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when the characteristics of interest are stable over the time frame of the sampling effort, or when
the objective of the sampling effort is to test a single specific hypothesis.
1.3.2.3 Systematic Samples
In the case of spatial sampling, systematic sampling involves establishing a two-
dimensional (or in some cases a three-dimensional) spatial grid and selecting a random starting
location within one of the cells. Sampling points in the other cells are located in a deterministic
way relative to that starting point. In addition, the orientation of the grid is sometimes chosen
randomly and various types of systematic samples are possible. For example, points may be
arranged in a pattern of squares (rectangular grid sampling) or a pattern of equilateral triangles
(triangular grid sampling). The result of either approach is a simple pattern of equally spaced
points at which sampling is to be performed.
Systematic sampling designs have several advantages over random sampling and some of
the other types of probability sampling. They are generally easier to implement, for example.
They are also preferred when one of the objectives is to locate "hot spots" within a site or
otherwise map the pattern of concentrations over a site. On the other hand, they should be used
with caution whenever there is a possibility of some type of cyclical pattern in the waste site or
process. Such a situation, combined with the uniform pattern of sampling points, could very
readily lead to biased results.
1.3.2.4 Stratified Samples
Another type of probability sample is the stratified random sample, in which the site or
process is divided into two or more non-overlapping strata, sampling units are defined for each
stratum, and separate simple random samples are employed to select the units in each stratum. (If
a systematic sample were employed within each stratum, then the design would be referred to as a
stratified systematic sample.) Strata should be defined so that physical samples within a stratum
are more similar to each other than to samples from other strata. If so, a stratified random sample
should result in more precise estimates of the overall population parameter than those that would
be obtained from a simple random sample with the same number of sampling units.
Stratification is a way to incorporate prior knowledge and professional judgment into a
probabilistic sampling design. Generally, units that are "alike" or anticipated to be "alike" are
placed together in the same stratum. Units that are contiguous in space (e.g., similar depths) or
time are often grouped together into the same stratum, but characteristics other than spatial or
temporal proximity can also be employed. Media, terrain characteristics, concentration levels,
previous cleanup attempts, and confounding contaminants can be used to create strata.
Advantages of stratified samples over random samples include their ability to ensure more
uniform coverage of the entire target population and, as noted above, their potential for achieving
greater precision in certain estimation problems. Even when imperfect information is used to
form strata, the stratified random sample will generally be more cost-effective than a simple
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random sample. A stratified design can also be useful when there is interest in estimating or
testing characteristics for subsets of the target population. Because different sampling rates can
be used in different strata, one can oversample in strata containing those subareas of particular
interest to ensure that they are represented in the sample. In general, statistical calculations for
data generated via stratified samples are more complex than for random samples, and certain
types of tests, for example, cannot be performed when stratified samples are employed.
Therefore, a statistician should be consulted when stratified sampling is used.
1.3.2.5 Compositing Physical Samples
When analysis costs are large relative to sampling costs, cost-effective plans can
sometimes be achieved by compositing physical samples or specimens prior to analysis, assuming
that there are no safety hazards or potential biases (for example, the loss of volatile organic
compounds from a matrix) associated with such compositing. For the same total cost,
compositing in this situation would allow a larger number of sampling units to be selected than
would be the case if compositing were not used. Composite samples reflect a physical rather than
a mathematical mechanism for averaging. Therefore, compositing should generally be avoided if
population parameters other than a mean are of interest (e.g., percentiles or standard deviations).
Composite sampling is also useful when the analyses of composited samples are to be used
in a two-staged approach in which the composite-sample analyses are used solely as a screening
mechanism to identify if additional, separate analyses need to be performed. This situation might
occur during an early stage of a study that seeks to locate those areas that deserve increased
attention due to potentially high levels of one or more contaminants.
1.3.2.6 Other Sampling Designs
Adaptive sampling involves taking a sample and using the resulting information to design
the next stage of sampling. The process may continue through several additional rounds of
sampling and analysis. A common application of adaptive sampling to environmental problems
involves subdividing the region of interest into smaller units, taking a probability sample of these
units, then sampling all units that border on any unit with a concentration level greater than some
specified level C. This process is continued until all newly sampled units are below C. The field
of adaptive sampling is currently undergoing active development and can be expected to have a
significant impact on environmental sampling.
Ranked set sampling (RSS) uses the availability of an inexpensive surrogate measurement
when it is correlated with the more expensive measurement of interest. The method exploits this
correlation to obtain a sample which is more representative of the population that would be
obtained by random sampling, thereby leading to more precise estimates of population parameters
than what would be obtained by random sampling. RSS consists of creating n groups, each of
size n (for a total of n2 initial samples), then ranking the surrogate from largest to smallest within
each group. One sample from each group is then selected according to a specified procedure and
these n samples are analyzed for the more expensive measurement of interest.
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CHAPTER 2
STEP 2: CONDUCT A PRELIMINARY DATA REVIEW
THE DATA QUALITY ASSESSMENT PROCESS
Review DQOs and Sampling Design
Conduct Preliminary Data Review
Select the Statistical Test
Verify the Assumptions
Draw Conclusions From the Data
CONDUCT PRELIMINARY DATA REVIEW
Purpose
Generate statistical quantities and graphical
representations that describe the data. Use this
information to learn about the structure of the data
and identify any patterns or relationships.
Activities
• Review Quality Assurance Reports
• Calculate Basic Statistical Quantities
•Graph the Data
Tools
• Statistical quantities
•Graphical representations
Step 2: Conduct a Preliminary Data Review
Review quality assurance reports.
P Look for problems or anomalies in the implementation of the sample collection and
analysis procedures.
P Examine QC data for information to verify assumptions underlying the Data Quality
Objectives, the Sampling and Analysis Plan, and the QA Project Plans.
Calculate the statistical quantities.
P Consider calculating appropriate percentiles (Section 2.2.1)
P Select measures of central tendency (Section 2.2.2) and dispersion (Section 2.2.3).
P If the data involve two variables, calculate the correlation coefficient (Section 2.2.4).
Display the data using graphical representations.
P Select graphical representations (Section 2.4) that illuminate the structure of the data
set and highlight assumptions underlying the Data Quality Objectives, the Sampling
and Analysis Plan, and the QA Project Plans.
P Use a variety of graphical representations that examine different features of the set.
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Boxes
Box 2-1: Directions for Calculating the Measure of Relative Standing (Percentiles) 2-6
Box 2-2: Directions for Calculating the Measures of Central Tendency 2-7
Box 2-3: Example Calculations of the Measures of Central Tendency 2-7
Box 2-4: Directions for Calculating the Measures of Dispersion 2-9
Box 2-5: Example Calculations of the Measures of Dispersion 2-9
Box 2-6: Directions for Calculating Pearson's Correlation Coefficient 2-10
Box 2-7: Directions for Calculating Spearman's Correlation 2-12
Box 2-8: Directions for Estimating the Serial Correlation Coefficient with a Example .... 2-13
Box 2-9: Directions for Generating a Histogram and a Frequency Plot 2-14
Box 2-10: Example of Generating a Histogram and a Frequency Plot 2-15
Box 2-11: Directions for Generating a Stem and Leaf Plot 2-16
Box 2-12: Example of Generating a Stem and Leaf Plot 2-16
Box 2-13: Directions for Generating a Box and Whiskers Plot 2-18
Box 2-14: Example of a Box and Whiskers Plot 2-18
Box 2-15: Directions for Generating a Ranked Data Plot 2-19
Box 2-16: Example of Generating a Ranked Data Plot 2-19
Box 2-17: Directions for Generating a Quantile Plot 2-22
Box 2-18: Example of Generating a Quantile Plot 2-22
Box 2-19: Directions for Constructing a Normal Probability Plot 2-23
Box 2-20: Example of Normal Probability Plot 2-24
Box 2-21: Directions for Generating a Scatter Plot and an Example 2-28
Box 2-22: Directions for Constructing an Empirical Q-Q Plot with an Example 2-31
Figures
Figure 2-1. Example of a Frequency Plot 2-13
Figure 2-2. Example of a Histogram 2-14
Figure 2-3. Example of a Box and Whisker Plot 2-17
Figure 2-4. Example of a Ranked Data Plot 2-20
Figure 2-5. Example of a Quantile Plot of Skewed Data 2-21
Figure 2-6. Normal Probability Paper 2-25
Figure 2-7. Example of Graphical Representations of Multiple Variables 2-26
Figure 2-8. Example of a Scatter Plot 2-27
Figure 2-9. Example of a Coded Scatter Plot 2-28
Figure 2-10. Example of a Parallel Coordinates Plot 2-29
Figure 2-11. Example of a Matrix Scatter Plot 2-29
Figure 2-12. Example of a Time Plot Showing a Slight Downward Trend 2-33
Figure 2-13. Example of a Correlogram 2-34
Figure 2-14. Example of a Posting Plot 2-37
Figure 2-15. Example of a Symbol Plot 2-38
Figure 2-16. The Normal Distribution 2-40
Figure 2-17. The Standard Normal Curve (Z-Curve) 2-40
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CHAPTER 2
STEP 2: CONDUCT A PRELIMINARY DATA REVIEW
2.1 OVERVIEW AND ACTIVITIES
In this step of DQA, the analyst conducts a preliminary evaluation of the data set,
calculates some basic statistical quantities, and examines the data using graphical representations.
A preliminary data review should be performed whenever data are used, regardless of whether
they are used to support a decision, estimate a population parameter, or answer exploratory
research questions. By reviewing the data both numerically and graphically, one can learn the
"structure" of the data and thereby identify appropriate approaches and limitations for using the
data. The DQA software Data Quality Evaluation Statistical Tools (DataQUEST) (G-9D) (EPA,
1996) will perform all of these functions as well as more sophisticated statistical tests.
There are two main elements of preliminary data review: (1) basic statistical quantities
(summary statistics); and (2) graphical representations of the data. Statistical quantities are
functions of the data that numerically describe the data set. Examples include a mean, median,
percentile, range, and standard deviation. They can be used to provide a mental picture of the
data and are useful for making inferences concerning the population from which the data were
drawn. Graphical representations are used to identify patterns and relationships within the data,
confirm or disprove hypotheses, and identify potential problems. For example, a normal
probability plot may allow an analyst to quickly discard an assumption of normality and may
identify potential outliers.
The preliminary data review step is designed to make the analyst familiar with the data.
The review should identify anomalies that could indicate unexpected events that may influence the
analysis of the data. The analyst may know what to look for based on the anticipated use of the
data documented in the DQO Process, the QA Project Plan, and any associated documents. The
results of the review are then used to select a procedure for testing a statistical hypotheses to
support the data user's decision.
2.1.1 Review Quality Assurance Reports
The first activity in conducting a preliminary data review is to review any relevant QA
reports that describe the data collection and reporting process as it actually was implemented.
These QA reports provide valuable information about potential problems or anomalies in the data
set. Specific items that may be helpful include:
• Data validation reports that document the sample collection, handling, analysis,
data reduction, and reporting procedures used;
Quality control reports from laboratories or field stations that document
measurement system performance, including data from check samples, split
samples, spiked samples, or any other internal QC measures; and
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Technical systems reviews, performance evaluation audits, and audits of data
quality, including data from performance evaluation samples.
When reviewing QA reports, particular attention should be paid to information that can be
used to check assumptions made in the DQO Process. Of great importance are apparent
anomalies in recorded data, missing values, deviations from standard operating procedures, and
the use of nonstandard data collection methodologies.
2.1.2 Calculate Basic Statistical Quantities
The goal of this activity is to summarize some basic quantitative characteristics of the data
set using common statistical quantities. Some statistical quantities that are useful to the analyst
include: number of observations; measures of central tendency, such as a mean, median, or mode;
measures of dispersion, such as range, variance, standard deviation, coefficient of variation, or
interquartile range; measures of relative standing, such as percentiles; measures of distribution
symmetry or shape; and measures of association between two or more variables, such as
correlation. These measures can then be used for description, communication, and to test
hypothesis regarding the population from which the data were drawn. Section 2.2 provides
detailed descriptions and examples of these statistical quantities.
The sample design may influence how the statistical quantities are computed. The
formulas given in this chapter are for simple random sampling, simple random sampling with
composite samples, and randomized systematic sampling. If a more complex design is used, such
as a stratified design, then the formulas may need to be adjusted.
2.1.3 Graph the Data
The goal of this step is to identify patterns and trends in the data that might go unnoticed
using purely numerical methods. Graphs can be used to identify these patterns and trends, to
quickly confirm or disprove hypotheses, to discover new phenomena, to identify potential
problems, and to suggest corrective measures. In addition, some graphical representations can be
used to record and store data compactly or to convey information to others. Graphical
representations include displays of individual data points, statistical quantities, temporal data,
spatial data, and two or more variables. Since no single graphical representation will provide a
complete picture of the data set, the analyst should choose different graphical techniques to
illuminate different features of the data. Section 2.3 provides descriptions and examples of
common graphical representations.
At a minimum, the analyst should choose a graphical representation of the individual data
points and a graphical representation of the statistical quantities. If the data set has a spatial or
temporal component, select graphical representations specific to temporal or spatial data in
addition to those that do not. If the data set consists of more than one variable, treat each
variable individually before developing graphical representations for the multiple variables. If the
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sampling plan or suggested analysis methods rely on any critical assumptions, consider whether a
particular type of graph might shed light on the validity of that assumption. For example, if a
small-sample study is strongly dependent on the assumption of normality, then a normal
probability plot would be useful (Section 2.3.6).
The sampling design may influence what data may be included in each representation.
Usually, the graphical representations should be applied to each complete unit of randomization
separately or each unit of randomization should be represented with a different symbol. For
example, the analyst could generate box plots for each stratum instead of generating one box plot
with all the strata.
2.2 STATISTICAL QUANTITIES
2.2.1 Measures of Relative Standing
Sometimes the analyst is interested in knowing the relative position of one of several
observations in relation to all of the observations. Percentiles are one such measure of relative
standing that may also be useful for summarizing data. A percentile is the data value that is
greater than or equal to a given percentage of the data values. Stated in mathematical terms, the
pth percentile is the data value that is greater than or equal to p% of the data values and is less
than or equal to (l-p)% of the data values. Therefore, if V is the pth percentile, then p% of the
values in the data set are less than or equal to x, and (100-p)% of the values are greater than or
equal to x. A sample percentile may fall between a pair of observations. For example, the 75th
percentile of a data set of 10 observations is not uniquely defined. Therefore, there are several
methods for computing sample percentiles, the most common of which is described in Box 2-1.
Important percentiles usually reviewed are the quartiles of the data, the 25th, 50th, and 75th
percentiles. The 50th percentile is also called the sample median (Section 2.2.2), and the 25th and
75th percentile are used to estimate the dispersion of a data set (Section 2.2.3). Also important for
environmental data are the 90th, 95th, and 99th percentile where a decision maker would like to be
sure that 90%, 95%, or 99% of the contamination levels are below a fixed risk level.
A quantile is similar in concept to a percentile; however, a percentile represents a
percentage whereas a quantile represents a fraction. If'x' is the pth percentile, then at least p% of
the values in the data set lie at or below x, and at least (100-p)% of the values lie at or above x,
whereas if x is the p/100 quantile of the data, then the fraction p/100 of the data values lie at or
below x and the fraction (l-p)/100 of the data values lie at or above x. For example, the .95
quantile has the property that .95 of the observations lie at or below x and .05 of the data lie at or
above x. For the example in Box 2-1, 9 ppm would be the .95 quantile and 10 ppm would be the
.99 quantile of the data.
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Box 2-1 : Directions for Calculating the Measure of Relative Standing (Percentiles)
with an Example
Let X.,, X2, ..., Xn represent the n data points. To compute the pth percentile, y(p), first list the data from
smallest to largest and label these points X(1), X(2), . . ., X(n) (so that X(1) is the smallest, X(2) is the
second smallest, and X, n } is the largest). Let t = p/1 00, and multiply the sample size n by t. Divide the
result into the integer part and the fractional part, i.e., let nt = j + g where j is the integer part and g is the
fraction part. Then the pth percentile, y(p), is calculated by:
If 9 = 0, y(p) = (X(j) + X(j + 1))/2
otherwise,
Example: The 90th and 95th percentile will be computed for the following 10 data points (ordered from
smallest to largest) : 4, 4, 4, 5, 5, 6, 7, 7, 8, and 10 ppb.
For the 95th percentile, t = p/1 00 = 95/100= .95 and nt = (10)(.95) = 9.5 = 9 + .5. Therefore,] = 9 and
g = .5. Because g = .5 ? 0, y(95) = X(j + 1) = X(9 + 1) = X(10) = 10 ppm. Therefore, 10 ppm is the 95th
percentile of the above data.
For the 90th percentile, t= p/1 00 = 90/100 = .9 and nt = (10)(.9) = 9. Therefore] = 9 and g = 0. Since g =
0, y(90) = (X(9) + X(10))/2 = (8 + 10) / 2 = 9 ppm.
2.2.2 Measures of Central Tendency
Measures of central tendency characterize the center of a sample of data points. The three
most common estimates are the mean, median, and the mode. Directions for calculating these
quantities are contained in Box 2-2; examples are provided in Box 2-3.
The most commonly used measure of the center of a sample is the sample mean, denoted
by x. This estimate of the center of a sample can be thought of as the "center of gravity" of the
sample. The sample mean is an arithmetic average for simple sampling designs; however, for
complex sampling designs, such as stratification, the sample mean is a weighted arithmetic
average. The sample mean is influenced by extreme values (large or small) and nondetects (see
Section 4.7).
The sample median (x) is the second most popular measure of the center of the data. This
value falls directly in the middle of the data when the measurements are ranked in order from
smallest to largest. This means that l/2 of the data are smaller than the sample median and l/2 of
the data are larger than the sample median. The median is another name for the 50th percentile
(Section 2.2.1). The median is not influenced by extreme values and can easily be used in the case
of censored data (nondetects).
The third method of measuring the center of the data is the mode. The sample mode is the
value of the sample that occurs with the greatest frequency. Since this value may not always
exist, or if it does it may not be unique, this value is the least commonly used. However, the
mode is useful for qualitative data.
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Box 2-2: Directions for Calculating the Measures of Central Tendency
LetX^ X2, ..., Xn represent the n data points.
Sample Mean: The sample mean x is the sum of all the data points divided by the total number of data
points (n):
X = -
n
Sample Median: The sample median (X) is the center of the data when the measurements are ranked in
order from smallest to largest. To compute the sample median, list the data from smallest to largest and
label these points X(1), X(2), . . ., X(n) (so that X(1) is the smallest, X(2) is the second smallest, and X(n) is
the largest).
If the number of data points is odd, then X= -^C-rn+nm
, ..., , , --^*- / i
If the number of data points is even, then X =
Sample Mode: The mode is the value of the sample that occurs with the greatest frequency. The mode
may not exist, or if it does, it may not be unique. To find the mode, count the number of times each
value occurs. The sample mode is the value that occurs most frequently.
Box 2-3: Example Calculations of the Measures of Central Tendency
Using the directions in Box 2-2 and the following 10 data points (in ppm): 4, 5, 6, 7, 4, 10, 4, 5, 7, and 8,
the following is an example of computing the sample mean, median, and mode.
Sample mean:
- 4 + 5 + 6 + 7 + 4+10 + 4 + 5 + 7 + 8 60 ,
X = = — = 6 ppm
10 10
Therefore, the sample mean is 6 ppm.
Sample median: The ordered data are: 4, 4, 4, 5, 5, 6, 7, 7, 8, and 10. Since n=10 is even, the sample
median is
X = X(io/2) + X([io/2]+i) = X(5) + X(6) = 5 + 6 = 5 5
2 2 2
Thus, the sample median is 5.5 ppm.
Sample mode: Computing the number of times each value occurs yields:
4 appears 3 times; 5 appears 2 times; 6 appears 1 time; 7 appears 2 times; 8 appears 1 time; and 10
appears 1 time.
Because the value of 4 ppm appears the most times, it is the mode of this data set.
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2.2.3 Measures of Dispersion
Measures of central tendency are more meaningful if accompanied by information on how
the data spread out from the center. Measures of dispersion in a data set include the range,
variance, sample standard deviation, coefficient of variation, and the interquartile range.
Directions for computing these measures are given in Box 2-4; examples are given in Box 2-5.
The easiest measure of dispersion to compute is the sample range. For small samples, the
range is easy to interpret and may adequately represent the dispersion of the data. For large
samples, the range is not very informative because it only considers (and therefore is greatly
influenced) by extreme values.
The sample variance measures the dispersion from the mean of a data set. A large sample
variance implies that there is a large spread among the data so that the data are not clustered
around the mean. A small sample variance implies that there is little spread among the data so
that most of the data are near the mean. The sample variance is affected by extreme values and by
a large number of nondetects. The sample standard deviation is the square root of the sample
variance and has the same unit of measure as the data.
The coefficient of variation (CV) is a unitless measure that allows the comparison of
dispersion across several sets of data. The CV is often used in environmental applications
because variability (expressed as a standard deviation) is often proportional to the mean.
When extreme values are present, the interquartile range may be more representative of
the dispersion of the data than the standard deviation. This statistical quantity does not depend on
extreme values and is therefore useful when the data include a large number of nondetects.
2.2.4 Measures of Association
Data often include measurements of several characteristics (variables) for each sample
point and there may be interest in knowing the relationship or level of association between two or
more of these variables. One of the most common measures of association is the correlation
coefficient. The correlation coefficient measures the relationship between two variables, such as a
linear relationship between two sets of measurements. However, the correlation coefficient does
not imply cause and effect. The analyst may say that the correlation between two variables is high
and the relationship is strong, but may not say that one variable causes the other variable to
increase or decrease without further evidence and strong statistical controls.
2.2.4.1 Pearson's Correlation Coefficient
The Pearson correlation coefficient measures a linear relationship between two variables.
A linear association implies that as one variable increases so does the other linearly, or as one
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Box 2-4: Directions for Calculating the Measures of Dispersion
LetX.,, X2, ..., Xn represent the n data points.
Sample Range: The sample range (R) is the difference between the largest value and the smallest value
of the sample, i.e., R = maximum - minimum.
Sample Variance: To compute the sample variance (s2), compute:
n ,-=
n-1
Sample Standard Deviation: The sample standard deviation (s) is the square root of the sample
variance, i.e.,
s = \l s2
Coefficient of Variation: The coefficient of variation (CV) is the standard deviation divided by the sample
mean (Section 2.2.2), i.e., CV = s/x. The CV is often expressed as a percentage.
Interquartile Range: Use the directions in Section 2.2.1 to compute the 25th and 75th percentiles of the
data (y(25) and y(75) respectively). The interquartile range (IQR) is the difference between these values,
IQR = y(75) - y(25).
Box 2-5: Example Calculations of the Measures of Dispersion
In this box, the directions in Box 2-4 and the following 10 data points (in ppm): 4, 5, 6, 7, 4, 10, 4, 5, 7,
and 8, are used to calculate the measures of dispersion. From Box 2-2, x = 6 ppm.
Sample Range: R = maximum - minimum =10-4 = 6 ppm
Sample Variance:
[42 + 52+ + ?2+g2] _ (4 +
2 10 10 A 2
s = - = - = 4 ppm
10-1 9
Sample Standard Deviation: s = \Js2 =
-------
variable decreases the other increases linearly. Values of the correlation coefficient close to +1
(positive correlation) imply that as one variable increases so does the other, the reverse holds for
values close to -1. A value of+1 implies a perfect positive linear correlation, i.e., all the data
pairs lie on a straight line with a positive slope. A value of-1 implies perfect negative linear
correlation. Values close to 0 imply little correlation between the variables. Directions and an
example for calculating Pearson's correlation coefficient are contained in Box 2-6.
Box 2-6: Directions for Calculating Pearson's Correlation Coefficient with an Example
LetX.,, X2, ..., Xn represent one variable of the n data points and let Y.,, Y2, ..., Yn represent a second
variable of the n data points. The Pearson correlation coefficient, r, between X and Y is computed by:
n
i= 1
n n
( \ ^V ^2 / \ ^v \2
/-i ' , ryy2 ,--i ,
J L ZJ i J
« ,= i n
1/2
Example: Consider the following data set (in ppb): Sample 1 — arsenic (X) = 8.0, lead (Y) = 8.0;
Sample 2 - arsenic = 6.0, lead = 7.0; Sample 3 - arsenic = 2.0, lead = 7.0; and Sample 4 - arsenic = 1.0,
lead = 6.0.
n n n n n
\ "V- _ i n \ V - ">9 \ "V"^— 1 fl^ \ V2_ 1 QO \ "V V - ^SvJ?\ _i_ _i_ ^1v/C\ - 1 T^
/_A - 1U, Jl .- Zo, 7-ji ~ ^•"•'5 / /, - lyo, / .A 1 - (oXo) + . . . + \iXO) — 1ZO.
;'= 1 ;'= 1 ;'= 1 ;'= 1 ;'= 1
126_ 02X28)
and r =
[105 - [198 -
4 4
1/2
= 0.865
Since r is close to 1, there is a strong linear relationship between these two contaminants.
The correlation coefficient does not detect nonlinear relationships so it should be used
only in conjunction with a scatter plot (Section 2.3.7.2). A scatter plot can be used to determine
if the correlation coefficient is meaningful or if some measure of nonlinear relationships should be
used. The correlation coefficient can be significantly changed by extreme values so a scatter plot
should be used first to identify such values.
An important property of the correlation coefficient is that it is unaffected by changes in
location of the data (adding or subtracting a constant from all of the X measurements and/or the
Y measurements) and by changes in scale of the data and/or Y values by a positive constant).
Thus linear transformations on the Xs and Ys do not affect the correlation of the measurements.
This is reasonable since the correlation reflects the degree to which linearity between X and Y
measurements occur and the degree of linearity is unaffected by changes in location or scale. For
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example, if a variable was temperature in Celsius, then the correlation should not change if Celsius
was converted to Fahrenheit.
On the other hand, if nonlinear transformations of the X and/or Y measurements are made,
then the Pearson correlation between the transformed values will differ from the correlation of the
original measurements. For example, if X and Y, respectively, represent PCB and dioxin
concentrations in soil, and x=log (X) and y=log(Y), then the Pearson correlations between X and
Y, X and y x and Y, and x and y, will all be different, in general, since the logarithmic
transformation is a nonlinear transformation.
Pearson's correlation may be sensitive to the presence of one or two extreme values,
especially when sample sizes are small. Such values may result in a high correlation, suggesting a
strong linear trend, when only moderate trend is present. This may happen, for instance, if a
single (X,Y) pair has very high values for both X and Y while the remaining data values are
uncorrelated. Extreme value may also lead to low correlations between X and Y, thus tending to
mask a strong linear trend. This may happen if all the (X, Y) pairs except one (or two) tend to
cluster tightly about a straight line, and the exceptional point has a very large X value paired with
a moderate or small Y value (or vice versa). Because of the influences of extreme values, it is
wise to use a scatter plot (Section 2.3.7.2) in conjunction with a correlation coefficient.
2.2.4.2 Spearman's Rank Correlation Coefficient
An alternative to the Pearson correlation is Spearman's rank correlation coefficient. It is
calculated by first replacing each X value by its rank (i.e., 1 for the smallest X value, 2 for the
second smallest, etc.) and each Y value by its rank. These pairs of ranks are then treated as the
(X,Y) data and Spearman's rank correlation is calculated using the same formulae as for
Pearson's correlation (Box 2-6). Directions and an example for calculating a correlation
coefficient are contained in Box 2-7.
Since meaningful (i.e., monotonic increasing) transformations of the data will not later the
ranks of the respective variables (e.g, the ranks for log (X) will be the same for the ranks for X),
Spearman's correlation will not be altered by nonlinear increasing transformations of the Xs or the
Ys. For instance, the Spearman correlation between PCB and dioxin concentrations (X and Y) in
soil will be the same as the correlations between their logarithms (x and y). This desirable
property and the fact that Spearman's correlation is less sensitive to extreme values than
Pearson's correlation make Spearman's correlation an attractive alternative or complement of
Pearson's correlation coefficient. There are some important theoretical differences between
Pearson's and Spearman's correlation but a full discussion is beyond this guidance. In general,
Pearson's correlation has a higher statistical power than Spearman's, although the latter has some
more varied applications.
2.2.4.3 Serial Correlation Coefficient
When data are truly independent, the correlation between data points is zero. For a
sequence of data points taken serially in time, or one-by-one in a row, the serial correlation
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Box 2-7: Directions for Calculating Spearman's Correlation Coefficient with an Example
LetX.,, X2, ..., Xn represent a set of ranks of the n data points of one data set and let Y.,, Y2, ..., Yn
represent a set of ranks of a second variable of the n data points. The Spearman correlation coefficient,
r, between X and Y is computed by:
v
~
i= 1 !'= 1
;=!
;=1
1/2
(15)
Example: Consider the following data set (in ppb): Sample 1 — arsenic (X) = 8.0, lead (Y) = 8.0;
Sample 2 - arsenic = 6.0, lead = 7.0; Sample 3 - arsenic = 2.0, lead = 7.0; and Sample 4 - arsenic = 1.0,
lead = 6.0.
Using Arsenic rank the data smallest to largest:
Sample No.
Arsenic
Lead
4
1.0
6.0
3
2.0
7.0
2
6.0
7.0
1
8.0
8.0
Convert the raw data to ranks, any ties being made an average of what ranks should have been
assigned.
Sample No.
Arsenic Rank
Lead Rank
3
2
2.5
2
3
2.5
4
4
4
(X)
(Y)
Note how 7.0 (two lead observations) was converted to the average rank (i.e., ranks 2 and 3, therefore
2.5 each).
;= 1
and r =
,.= 10,
;= 1 ;= 1
...+ (4x4) = 29.5.
;= 1
;= 1
[30 -
[29 5 _
1/2
= 0.948
4 4
Since r is close to 1, there is a strong linear relationship between these two contaminants.
coefficient can be calculated by replacing the sequencing variable by the numbers 1 through n and
calculating Pearon's correlation coefficient with z being the actual data values, and y being the
numbers 1 through n. For example, for a sequence of data collected at a waste site along a
straight transit line, the distances on the transit line of the data points are replaced by the numbers
1 through n, e.g., first 10-foot sample point = 1, the 20-foot sample point = 2, the 30-foot sample
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point = 3, etc., for samples taken at 10-foot intervals. Directions for the Serial correlation
coefficient, along with an example, are given in Box 2-8.
Box 2-8: Directions for Estimating the
Serial Correlation Coefficient with a Example
Directions:
LetX.,, X2, . . . , Xn represent the data values collected in sequence over equally spared periods of
time. Label the periods of time 1, 2, ..., n to match the data values. Use the directions in Box 2-6 to calculate
the Pearson's Correlation Coefficient between the data X and the time periods Y.
Example: The following are hourly readings from a discharge monitor. Notice how the actual 24-hour times are
replaced by the numbers 1 through 13.
Time
Reading
Time
Periods
12:00
6.5
1
13:00
6.6
2
14:00
6.7
3
15:00
6.4
4
16:00
6.3
5
17:00
6.4
6
18:00
6.2
7
19:00
6.2
8
20:00
6.3
9
21:00
6.6
10
22:00
6.8
11
23:00
6.9
12
24:00
7.0
13
Using Box 2-6, with the readings being the X values and the Time Periods being the Y values gives a serial
correlation coefficient of 0.432.
2.3 GRAPHICAL REPRESENTATIONS
2.3.1 Histogram/Frequency Plots
10
Two of the oldest methods for summarizing data distributions are the frequency plot
(Figure 2-1) and the histogram (Figure 2-2).
Both the histogram and the frequency plot use
the same basic principles to display the data:
dividing the data range into units, counting the
number of points within the units, and
displaying the data as the height or area within
a bar graph. There are slight differences
between the histogram and the frequency plot.
In the frequency plot, the relative height of the
bars represents the relative density of the data.
In a histogram, the area within the bar
represents the relative density of the data. The
difference between the two plots becomes
more distinct when unequal box sizes are used.
01 6
M
.Q
O
10
15 20 25
Concentration (ppm)
30
35
40
Figure 2-1. Example of a Frequency Plot
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The histogram and frequency plot
provide a means of assessing the symmetry and
variability of the data. If the data are symmetric,
then the structure of these plots will be
symmetric around a central point such as a
mean. The histogram and frequency plots will
generally indicate if the data are skewed and the
direction of the skewness.
Directions for generating a histogram
and a frequency plot are contained in Box 2-9
and an example is contained in Box 2-10. When
plotting a histogram for a continuous variable
(e.g., concentration), it is necessary to decide on
an endpoint convention; that is, what to do with cases that fall on the boundary of a box. With
discrete variables, (e.g., family size) the intervals can be centered in between the variables. For
the family size data, the intervals can span between 1.5 and 2.5, 2.5 and 3.5, and so on, so that the
whole numbers that relate to the family size can be centered within the box. The visual
impression conveyed by a histogram or a frequency plot can be quite sensitive to the choice of
interval width. The choice of the number of intervals determines whether the histogram shows
more detail for small sections of the data or whether the data will be displayed more simply as a
smooth overview of the distribution.
1 Percentage of Observations (per ppm)
O M ^ O) 00 |
-
) 5 10 15 20 25 30 35 40
Concentration (ppm)
Figure 2-2. Example of a Histogram
Box 2-9: Directions for Generating a Histogram and a Frequency Plot
LetX.,, X2, ..., Xn represent the n data points. To develop a histogram or a frequency plot:
STEP 1: Select intervals that cover the range of observations. If possible, these intervals should have
equal widths. A rule of thumb is to have between 7 to 11 intervals. If necessary, specify an
endpoint convention, i.e., what to do with cases that fall on interval endpoints.
STEP 2: Compute the number of observations within each interval. For a frequency plot with equal
interval sizes, the number of observations represents the height of the boxes on the frequency
plot.
STEP 3: Determine the horizontal axis based on the range of the data. The vertical axis for a frequency
plot is the number of observations. The vertical axis of the histogram is based on percentages.
STEP 4: For a histogram, compute the percentage of observations within each interval by dividing the
number of observations within each interval (Step 3) by the total number of observations.
STEP 5: For a histogram, select a common unit that corresponds to the x-axis. Compute the number of
common units in each interval and divide the percentage of observations within each interval
(Step 4) by this number. This step is only necessary when the intervals (Step 1) are not of
equal widths.
STEP 6: Using boxes, plot the intervals against the results of Step 5 for a histogram or the intervals
against the number of observations in an interval (Step 2) for a frequency plot.
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Consider
17.2 19.1
STEP 1:
STEP 2:
STEP 3:
STEP 4:
STEP 5:
STEP 6:
Box 2-10: Example of Generating a Histogram and a Frequency Plot
the following 22 samples of a contaminant concentration (in ppm): 17.7, 17.4, 22.8, 35.5, 28.6,
<4 72 <4 152 147 149 109 124 124 116 147 102 52 165 and 8 9
This data spans 0-40 ppm. Equally sized intervals of 5 ppm will be used: 0-5 ppm; 5-10
ppm; etc. The endpoint convention will be that values are placed in the highest interval
containing the value. For example, a value of 5 ppm will be placed in the interval 5-10 ppm
instead of 0 - 5 ppm.
The table below shows the number of observations within each interval defined in Step 1 .
The horizontal axis for the data is from 0 to 40 ppm. The vertical axis for the frequency plot is
from 0-10 and the vertical axis for the histogram is from 0% - 10%.
There are 22 observations total, so the number observations shown in the table below will be
divided by 22. The results are shown in column 3 of the table below.
A common unit for this data is 1 ppm. In each interval there are 5 common units so the
percentage of observations (column 3 of the table below) should be divided by 5 (column 4).
The frequency plot is shown in Figure 2-1 and the histogram is shown in Figure 2-2.
# of Obs % of Obs % of Obs
Interval in Interval in Interval per ppm
0 - 5 ppm 2 9.10 1.8
5 -10 ppm 3 13.60 2.7
10 -15 ppm 8 36.36 7.3
15 -20 ppm 6 27.27 5.5
20 -25 ppm 1 4.55 0.9
25 -30 ppm 1 4.55 0.9
30 - 35 ppm 0 0.00 0.0
35 -40 ppm 1 4.55 0.9
2.3.2 Stem-and-LeafPlot
The stem-and-leaf plot is used to show both the numerical values themselves and
information about the distribution of the data. It is a useful method for storing data in a compact
form while, at the same time, sorting the data from smallest to largest. A stem-and-leaf plot can
be more useful in analyzing data than a histogram because it not only allows a visualization of the
data distribution, but enables the data to be reconstructed and lists the observations in the order of
magnitude. However, the stem-and-leaf plot is one of the more subjective visualization
techniques because it requires the analyst to make some arbitrary choices regarding a partitioning
of the data. Therefore, this technique may require some practice or trial and error before a useful
plot can be created. As a result, the stem-and-leaf plot should only be used to develop a picture
of the data and its characteristics. Directions for constructing a stem-and-leaf plot are given in
Box 2-11 and an example is contained in Box 2-12.
Each observation in the stem-and-leaf plot consist of two parts: the stem of the
observation and the leaf. The stem is generally made up of the leading digit of the numerical
values while the leaf is made up of trailing digits in the order that corresponds to the order of
magnitude from left to right. The stem is displayed on the vertical axis and the data points make
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Box 2-11: Directions for Generating a Stem and Leaf Plot
Let X.,, X2, ..., Xn represent the n data points. To develop a stem-and-leaf plot, complete the following steps:
STEP 1: Arrange the observations in ascending order. The ordered data is usually labeled (from smallest
to largest) X(1),X, 2,, ..., X(n).
STEP 2: Choose either one or more of the leading digits to be the stem values. As an example, for the
value 16, 1 could be used as the stem as it is the leading digit.
STEP 3: List the stem values from smallest to largest at the left (along a vertical axis). Enter the leaf (the
remaining digits) values in order from lowest to highest to the right of the stem. Using the value
16 as an example, if the 1 is the stem then the 6 will be the leaf.
Box 2-12: Example of Generating a Stem and Leaf Plot
Consider the following 22 samples of trifluorine (in ppm): 17.7, 17.4, 22.8, 35.5, 28.6, 17.2 19.1, <4, 7.2,
<4, 15.2, 14.7, 14.9, 10.9, 12.4, 12.4, 11.6, 14.7, 10.2, 5.2, 16.5, and 8.9.
STEP 1: Arrange the observations in ascending order: <4, <4, 5.2, 7.7, 8.9, 10.2, 10.9, 11.6, 12.4, 12.4,
14.7, 14.7, 14.9, 15.2, 16.5, 17.2, 17.4, 17.7, 19.1, 22.8, 28.6, 35.5.
STEP 2: Choose either one or more of the leading digits to be the stem values. For the above data, using
the first digit as the stem does not provide enough detail for analysis. Therefore, the first digit
will be used as a stem; however, each stem will have two rows, one for the leaves 0-4, the other
for the leaves 5-9.
STEP 3: List the stem values at the left (along a vertical axis) from smallest to largest. Enter the leaf (the
remaining digits) values in order from lowest to highest to the right of the stem. The first digit of
the data was used as the stem values; however, each stem value has two leaf rows.
0(0,1,2,3,4) | <4 <4
0(5, 6, 7, 8, 9) | 5.2 7.7 8.9
1 (0, 1, 2, 3, 4) | 0.2 0.9 1.6 2.4 2.4 4.7 4.7 4.9
1 (5, 6, 7, 8, 9) | 5.2 6.5 7.2 7.4 7.7 9.1
2(0,1,2,3,4) | 2.8
2 (5, 6, 7, 8, 9) | 8.6
3(0,1,2,3,4) |
3(5,6,7,8,9) | 5.5
Note: If nondetects are present, place them first in the ordered list, using a symbol such as
-------
up the magnitude from left to right. The stem is displayed on the vertical axis and the data points
make up the leaves. Changing the stem can be accomplished by increasing or decreasing the
digits that are used, dividing the groupings of one stem (i.e., all numbers which start with the
numeral 6 can be divided into smaller groupings), or multiplying the data by a constant factor (i.e.,
multiply the data by 10 or 100). Nondetects can be placed in a single stem.
A stem-and-leaf plot roughly displays the distribution of the data. For example, the stem-
and-leaf plot of normally distributed data is approximately bell shaped. Since the stem-and-leaf
roughly displays the distribution of the data, the plot may be used to evaluate whether the data are
skewed or symmetric. The top half of the stem-and-leaf plot will be a mirror image of the bottom
half of the stem-and-leaf plot for symmetric data. Data that are skewed to the left will have the
bulk of data in the top of the plot and less data spread out over the bottom of the plot.
2.3.3 Box and Whisker Plot
A box and whisker plot or box plot (Figure 2-3) is a schematic diagram useful for
visualizing important statistical quantities of the data. Box plots are useful in situations where it is
not necessary or feasible to portray all the details of a distribution. Directions for generating a
box and whiskers plot are contained in Box 2-13, and an example is contained in Box 2-14.
A box and whiskers plot is composed of a central box divided by a
line and two lines extending out from the box called whiskers. The length of
the central box indicates the spread of the bulk of the data (the central 50%)
while the length of the whiskers show how stretched the tails of the
distribution are. The width of the box has no particular meaning; the plot
can be made quite narrow without affecting its visual impact. The sample
median is displayed as a line through the box and the sample mean is
displayed using a '+' sign. Any unusually small or large data points are
displayed by a '*' on the plot. A box and whiskers plot can be used to
assess the symmetry of the data. If the distribution is symmetrical, then the
box is divided in two equal halves by the median, the whiskers will be the
same length and the number of extreme data points will be distributed
equally on either end of the plot.
2.3.4 Ranked Data Plot
Figure 2-3.
Example of a Box
and Whisker Plot
A ranked data plot is a useful graphical representation that is easy to
construct, easy to interpret, and makes no assumptions about a model for the data. The analyst
does not have to make any arbitrary choices regarding the data to construct a ranked data plot
(such as cell sizes for a histogram). In addition, a ranked data plot displays every data point;
therefore, it is a graphical representation of the data instead of a summary of the data. Directions
for developing a ranked data plot are given in Box 2-15 and an example is given in Box 2-16.
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Box 2-13: Directions for Generating a Box and Whiskers Plot
STEP 1 :
STEP 2:
Set the vertical scale of the plot based on the maximum and minimum values of the data set.
Select a width for the box plot keeping in mind that the width is only a visualization tool. Label the
width w; the horizontal scale then ranges from -1/4Wto 1/
Compute the upper quartile (Q(.75), the 75th percentile) and the lower quartile (Q(.25), the 25th
percentile) using Box 2-1. Compute the sample mean and median using Box 2-2. Then, compute
the interquartile range (IQR) where IQR = Q(.75) - Q(.25).
STEP 3: Draw a box through points ( -1/2W, Q(.75) ), ( -1/2W, Q (.25) ), ( 1/2W, Q(.25) ) and ( 1/2W, Q(.75) ).
Draw a line from (1/4W, Q(.5)) to (-1/4W, Q(.5)) and mark point (0, x) with (+).
STEP 4'. Compute the upper end of the top whisker by finding the largest data value X less than
Q(.75) + 1.5( Q(.75) - Q(.25) ). Draw a line from (0, Q(.75)) to (0, X).
Compute the lower end of the bottom whisker by finding the smallest data value Y greater than
Q(.25) - 1.5( Q(.75) - Q(.25) ). Draw a line from (0, Q(.25)) to (0, Y).
STEP 5: For all points X* > X, place an asterisk (*) at the point (0, X*).
For all points Y* < Y, place an asterisk (*) at the point (0, Y*).
Box 2-14: Example of a Box and Whiskers Plot
Consider the following 22 samples of trifluorine (in ppm) listed in order from smallest to largest: 4.0, 6.1, 9.8,
10.7, 10.8, 11.5, 11.6, 12.4, 12.4, 14.6, 14.7, 14.7, 16.5, 17, 17.5, 20.6, 20.8, 25.7, 25.9, 26.5, 32.0, and 35.5.
STEP 1: The data ranges from 4.0 to 35.5 ppm. This is the range of the vertical axis. Arbitrarily, a width of
4 will be used for the horizontal axis.
STEP 2: Using the formulas in Box 2-2, the sample mean = 16.87 and the median = 14.70. Using Box 2-1,
Q(.75) = 20.8 and Q(.25) = 11.5. Therefore, IQR = 20.8 -11.5 =
9.3.
STEP 3: In the figure, a box has been drawn through points (-2, 20.8),
(-2, 11.5), ( 2, 11.5 ), (2, 20.8). A line has been drawn from (-2 ,
14.7 ) to ( 2, 14.7 ), and the point (0, 16.87) has been marked with
a '+' sign.
STEP 4: Q(.75) + 1.5(9.3) = 34.75. The closest data value to this number,
but less than it, is 32.0. Therefore, a line has been drawn in the
figure from ( 0, 20.8) to (0, 32.0).
Q(.25) -1.5( 9.3 ) = -2.45. The closest data value to this number,
but greater than it, is 4.0. Therefore, a line has been drawn in the
figure from ( 0, 4) to ( 0, 11.5).
STEP 5: There is only 1 data value greater than 32.0 which is 35.5.
Therefore, the point ( 0, 35.5) has been marked with an asterisk.
There are no data values less than 4.0.
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Box 2-15: Directions for Generating a Ranked Data Plot
LetX.,, X2, ..., Xn represent the n data points. LetX(i), for i=1 to
n, be the data listed in order from smallest to largest so that X,.,}
(i = 1) is the smallest, X(2) (i = 2) is the second smallest, and X(n
) (i = n) is the largest. To generate a ranked data plot, plot the
ordered X values at equally spaced intervals along the horizontal
axis.
Box 2-16: Example of Generating a Ranked Data Plot
Consider the following 22 samples of triflourine (in ppm): 17.7, 17.4, 22.8, 35.5, 28.6, 17.2
191 49 72 40 152 147 149 109 124 124 116 147 102 52 165 and 8 9 The
data listed in order from smallest to largest X(i) along with the ordered number of the
observation (i) are:
1~
2
3
4
5
6
7
8
9
10
11
A ranked data
4.0" 12 14.7"
4.9 13 14.9
5.2 14 15.2
7.7 15 16.5
8.9 16 17.2
10.2 17 17.4
10.9 18 17.7
11.6 19 19.1
12.4 20 22.8
12.4 21 28.6
14.7 22 35.5
plot of this data is a plot of the pairs ( i, X(i)). This plot is shown below:
40
35
30
Q.
5=20
ro
° 15
10
5
0
-
-
"
. • • *
•
. *
I I I I I I I I I I I I I I I I I I I I I I I
Smallest ^ Largest
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A ranked data plot is a plot of the data from smallest to largest at evenly spaced intervals
(Figure 2-4). This graphical representation is very similar to the quantile plot described in Section
2.3.5. A ranked data plot is marginally easier to generate than a quantile plot; however, a ranked
data plot does not contain as much information as a quantile plot. Both plots can be used to
determine the density of the data points and the skewness of the data; however, a quantile plot
contains information on the quartiles of the data whereas a ranked data plot does not.
Smallest
Largest
Figure 2-4. Example of a Ranked Data Plot
A ranked data plot can be used to determine the density of the data values, i.e., if all the
data values are close to the center of the data with relatively few values in the tails or if there is a
large amount of values in one tail with the rest evenly distributed. The density of the data is
displayed through the slope of the graph. A large amount of data values has a flat slope, i.e., the
graph rises slowly. A small amount of data values has a large slope, i.e., the graph rises quickly.
Thus the analyst can determine where the data lie, either evenly distributed or in large clusters of
points. In Figure 2-4, the data rises slowly up to a point where the slope increases and the graph
rises relatively quickly. This means that there is a large amount of small data values and relatively
few large data values.
A ranked data plot can be used to determine if the data are skewed or if they are
symmetric. A ranked data plot of data that are skewed to the right extends more sharply at the
top giving the graph a convex shape. A ranked data plot of data that are skewed to the left
increases sharply near the bottom giving the graph a concave shape. If the data are symmetric,
then the top portion of the graph will stretch to upper right corner in the same way the bottom
portion of the graph stretches to lower left, creating a s-shape. Figure 2-4 shows a ranked data
plot of data that are skewed to the right.
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2.3.5 Quantile Plot
A quantile plot (Figure 2-5) is a graphical representation of the data that is easy to
construct, easy to interpret, and makes no assumptions about a model for the data. The analyst
does not have to make any arbitrary choices regarding the data to construct a quantile plot (such
as cell sizes for a histogram). In addition, a quantile plot displays every data point; therefore, it is
a graphical representation of the data instead of a summary of the data.
0.2 0.4 0.6 0.8
Fraction of Data (f-values)
Figure 2-5. Example of a Quantile Plot of Skewed Data
A quantile plot is a graph of the quantiles (Section 2.2.1) of the data. The basic quantile
plot is visually identical to a ranked data plot except its horizontal axis varies from 0.0 to 1.0, with
each point plotted according to the fraction of the points it exceeds. This allows the addition of
vertical lines indicating the quartiles or, any other quantiles of interest. Directions for developing
a quantile plot are given in Box 2-17 and an example is given in Box 2-18.
A quantile plot can be used to read the quantile information such as the median, quartiles,
and the interquartile range. In addition, the plot can be used to determine the density of the data
points, e.g., are all the data values close to the center with relatively few values in the tails or are
there a large amount of values in one tail with the rest evenly distributed? The density of the data
is displayed through the slope of the graph. A large amount of data values has a flat slope, i.e.,
the graph rises slowly. A small amount of data values has a large slope, i.e., the graph rises
quickly. A quantile plot can be used to determine if the data are skewed or if they are symmetric.
A quantile plot of data that are skewed to the right is steeper at the top right than the bottom left,
as in Figure 2-5. A quantile plot of data that are skewed to the left increases sharply near the
bottom left of the graph. If the data are symmetric then the top portion of the graph will stretch
to the upper right corner in the same way the bottom portion of the graph stretches to the lower
left, creating an s-shape.
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Box 2-17: Directions for Generating a Quantile Plot
LetX.,, X2, ..., Xn represent the n data points. To obtain a quantile plot, letX(i),
for
i = 1 to n, be the data listed in order from smallest to largest so that X,.,} (i = 1)
is the smallest, X(2) (i = 2) is the second smallest, and X(n) (i = n) is the
largest. For each i, compute the fraction f,= (i - 0.5)/n. The quantile plot is a
plot of the pairs (fh X(i)), with straight lines connecting consecutive points.
Box 2-18: Example of Generating a Quantile Plot
Consider the following 10 data points: 4 ppm, 5 ppm, 6 ppm, 7 ppm, 4 ppm, 10 ppm, 4 ppm, 5 ppm, 7
ppm, and 8 ppm. The data ordered from smallest to largest, X(i), are shown in the first column of the
table below and the ordered number for each observation, i, is shown in the second column. The third
column displays the values f, for each i where f,= (i - 0.5)/n.
4
4
4
5
5
1
2
3
4
5
0.05
0.15
0.25
0.35
0.45
6
7
7
8
10
I
6
7
8
9
10
0.55
0.65
0.75
0.85
0.95
The pairs (fh X(i)) are then plotted to yield the following quantile plot:
10
ro
•%
a 6
0 0.2 0.4 0.6 0.8 1
Fraction of Data (f-values)
Note that the graph curves upward; therefore, the data appear to be skewed to the right.
2.3.6 Normal Probability Plot (Quantile-Quantile Plot)
There are two types of quantile-quantile plots or q-q plots. The first type, an empirical
quantile-quantile plot (Section 2.3.7.4), involves plotting the quantiles of two data variables
against each other. The second type of a quantile-quantile plot, a theoretical quantile-quantile
plot, involves graphing the quantiles of a set of data against the quantiles of a specific distribution.
The following discussion will focus on the most common of these plots for environmental data,
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the normal probability plot (the normal q-q plot); however, the discussion holds for other q-q
plots. The normal probability plot is used to roughly determine how well the data set is modeled
by a normal distribution. Formal tests are contained in Chapter 4, Section 2. Directions for
developing a normal probability plot are given in Box 2-19 and an example is given in Box 2-20.
A discussion of the normal distribution is contained in Section 2.4.
A normal probability plot is the graph of the quantiles of a data set against the quantiles of
the normal distribution using normal probability graph paper (Figure 2-6). If the graph is linear,
the data may be normally distributed. If the graph is not linear, the departures from linearity give
important information about how the data distribution deviates from a normal distribution.
If the graph of the normal probability plot is not linear, the graph may be used to
determine the degree of symmetry (or asymmetry) displayed by the data. If the data in the upper
tail fall above and the data in the lower tail fall below the quartile line, the data are too slender to
be well modeled by a normal distribution, i.e., there are fewer values in the tails of the data set
than what is expected from a normal distribution. If the data in the upper tail fall below and the
data in the lower tail fall above the quartile line, then the tails of the data are too heavy to be well
modeled using a normal distribution, i.e., there are more values in the tails of the data than what is
expected from a normal distribution. A normal probability plot can be used to identify potential
outliers. A data value (or a few data values) much larger or much smaller than the rest will cause
the other data values to be compressed into the middle of the graph, ruining the resolution.
Box 2-19: Directions for Constructing a Normal Probability Plot
LetX.,, X2, ..., Xn represent the n data points.
STEP 1: For each data value, compute the absolute frequency, AF,. The absolute frequency is the
number of times each value occurs. For distinct values, the absolute frequency is 1 . For
non-distinct observations, count the number of times an observation occurs. For example,
consider the data 1, 2, 3, 3. The absolute frequency of value 1 is 1 and the absolute
frequency of value 2 is 1. The absolute frequency of value 3 is 2 since 3 appears 2 times
in the data set.
STEP 2: Compute the cumulative frequencies, CF,. The cumulative frequency is the number of data
;'
points that are less than or equal to Xh i.e., CFt = /AF .. Using the data given in step
y=i
2, the cumulative frequency for value 1 is 1, the cumulative frequency for value 2 is 2
(1 + 1), and the cumulative frequency for value 3 is 4 (1 + 1+2).
CFi
STEP 3: Compute Y = 100 x - and plot the pairs (Yh X,) using normal probability paper
(Figure 2-6). If the graph of these pairs approximately forms a straight line, then the data
are probably normally distributed. Otherwise, the data may not be normally distributed.
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Box 2-20: Example of Normal Probability Plot
Considerthe following 15 data points: 5, 5, 6, 6, 8, 8, 9, 10, 10, 10, 10, 10, 12, 14, and 15.
STEP 1: Because the value 5 appears 2 times, its absolute frequency is 2. Similarly, the absolute frequency
of 6 is 2, of 8 is 2, of 9 is 1, of 10 is 5, etc. These values are shown in the second column of the
table below.
STEP 2: The cumulative frequency of the data value 8 is 6 because there are 2 values of 5, 2 values of 6, and
2 values of 8. The cumulative frequencies are shown in the 3rd column of the table.
CFt
STEP 3: The values Y. = 100 x ( )for each data point are shown in column 4 of the table below. A
n+ 1
plot of these pairs (Yh X,) using normal probability paper is also shown below.
i
1
2
3
4
5
6
7
8
Individual
Xj
5
6
8
9
10
12
14
15
Absolute
Frequency AFi
2
2
2
1
5
1
1
1
Cumulative
Frequency CFi
2
4
6
7
12
13
14
15
Y:
12.50
25.00
37.50
43.75
75.00
81.25
87.50
93.75
X
18
14
12
10
20 30 40 50 60 70 80
Y
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20 30 40 50 60 70 80 90 95 98 99
Figure 2-6. Normal Probability Paper
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2.3.7 Plots for Two or More Variables
Data often consist of measurements of several characteristics (variables) for each sample
point in the data set. For example, a data set may consist of measurements of weight, sex, and
age for each animal in a sample or may consist of daily temperature readings for several cities. In
this case, graphs may be used to compare and contrast different variables. For example, the
analyst may wish to compare and contrast the temperature readings for different cities, or
different sample points (each containing several variables) such the height, weight, and sex across
individuals in a study.
To compare and contrast individual data points, some special plots have been developed
to display multiple variables. These plots are discussed in Section 2.3.7.1. To compare and
contrast several variables, collections of the single variable displays described in previous sections
are useful. For example, the analyst may generate box and whisker plots or histograms for each
variable using the same axis for all of the variables. Separate plots for each variable may be
overlaid on one graph, such as overlaying quantile plots for each variable on one graph. Another
useful technique for comparing two variables is to place the stem and leaf plots back to back. In
addition, some special plots have been developed to display two or more variables. These plots
are described in Sections 2.3.7.2 through 2.3.7.4.
2.3.7.1 Plots for Individual Data Points
Since it is difficult to visualize data in more than 2 or 3 dimensions, most of the plots
developed to display multiple variables for individual data points involve representing each
variable as a distinct piece of a two-dimensional figure. Some such plots include Profiles, Glyphs,
and Stars (Figure 2-7). These graphical representations start with a specific symbol to represent
each data point, then modify the various features of the symbol in proportion to the magnitude of
each variable. The proportion of the magnitude is determined by letting the minimum value for
each variable be of length 0, the maximum be of length 1. The remaining values of each variable
are then proportioned based on the magnitude of each value in relation to the maximum and
minimum.
Profile Plot
Glyph Plot
Star Plot
Figure 2-7. Example of Graphical Representations of
Multiple Variables
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A profile plot starts with a line segment of a fixed length. Then lines spaced an equal
distance apart and extended perpendicular to the line segment represent each variable. A glyph
plot uses a circle of fixed radius. From the perimeter, parallel rays whose sizes are proportional to
the magnitude of the variable extend from the top half of the circle. A star plot starts with a point
where rays spaced evenly around the circle represent each variable and a polygon is then drawn
around the outside edge of the rays.
2.3.7.2
Scatter Plot
For data sets consisting of paired observations where two or more continuous variables
are measured for each sampling point, a scatter plot is one of the most powerful tools for
analyzing the relationship between two or more variables. Scatter plots are easy to construct for
two variables (Figure 2-8) and many computer graphics packages can construct 3-dimensional
scatter plots. Directions for constructing a scatter plot for two variables are given in Box 2-21
along with an example.
40
30
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Q.
^20
"•
10
°c
>K ^
-
*
^ ^ >K
* i*r"% " **
2468
Chromium VI (ppb)
Figure 2-8. Example of a Scatter Plot
A scatter plot clearly shows the relationship between two variables. Both potential
outliers from a single variable and potential outliers from the paired variables may be identified on
this plot. A scatter plot also displays the correlation between the two variables. Scatter plots of
highly linearly correlated variables cluster compactly around a straight line. In addition, nonlinear
patterns may be obvious on a scatter plot. For example, consider two variables where one
variable is approximately equal to the square of the other. A scatter plot of this data would
display a u-shaped (parabolic) curve. Another important feature that can be detected using a
scatter plot is any clustering effect among the data.
2.3.7.3
Extensions of the Scatter Plot
It is easy to construct a 2-dimensional scatter plot by hand and many software packages
can construct a useful 3-dimensional scatter plot. However, with more than 3 variables, it is
difficult to construct and interpret a scatter plot. Therefore, several graphical representations
have been developed that extend the idea of a scatter plot for data consisting of 2 or more
variables.
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Box 2-21: Directions for Generating a Scatter Plot and an Example
LetX.,, X2, ..., Xn represent one variable of the n data points and let Y.,, Y2, ..., Yn represent a second variable of
the n data points. The paired data can be written as (Xh Y,) for i = 1 , ..., n. To construct a scatter plot, plot the
first variable along the horizontal axis and the second variable along the vertical axis. It does not matter which
variable is placed on which axis.
Example: /
and Chrom
\ scatter plot will be developed for the data below. PCE values are displayed on the vertical axis
um VI values are displayed on the horizontal axis of Figure 2-8.
PCE
(PPb)
14.49
37.21
10.78
18.62
7.44
37.84
13.59
4.31
Chromium
VI (ppb)
3.76
6.92
1.05
6.30
1.43
6.38
5.07
3.56
PCE
(PPb)
2.23
3.51
6.42
2.98
3.04
12.60
3.56
7.72
Chromium
VI (ppb)
0.77
1.24
3.48
1.02
1.15
5.44
2.49
3.01
PCE
(PPb)
4.14
3.26
5.22
4.02
6.30
8.22
1.32
7.73
5.88
Chromium
VI (ppb)
2.36
0.68
0.65
0.68
1.93
3.48
2.73
1.61
1.42
30
20
10
Chromium vs. PCE
Atrazine vs. PCE
Atrazine vs. Chromium IV
The simplest of these graphical representations is a coded scatter plot. In this case, all
possible pairs of data are given a code and plotted on one scatter plot. For example, consider a
data set of 3 variables: variable A, variable B, and variable C. Using the first variable to
designate the horizontal axis, the
analyst may choose to display the
pairs (A, B) using an X, the pairs (A,
C) using a Y, and the pairs (B, C)
using a Z on one scatter plot. All of
the information described above for
a scatter plot is also available on a
coded scatter plot. However, this
method assumes that the ranges of
the three variables are comparable
and does not provide information on
three-way or higher interactions
between the variables. An example
of a coded scatter plot is given in
Figure 2-9.
10
20
Figure 2-9. Example of a Coded Scatter Plot
A parallel coordinate plot also extends the idea of a scatter plot to higher dimensions. The
parallel coordinates method employs a scheme where coordinate axes are drawn in parallel
(instead of perpendicular). Consider a sample point X consisting of values Xt for variable 1, X2
for variable 2, and so on up to Xp for variable p. A parallel coordinate plot is constructed by
placing an axis for each of the p variables parallel to each other and plotting Xt on axis 1, X2 on
axis 2, and so on through Xp on axis p and joining these points with a broken line. This method
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contains all of the information
available on a scatter plot in
addition to information on 3-way
and higher interactions (e.g.,
clustering among three variables).
However, for p variables one
must construct (p+l)/2 parallel
coordinate plots in order to
display all possible pairs of
variables. An example of a
parallel coordinate plot is given in
Figure 2-10.
A scatter plot matrix is
another useful method of
extending scatter plots to higher
dimensions. In this case, a scatter plot is developed for all possible pairs of the variables which
are then displayed in a matrix format. This method is easy to implement and provides a concise
method of displaying the individual scatter plots. However, this method does not contain
information on 3-way or higher interactions between variables. An example of a scatter plot
matrix is contained in Figure 2-11.
Figure 2-10. Example of a Parallel Coordinates Plot
40
Q.
S
I 20
Chromil
o
0
40
£•30
a.
Q.
5
1 20
Chromil
o o
14
12
s-10
Q.
£ 8
HI
=
5 6
~~r 4
/, , , , \
r
- +
+
++ +
*^ +
"^
10 20 30 40 0 10 20 30 40
Chromium IV (ppb) 14
12
-10
Q.
S 8
HI
=
S 6
\ ^ 4
Chromium IV (ppb)
+
++
A-
:+++
2 4 6 8 10 12 14 0 2 4 6 8 10 12 14
Atrazine (ppb) Atrazine (ppb)
Figure 2-11. Example of a Matrix Scatter Plot
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2.3.7.4 Empirical Quantile-Quantile Plot
An empirical quantile-quantile (q-q) plot involves plotting the quantiles (Section 2.2.1) of
two data variables against each other. This plot is used to compare distributions of two or more
variables; for example, the analyst may wish to compare the distribution of lead and iron samples
from a drinking water well. This plot is similar in concept to the theoretical quantile-quantile plot
and yields similar information in regard to the distribution of two variables instead of the
distribution of one variable in relation to a fixed distribution. Directions for constructing an
empirical q-q plot with an example are given in Box 2-22.
An empirical q-q plot is the graph of the quantiles of one variable of a data set against the
quantiles of another variable of the data set. This plot is used to determine how well the
distribution of the two variables match. If the distributions are roughly the same, the graph is
linear or close to linear. If the distributions are not the same, than the graph is not linear. Even if
the graph is not linear, the departures from linearity give important information about how the
two data distributions differ. For example, a q-q plot can be used to compare the tails of the two
data distributions in the same manner a normal probability plot was used to compare the tails of
the data to the tails of a normal distribution. In addition, potential outliers (from the paired data)
may be identified on this graph.
2.3.8 Plots for Temporal Data
Data collected over specific time intervals (e.g., monthly, biweekly, or hourly) have a
temporal component. For example, air monitoring measurements of a pollutant may be collected
once a minute or once a day; water quality monitoring measurements of a contaminant level may
be collected weekly or monthly. An analyst examining temporal data may be interested in the
trends over time, correlation among time periods, and cyclical patterns. Some graphical
representations specific to temporal data are the time plot, correlogram, and variogram.
Data collected at regular time intervals are called time series. Time series data may be
analyzed using Box-Jenkins modeling and spectral analysis. Both of these methods require a large
amount of data collected at regular intervals and are beyond the scope of this guidance. It is
recommended that the interested reader consult a statistician.
The graphical representations presented in this section are recommended for all data that
have a temporal component regardless of whether formal statistical time series analysis will be
used to analyze the data. If the analyst uses a time series methodology, the graphical
representations presented below will play an important role in this analysis. If the analyst decides
not to use time series methodologies, the graphical representations described below will help
identify temporal patterns that need to be accounted for in the analysis of the data
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Box 2-22: Directions for Constructing an Empirical Q-Q Plot with an Example
LetX.,, X2, ..., Xn represent n data points of one variable and let Y.,, Y2, ..., Ym represent a second variable
of m data points. Let X,,}, for i = 1 to n, be the first variable listed in order from smallest to largest so that
X,!) (i = 1) is the smallest, X, 2} (i = 2) is the second smallest, and X, n} (i = n) is the largest. Let Y,,}, for i
= 1 to n, be the second variable listed in order from smallest to largest so that Y,.,} (i = 1) is the smallest,
Y(2) (i = 2) is the second smallest, and Y(m) (i = m) is the largest.
If m = n: If the two variables have the same number of observations, then an empirical q-q plot of the
two variables is simply a plot of the ordered values of the variables. Since n=m, replace m by n. A plot
of the pairs (X(1), Y(1)), (X(2), Y,
x(2). '(2)!
(X(n), Y(n)) is an empirical quantile-quantile plot.
If n > m: If the two variables have a different number of observations, then the empirical quantile-quantile
plot will consist of m (the smaller number) pairs. The empirical q-q plot will then be a plot of the ordered
Y values against the interpolated X values. For i = 1, i = 2, ..., i = m, let v = (n/m)(i - 0.5) + 0.5 and
separate the result into the integer part and the fractional part, i.e., let v = j + g where j is the integer part
and g is the fraction part. If g = 0, plot the pair (Y(i), X(i)). Otherwise, plot the pair (Y(i), (1-g)X(j) + gX(j +
!)). A plot of these pairs is an empirical quantile-quantile plot.
Example: Consider two sets of contaminant readings from two separate drinking water wells at the same
site. The data from well 1 are: 1.32, 3.26, 3.56, 4.02, 4.14, 5.22, 6.30, 7.72, 7.73, and 8.22. The data
from well 2 are: 0.65, 0.68, 0.68, 1.42, 1.61, 1.93, 2.36, 2.49, 2.73, 3.01, 3.48, and 5.44. An empirical
q-q plot will be used to compare the distributions of these two wells. Since there are 10 observations in
well 1, and 12 observations in well, the case for n ? m will be used. Therefore, for i = 1, 2, ..., 10,
compute:
i = 1: v = —(l-.5)+.5 = 1.1 soj=1 andg=.1. Since g*0, plot (1.32,(.9).65+(.1).68)=(1.32,
0.653)
12,
1 = 2: v = —(2-.5)+.5 = 2.3 so j=2 and g=3. Since g^O, plot (3.26,(.7).68+(.3).68)=(3.26, 0.68)
1 = 3: v = —(3-.5)+.5 = 3.5 so j=3 and g=5. Since g^O, plot (3.56,(.5).68+(.5)1.42)=(3.56,1.05)
Continue this process for i =4, 5, 6, 7, 8, 9, and 10 to yield the following 10 data pairs (1.32, 0.653),
(3.26, 0.68), (3.56, 1.05), (4.02, 1.553), (4.14, 1.898), (5.22, 2.373), (6.30, 2.562), (7.72, 2.87), (7.73,
3.339), and (8.22, 5.244). These pairs are plotted below, along with the best fitting regression line.
10
tM
~s
!6
4>
'c ^
ro
O
4 6
QuantilesofWell 1
10
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The analyst examining temporal environmental data may be interested in seasonal trends,
directional trends, serial correlation, and stationarity. Seasonal trends are patterns in the data that
repeat over time, i.e., the data rise and fall regularly over one or more time periods. Seasonal
trends may be large scale, such as a yearly trend where the data show the same pattern of rising
and falling over each year, or the trends may be small scale, such as a daily trend where the data
show the same pattern for each day. Directional trends are downward or upward trends in the
data which is of importance to environmental applications where contaminant levels may be
increasing or decreasing. Serial correlation is a measure of the extent to which successive
observations are related. If successive observations are related, statistical quantities calculated
without accounting for serial correlation may be biased. Finally, another item of interest for
temporal data is stationarity (cyclical patterns). Stationary data look the same over all time
periods. Directional trends and increasing (or decreasing) variability among the data imply that
the data are not stationary.
Temporal data are sometimes used in environmental applications in conjunction with a
statistical hypothesis test to determine if contaminant levels have changed. If the hypothesis test
does not account for temporal trends or seasonal variations, the data must achieve a "steady state"
before the hypothesis test may be performed. Therefore, the data must be essentially the same for
comparable periods of time both before and after the hypothesized time of change.
Sometimes multiple observations are taken in each time period. For example, the
sampling design may specify selecting 5 samples every Monday for 3 months. If this is the case,
the time plot described in Section 2.3.8.1 may be used to display the data, display the mean
weekly level, display a confidence interval for each mean, or display a confidence interval for each
mean with the individual data values. A time plot of all the data can be used to determine if the
variability for the different time periods changes. A time plot of the means can be used to
determine if the means are possibly changing between time periods. In addition, each time period
may be treated as a distinct variable and the methods of Section 2.3.7 may be applied.
2.3.8.1 Time Plot
One of the simplest plots to generate that provides a large amount of information is a time
plot. A time plot is a plot of the data over time. This plot makes it easy to identify large-scale
and small-scale trends over time. Small-scale trends show up on a time plot as fluctuations in
smaller time periods. For example, ozone levels over the course of one day typically rise until the
afternoon, then decrease, and this process is repeated every day. Larger scale trends, such as
seasonal fluctuations, appear as regular rises and drops in the graph. For example, ozone levels
tend to be higher in the summer than in the winter so ozone data tend to show both a daily trend
and a seasonal trend. A time plot can also show directional trends and increased variability over
time. Possible outliers may also be easily identified using a time plot.
A time plot (Figure 2-12) is constructed by numbering the observations in order by time.
The time ordering is plotted on the horizontal axis and the corresponding observation is plotted
on the vertical axis. The points plotted on a time plot may be joined by lines; however, it is
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recommended that the plotted points not be connected to avoid creating a false sense of
continuity. The scaling of the vertical axis of a time plot is of some importance. A wider scale
tends to emphasize large-scale trends, whereas a smaller scale tends to emphasize small-scale
trends. Using the ozone example above, a wide scale would emphasize the seasonal component
of the data, whereas a smaller scale would tend to emphasize the daily fluctuations. Directions for
constructing a time plot are contained in Box 2-23 along with an example.
20
15
in
> 10
re
re
Q
5
0
• *
* ^ ^
** "" * ** *%** * "" **********
** ** ** * *
) 5 10 15 20 25 30 35 40 45 50
Time
Figure 2-12. Example of a Time Plot Showing a Slight Downward Trend
Box 2-23: Directions for Generating a Time Plot and an Example
LetX.,, X2, ..., Xn represent n data points listed in order by time, i.e., the subscript represents the ordered time
interval. A plot of the pairs (i, X,) is a time plot of this data.
Example: Consider the following 50 daily observations (listed in order by day): 10.05, 11.22, 15.9, 11.15,
10.53, 13.33, 11.81, 14.78, 10.93, 10.31, 7.95, 10.11, 10.27, 14.25, 8.6, 9.18, 12.2, 9.52, 7.59, 10.33, 12.13,
11.31, 10.13, 7.11, 6.72, 8.97, 10.11, 7.72, 9.57, 6.23, 7.25, 8.89, 9.14, 12.34, 9.99, 11.26, 5.57, 9.55, 8.91,
7.11, 6.04, 8.67, 5.62, 5.99, 5.78, 8.66, 5.8, 6.9, 7.7, 8.87. By labeling day 1 as 1, day 2 as 2, and soon, a
time plot is constructed by plotting the pairs (i, X,) where i represents the number of the day and X, represents
the concentration level. A time plot of this data is shown in Figure 2-12.
2.3.8.2
Plot of the Autocorrelation Function (Correlogram)
Serial correlation is a measure of the extent to which successive observations are related.
If successive observations are related, either the data must be transformed or this relationship
must be accounted for in the analysis of the data. The correlogram is a plot that is used to display
serial correlation when the data are collected at equally spaced time intervals. The autocorrelation
function is a summary of the serial correlations of data. The 1st autocorrelation coefficient (r^ is
the correlation between points that are 1 time unit (kj) apart; the 2nd autocorrelation coefficient
(r2) is the correlation between points that are 2 time units (k2) apart; etc. A correlogram (Figure
2-13) is a plot of the sample autocorrelation coefficients in which the values of k versus the values
of rk are displayed. Directions for constructing a correlogram are contained in Box 2-24; example
calculations are contained in Box 2-25. For large sample sizes, a correlogram is tedious to
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construct by hand; therefore, software like Data Quality Evaluation Statistical Tools
(DataQUEST) (G-9D) should be used.
1
0.75
_ 0.5
tr
0.25
0
-0.25
-0.5
-*
-
*
*. ^
^ ^ ^ ^
* ** ^
** * '* * **
*
0 5 10 15 20 25 30
k
Figure 2-13. Example of a Correlogram
Box 2-24: Directions for Constructing a Correlogram
Let X.,, X2, ..., Xn represent the data points ordered by time for equally spaced time points, i.e., X., was collected
at time 1, X2 was collected at time 2, and so on. To construct a Correlogram, first compute the sample
autocorrelation coefficients. So for k = 0, 1, ..., compute rk where
= - and 8k = T,(Xt-X)(Xt.k-X).
t=k+
Once the rk have been computed, a Correlogram is the graph (k, rk) for k = 0, 1, . . . , and so on. As a
approximation, compute up to approximately k = n/6. Also, note that r0 = 1. Finally, place horizontal lines at
±2//R.
The correlogram is used for modeling time series data and may be used to determine if
serial correlation is large enough to create problems in the analysis of temporal data using other
methodologies besides formal time series methodologies. A quick method for determining if serial
correlation is large is to place horizontal lines at ±2//n on the correlogram (shown as dashed lines
on Figure 2-13). Autocorrelation coefficients that exceed this value require further investigation.
In application, the correlogram is only useful for data at equally spaced intervals. To relax
this restriction, a variogram may be used instead. The variogram displays the same information as
a correlogram except that the data may be based on unequally spaced time intervals. For more
information on the construction and uses of the variogram, consult a statistician.
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2.3.8.3 Multiple Observations Per Time Period
Sometimes in environmental data collection, multiple observations are taken for each time
period. For example, the data collection design may specify collecting and analyzing 5 samples
from a drinking well every Wednesday for three months. If this is the case, the time plot
described in Section 2.3.8.1. may be used to display the data, display the mean weekly level,
display a confidence interval for each mean, or display a confidence interval for each mean with
the individual data values. A time plot of all the data will allow the analyst to determine if the
variability for the different collection periods varies. A time plot of the means will allow the
analyst to determine if the means may possibly be changing between the collection periods. In
addition, each collection period may be treated as a distinct variable and the methods described in
Section 2.3.7 may be applied.
Box 2-25: Example Calculations for Generating a Correlogram
A correlogram will be constructed using the following four hourly data points: hour 1: 4.5, hour 2: 3.5, hour 3:
2.5, and hour 4: 1.5. Only four data points are used so that all computations may be shown. Therefore, the
idea that no more than n/6 autocorrelation coefficients should be computed will be broken for illustrative
purposes. The first step to constructing a correlogram is to compute the sample mean (Box 2-2) which is 3 for
the 4 points. Then,
_ \ A, _ V\(M - -\!\ = = v ' v ' v ' v ' = 1 9S
g°" gy<-y>(y<-°-y> 4 4
(v3-3)(y2-3) + (v4-3)(y3-3)
= (3.5-3)(4.5-3) +(2.5-3)(3.5-3) +(1.5-3X2.5-3) = 1.25 =
0>4-3)(y2-3)
(2.5-3)(4.5-3) + (1.5-3)(3.5-3) _ -1.5
= ^-^- = (1.5-3)(4.5-3) = -2.25 =
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Box 2-25: Example Calculations for Generating a Correlogram - Continued
So r, = —- =
0.3125
1.25
= 0.25 , r? = ±1 =
-.375
1.25
= -0.3 , and
-0.5625
1.25
= -0.45.
Remember r0 = 1. Thus, the correlogram of these data is a plot of (0, 1) (1, 0.25), (2, -0.3) and (3, -0.45) with
two horizontal lines at±2//4 (±1). This graph is shown below.
In this case, it appears that the observations are not serially correlated because all of the correlogram points
are within the bounds of ±2//4 (±1.0). In Figure 2-13, if k represents months, then the correlogram shows a
yearly correlation between data points since the points at k=12 and k=24 are out of the bounds of ±2//n. This
correlation will need to be accounted for when the data are analyzed.
1
0.8
0.6
0.4
0.2
o
-0.2
-0.4
-0.6
-0.8
-1
\i/
+ 1.0
-1.0
1 2
k
(Hours)
2.3.9 Plots for Spatial Data
The graphical representations of the preceding sections may be useful for exploring spatial
data. However, an analyst examining spatial data may be interested in the location of extreme
values, overall spatial trends, and the degree of continuity among neighboring locations.
Graphical representations for spatial data include postings, symbol plots, correlograms, h-scatter
plots, and contour plots.
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The graphical representations presented in this section are recommended for all spatial
data regardless of whether or not geostatistical methods will be used to analyze the data. The
graphical representations described below will help identify spatial patterns that need to be
accounted for in the analysis of the data. If the analyst uses geostatistical methods such as kriging
to analyze the data, the graphical representations presented below will play an important role in
geostatistical analysis.
2.3.9.1
Posting Plots
A posting plot (Figure 2-14) is a map of data locations along with corresponding data
values. Data posting may reveal obvious errors in data location and identify data values that may
be in error. The graph of the sampling locations gives the analyst an idea of how the data were
collected (i.e., the sampling design), areas that may have been inaccessible, and areas of special
interest to the decision maker which may have been heavily sampled. It is often useful to mark
the highest and lowest values of the data to see if there are any obvious trends. If all of the
highest concentrations fall in one region of the plot, the analyst may consider some method such
as post-stratifying the data (stratification after the data are collected and analyzed) to account for
this fact in the analysis. Directions for generating a posting of the data (a posting plot) are
contained in Box 2-26.
4.0 11.6 14.9 17.4 17.7 12.4
28.6 7.7 15.2 35.5 14.7 16.5
14.7 10.9 12.4 22.8 19.1
10.2 5.2 4.9 17.2
2.3.9.2
Figure 2-14. Example of a Posting Plot
Symbol Plots
For large amounts of data, a posting plot may not be feasible and a symbol plot (Figure 2-
15) may be used. A symbol plot is basically the same as a posting plot of the data, except that
instead of posting individual data values, symbols are posted for ranges of the data values. For
example, the symbol '0' could represent all concentration levels less than 100 ppm, the symbol T
could represent all concentration levels between 100 ppm and 200 ppm, etc. Directions for
generating a symbol plot are contained in Box 2-26.
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5 1 3
3 3
7 2
2 4
1 0
Figure 2-15. Example of a Symbol Plot
Box 2-26: Directions for Generating a Posting Plot and a Symbol Plot
with an Example
On a map of the site, plot the location of each sample. At each location, either indicate the value of the
data point (a posting plot) or indicate by an appropriate symbol (a symbol plot) the data range within
which the value of the data point falls for that location, using one unique symbol per data range.
Example: The spatial data displayed in the table below contains both a location (Northing and Easting)
and a concentration level ([c]). The data range from 4.0 to 35.5 so units of 5 were chosen to group the
data:
Range Symbol Range Symbol
0.0- 4.9 0 20.0-24.9 4
5.0- 9.9 1 25.0-29.9 5
10.0-14.9 2 30.0-34.9 6
15.0-19.9 3 35.0-39.9 7
The data values with corresponding symbols then become:
Northing Easting fcl Symbol Northing Easting fcl
Symbol
25.0
25.0
25.0
25.0
20.0
20.0
20.0
20.0
15.0
15.0
15.0
The posting plot of this data
0.0
5.0
10.0
15.0
0.0
5.0
10.0
15.0
0.0
5.0
10.0
4.0
11.6
14.9
17.4
17.7
12.4
28.6
7.7
15.2
35.5
14.7
is displayed in
0
2
2
3
3
2
5
1
3
7
2
Figure 2-14
15.0
15.0
10.0
10.0
10.0
5.0
5.0
5.0
5.0
0.0
0.0
and the symbol
15.0
0.0
5.0
10.0
15.0
0.0
5.0
10.0
15.0
5.0
15.0
plot is
16.5
8.9
14.7
10.9
12.4
22.8
19.1
10.2
5.2
4.9
17.2
displayed
3
1
2
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3
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1
0
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in Figure 2-15.
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2.3.9.3 Other Spatial Graphical Representations
The two plots described in Sections 2.3.9.1 and 2.3.9.2 provide information on the
location of extreme values and spatial trends. The graphs below provide another item of interest
to the data analyst, continuity of the spatial data. The graphical representations are not described
in detail because they are used more for preliminary geostatistical analysis. These graphical
representations can be difficult to develop and interpret. For more information on these
representations, consult a statistician.
An h-scatterplot is a plot of all possible pairs of data whose locations are separated by a
fixed distance in a fixed direction (indexed by h). For example, a h-scatter plot could be based on
all the pairs whose locations are 1 meter apart in a southerly direction. A h-scatter plot is similar
in appearance to a scatter plot (Section 2.3.7.2). The shape of the spread of the data in a h-
scatter plot indicates the degree of continuity among data values a certain distance apart in
particular direction. If all the plotted values fall close to a fixed line, then the data values at
locations separated by a fixed distance in a fixed location are very similar. As data values become
less and less similar, the spread of the data around the fixed line increases outward. The data
analyst may construct several h-scatter plots with different distances to evaluate the change in
continuity in a fixed direction.
A correlogram is a plot of the correlations of the h-scatter plots. Because the h-scatter
plot only displays the correlation between the pairs of data whose locations are separated by a
fixed distance in a fixed direction, it is useful to have a graphical representation of how these
correlations change for different separation distances in a fixed direction. The correlogram is such
a plot which allows the analyst to evaluate the change in continuity in a fixed direction as a
function of the distance between two points. A spatial correlogram is similar in appearance to a
temporal correlogram (Section 2.3.8.2). The correlogram spans opposite directions so that the
correlogram with a fixed distance of due north is identical to the correlogram with a fixed distance
of due south.
Contour plots are used to reveal overall spatial trends in the data by interpolating data
values between sample locations. Most contour procedures depend on the density of the grid
covering the sampling area (higher density grids usually provide more information than lower
densities). A contour plot gives one of the best overall pictures of the important spatial features.
However, contouring often requires that the actual fluctuations in the data values are smoothed so
that many spatial features of the data may not be visible. The contour map should be used with
other graphical representations of the data and requires expert judgement to adequately interpret
the findings.
2.4 Probability Distributions
2.4.1 The Normal Distribution
Data, especially measurements, occur in natural patterns that can be considered to be a
distribution of values. In most instances the data values will be grouped around some measure of
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control tendency such as the mean or median. The spread of the data (as determined by the sum
of the squared distances from data point to the mean) is called the variance (the square root of
this is called the standard deviation). A distribution with a large variance will be more spread out
than one with a small variance (Figure 2-16). When the data values fall in a systematic pattern
around the mean and then taper off rapidly to the tails, it is often a normal distribution or bell-
shaped curve.
Figure 2-16. The Normal
Distribution
Figure 2-17. The Standard
Normal Curve (Z-Curve)
The characteristics of a normal distribution are well known mathematically and when
referred to, usually written as "data are distributed N (//, a2)" where the first characteristic is the
mean (//) and the second, the variance (a2). It may be shown that any normal distribution can be
transformed to a standard normal distribution, N(0,l), and this standard normal referred to as
simply Z (Figure 2-17). It is frequently necessary to refer to the percentiles of a standard normal
and in this guidance document, the subscript to a quoted Z-value will denote the percentile (or
area under the curve, cumulative from the left), see Figure 2-17.
2.4.2 The t-Distribution
The standard normal curve is used when exact information on the mean and variance are
available, but when only estimates from a sample are available, a different distribution applies.
When only information from a random sample on sample mean and sample variance is known for
decision making purposes, a Student's t distribution is appropriate. It resembles a standard
normal but is lower in the center and fatter in the tails. The degree of fatness in the tails is a
function of the degrees of freedom available, which in turn is related to sample size.
2.4.3 The Lognormal Distribution
A commonly met distribution in environmental work is
the lognormal distribution which has a more skewed (lopsided)
shape than a normal, see Figure 2-18. The lognormal is
bounded by zero and has a fatter tail than the normal. It is
related to the normal by the simple relationship: if X is
distributed lognormally, then Y = In (X) is distributed normally.
It is common practice to transform data (and any standard
being tested against) to achieve approximate normality prior to
conducting statistical tests.
Figure 2-18. Three Different
Lognormal Distributions
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2.4.4 Central Limit Theorem
In nearly all estimation situations in environmental work, the focus of the investigation
centers on the mean of a random sample of observations or measurements. It is rare that true
normality of the observations can be assumed and therefore a question as whether to use
statistical tests based on normality may be considered.
In many cases, the normally-based statistical tests are not overly affected by the lack of
normality as tests are very robust (sturdy) and perform tolerably well unless gross non-normality
is present. In addition, the tests become increasingly tolerant of deviations from normality as the
number of individual samples constituting the sample mean increases. In simple terms, as the size
of the sample increases, the mean of that sample acts increasingly as if it came from a normal
distribution regardless of the true distribution of the individual values. The taking of large
samples "stabilizes" the mean, so it then acts as if normality was present and the statistical test
remain valid.
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CHAPTER 3
STEP 3: SELECT THE STATISTICAL TEST
THE DATA QUALITY ASSESSMENT PROCESS
Review DQOs and Sampling Design
Conduct Preliminary Data Review
Select the Statistical Test
Verify the Assumptions
Draw Conclusions From the Data
SELECT THE STATISTICAL TEST
Purpose
Select an appropriate procedure for analyzing
data based on the preliminary data review.
Activities
• Select Statistical Hypothesis Test
• Identify Assumptions Underlying Test
Tools
• Hypothesis tests for a single population
• Hypothesis tests for comparing two populations
Step 3: Select the Statistical Test
Select the statistical hypothesis test based on the data user's objectives and the results of the
preliminary data review.
P If the problem involves comparing study results to a fixed threshold, such as a regulatory
standard, consider the hypothesis tests in Section 3.2.
P If the problem involves comparing two populations, such as comparing data from two
different locations or processes, then consider the hypothesis tests in Section 3.3.
Identify the assumptions underlying the statistical test.
P List the key underlying assumptions of the statistical hypothesis test, such as distributional
form, dispersion, independence, or others as applicable.
P Note any sensitive assumptions where relatively small deviations could jeopardize the
validity of the test results.
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List of Boxes
Page
Box 3-1: Directions for a One-Sample t-Test 3-7
Box 3-2: An Example of a One-Sample t-Test 3-8
Box 3-3: Directions for a One-Sample t-Test for a Stratified Random Sample 3-9
Box 3-4: An Example of a One-Sample t-Test for a Stratified Random Sample 3-10
Box 3-5: Directions for the Wilcoxon Signed Rank Test 3-12
Box 3-6: An Example of the Wilcoxon Signed Rank Test 3-13
Box 3-7: Directions for the Large Sample Approximation to the Wilcoxon
Signed Rank Test 3-14
Box 3-8: Directions for the Chen Test 3-16
Box 3-9: Example of the Chen Test 3-17
Box 3-10: Directions for the One-Sample Test for Proportions 3-19
Box 3-11: An Example of the One-Sample Test for Proportions 3-20
Box 3-12: Directions for a Confidence Interval for a Mean 3-21
Box 3-13: An Example of a Confidence Interval for a Mean 3-21
Box 3-14: Directions for the Student's Two-Sample t-Test (Equal Variances) 3-24
Box 3-15: An Example of a Student's Two-Sample t-Test (Equal Variances 3-25
Box 3-16: Directions for Satterthwaite's t-Test (Unequal Variances 3-26
Box 3-17: An Example of Satterthwaite's t-Test (Unequal Variances) 3-27
Box 3-18: Directions for a Two-Sample Test for Proportions 3-29
Box 3-19: An Example of a Two-Sample Test for Proportions 3-30
Box 3-20: Directions for the Wilcoxon Rank Sum Test 3-32
Box 3-21: An Example of the Wilcoxon Rank Sum Test 3-33
Box 3-22: Directions for the Large Sample Approximation to the
Wilcoxon Rank Sum Test 3-34
Box 3-23: Directions for a Modified Quantile Test 3-36
Box 3-24: A Example of a Modified Quantile Test 3-37
Box 3-25: Directions for Dunnett's Test for Simple Random and Systematic Samples .... 3-39
Box 3-26: An Example of Dunnett's Test for Simple Random and Systematic Samples ... 3-40
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CHAPTER 3
STEP 3: SELECT THE STATISTICAL TEST
3.1 OVERVIEW AND ACTIVITIES
This chapter provides information that the analyst can use in selecting an appropriate
statistical hypothesis test that will be used to draw conclusions from the data. A brief review of
hypothesis testing is contained in Chapter 1. There are two important outputs from this step: (1)
the test itself, and (2) the assumptions underlying the test that determine the validity of
conclusions drawn from the test results.
This section describes the two primary activities in this step of DQA. The remaining
sections in this chapter contain statistical tests that may be useful for analyzing environmental
data. In the one-sample tests discussed in Section 3.2, data from a population are compared with
an absolute criterion such as a regulatory threshold or action level. In the two-sample tests
discussed in Section 3.3, data from a population are compared with data from another population
(for example, an area expected to be contaminated might be compared with a background area).
For each statistical test, this chapter presents its purpose, assumptions, limitations, robustness,
and the sequence of steps required to apply the test.
The directions for each hypothesis test given in this chapter are for simple random
sampling and randomized systematic sampling designs, except where noted otherwise. If a more
complex design is used (such as a stratified design or a composite random sampling design) then
different formulas are needed, some of which are contained in this chapter.
3.1.1 Select Statistical Hypothesis Test
If a particular test has been specified either in the DQO Process, the QA Project Plan, or
by the particular program or study, the analyst should use the results of the preliminary data
review to determine if this statistical test is legitimate for the data collected. If the test is not
legitimate, the analyst should document why this particular statistical test should not be applied to
the data and then select a different test, possibly after consultation with the decision maker. If a
particular test has not been specified, the analyst should select a statistical test based on the data
user's objectives, preliminary data review, and likely viable assumptions.
3.1.2 Identify Assumptions Underlying the Statistical Test
All statistical tests make assumptions about the data. Parametric tests assume the data
have some distributional form (e.g., the t-test assumes normal distribution), whereas
nonparametric tests do not make this assumption (e.g., the Wilcoxon test only assumes the data
are symmetric but not necessarily normal). However, both parametric and nonparametric tests
may assume that the data are statistically independent or that there are no trends in the data.
While examining the data, the analyst should always list the underlying assumptions of the
statistical hypothesis test, such as distribution, dispersion, or others as applicable.
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Another important feature of statistical tests is their sensitivities (nonrobustness) to
departures from the assumptions. A statistical procedure is called robust if its performance is not
seriously affected by moderate deviations from its underlying assumptions. The analyst should
note any sensitive assumptions where relatively small deviations could jeopardize the validity of
the test results.
3.2 TESTS OF HYPOTHESES ABOUT A SINGLE POPULATION
A one-sample test involves the comparison of a population parameter (e.g., a mean,
percentile, or variance) to a threshold value. Both the threshold value and the population
parameter were specified during Step 1: Review DQOs and Sampling Design. In a one-sample
test, the threshold value is a fixed number that does not vary. If the threshold value was estimated
(and therefore contains variability), a one-sample test is not appropriate. An example of a one-
sample test would be to determine if 95% of all companies emitting sulfur dioxide into the air are
below a fixed discharge level. For this example, the population parameter is a percentage
(proportion) and the threshold value is 95% (.95). Another example is a common Superfund
problem that involves comparing the mean contaminant concentration to a risk-based standard. In
this case, the risk-based standard (which is fixed) is the threshold value and the statistical
parameter is the true mean contaminant concentration level of the site. However, comparing the
mean concentration in an area to the mean concentration of a reference area (background) would
not be a one-sample test because the mean concentration in the reference area would need to be
estimated.
The statistical tests discussed in this section may be used to determine if 6 < 60 or 6 > 60,
where 6 represents either the population mean, median, a percentile, or a proportion and 60
represents the threshold value. Section 3.2.1 discusses tests concerning the population mean,
Section 3.2.2 discusses tests concerning a proportion or percentile, and Section 3.2.2 discusses
tests for a median.
3.2.1 Tests for a Mean
A population mean is a measure of the center of the population distribution. It is one of
the most commonly used population parameters in statistical hypothesis testing because its
distribution is well known for large sample sizes. The hypotheses considered in this section are:
Case 1: H0: ji < C vs. HA: |i> C; and
Case 2: H0: |i > C vs. HA: |i < C
where C represents a given threshold such as a regulatory level, and |i denotes the (true) mean
contaminant level for the population. For example, C may represent the arsenic concentration
level of concern. Then if the mean of the population exceeds C, the data user may wish to take
action.
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The information required for this test (defined in Step 1) includes the null and alternative
hypotheses (either Case 1 or Case 2); the gray region, i.e., a value \il > C for Case 1 or a value \il
< C for Case 2 representing the bound of the gray region; the false rejection error rate a at C; the
false acceptance error rate P at nt; and any additional limits on decision errors. It may be helpful
to label any additional false rejection error limits as a2 at C2, a3 at C3, etc., and to label any
additional false acceptance error limits as P2 at |i2, P3 at |i3, etc. For example, consider the
following decision: determine whether the mean contaminant level at a waste site is greater than
10 ppm. The null hypothesis is H0: |i > 10 ppm and the alternative hypothesis is HA: |i < 10
ppm. A gray region has been set from 10 to 8 ppm, a false rejection error rate of 5% has been set
at 10 ppm, and a false acceptance error rate of 10% has been set at 8 ppm. Thus, C = 10 ppm, \il
= 8 ppm, a = 0.05, and P = 0.1. If an additional false acceptance error rate was set, for example,
an error rate of 1% at 4 ppm, then P2 = .01 and |i2 = 4 ppm.
3.2.1.1 The One-Sample t-Test
PURPOSE
Given a random sample of size n (or a composite sample of size n, each composite
consisting of k aliquots), the one-sample t-test can be used to test hypotheses involving the mean
(|i) of the population from which the sample was selected.
ASSUMPTIONS AND THEIR VERIFICATION
The primary assumptions required for validity of the one-sample t-test are that of a
random sample (independence of the data values) and that the sample mean x is approximately
normally distributed. Because the sample mean and standard deviation are very sensitive to
outliers, the t-test should be preceded by a test for outliers (see Section 4.4).
Approximate normality of the sample mean follows from approximate normality of the
data values. In addition, the Central Limit Theorem states that the sample mean of a random
sample from a population with an unknown distribution will be approximately normally distributed
provided the sample size is large. This means that although the population distribution from
which the data are drawn can be distinctly different from the normal distribution, the distribution
of the sample mean can still be approximately normal when the sample size is relatively large.
Although preliminary tests for normality of the data can and should be done for small sample
sizes, the conclusion that the sample does not follow a normal distribution does not automatically
invalidate the t-test, which is robust to moderate violations of the assumption of normality for
large sample sizes.
LIMITATIONS AND ROBUSTNESS
The t-test is not robust to outliers because the sample mean and standard deviation are
influenced greatly by outliers. The Wilcoxon signed rank test (see Section 3.2.1.2) is more
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robust, but is slightly less powerful. This means that the Wilcoxon signed rank test is slightly less
likely to reject the null hypothesis when it is false than the t-test.
The t-test has difficulty dealing with less-than values, e.g., values below the detection
limit, compared with tests based on ranks or proportions. Tests based on a proportion above a
given threshold (Section 3.2.2) are more valid in such a case, if the threshold is above the
detection limit. It is also possible to substitute values for below detection-level data (e.g., /^ the
detection level) or to adjust the statistical quantities to account for nondetects (e.g., Cohen's
Method for normally or lognormally distributed data). See Chapter 4 for more information on
dealing with data that are below the detection level.
SEQUENCE OF STEPS
Directions for a one-sample t-test for a simple, systematic, and composite random samples
are given in Box 3-1 and an example is given in Box 3-2. Directions for a one-sample t-test for a
stratified random sample are given in Box 3-3 and an example is given in Box 3-4.
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Box 3-1: Directions for a One-Sample t-Test
for Simple and Systematic Random Samples
with or without Compositing
Let X.,, X2, . . . , Xn represent the n data points. These could be either n individual samples or n composite
samples consisting of k aliquots each. These are the steps for a one-sample t-test for Case 1 (H0: u < C);
modifications for Case 2 (H0: u > C) are given in braces {}.
STEP 1: Calculate the sample mean, x (Section 2.2.2), and the standard deviation, s (Section 2.2.3).
STEP 2: Use Table A-1 of Appendix A to find the critical value t.,_,., such that 100(1-a)% of the t distribution with
n -1 degrees of freedom is below !,_„. For example, if a = 0.05 and n = 16, then n-1 =15 and !,_„ =
1.753.
STEP 3: Calculate the sample value t = (X- C) I (s/\fn) .
STEP 4: Compare t with t.,_a.
1) If t > ti-a {t < -t-i-J, the null hypothesis may be rejected. Go to Step 6.
2) If t > t.,_a {t < -t^J, there is not enough evidence to reject the null hypothesis and the false
acceptance error rate should be verified. Go to Step 5.
STEP 5: As the null hypothesis (H0) was not rejected, calculate either the power of the test or the sample size
necessary to achieve the false rejection and false acceptance error rates. To calculate the power,
assume that the true values for the mean and standard deviation are those obtained in the sample
and use a software package like the Decision Error Feasibility Trial (DEFT) software (EPA, 1994) to
generate the power curve of the test.
If only one false acceptance error rate (P) has been specified (at u^, it is possible to calculate the
sample size which achieves the DQOs, assuming the true mean and standard deviation are equal to
the values estimated from the sample, instead of calculating the power of the test. To do this,
s 2(z +z )2
calculates = — + (0.5)zj where zp is the pth percentile of the standard normal
(nrQ2
distribution (Table A-1 of Appendix A). Round m up to the next integer. If m < n, the false
acceptance error rate has been satisfied. If m > n, the false acceptance error rate has not been
satisfied.
STEP 6: The results of the test may be:
1) the null hypothesis was rejected and it seems that the true mean is greater than C {less than C};
2) the null hypothesis was not rejected and the false acceptance error rate was satisfied and it seems
that the true mean is less than C {greater than C}; or
3) the null hypothesis was not rejected and the false acceptance error rate was not satisfied and it
seems that the true mean is less than C {greater than C} but conclusions are uncertain since the
sample size was too small.
Report the results of the test, the sample size, sample mean, standard deviation, t and t1_a.
Note: The calculations for the t-test are the same for both simple random or composite random sampling. The
use of compositing will usually result in a smaller value of "s" than simple random sampling.
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Box 3-2: An Example of a One-Sample t-Test
for a Simple Random or Composite Sample
Consider the following 9 random (or composite samples each of k aliquots) data points: 82.39 ppm,
103.46 ppm, 104.93 ppm, 105.52 ppm, 98.37 ppm, 113.23 ppm, 86.62 ppm, 91.72 ppm, and 108.21
ppm. This data will be used to test the hypothesis: H0: u < 95 ppm vs. HA: u > 95 ppm. The decision
maker has specified a 5% false rejection decision error limit (a) at 95 ppm (C), and a 20% false
acceptance decision error limit (P) at 105 ppm (m).
STEP 1 : Using the directions in Box 2-2 and Box 2-3, it was found that
X = 99.38 ppm and s = 10.41 ppm.
STEP 2: Using Table A-1 of Appendix A, the critical value of the t distribution with 8 degrees of freedom
is t0 95 =1.86.
STEP3: t=L-C= 99-38 - 95 = 1.26
s/fi 10.41/^9
STEP 4: Because 1.26 > 1.86, there is not enough evidence to reject the null hypothesis and the false
acceptance error rate should be verified.
STEP 5: Because there is only one false acceptance error rate, it is possible to use the sample size
formula to determine if the error rate has been satisfied. Therefore,
- - * «"*•-•
0.842)2 +
(95 - 105)2
i.e., 9
Notice that it is customary to round upwards when computing a sample size. Since m=n, the
false acceptance error rate has been satisfied.
STEP 6: The results of the hypothesis test were that the null hypothesis was not rejected but the false
acceptance error rate was satisfied. Therefore, it seems that the true mean is less than 95
ppm.
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Box 3-3: Directions for a One-Sample t-Test
for a Stratified Random Sample
Let h=1 , 2, 3, . . . , L represent the L strata and nh represent the sample size of stratum h. These steps are for a
one-sample t-test for Case 1 (H0: u < C); modifications for Case 2 (H0: u > C) are given in braces { }.
STEP 1: Calculate the stratum weights (Wh) by calculating the proportion of the volume in
V,
stratum h, Wh = - where Vh is the surface area of stratum h multiplied by
h=l
the depth of sampling in stratum h.
STEP 2: For each stratum, calculate the sample stratum mean Xh = and the sample stratum
standard error sh =
,,2
STEPS: Calculate overall mean XST = 2_/V^/i' ar|d variance SST = /]Vh—.
A=i A=I nh
2
STEP 4: Calculate the degrees of freedom (dof): dof =
L Wist
£-
Use Table A-1 of Appendix A to find the critical value t .,_,., so that 100(1-a)% of the t
distribution with the above degrees of freedom (rounded to the next highest integer) is
below t.,_a.
-A. OT-T ~~ \_,
STEP 5: Calculate the sample value: t =
STEP 6: Compare t to t.,_a. If t > t.|_a {t < -t^J, the null hypothesis may be rejected. Go to Step 8. If t > t^,, {t < -
t.,.,.,}, there is not enough evidence to reject the null hypothesis and the false acceptance error rate
should be verified. Go to Step 7.
STEP 7: If the null hypothesis was not rejected, calculate either the power of the test or the sample size
necessary to achieve the false rejection and false acceptance error rates (see Step 5, Box 3-2).
STEP 8: The results of the test may be: 1) the null hypothesis was rejected so it seems that the true mean is
less than C {greater than C}; 2) the null hypothesis was not rejected and the false acceptance error
rate was satisfied and it seems that the true mean is greater than C {less than C}; or 3) the null
hypothesis was not rejected and the false acceptance error rate was not satisfied and it seems that
the true mean is greater than C {less than C} but conclusions are uncertain since the sample size was
too small.
Report the results of the test, as well as the sample size, sample mean, and sample standard
deviation for each stratum, the estimated t, the dof, and !.,_„.
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Box 3-4: An Example of a One-Sample t-Test
for a Stratified Random Sample
Consider a stratified sample consisting of two strata where stratum 1 comprises 10% of the total site
surface area and stratum 2 comprises the other 90%, and 40 samples were collected from stratum 1, and
60 samples were collected from stratum 2. For stratum 1, the sample mean is 23 ppm and the sample
standard deviation is 18.2 ppm. For stratum 2, the sample mean is 35 ppm, and the sample standard
deviation is 20.5 ppm. This information will be used to test the null hypothesis that the overall site mean
is greater than or equal to 40 ppm, i.e., H0: u > 40 ppm (Case 2). The decision maker has specified a
1% false rejection decision limit (a) at 40 ppm and a 20% false acceptance decision error limit (P) at 35
STEP 1: W., = 10/100 = 0.10, W2 = 90/100 = 0.9.
STEP 2: From above, x., = 23 ppm, X2 = 35 ppm, s., = 18.2, and s2 = 20.5. This information was
developed using the equations in step 2 of Box 3-3.
STEP 3: The estimated overall mean concentration is:
_ L _ _ _
XST = YFhXh = W\X\ + W2X2 = (-!X23) + (.9)(35) = 33.8 ppm.
h=l
and the estimated overall variance is:
L 2 T T T T
vv 2 sh (.1) (18.2) (.9) (20.5)
SVT = / "\i — = "~ :— + "~ :— = 5.76
w hTf nh 40 60
STEP 4: The approximate degrees of freedom (dof) is:
(O2 (5 76)2
dof = -±i- = ^-^. = 60.8, i.e.,
.1)4(18.2)4 + (.9)4(20.5)4
(40)239 (60)259
v ' v '
~ 2 2
61
Note how the degrees of freedom has been rounded up to a whole number.
Using Table A-1 of Appendix A, the critical value i^_a of the t distribution with
61 dof is approximately 2.39.
X?T ~ C QQ « _ AH
STEPS: Calculate the sample value t = — - = = -2.58
STEP 6: Because -2.58 < -2.39 the null hypothesis may be rejected.
STEP 7: Because the null hypothesis was rejected, it is concluded that the mean is probably less than
40 ppm. In this example there is no need to calculate the false acceptance rate as the null
hypothesis was rejected and so the chance of making a false acceptance error is zero by
definition.
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3.2.1.2 The Wilcoxon Signed Rank (One-Sample) Test for the Mean
PURPOSE
Given a random sample of size n (or composite sample size n, each composite consisting
of k aliquots), the Wilcoxon signed rank test can be used to test hypotheses regarding the
population mean or median of the population from which the sample was selected.
ASSUMPTIONS AND THEIR VERIFICATION
The Wilcoxon signed rank test assumes that the data constitute a random sample from a
symmetric continuous population. (Symmetric means that the underlying population frequency
curve is symmetric about its mean/median.) Symmetry is a less stringent assumption than
normality since all normal distributions are symmetric, but some symmetric distributions are not
normal. The mean and median are equal for a symmetric distribution, so the null hypothesis can
be stated in terms of either parameter. Tests for symmetry can be devised which are based on the
chi-squared distribution, or a test for normality may be used. If the data are not symmetric, it may
be possible to transform the data so that this assumption is satisfied. See Chapter 4 for more
information on transformations and tests for symmetry.
LIMITATIONS AND ROBUSTNESS
Although symmetry is a weaker assumption than normality, it is nonetheless a strong
assumption. If the data are not approximately symmetric, this test should not be used. For large
sample sizes (n > 50), the t-test is more robust to violations of its assumptions than the Wilcoxon
signed rank test. For small sample sizes, if the data are not approximately symmetric and are not
normally distributed, this guidance recommends consulting a statistician before selecting a
statistical test or changing the population parameter to the median and applying a different
statistical test (Section 3.2.3).
The Wilcoxon signed rank test may produce misleading results if many data values are the
same. When values are the same, their relative ranks are the same, and this has the effect of
diluting the statistical power of the Wilcoxon test. Box 3-5 demonstrates the method used to
break tied ranks. If possible, results should be recorded with sufficient accuracy so that a large
number of equal values do not occur. Estimated concentrations should be reported for data
below the detection limit, even if these estimates are negative, as their relative magnitude to the
rest of the data is of importance.
SEQUENCE OF STEPS
Directions for the Wilcoxon signed rank test for a simple random sample and a systematic
simple random sample are given in Box 3-5 and an example is given in Box 3-6 for samples sizes
smaller than 20. For sample sizes greater than 20, the large sample approximation to the
Wilcoxon Signed Rank Test should be used. Directions for this test are given in Box 3-7.
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Box 3-5: Directions for the Wilcoxon Signed Rank Test
for Simple and Systematic Random Samples
Let X.,, X2, . . . , Xn represent the n data points. The following describes the steps for applying the
Wilcoxon signed rank test for a sample size (n) less than 20 for Case 1 (H0: u < C); modifications for
Case 2 (H0: u > C) are given in braces {}. If the sample size is greater than or equal to 20, use Box 3-7.
STEP 1: If possible, assign values to any measurements below the detection limit. If
this is not possible, assign the value "Detection Limit divided by 2" to each
value. Then subtract each observation X, from C to obtain the deviations d, =
C - X,. If any of the deviations are zero delete them and correspondingly
reduce the sample size n.
STEP 2: Assign ranks from 1 to n based on ordering the absolute deviations |dj (i.e.,
magnitude of differences ignoring the sign) from smallest to largest. The rank
1 is assigned to the smallest value, the rank 2 to the second smallest value,
and so forth. If there are ties, assign the average of the ranks which would
otherwise have been assigned to the tied observations.
STEP 3: Assign the sign for each observation to create the signed rank. The sign is
positive if the deviation d, is positive; the sign is negative if the deviation d, is
negative.
STEP 4: Calculate the sum R of the ranks with a positive sign.
STEP 5: Use Table A-6 of Appendix A to find the critical value wa.
If R < wa, {R > n(n+1)/2 - wj, the null hypothesis may be rejected; proceed to Step 7.
Otherwise, there is not enough evidence to reject the null hypothesis, and the false
acceptance error rate will need to be verified; proceed to Step 6.
STEP 6: If the null hypothesis (H0) was not rejected, calculate either the power of the
test or the sample size necessary to achieve the false rejection and false
acceptance error rates using a software package like the DEFT software (EPA
, 1994). For large sample sizes, calculate,
where zp is the pth percentile of the standard normal distribution (Table A-1 of Appendix A). If
1.16m < n, the false acceptance error rate has been satisfied.
STEP 7: The results of the test may be:
1) the null hypothesis was rejected and it seems that the true mean is greater than C {less
than C};
2) the null hypothesis was not rejected and the false acceptance error rate was satisfied and
it seems that the true mean is less than C {greater than C}; or
3) the null hypothesis was not rejected and the false acceptance error rate was not satisfied
and it seems that the true mean is greater than C {less than C} but conclusions are uncertain
since the sample size was too small.
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Box 3-6: An Example of the Wilcoxon Signed Rank Test
for a Simple Random Sample
Consider the following 10 data points: 974 ppb, 1044 ppb, 1093 ppb, 897 ppb, 879 ppb, 1161 ppb, 839
ppb, 824 ppb, 796 ppb, and one observation below the detection limit of 750 ppb. This data will be used
to test the hypothesis: H0: u > 1000 ppb vs. HA: u < 1000 ppb (Case 2). The decision maker has
specified a 10% false rejection decision error limit (a) at 1000 ppb (C), and a 20% false acceptance
decision error limit (P) at 900 ppb (u^.
STEP 1:
x.
|d,|
rank
s-rank
STEP 2:
STEP 3:
STEP 4:
STEP 5:
STEP 7:
Assign the value 375 ppb (750 divided by 2) to the data point below the detection limit.
Subtract C (1 000) from each of the n observations X, to obtain the deviations d, = 1 000 - X,.
This is shown in row 2 of the table below.
974 1044 1093 897 879 1161 839 824 796 375
26 -44 -93 103 121 -161 161 176 204 625
26 44 93 103 121 161 161 176 204 625
12 2 3 4 5 6.5 6.5 8 9 10
1-2-3 45 -6.5 6.5 8 9 10
Assign ranks from 1 to n based on ordering the absolute deviations |d,| (magnitude ignoring
any negative sign) from smallest to largest. The absolute deviations are listed in row 3 of the
table above. Note that the data have been sorted (rearranged) for clarity so that the absolute
deviations are ordered from smallest to largest.
The rank 1 is assigned to the smallest value, the rank 2 to the second smallest value, and so
forth. Observations 6 and 7 are ties, therefore, the average (6+7)/2 = 6.5 will be assigned to
the two observations. The ranks are shown in row 4.
Assign the sign for each observation to create the signed rank. The sign is positive if the
deviation d, is positive; the sign is negative if the deviation d, is negative. The signed rank is
shown in row 5.
R = 1+4 + 5 + 6.5 + 8 + 9+10 = 43.5.
Table A-6 of Appendix A was used to find the critical value wa where a = 0.10. For this
example, w010 = 15. Since 43.5 > (10x11)72 - 15 = 40, the null hypothesis may be rejected.
The null hypothesis was rejected with a 10% significance level using the Wilcoxon signed rank
test. Therefore, it would seem that the true mean is below 1 000 ppb.
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Box 3-7: Directions for the Large Sample Approximation to the Wilcoxon Signed Rank Test
for Simple and Systematic Random Samples
Let X.,, X2, . . . , Xn represent the n data points where n is greater than or equal to 20. The following
describes the steps for applying the large sample approximation for the Wilcoxon signed rank test for
Case 1 (H0: u < C); modifications for Case 2 (H0: u > C) are given in braces {}.
STEP 1: If possible, assign values to any measurements below the detection limit. If this is not
possible, assign the value "Detection Limit divided by 2" to each value. Then subtract each
observation X, from C to obtain the deviations d, = C - X,. If any of the deviations are zero
delete them and correspondingly reduce the sample size n.
STEP 2: Assign ranks from 1 to n based on ordering the absolute deviations |d,| (i.e., magnitude of
differences ignoring the sign) from smallest to largest. The rank 1 is assigned to the smallest
value, the rank 2 to the second smallest value, and so forth. If there are ties, assign the
average of the ranks which would otherwise have been assigned to the tied observations.
STEP 3: Assign the sign for each observation to create the signed rank. The sign is positive if the
deviation d, is positive; the sign is negative if the deviation d, is negative.
STEP 4: Calculate the sum R of the ranks with a positive sign.
n(n + 1)
STEPS: Calculate w = — + x \jn(n + l)(2n + 1)124 where p = 1-a {p = a} and zp
4 p
is the pth percentile of the standard normal distribution (Table A-1 of Appendix A).
STEP 6: If R < w {R > w}, the null hypothesis may be rejected. Go to Step 8.
Otherwise, there is not enough evidence to reject the null hypothesis, and the false
acceptance error rate will need to be verified. Go to Step 7.
STEP 7: If the null hypothesis (H0) was not rejected, calculate either the power of the test or the sample
size necessary to achieve the false rejection and false acceptance error rates using a software
package like the DEFT software (EPA , 1994). For large sample sizes, calculate,
m = 11«—i_p^_ + (Q.5)Zl2_a
(MI~ O
where zp is the pth percentile of the standard normal distribution (Table A-1 of Appendix
A). If 1.16m < n, the false acceptance error rate has been satisfied.
STEPS: The results of the test may be:
1) the null hypothesis was rejected and it seems that the true mean is greater {less} than C;
2) the null hypothesis was not rejected and the false acceptance error rate was satisfied and it
seems that the true mean is less than C {greater than C}; or
3) the null hypothesis was not rejected and the false acceptance error rate was not satisfied
and it seems that the true mean is greater than C {less than C} but conclusions are uncertain
since the sample size was too small.
Report the results of the test, the sample size, R, and w.
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3.2.1.3 The Chen Test
PURPOSE
Environmental data such as concentration measurements are often confined to positive
values and appear to follow a distribution with most of the data values relatively small or near
zero but with a few relatively large data values. Underlying such data is some distribution which
is not symmetrical (like a normal) but which is skewed to the right (like a log normal). Given a
random sample of size 'n' from a right-skewed distribution, the Chen test can be used to compare
the mean (|i) of the distribution with a threshold level or regulatory value. The null hypothesis
has the form H0: ji < C, where C is a given threshold level; the alternative hypothesis is HA: ji > C.
The method is not recommended for testing null hypotheses of the form H0: |i > C against
HA:n
-------
Box 3-8: Directions for the Chen Test
LetX.,, X2, . . . , Xn represent the n data points. Let C denote the threshold level of interest. The null hypothesis
is H0: U < C and the alternative is HA: |J > C, the level of significance is a.
STEP 1: If at most 15% of the data points are below the detection limit (DL) and C is much larger than the
DL, then replace values below the DL with DL/2.
STEP 2: Visually check the assumption of right-skewness by inspecting a histogram or frequency plot for the
data. (The Chen test is appropriate only for data which are skewed to the right.)
STEP 3: Calculate the sample mean, x (Section 2.2.2), and the standard deviation, s (Section 2.2.3).
STEP 4: Calculate the sample skewness b = — — - ' the quantity a = b/6i/n , the t-statistic
(n - V)(n - 2>3 '
t = - — ^ — - , and then compute T = t + a(l + 2t2 ) + 4a2 (t + 2t3 ).
NOTE: The skewness b should be greater than 1 to confirm the data are skewed to the right.
STEP 5: Use the last row of Table A-1 in Appendix A to find the critical value z.|_a such that 100(1-a)% of the
Normal distribution is below !.,_„. For example, if a = 0.05 then z1_a = 1 .645.
STEP 6: Compare t with z.,_a.
1 ) If t > z.,.,, the null hypothesis may be rejected and it seems that the true mean is greater than C;
2) If t > z1_a, there is not enough evidence to reject the null hypothesis so it seems that the true mean
is less than C.
3.2.2 Tests for a Proportion or Percentile
This section considers hypotheses concerning population proportions and percentiles.
A population proportion is the ratio of the number of elements of a population that has some
specific characteristic to the total number of elements. A population percentile represents the
percentage of elements of a population having values less than some threshold C. Thus, if x is the
95th percentile of a population, 95% of the elements of the population have values less than C and
5% of the population have values greater than C.
This section of the guidance covers the following hypothesis: Case 1: H0: P < P0 vs.
HA: P > P0 and Case 2: H0: P > P0 vs. HA: P < P0 where P is a proportion of the population,
and P0 represents a given proportion (0 < P0 < 1). Equivalent hypotheses written in terms of
percentiles are H0: the 100Pth percentile is C or larger for Case 1, and H0: the 100Pth percentile is
C or smaller for Case 2. For example, consider the decision to determine whether the 95th
percentile of a container of waste is less than 1 mg/L cadmium. The null hypothesis in this case is
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Box 3-9: Example of the Chen Test
Consider the following sample of a contaminant concentration measurements in (in ppm):2.0, 2.0, 5.0, 5.2, 5.9,
6.6, 7.4, 7.4, 9.7, 9.7, 10.2, 11.5, 12.4, 12.7, 14.1, 15.2, 17.7, 18.9, 22.8, 28.6, 30.5, 35.5. We want to test the
null hypothesis that the mean u is less than 10 ppm versus the alternative that it exceeds 10 ppm. A
significance level of 0.05 is to be used.
STEP 1: Since all of the data points exceed the detection limit, there is no need to substitute values for below
the detection limit.
STEP 2: A frequency plot of the 23 data points confirms the right-skewness.
STEP 3: Using boxes 2-2 and 2-4 of Chapter 2, it is found thatX = 13.08 ppm
and s = 8.99 ppm.
0 5 10 15 20 25 30 35 40
23 Concentration (ppm)
, -13.08)3
STEP 4: b = — ^ - r = - — - ;— = 1.14 ,
(fj-l)(fi-2>3 (22)(21)(8.99)3
a = b/6*Jn = 114/6^23 = 0.0396 ,
,
8.997 '
/V23
compute T = t + a(l + 2t2 ) + 4a2 (t + 2t3 ) = 1.965
(The value of 1.14 for skewness confirms that the data are skewed to the right.)
STEP 5: Using the last row of Table A-1 of Appendix A, the critical value z095 of the Normal distribution is
1.645.
STEP 6: Since T > z095 (1.965 > 1.645), the null hypothesis is rejected and we conclude that the true mean is
greater than 10 ppm.
H0: the 95th percentile of cadmium is less than 1 mg/L. Now, instead of considering the
population to consist of differing levels of cadmium, consider the population to consist of a binary
variable that is T if the cadmium level is above 1 mg/L or is '0' if the level is below 1 mg/L. In
this case, the hypothesis may be changed to a test for a proportion so that the null hypothesis
becomes H0: P < .95 where P represents the proportion of 1's (cadmium levels above 1 mg/L) in
the container of waste. Thus, any hypothesis about the proportion of the site below a threshold
can be converted to an equivalent hypothesis about percentiles. Therefore, only hypotheses about
the proportion of the site below a threshold will be discussed in this section. The information
required for this test includes the null and alternative hypotheses, the gray region, the false
rejection error rate a at P0, the false acceptance error rate P at Pl3 and any additional limits on
decision errors. It may be helpful to label any additional false rejection error limits as a2 at Pa2, a3
at Pa3, etc., and any additional false acceptance error limits as P2 at Pp2, P3 at Pp3, etc.
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3.2.2.1 The One-Sample Proportion Test
PURPOSE
Given a random sample of size n, the one-sample proportion test may be used to test
hypotheses regarding a population proportion or population percentile for a distribution from
which the data were drawn. Note that for P=.5, this test is also called the Sign test.
ASSUMPTIONS AND THEIR VERIFICATION
The only assumption required for the one-sample proportion test is the assumption of a
random sample. To verify this assumption, review the procedures and documentation used to
select the sampling points and ascertain that proper randomization has been used.
LIMITATIONS AND ROBUSTNESS
Since the only assumption is that of a random sample, the procedures are valid for any
underlying distributional shape. The procedures are also robust to outliers, as long as they do not
represent data errors.
SEQUENCE OF STEPS
Directions for the one-sample test for proportions for a simple random sample and a
systematic random sample are given in Box 3-10, an example is given in Box 3-11.
3.2.3 Tests for a Median
A population median (jl) is another measure of the center of the population distribution.
This population parameter is less sensitive to extreme values and nondetects than the sample
mean. Therefore, this parameter is sometimes used instead of the mean when the data contain a
large number of nondetects or extreme values. The hypotheses considered in this section are:
Case 1: H0: jl < C vs. HA: jl > C; and
Case 2: H0: ji > C vs. HA: £< C
where C represents a given threshold such as a regulatory level.
It is worth noting that the median is the 50th percentile, so the methods described in
Section 3.2.2 may be used to test hypotheses concerning the median by letting P0 = 0.50. In this
case, the one-sample test for proportions is also called the Sign Test for a median. The Wilcoxon
signed rank test (Section 3.2.1.2) can also be applied to a median in the same manner as it is
applied to a mean. In
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Box 3-10: Directions for the One-Sample Test for Proportions
for Simple and Systematic Random Samples
This box describes the steps for applying the one-sample test for proportions for Case 1 (H0: P < P0);
modifications for Case 2 (H0: P > P0) are given in braces { }.
STEP 1: Given a random sample X.,, X2, . . . , Xn of measurements from the population, let
p (small p) denote the proportion of X's that do not exceed C, i.e., p is the number
(k) of sample points that are less than or equal to C, divided by the sample size n.
STEP 2: Compute np, and n(1-p). If both np and n(1-p) are greater than or equal to 5, use
Steps 3 and 4. Otherwise, consult a statistician as analysis may be complex.
p - .5/n - P p + .5/n - PQ
STEP 3: Calculate z = — - for Case 1 or z = — - for
Case 2.
STEP 4: Use Table A-1 of Appendix A to find the critical value z^ such that 100(1-a)% of
the normal distribution is below z^.a. For example, if a = 0.05 then z^.a = 1 .645.
If z > z.,.,, {z < -z-i.J, the null hypothesis may be rejected. Go to Step 6.
If z > z1_cl {z < -z .,_„}, there is not enough evidence to reject the null hypothesis. Therefore, the
false acceptance error rate will need to be verified. Go to Step 5.
STEP 5: To calculate the power of the test, assume that the true values for the mean and
standard deviation are those obtained in the sample and use a statistical software
package like the DEFT software (EPA, 1994) or the DataQUEST software (EPA,
1996) to generate the power curve of the test.
If only one false acceptance error rate (P) has been specified (at P.,), it is possible to calculate
the sample size which achieves the DQOs. To do this, calculate
m =
If m < n, the false acceptance error rate has been satisfied. Otherwise, the false acceptance
error rate has not been satisfied.
STEP 6: The results of the test may be:
1) the null hypothesis was rejected and it seems that the proportion is greater than {less than}
PO;
2) the null hypothesis was not rejected, the false acceptance error rate was satisfied, and it
seems that proportion is less than {greater than} P0; or
3) the null hypothesis was not rejected, the false acceptance error rate was not satisfied, and it
would seem the proportion was less than {greater than} P0, but the conclusions are uncertain
because the sample size was too small.
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Box 3-1 1 : An Example of the One-Sample Test for Proportions
for a Simple Random Sample
Consider 85 samples of which 1 1 samples have concentrations greater than the clean-up standard. This
data will be used to test the null hypothesis H0: P > .20 vs. HA: P < .20 (Case 2). The decision maker
has specified a 5% false rejection rate (a) for P0 = .2, and a false acceptance rate (P) of 20% for P., =
0.15.
STEP 1: From the data, the observed proportion (p) is p = 11/85 = .1294
STEP 2: np = (85)(.1294) = 1 1 and n(1-p) = (85)(1-.1294) = 74. Since both np and n(1-p) are
greater than or equal to 5, Steps 3 and 4 will be used.
STEP 3: Because H0: P > .20, Case 2 formulas will be used.
P + -5/n ~ Po .1294 + .5/85 - .2 _
JP0(\-P0)/n J. 2(1 -.2)785
STEP 4: Using Table A-1 of Appendix A, it was found that z., 05 = z95 = 1.645. Because z < -z., „
(i.e.,
-1.492 < -1.645), the null hypothesis is not rejected so Step 5 will need to be completed.
STEP 5: To determine whether the test was powerful enough, the sample size necessary to
achieve the DQOs was calculated as follows:
m =
1.64/2(1- .2)+ 1.04/15(1- .15)
= 422.18
.15 - .2
So 423 samples are required, many more than were actually taken.
STEP 6: The null hypothesis was not rejected and the false acceptance error rate was not
satisfied. Therefore, it would seem the proportion is greater than 0.2, but this
conclusion is uncertain because the sample size is too small.
addition, this test is more powerful than the Sign Test for symmetric distributions. Therefore, the
Wilcoxon signed rank test is the preferred test for the median.
3.2.4 Confidence Intervals
In some instances, a test of hypotheses for the estimated parameter (i.e. mean or difference
of two means) is not required, but an idea of the uncertainty of the estimate with respect to the
parameter is needed. The most common type of interval estimate is a confidence interval. A
confidence interval may be regarded as combining an interval around an estimate with a
probabilistic statement about the unknown parameter. When interpreting a confidence interval
statement such as "The 95% confidence interval for the mean is 19.1 to 26.3", the implication is
that the best estimate for the unknown population mean is 22.7 (halfway between 19.1 and 26.3),
and that we are 95% certain that the interval 19.1 to 26.3 captures the unknown population mean.
Box 3-12 gives the directions on how to calculate a confidence interval for the mean, Box 3-13
gives and example of the method.
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The concept of a confidence interval can be shown by a simple example. Suppose a stable
situation producing data without any anomalies was sampled many times. Each time the sample
was taken, the mean and standard deviation was calculated from the sample and a confidence
interval constructed using the method of Box 3-10.
Box 3-12: Directions for a Confidence Interval for a Mean
for Simple and Systematic Random Samples
Let X.,, X2, ..., Xn represent a sample of size n from a population of normally distributed values.
Step 1: Use the directions in Box 2-2 to calculate the sample mean, X. Use the directions in Box 2-3 to
calculate the sample standard deviation, s.
Step 2: Use Table A-1 of Appendix A to find the critical value t^ such that 100(1-a/2)% of the t distribution
with n -1 degrees of freedom is below t^^. For example, if a = 0.10 and n = 16, then n-1 =15 and t
= 1.753.
f v / V
Step 3: The (1-a)100% confidence interval is: X ~"_ to X '
V« \n
Box 3-13: An Example of a Confidence Interval for a Mean
for a Random or Systematic Random Samples
The effluent from a discharge point in a plating manufacturing plant was sampled 7 times over the course of 4
days for the presence of Arsenic with the following results: 8.1, 7.9, 7.9. 8.2, 8.2, 8.0,7.9. The directions in Box
3-12 will be used to develop a 95% confidence interval for the mean.
Stepl: Using Box 2-2, X=8.Q3. Use Box 2-3, s=0.138.
Step 2: Using Table A-1 of Appendix A and 6 degrees of freedom, t^^ = 2.447.
Step 3: The (1-a)100% confidence interval is:
2.447x0.138 2.447x0.138
803 p= to 803 + p= or 7.902 to 8.158.
V7
3.3 TESTS FOR COMPARING TWO POPULATIONS
A two-sample test involves the comparison of two populations or a "before and after"
comparison. In environmental applications, the two populations to be compared may be a
potentially contaminated area with a background area or concentration levels from an upgradient
and a downgradient well. The comparison of the two populations may be based on a statistical
parameter that characterizes the relative location (e.g., a mean or median), or it may be based on a
distribution-free comparison of the two population distributions. Tests that do not assume an
underlying distributions (e.g., normal or lognormal) are called distribution-free or nonparametric
tests. These tests are often more useful for comparing two populations than those that assume a
specific distribution because they make less stringent assumptions. Section 3.3.1 covers tests for
differences in the means of two populations. Section 3.3.2 covers tests for differences in the
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proportion or percentiles of two populations. Section 3.3.3 describes distribution-free
comparisons of two populations. Section 3.3.4 describes tests for comparing two medians.
Often, a two-sample test involves the comparison of the difference of two population
parameters to a threshold value. For environmental applications, the threshold value is often zero,
representing the case where the data are used to determine which of the two population
parameters is greater than the other. For example, concentration levels from a Superfund site may
be compared to a background site. Then, if the Superfund site levels exceed the background
levels, the site requires further investigation. A two-sample test may also be used to compare
readings from two instruments or two separate populations of people.
If the exact same sampling locations are used for both populations, then the two samples
are not independent. This case should be converted to a one-sample problem by applying the
methods described in Section 3.2 to the differences between the two populations at the same
location. For example, one could compare contaminant levels from several wells after treatment
to contaminant levels from the same wells before treatment. The methods described in Section
3.2 would then be applied to the differences between the before and after treatment contaminant
levels for each well.
3.3.1 Comparing Two Means
Let jij represent the mean of population 1 and |i2 represent the mean of population 2. The
hypotheses considered in this section are:
Case 1: H0: jij - |i2 < 50 vs. HA: jij - |i2 > 60; and
Case 2: H0: ^ - |i2 > 50 vs. HA: ^ - |i2 < 50.
An example of a two-sample test for population means is comparing the mean contaminant level
at a remediated Superfund site to a background site; in this case, 50 would be zero. Another
example is a Record of Decision for a Superfund site which specifies that the remediation
technique must reduce the mean contaminant level by 50 ppm each year. Here, each year would
be considered a separate population and 50 would be 50 ppm.
The information required for these tests includes the null and alternative hypotheses (either
Case 1 or Case 2); the gray region (i.e., a value 6X > 50 for Case 1 or a value 6X < 50 for Case 2
representing the bound of the gray region); the false rejection error rate a at 50; the false
acceptance error rate P at 5t; and any additional limits on decision errors. It may be helpful to
label additional false rejection error limits as a2 at 5a2, a3 at 6a3, etc., and to label additional false
acceptance error limits as P2 at 5p2, P3 at 6p3, etc.
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3.3.1.1 Student's Two-Sample t-Test (Equal Variances)
PURPOSE
Student's two-sample t-test can be used to compare two population means based on the
independent random samples Xl3 X2, . . . , X,,, from the first population, and Yl3 Y2, . . . , Yn from
the second population. This test assumes the variabilities (as expressed by the variance) of the
two populations are approximately equal. If the two variances are not equal (a test is described in
Section 4.5), use Satterthwaite's t test (Section 3.3.1.2).
ASSUMPTIONS AND THEIR VERIFICATION
The principal assumption required for the two-sample t-test is that a random sample of
size m (Xl3 X2, . . . , XjJ is drawn from population 1, and an independent random sample of size n
(Yl3 Y2, . . . , Yn) is drawn from population 2. Validity of the random sampling and independence
assumptions should be confirmed by reviewing the procedures used to select the sampling points.
The second assumption required for the two-sample t-tests are that the sample means x
(sample 1) and Y (sample 2) are approximately normally distributed. If both m and n are large,
one may make this assumption without further verification. For small sample sizes, approximate
normality of the sample means can be checked by testing the normality of each of the two
samples.
LIMITATIONS AND ROBUSTNESS
The two-sample t-test with equal variances is robust to violations of the assumptions of
normality and equality of variances. However, if the investigator has tested and rejected
normality or equality of variances, then nonparametric procedures may be applied. The t-test is
not robust to outliers because sample means and standard deviations are sensitive to outliers.
SEQUENCE OF STEPS
Directions for the two-sample t-test for a simple random sample and a systematic simple
random sample are given in Box 3-14 and an example in Box 3-15.
3.3.1.2 Satterthwaite's Two-Sample t-Test (Unequal Variances)
Satterthwaite's t-test should be used to compare two population means when the variances
of the two populations are not equal. It requires the same assumptions as the two-sample t-test
(Section 3.3.1.1) except the assumption of equal variances.
Directions for Satterthwaite's t-test for a simple random sample and a systematic simple
random sample are given in Box 3-16 and an example in Box 3-17.
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Box 3-14: Directions for the Student's Two-Sample t-Test (Equal Variances)
for Simple and Systematic Random Samples
This describes the steps for applying the two-sample t-tests for differences between the population
means when the two population variances are equal for Case 1 (H0: U., - u2 < 50). Modifications for Case
2
(H0: U., - u2 > 50) are given in parentheses {}.
STEP 1: Calculate the sample mean X and the sample variance sx2 for sample 1 and compute
the sample mean Y and the sample variance sY2 for sample 2.
STEP 2: Use Section 4.5 to determine if the variances of the two populations are equal. If the
variances of the two populations are not equal, use Satterthwaite's ttest (Section
3.3.1.2). Otherwise, compute the pooled standard deviation
(iw-!)+(«-!)
X- Y- 60
STEP 3: Calculate t = -
II m
Use Table A-1 of Appendix A to find the critical value t.,_a such that 100(1-a)% of the t-
distribution with (m+n-2) degrees of freedom is below t^.
If t > t.,.,, {t < -t^J, the null hypothesis may be rejected. Go to Step 5.
If t > i^ {t < -t^J, there is not enough evidence to reject the null hypothesis. Therefore, the
false acceptance error rate will need to be verified. Go to Step 4.
STEP 4: To calculate the power of the test, assume that the true values for the mean and
standard deviation are those obtained in the sample and use a statistical software
package like the DEFT software (EPA, 1994) or the DataQUEST software (EPA, 1996)
to generate the power curve of the two-sample t-test. If only one false acceptance error
rate (P) has been specified (at 6.,), it is possible to calculate the sample size which
achieves the DQOs, assuming the true mean and standard deviation are equal to the
values estimated from the sample, instead of calculating the power of the test.
Calculate
If m* < m and n* < n, the false acceptance error rate has been satisfied. Otherwise, the false
acceptance error rate has not been satisfied.
STEP 5: The results of the test could be:
1) the null hypothesis was rejected, and it seems U., - u2 > 50 {p., - u2 < 50};
2) the null hypothesis was not rejected, the false acceptance error rate was satisfied, and it
seems U., - u2 < 50 {p., - u2 > 50}; or
3) the null hypothesis was not rejected, the false acceptance error rate was not satisfied, and
it seems U., - u2 < 50 {p., - u2 > 50}, but this conclusion is uncertain because the sample size
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Box 3-15: An Example of a Student's Two-Sample t-Test (Equal Variances)
for Simple and Systematic Random Samples
At a hazardous waste site, area 1 (cleaned using an in-situ methodology) was compared with a similar
(but relatively uncontaminated) reference area, area 2. If the in-situ methodology worked, then the two
sites should be approximately equal in average contaminant levels. If the methodology did network,
then area 1 should have a higher average than the reference area. Seven random samples were taken
from area 1, and eight were taken from area 2. Because the contaminant concentrations in the two areas
are supposedly equal, the null hypothesis is H0: U., - u2 < 0 (Case 1). The false rejection error rate was
set at 5% and the false acceptance error rate was set at 20% (P) if the difference between the areas is 2.5
ppb.
STEP 1: Sample Mean Sample Variance
Area 1 7.8 ppm 2.1 ppm2
Area 2 6.6 ppm 2.2 ppm2
STEP 2: Methods described in Section 4.5 were used to determine that the variances were
essentially equal. Therefore,
SE=
E
- D2.1 + (8- 1)2.2 =
(7-1) +(8-1)
STEPS: t = 7-8"6-6"0 = 1.5798
1.4676^/1/7+1/8
Table A-1 of Appendix A was used to find that the critical value t095 with (7 + 8 - 2) = 13
degrees of freedom is 1.771.
Because t > i^_a (i.e., 1.5798 > 1.771), there is not enough evidence to reject the null
hypothesis. The false acceptance error rate will need to be verified.
STEP 4: Assuming the true values for the mean and standard deviation are those obtained in the
sample:
2(1.46762)(1.645 + 0.842)2 ^^,^,2 „ noo • c
m = n — - — - — + (0.25)1.645 = 4.938, i.e., 5.
(2.5 - O)2
Because m* < m (7) and n* < n (8), the false acceptance error rate has been satisfied.
STEP 5: The null hypothesis was not rejected and the false acceptance error rate was satisfied.
Therefore, it seems there is no difference between the two areas and that the in-situ
methodology worked as expected.
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Box 3-16: Directions for Satterthwaite's t-Test (Unequal Variances)
for Simple and Systematic Random Samples
This describes the steps for applying the two-sample t-test for differences between the population means
for Case 1 (H0: U., - u2 < 50). Modifications for Case 2 (H0: U., - u2 > 50) are given in parentheses {}.
STEP 1: Calculate the sample mean X and the sample variance sx2 for sample 1 and compute
the sample mean Y and the sample variance sY2 for sample 2.
STEP 2: Using Section 4.5, test whether the variances of the two populations are equal. If the
variances of the two populations are not equal, compute:
SNE \
o a
y Y
m n
If the variances of the two populations appear approximately equal, use Student's two-
sample t-test (Section 3.3.1.1, Box 3-14).
X- Y- 60
STEP 3: Calculate =
SNE
Use Table A-1 of Appendix A to find the critical value t.,_a such that 100(1-a)% of the t-
distribution with f degrees of freedom is below t.,_a, where
2 2?
/=•
m
sx s¥
m2(m-V) n\n-Y)
(Round f down to the nearest integer.)
If t > t.,.,, {t < -t .,_„}, the null hypothesis may be rejected. Go to Step 5.
If t > t1_cl {t i -t-i.J, there is not enough evidence to reject the null hypothesis and therefore,
the false acceptance error rate will need to be verified. Go to Step 4.
STEP 4: If the null hypothesis (H0) was not rejected, calculate either the power of the test or the
sample size necessary to achieve the false rejection and false acceptance error rates.
To calculate the power of the test, assume that the true values for the mean and
standard deviation are those obtained in the sample and use a statistical software
package to generate the power curve of the two-sample t-test. A simple method to
check on statistical power does not exist.
STEP 5: The results of the test could be:
1) the null hypothesis was rejected, and it seems U., - u2 > 50 {p., - u2 < 50};
2) the null hypothesis was not rejected, the false acceptance error rate was satisfied, and it
seems ^ - u2 < 50 {^ - u2 > 50}; or
3) the null hypothesis was not rejected, the false acceptance error rate was not satisfied,
and it seems U., - u2 < 50 {p., - u2 > 50}, but this conclusion is uncertain because the sample
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Box 3-17: An Example of Satterthwaite's t-Test (Unequal Variances)
for Simple and Systematic Random Samples
At a hazardous waste site, area 1 (cleaned using an in-situ methodology) was compared with a similar
(but relatively uncontaminated) reference area, area 2. If the in-situ methodology worked, then the two
sites should be approximately equal in average contaminant levels. If the methodology did network,
then area 1 should have a higher average than the reference area. Seven random samples were taken
from area 1, and eight were taken from area 2. Because the contaminant concentrations in the two areas
are supposedly equal, the null hypothesis is H0: U., - u2 < 0 (Case 1). The false rejection error rate was
set at 5% and the false acceptance error rate was set at 20% (P) if the difference between the areas is 2.5
ppb.
STEP 1: Sample Mean Sample Variance
Area 1 9.2 ppm 1.3ppm2
Area 2 6.1 ppm 5.7 ppm2
STEP 2: Using Section 4.5, it was determined that the variances of the two populations were not
equal, and therefore using Satterthwaite's method is appropriate:
SNE = ^1.3/7 + 5.7/8 = 0.9477
STEPS: ,= 9-2-6-1-° =3.271
0.9477
Table A-1 was used with f degrees of freedom, where
/ = —i— '• ! = 10.307 (i.e., 10 degrees of freedom)
1.32 5.72
72(7-l) 82(8-l)
(recall that f is rounded down to the nearest integer), to find t1_c, = 1.812.
Because t > t095 (3.271 > 1.812), the null hypothesis may be rejected.
STEP 5: Because the null hypothesis was rejected, it would appear there is a difference between
the two areas (area 1 being more contaminated than area 2, the reference area) and
that the in-situ methodology has networked as intended.
3.3.2 Comparing Two Proportions or Percentiles
This section considers hypotheses concerning two population proportions (or two
population percentiles); for example, one might use these tests to compare the proportion of
children with elevated blood lead in one urban area compared with the proportion of children with
elevated blood lead in another area. The population proportion is the ratio of the number of
elements in a subset of the total population to the total number of elements, where the subset has
some specific characteristic that the rest of the elements do not. A population percentile
represents the percentage of elements of a population having values less than some threshold
value C.
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Let Pj represent the true proportion for population 1, and P2 represent the true proportion
of population 2. The hypotheses considered in this section are:
Case 1: H0: P1 - P2 < 50 vs. HA: P1 - P2 > 60; and
Case 2: H0: Pt - P2 > 50 vs. HA: Pt - P2 < 50
where 50 is some numerical value. An equivalent null hypothesis for Case 1, written in terms of
percentiles, is H0: the lOOP/11 percentile minus the 100P2th percentile is C or larger, the reverse
applying to Case 2. Since any hypothesis about the proportion below a threshold can be
converted to an equivalent hypothesis about percentiles (see Section 3.2.2), this guidance will
only consider hypotheses concerning proportions.
The information required for this test includes the null and alternative hypotheses (either
Case 1 or Case 2); the gray region (i.e., a value 5j > 50 for Case 1 or a value 5j < 50 for Case 2,
representing the bound of the gray region); the false rejection error rate a at 60; the false
acceptance error rate P at 5^ and any additional limits on decision errors.
3.3.2.1 Two-Sample Test for Proportions
PURPOSE
The two-sample test for proportions can be used to compare two population percentiles or
proportions and is based on an independent random sample of m (Xl3 X2, . . . , XjJ from the first
population and an independent random sample size n (Yl3 Y2, . . . , Yn) from the second
population.
ASSUMPTIONS AND THEIR VERIFICATION
The principal assumption is that of random sampling from the two populations.
LIMITATIONS AND ROBUSTNESS
The two-sample test for proportions is valid (robust) for any underlying distributional
shape and is robust to outliers, providing they are not pure data errors.
SEQUENCE OF STEPS
Directions for a two-sample test for proportions for a simple random sample and a
systematic simple random sample are given in Box 3-18; an example is provided in Box 3-19.
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Box 3-18: Directions for a Two-Sample Test for Proportions
for Simple and Systematic Random Samples
The following describes the steps for applying the two-sample test for proportions for Case 1 (H0: P., - P2
< 0). Modifications for Case 2 (H0: P., - P2 > 0) are given in braces {}.
STEP 1: Given m random samples X.,, X2, . . . , Xm from the first population, and n samples from the
second population, Y.,, Y2, . . . , Yn, let k., be the number of points from sample 1 which
exceed C, and let k2 be the number of points from sample 2 which exceed C. Calculate the
sample proportions p., = k/m and p2 = k2/n. Then calculate the pooled proportion
p = (kl + k2) / (m + n).
STEP 2: Compute imp.,, m(1-p.|), np2, n(1-p2). If all of these values are greater than or equal to 5,
continue. Otherwise, seek assistance from a statistician as analysis is complicated.
STEP 3: Calculate z = (pl - p2) I Jp(\ - p)(\lm + lln).
Use Table A-1 of Appendix A to find the critical value z^.a such that 100(1-a)% of the normal
distribution is below z^.a. For example, if a = 0.05 then z^.a = 1 .645.
If z > z.,.,, {z < -z .,_„}, the null hypothesis may be rejected. Go to Step 5.
If z > z1_cl {z < -z^J, there is not enough evidence to reject the null hypothesis. Therefore, the
false acceptance error rate will need to be verified. Go to Step 4.
STEP 4: If the null hypothesis (H0) was not rejected, calculate either the power of the test or the
sample size necessary to achieve the false rejection and false acceptance error rates. If only
one false acceptance error rate (P) has been specified at P., - P2, it is possible to calculate
the sample sizes that achieve the DQOs (assuming the proportions are equal to the values
estimated from the sample) instead of calculating the power of the test. To do this, calculate
2(Z, + Z, n
m* = n* = — -^ - li£ — - - - where P =
and zp is the pth percentile of the standard normal distribution (Table A-1 of Appendix A). If
both m and n exceed m*, the false acceptance error rate has been satisfied. If both m and n
are below m*, the false acceptance error rate has not been satisfied.
If m* is between m and n, use a software package like the DEFT software (EPA, 1994) or the
DataQUEST software (EPA, 1996) to calculate the power of the test, assuming that the true
values for the proportions P., and P2 are those obtained in the sample. If the estimated
power is below 1-(3, the false acceptance error rate has not been satisfied.
STEP 5: The results of the test could be:
1) the null hypothesis was rejected, and it seems the difference in proportions is greater
than 0 {less than 0};
2) the null hypothesis was not rejected, the false acceptance error rate was satisfied, and it
seems the difference in proportions is less than or equal to 0 {greater than or equal to 0}; or
3) the null hypothesis was not rejected, the false acceptance error rate was not satisfied,
and it seems the difference in proportions is less than or equal to 0 {greater than or equal to
0}, but this outcome is uncertain because the sample size was probably too small.
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Box 3-19: An Example of a Two-Sample Test for Proportions
for Simple and Systematic Random Samples
At a hazardous waste site, investigators must determine whether an area suspected to be contaminated
with dioxin needs to be remediated. The possibly contaminated area (area 1) will be compared to a
reference area (area 2) to see if dioxin levels in area 1 are greater than dioxin levels in the reference area.
An inexpensive surrogate probe was used to determine if each individual sample is either "contaminated,"
i.e., over the health standard of 1 ppb, or "clean," i.e., less than the health standard of 1 ppb. The null
hypothesis will be that the proportion or contaminant levels in area 1 is less than or equal to the
proportion in area 2, or H0: P., - P2 < 0 (Case 1). The decision maker is willing to accept a false rejection
decision error rate of 10% (a) and a false-negative decision error rate of 5% (P) when the difference in
proportions between areas exceeds 0.10. A team collected 92 readings from area 1 (of which 12 were
contaminated) and 80 from area 2, the reference area, (of which 10 were contaminated).
STEP 1: The sample proportion for area 1 is p., = 12/92 = 0.130, the sample proportion for area 2 is
p2 = 10/80 = 0.125, and the pooled proportion p = (12 + 10) / (92 + 80 ) = 0.128.
STEP 2: imp., = 12, m(1-p.,) = 80, np2 = 10, n(1-p2) =70. Because these values are greater than or
equal to 5, continue to step 3.
STEPS: z = (0.130 - 0.125) / JO.128(1 - 0.128)(l/92 + 1/80) = 0.098
Table A-1 of Appendix A was used to find the critical value zogo = 1.282.
Because z > zogo (0.098 > 1.282), there is not enough evidence to reject the null hypothesis
and the false acceptance error rate will need to be verified. Go to Step 4.
STEP 4: Because the null hypothesis (H0) was not rejected, calculate the sample size necessary to
achieve the false rejection and false acceptance error rates. Because only one false
acceptance error rate (p = 0.05) has been specified (at a difference of P., - P2 = 0.1), it is
possible to calculate the sample sizes that achieve the DQOs, assuming the proportions are
equal to the values estimated from the sample:
2(1.282+ 1.645)20.1275(1-0.1275) 1on,,. im , ,
m = n = — - = 190.6 (i.e., 191 samples)
n m* P °-115 + °-055
where 0.1275 = P =
2
Because both m and n are less than m*, the false acceptance error rate has not been
satisfied.
STEP 5: The null hypothesis was not rejected, and the false acceptance error rate was not satisfied.
Therefore, it seems that there is no difference in proportions and that the contaminant
concentrations of the investigated area and the reference area are probably the same.
However, this outcome is uncertain because the sample sizes obtained were in all likelihood
too small.
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3.3.3 Nonparametric Comparisons of Two Populations
In many cases, assumptions on distributional characteristics are difficult to verify or
difficult to satisfy for both populations. In this case, several distribution-free test procedures are
available that compare the shape and location of the two distributions instead of a statistical
parameter (such as a mean or median). The statistical tests described below test the null
hypothesis "H0: the distributions of population 1 and population 2 are identical (or, the site is not
more contaminated than background)" versus the alternative hypothesis "HA: part of the
distribution of population 1 is located to the right of the distribution of population 2 (or the site is
more contaminated than background)." Because of the structure of the hypothesis tests, the
labeling of populations 1 and 2 is of importance. For most environmental applications, population
1 is the area of interest (i.e., the potentially contaminated area) and population 2 is the reference
area.
There is no formal statistical parameter of interest in the hypotheses stated above.
However, the concept of false rejection and false acceptance error rates still applies.
3.3.3.1 The Wilcoxon Rank Sum Test
PURPOSE
The Wilcoxon rank sum test can be used to compare two population distributions based
on m independent random samples Xl3 X2, . . . , X,,, from the first population, and n independent
random samples Yl3 Y2, . . . , Yn from the second population. When applied with the Quantile test
(Section 3.3.3.2), the combined tests are most powerful for detecting true differences between
two population distributions.
ASSUMPTIONS AND THEIR VERIFICATION
The validity of the random sampling and independence assumptions should be verified by
review of the procedures used to select the sampling points. The two underlying distributions are
assumed to have the same shape and dispersion, so that one distribution differs by some fixed
amount (or is increased by a constant) when compared to the other distribution. For large
samples, to test whether both site distributions have approximately the same shape, one can create
and compare histograms for the samples.
LIMITATIONS AND ROBUSTNESS
The Wilcoxon rank sum test may produce misleading results if many data values are the
same. When values are the same, their relative ranks are the same, and this has the effect of
diluting the statistical power of the Wilcoxon rank sum test. Estimated concentrations should be
reported for data below the detection limit, even if these estimates are negative, because their
relative magnitude to the rest of the data is of importance. An important advantage of the
Wilcoxon rank sum test is its partial robustness to outliers, because the analysis is conducted in
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terms of rankings of the observations. This limits the influence of outliers because a given data
point can be no more extreme than the first or last rank.
SEQUENCE OF STEPS
Directions and an example for the Wilcoxon rank sum test are given in Box 3-20 and Box
3-21. However, if a relatively large number of samples have been taken, it is more efficient in
terms of statistical power to use a large sample approximation to the Wilcoxon rank sum test
(Box 3-22) to obtain the critical values of W.
Box 3-20: Directions for the Wilcoxon Rank Sum Test
for Simple and Systematic Random Samples
LetX.,, X2, . . . , Xn represent the n data points from population 1 and Y.,, Y2, . . . , Ym represent the m data points
from population 2 where both n and m are less than or equal to 20. For Case 1, the null hypothesis will be that
population 1 is shifted to the left of population 2 with the alternative that population 1 is either the same as or
shifted to the right of population 2; Case 2 will be that population 1 is shifted to the right of population 2 with the
alternative that population 1 is the same as or shifted to the left of population 2; for Case 3, the null hypothesis
will be that there is no difference between the two populations and the alternative hypothesis will be that
population 1 is shifted either to the right or left of population 2. If either m or n are larger than 20, use Box 3-22.
STEP 1: List and rank the measurements from both populations from smallest to largest,
keeping track of which population contributed each measurement. The rank of 1 is
assigned to the smallest value, the rank of 2 to the second smallest value, and so
forth. If there are ties, assign the average of the ranks that would otherwise have
been assigned to the tied observations.
STEP 2: Calculate R as the sum of the ranks of the data from population 1, then calculate
W=R- H(n+l\
2
STEP 3: Use Table A-7 of Appendix A to find the critical value wa (or wa/2 for Case 3). For
Case 1, reject the null hypothesis if W > nm - wa. For Case 2, reject the null
hypothesis if W < wa. For Case 3, reject the null hypothesis if W > nm - wa/2 or W <
wa/2. If the null hypothesis is rejected, go to Step 5. Otherwise, go to Step 4.
STEP 4: If the null hypothesis (H0) was not rejected, the power of the test or the sample size
necessary to achieve the false rejection and false acceptance error rates should be
calculated. For small samples sizes, these calculations are too complex for this
document.
STEP 5: The results of the test could be:
1) the null hypothesis was rejected and it seems that population 1 is shifted to the right (Case 1), to
the left (Case 2) or to the left or right (Case 3) of population 2.
2) the null hypothesis was not rejected and it seems that population 1 is shifted to the left (Case 1)
or to the right (Case 2) of population 2, or there is no difference between the two populations (Case
3).
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Box 3-21: An Example of the Wilcoxon Rank Sum Test
for Simple and Systematic Random Samples
At a hazardous waste site, area 1 (cleaned using an in-situ methodology) was compared with a similar
(but relatively uncontaminated) reference area, area 2. If the in-situ methodology worked, then the two
sites should be approximately equal in average contaminant levels. If the methodology did network,
then area 1 should have a higher average than the reference area. The null hypothesis will be that area 1
is shifted to the right of area 2 and the alternative hypothesis will be that there is no difference between
the two areas or that area 1 is shifted to the left of area 2 (Case 2). The false rejection error rate was set
at 10% and the false acceptance error rate was set at 20% (P) if the difference between the areas is 2.5
ppb. Seven random samples were taken from area 1 and eight samples were taken from area 2:
Area 1 Area 2
17,23,26,5 16,20,5,4
13,13,12 8,10,7,3
STEP 1: The data listed and ranked by size are (Area 1 denoted by *):
Data (ppb): 3, 4, 5, 5*, 7, 8, 10, 12*, 13*, 13*, 16, 17*, 20, 23*, 26*
Rank: 1, 2,3.5,3.5*, 5, 6, 7, 8*, 9.5*, 9.5* 11, 12*, 13, 14*, 15*
STEP 2: R = 3.5+ 8 +9.5 +9.5+12+14+15 = 71..5. W= 71.5 - 7(7 + 1)/2 = 43.5
STEPS: Using Table A-7 of Appendix A, a = 0.10 and Wa = 17. Since 43.5 > 17, do
not reject the null hypothesis.
STEP 4: The null hypothesis was not rejected and it would be appropriate to calculate
the probable power of the test. However, because the number of samples is
small, extensive computer simulations are required in order to estimate the
power of this test which is beyond the scope of this guidance.
STEP 5: The null hypothesis was not rejected. Therefore, it is likely that there is no
difference between the investigated area and the reference area, although the
statistical power is low due to the small sample sizes involved.
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Box 3-22: Directions for the Large Sample Approximation
to the Wilcoxon Rank Sum Test for Simple and Systematic Random Samples
LetX.,, X2, . . . , Xn represent the n data points from population 1 and Y.,, Y2, . . . , Ym represent the m data
points from population 2 where both n and m are greater than 20. For Case 1, the null hypothesis will be
that population 1 is shifted to the left of population 2 with the alternative that population 1 is the same as
or shifted to the right of population 2; for Case 2, the null hypothesis will be that population 1 is shifted to
the right of population 2 with the alternative that population 1 is the same as or shifted to the left of
population 2; for Case 3, the null hypothesis will be that there is no difference between the populations
and the alternative hypothesis will be that population 1 is shifted either to the right or left of population 2.
STEP 1: List and rank the measurements from both populations from smallest to
largest, keeping track of which population contributed each measurement.
The rank of 1 is assigned to the smallest value, the rank of 2 to the second
smallest value, and so forth. If there are ties, assign the average of the ranks
that would otherwise have been assigned to the tied observations.
STEP 2: Calculate Was the sum of the ranks of the data from population 1.
mn
STEPS: Calculate w = - +Zp\lmn(n + m + 1)/12 where p = 1 - a for Case
1, p = a for Case 2, and zp is the pth percentile of the standard normal
distribution (Table A-1 of Appendix A). For Case 3, calculate both wa/2 (p =
a/2) and w^^ (p = 1 -a/2).
STEP 4: For Case 1 , reject the null hypothesis if W > \N^a. For Case 2, reject the null
hypothesis if W < wa. For Case 3, reject the null hypothesis if W > w^^ or
W < wa/2. If the null hypothesis is rejected, go to Step 6. Otherwise, go to
Step 5.
STEP 5: If the null hypothesis (H0) was not rejected, calculate either the power of the
test or the sample size necessary to achieve the false rejection and negative
error rates. If only one false acceptance error rate (P) has been specified (at
5.,), it is possible to calculate the sample size that achieves the DQOs,
assuming the true mean and standard deviation are equal to the values
estimated from the sample, instead of calculating the power of the test. If m
and n are large, calculate:
where zp is the pth percentile of the standard normal distribution (Table A-1 of Appendix A). If
1.16m* < m and 1.1 6n* < n, the false acceptance error rate has been satisfied.
STEP 6: The results of the test could be:
1) the null hypothesis was rejected, and it seems that population 1 is shifted to the right
(Case 1 ), to the left (Case 2) or to the left or right (Case 3) of population 2.
2) the null hypothesis was not rejected, the false acceptance error rate was satisfied, and it
seems that population 1 is shifted to the left (Case 1 ) or to the right (Case 2) of population 2,
or there is no difference between the two populations (Case 3).
3) the null hypothesis was not rejected, the false acceptance error rate was not satisfied, and
it seems that population 1 is shifted to the left (Case 1 ) or to the right (Case 2) of population
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3.3.3.2 The Quantile Test
PURPOSE
The Quantile test can be used to compare two populations based on the independent
random samples Xt, X2, . . ., X,,, from the first population and Yl5 Y2, . . ., Yn from the second
population. When the Quantile test and the Wilcoxon rank sum test (Section 3.3.3.1) are applied
together, the combined tests are the most powerful at detecting true differences between two
populations. The Quantile test is useful in detecting instances where only parts of the data are
different rather than a complete shift in the data. It essentially looks at a certain number of the
largest data values to determine if too many data values from one population are present to be
accounted for by pure chance.
ASSUMPTIONS AND THEIR VERIFICATION
The Quantile test assumes that the data Xl3 X2, . . ., X,,, are a random sample from
population 1, and the data Yl3 Y2, . . ., Yn are a random sample from population 2, and the two
random samples are independent of one another. The validity of the random sampling and
independence assumptions is assured by using proper randomization procedures, either random
number generators or tables of random numbers. The primary verification required is to review
the procedures used to select the sampling points. The two underlying distributions are assumed
to have the same underlying dispersion (variance).
LIMITATIONS AND ROBUSTNESS
The Quantile test is not robust to outliers. In addition, the test assumes either a systematic
(e.g., a triangular grid) or simple random sampling was employed. The Quantile test may not be
used for stratified designs. In addition, exact false rejection error rates are not available, only
approximate rates.
SEQUENCE OF STEPS
The Quantile test is difficult to implement by hand. Therefore, directions are not included
in this guidance but the DataQUEST software (EPA, 1996) can be used to conduct this test.
However, directions for a modified Quantile test that can be implemented by hand are contained
in Box 3-23 and an example is given in Box 3-24.
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Box 3-23: Directions for a Modified Quantile Test for
Simple and Systematic Random Samples
Let there be 'm' measurements from population 1 (the reference area or group) and 'n' measurement from
population 2 (the test area or group). The Modified Quantile test can be used to detect differences in shape
and location of the two distributions. For this test, the significance level (a) can either be approximately 0.10
or approximately 0.05. The null hypothesis for this test is that the two population are the same (i.e., the test
group is the same as the reference group) and the alternative is that population 2 has larger measurements
than population 1 (i.e., the test group has larger values than the reference group).
STEP 1: Combine the two samples and order them from smallest to largest keeping track
of which sample a value came from.
STEP 2: Using Table A-13 of Appendix A, determine the critical number (C) for a sample
size n from the reference area, sample size m from the test area using the
significance level a. If the Cth largest measurement of the combined population is
the same as others, increase C to include all of these tied values.
STEP 3: If the largest C measurements from the combined samples are all from
population 2 (the test group), then reject the null hypothesis and conclude that
there are differences between the two populations. Otherwise, the null hypothesis
is not rejected and it appears that there is no difference between the two
populations.
3.3.4 Comparing Two Medians
Let jlj represent the median of population 1 and £2 represent the median of population 2.
The hypothesis considered in this section are:
Case 1: H0: j^ - J12 < 50 vs. HA: j^ - J12 > 50; and
Case 2: H0: ^ - £2 > 50 vs. HA: ^ - £2 < 50.
An example of a two-sample test for the difference between two population medians is comparing
the median contaminant level at a Superfund site to the median of a background site. In this case,
50 would be zero.
The median is also the 50th percentile, and, therefore, the methods described in Section
3.3.2 for percentiles and proportions may be used to test hypotheses concerning the difference
between two medians by letting ?! = ?„ = 0.50. The Wilcoxon rank sum test (Section 3.3.3.1) is
also recommended for comparing two medians. This test is more powerful than those for
proportions for symmetric distributions.
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Box 3-24: A Example of a Modified Quantile Test for
Simple and Systematic Random Samples
At a hazardous waste site a new, cheaper, in-site methodology was compared against an existing
methodology by remediating separate areas of the site using each method. If the new methodology works,
the overall contamination of the area of the site remediated using the new methodology should be the same as
the area of the site remediated using the standard methodology. If the new methodology does network, then
there would be higher contamination values remaining on the area of the site remediated using the new
methodology. The site manager wishes to determine if the new methodology works and has chosen a 5%
significance level. A modified Quantile Test will be used to make this determination based on 7 samples from
the area remediated using the standard methodology (population 1) and 12 samples from the area remediated
using the new methodology (population 2). The sampled values are:
Standard Methodology New Methodology
17, 8, 20, 4, 6, 5, 4 7, 18, 2, 4, 6, 11, 5, 9, 10, 2, 3, 3
STEP 1: Combine the two samples and ordering them from smallest to largest yields:
2* 2* 3* 3* 4 4 4* 5* 5 6 6* 7* 8 9* 10* 11* 17 18* 20
where * denoted samples from the new methodology portion of the site (population 2).
STEP 2: Using Table A-13 of Appendix A with m = 7, n = 12, and a = 0.05, the critical value C = 5. Since
the 5th largest value is 10, there is not need to increase C.
(Note however, if the data were 2* 2* 3* 3* 4 4 4* 5* 5 6 6* 7* 8 9*9* 11* 17 18* 20
then the 5th largest value would have been 9 which is the same tied with 1 other value. In this
case, C would have been raised to 6 to include the tied value.)
STEPS: From Step 1, the 5 largest values are 10* 11* 17 18* 20. Only 3 of these 5
values come from population 2, therefore the null hypothesis can not be rejected
and the site manager concludes that it seems that the new in-situ methodology
works as well as the standard methodology.
3.4 Tests for Comparing Several Populations
3.4.1 Tests for Comparing Several Means
This section describes procedures to test the differences between several sample means
from different populations either against a control population or among themselves. For example,
the test described in this section could be used identify whether or not there are differences
between several drinking water wells or could be used to identify if several downgradient wells
differ from an upgradient well.
In this situation, it would be possible to apply the tests described in Section 3.3.1 multiple
times. However, applying a test multiple times underestimates the true false rejection decision
error rate. Therefore, the test described in this section controls the overall false rejection decision
error rate by making the multiple comparisons simultaneously.
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3.4.1.1 Dunnett's Test
PURPOSE
Dunnett's test is used to test the difference between sample means from different
populations against a control population. A typical application would involve different cleaned
areas of a hazardous waste site being compared to a reference sample; this reference sample
having been obtained from a uncontaminated part of the hazardous waste site.
ASSUMPTIONS AND THEIR VERIFICATION
Multiple application of any statistical test is inappropriate because the continued use of the
same reference sample violates the assumption that the two samples were obtained independently
for each statistical test. The tests are strongly correlated between themselves with the degree of
correlation depending on the degree of similarity in number of samples used for the control group
and investigated groups. The test is really best suited for approximately equal sample sizes in
both the control group and the groups under investigation.
LIMITATIONS AND ROBUSTNESS
Dunnett's method is the same in operation as the standard two-sample t-test of Section
3.3.1 except for the use of a larger pooled estimate of variance and the need for special t-type
tables (Table A-14 of Appendix A). These tables are for the case of equal number of samples in
the control and each of the investigated groups, but remain valid provided the number of samples
from the investigated group are approximately more than half but less than double the size of the
control group. In this guidance, only the null hypothesis that the mean of the sample populations
is the same as the mean of the control population will be considered.
SEQUENCE OF STEPS
Directions for the use of Dunnett's method for a simple random sample or a systematic
random sample are given in Box 3-25 and an example is contained in Box 3-26.
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Box 3-25: Directions for Dunnett's Test for
Simple Random and Systematic Samples
Let k represent the total number of populations to be compared so there are (k-1) sample populations and a
single control population. Let n.,, n2 ... nk_., represent the sample sizes of each ofthe (k-1) sample populations
and let m represent the sample size ofthe control population. The null hypothesis is H0: ur uc < 0 (i.e., no
difference between the sample means and the control mean) and the alternative hypothesis is HA: ur uc > 0 for
1 =1,2, ..., k-1 where u, represents the mean ofthe ith sample population and uc represents the mean ofthe
control population. Let a represent the chosen significance level for the test.
STEP 1: For each sample population, make sure that approximately 0.5 < m/n, < 2. If not, Dunnett's Test
should not be used.
STEP 2: Calculate the sample mean, x, (Section 2.2.2), and the variance, s2 (Section 2.2.3) for each ofthe k
populations (i.e., i = 1, 2, ... k).
STEP 3: Calculated the pooled standard:
(m—!) + («! —1)+...+(«£_! —1)
STEP 4: For each ofthe k-1 sample populations, compute
Xi - Xc
STEP 5: Use Table A-14 of Appendix A to determine the critical value TD(1_a) where the degrees of freedom is
(m -1) + (n.,-1) + . . . + (nk_.| -1).
STEP 6: Compare t, to TD(1_a) for each of the k-1 sample populations. If t, >TD(1_a) for any of the sample
populations, then reject the null hypothesis and conclude that there are differences between the
means ofthe sample populations and the mean ofthe control populations. Otherwise, conclude that
there is no difference between the sample and control population means.
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Number of
Samples:
Mean:
Variance:
Ratio: m/n
t;:
Reference
Area
7
10.3
2.5
IAK3
6
11.4
2.6
7/6 = 1.16
1.18
ZBF6
5
12.2
3.3
7/5 = 1.4
1.93
3BG5
6
10.2
3.0
7/6 = 1.16
0.11
4GH2
7
11.4
3.2
7/7 = 1
1.22
5FF3
8
11.9
2.6
7/8 = 0.875
1.84
6GW4
7
12.1
2.8
7/7 = 1
2.00
Box 3-26: An Example of Dunnett's Test for
Simple Random and Systematic Samples
At a hazardous work site, 6 designated areas previously identified as 'hotspots' have been cleaned. In order for
this site to be a potential candidate for the local Brownfields program, it must be demonstrated that these areas
are not longer contaminated. Therefore, the means of these areas will be compared to mean of a reference area
also located on the site using Dunnett's test. The null hypothesis will be that there is no difference between the
means of the 'hotspot' areas and the mean of the reference area. A summary of the data from the areas follows.
STEP 1: Calculate the ratio m/n for each investigated area. These are shown in the 4th row of the table above.
Since all of these ration fall within the range of 0.5 to 2.0, Dunnett's test may be used.
STEP 2: The sample means, x, and the variance, s2 were calculated using Sections 2.2.2 and 2.2.3 of
Chapter 2. These are shown in the 2nd and 3rd row of the table above.
STEP 3: The pooled standard deviation for all 7 areas is:
SD =
(7 -1)2.5 + (6 -1)2.6+.. .+(7 ~ 1)2.8
11.4-10.3
STEP 4: For each 'hotspot' area, t, was computed. For example, t\ = , — = 1.18
1.68^/1/6+1/7
These are shown in the 5th row of the table above.
STEP 5: The degrees of freedom is (7 -1) + (6 -1) + . . . + (7 -1) = 39. So using Table A-14 of Appendix A
with 39 for the degrees of freedom, the critical value TD(095) = 2.37 and TD(090) = 2.03.
STEP 6: Since none of the values in row 5 of the table are greater than either 2.37 or 2.03, it appears that
none of the 'hotspot' areas have contamination levels that are significantly different than the
reference area. Therefore, this site may be a potential candidate to be a Brownsfield site.
NOTE: If an ordinary 2-sample t-test (see Section 3.3.1.1) had been used to compare each 'hotspot' area with
the reference area at the 5% level of significance, areas 2BF6, 5FF3, and 69W4 would have erroneously been
declared different from the reference area, which would probably alter the final conclusion to include the site as a
Brownfields candidate.
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CHAPTER 4
STEP 4: VERIFY THE ASSUMPTIONS OF THE STATISTICAL TEST
THE DATA QUALITY ASSESSMENT PROCESS
Review DQOs and Sampling Design
Conduct Preliminary Data Review
Select the Statistical Test
Verify the Assumptions
Draw Conclusions From the Data
VERIFY THE ASSUMPTIONS OF THE
STATISTICAL TEST
Purpose
Examine the underlying assumptions of the statistical
hypothesis test in light of the environmental data.
Activities
• Determine Approach for Verifying Assumptions
• Perform Tests of Assumptions
• Determine Corrective Actions
Tools
• Tests of distributional assumptions
• Tests for independence and trends
• Tests for dispersion assumptions
Step 4: Verify the Assumptions of the Statistical Test
! Determine approach for verifying assumptions.
P Identify any strong graphical evidence from the preliminary data review.
P Review (or develop) the statistical model for the data.
P Select the tests for verifying assumptions.
! Perform tests of assumptions.
P Adjust for bias if warranted.
P Perform the calculations required for the tests.
! If necessary, determine corrective actions.
P Determine whether data transformations will correct the problem.
P If data are missing, explore the feasibility of using theoretical justification or
collecting new data.
P Consider robust procedures or nonparametric hypothesis tests.
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List of Boxes
Box 4-1: Directions for the Coefficient of Variation Test and an Example 4-9
Box 4-2: Directions for Studentized Range Test and an Example 4-10
Box 4-3: Directions for Geary's Test 4-11
Box 4-4: Example of Geary's Test 4-11
Box 4-5: Directions for the Test for a Correlation Coefficient and an Example 4-15
Box 4-6: "Upper Triangular" Data for Basic Mann-Kendall Trend Test 4-17
Box 4-7: Directions for the Mann-Kendall Trend Test for Small Sample Sizes 4-18
Box 4-8: An Example of Mann-Kendall Trend Test for Small Sample Sizes 4-18
Box 4-9: Directions for the Mann-Kendall Procedure Using Normal Approximation 4-19
Box 4-10: An Example of Mann-Kendall Trend Test by Normal Approximation 4-20
Box 4-11: Data for Multiple Times and Multiple Stations 4-21
Box 4-12: Testing for Comparability of Stations and an Overall Monotonic Trend 4-22
Box 4-13: Directions for the Wald-Wolfowitz Runs Test 4-25
Box 4-14: An Example of the Wald-Wolfowitz Runs Test 4-26
Box 4-15: Directions for the Extreme Value Test (Dixon's Test) 4-28
Box 4-16: An Example of the Extreme Value Test (Dixon's Test) 4-28
Box 4-17: Directions for the Discordance Test 4-29
Box 4-18: An Example of the Discordance Test 4-29
Box 4-19: Directions for Rosner's Test for Outliers 4-30
Box 4-20: An Example of Rosner's Test for Outliers 4-31
Box 4-21: Directions for Walsh's Test for Large Sample Sizes 4-32
Box 4-22: Directions for Constructing Confidence Intervals and Confidence Limits 4-34
Box 4-23: Directions for Calculating an F-Test to Compare Two Variances 4-34
Box 4-24: Directions for Bartletf s Test 4-35
Box 4-25: An Example of Bartletf s Test 4-36
Box 4-26: Directions for Levene's Test 4-37
Box 4-27: An Example of Levene's Test 4-38
Box 4-28: Directions for Transforming Data and an Example 4-40
Box 4-29: Directions for Cohen's Method 4-44
Box 4-30: An Example of Cohen's Method 4-44
Box 4-31: Double Linear Interpolation 4-45
Box 4-32: Directions for Developing a Trimmed Mean 4-46
Box 4-33: An Example of the Trimmed Mean 4-46
Box 4-34: Directions for Developing a Winsorized Mean and Standard Deviation 4-47
Box 4-35: An Example of a Winsorized Mean and Standard Deviation 4-47
Box 4-36: 11 Directions for Aitchison's Method to Adjust Means and Variances 4-48
Box 4-37: An Example of Aitchison's Method 4-48
Box 4-38: Directions for Selecting Between Cohen's Method or Aitchison's Method .... 4-49
Box 4-39: Example of Determining Between Cohen's Method and Aitchison's Method .. 4-49
Box 4-40: Directions for the Rank von Neumann Test 4-52
Box 4-41: An Example of the Rank von Neumann Test 4-53
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CHAPTER 4
STEP 4: VERIFY THE ASSUMPTIONS OF THE STATISTICAL TEST
4.1 OVERVIEW AND ACTIVITIES
In this step, the analyst should assess the validity of the statistical test chosen in step 3 by
examining its underlying assumptions in light of the newly generated environmental data. The
principal thrust of this section is the determination of whether the data support the underlying
assumptions necessary for the selected test, or if modifications to the data are necessary prior to
further statistical analysis.
This determination can be performed quantitatively using statistical analysis of data to
confirm or reject the assumptions that accompany any statistical test. Almost always, however,
the quantitative techniques must be supported by qualitative judgments based on the underlying
science and engineering aspects of the study. Graphical representations of the data, such as those
described in Chapter 2, can provide important qualitative information about the reasonableness of
the assumptions. Documentation of this step is important, especially when subjective judgments
play a pivotal role in accepting the results of the analysis.
If the data support all of the key assumptions of the statistical test, then the DQA
continues to the next step, drawing conclusions from the data (Chapter 5). However, often one
or more of the assumptions will be called into question which may trigger a reevaluation of one of
the previous steps. This iteration in the DQA is an important check on the validity and
practicality of the results.
4.1.1 Determine Approach for Verifying Assumptions
In most cases, assumptions about distributional form, independence, and dispersion can be
verified formally using the statistical tests described in the technical sections in the remainder of
this chapter, although in some situations, information from the preliminary data review may serve
as sufficiently strong evidence to support the assumptions. As part of this activity, the analyst
should identify methods to verify that the type and quantity of data required to perform the
desired test are available. The outputs of this activity should include a list of the specific tests that
will be used to verify the assumptions.
For each statistical test it will be necessary for the investigator to select the "level of
significance." For the specific null hypothesis for the test under consideration, the level of
significance is the chance that this null hypothesis is rejected even though it is true. For example,
if testing for normality of data, the null hypothesis is that the data do indeed exhibit normality.
When a test statistic is computed, choosing a level of significance of 5% is saying that if the null
hypothesis is true then the chance that normally distributed data will produce a statistic more
extreme than that value tabulated is only 1 in 20 (5%).
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The choice of specific level of significance is up to the investigator and is a matter of
experience or personal choice. It does not have to be the same as that chosen in Step 3 (Select
the Statistical Test). If more than a couple of statistical tests are contemplated, it is advisable to
choose a numerically low value for the level of significance to prevent the accumulation of
potential errors. The level of significance for a statistical test is by definition the same as false
rejection error.
The methods and approach chosen for assumption verification depend on the nature of the
study and its documentation. For example, if computer simulation was used to estimate the
theoretical power of the statistical test, then this simulation model should be the basis for
evaluation of the effect of changes to assumptions using estimates calculated from the data to
replace simulation values.
If it is not already part of the design documentation, the analyst may need to formulate a
statistical model that describes the data. In a statistical model, the data are conceptually
decomposed into elements that are assumed to be "fixed" (i.e., the component is either a constant
but unknown feature of the population or is controlled by experimentation) or "random" (i.e., the
component is an uncontrolled source of variation). Which components are considered fixed and
which are random is determined by the assumptions made for the statistical test and by the
inherent structure of the sampling design. The random components that represent the sources of
uncontrolled variation could include several types of measurement errors, as well as other sources
such as temporal and/or spatial components.
In addition to identifying the components that make up an observation and specifying
which are fixed and which are random, the model should also define whether the various
components behave in an additive or multiplicative fashion (or some combination). For example,
if temporal or spatial autocorrelations are believed to be present, then the model needs to identify
the autocorrelation structure (see Section 2.3.8).
4.1.2 Perform Tests of Assumptions
For most statistical tests, investigators will need to assess the reasonableness of
assumptions in relation to the structure of the components making up an observation. For
example, a t-test assumes that the components, or errors, are additive, uncorrelated, and normally
distributed with homogeneous variance. Basic assumptions that should be investigated include:
(1) Is it reasonable to assume that the errors (deviations from the model) are
normally distributed? If adequate data are available, then standard tests for
normality can be conducted (e.g., the Shapiro-Wilk test or the Kolmogorov-
Smirnov test).
(2) Is it reasonable to assume that errors are uncorrelated? While it is natural to
assume that analytical errors imbedded in measurements made on different sample
units are independent, other errors from other sources may not be independent. If
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sample units are "too close together," either in time or space, independence may
not hold. If the statistical test assumes independence and this assumption is not
correct, the proposed false rejection and false acceptance error rates for the
statistical test cannot be verified.
(3) Is it reasonable to assume that errors are additive and have a constant
variability? If sufficient data are available, a plot of the relevant standard
deviations versus mean concentrations may be used to discern if variability tends to
increase with concentration level. If so, transformations of the data may make the
additivity assumption more tenable.
One of the most important assumptions underlying the statistical procedures described
herein is that there is no inherent bias (systematic deviation from the true value) in the data. The
general approach adopted here is that if a long term bias is known to exist, then adjustment for
this bias should be made. If bias is present, then the basic effect is to shift the power curves
associated with a given test to the right or left, depending on the direction of the bias. Thus
substantial distortion of the nominal false rejection and false acceptance decision error rates may
occur and so the level of significance could be very different than that assumed, and the power of
the test be far less than expected. In general, bias cannot be discerned by examination of routine
data; rather, appropriate and adequate QA data are needed, such as performance evaluation data.
If one chooses not to make adjustment for bias on the basis of such data, then one should, at a
minimum, construct the estimated worse-case power curves so as to understand the potential
effects of the bias.
4.1.3 Determine Corrective Actions
Sometimes the assumptions underlying the primary statistical test will not be satisfied and
some type of corrective action will be required before proceeding. In some cases, a
transformation of the data will correct a problem with distributional assumptions. In other cases,
the data for verifying some key assumption may not be available, and existing information may not
support a theoretical justification of the validity of the assumption. In this situation, it may be
necessary to collect additional data to verify the assumptions. If the assumptions underlying a
hypothesis test are not satisfied, and data transformations or other modifications do not appear
feasible, then it may be necessary to consider an alternative statistical test. These include robust
test procedures and nonparametric procedures. Robust test procedures involve modifying the
parametric test by using robust estimators. For instance, as a substitute for a t-test, a trimmed
mean and its associated standard error (Section 4.7.2) might be used to form a t-type statistic.
4.2 TESTS FOR DISTRIBUTIONAL ASSUMPTIONS
Many statistical tests and models are only appropriate for data that follow a particular
distribution. This section will aid in determining if a distributional assumption of a statistical test
is satisfied, in particular, the assumption of normality. Two of the most important distributions
for tests involving environmental data are the normal distribution and the lognormal distribution,
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both of which are discussed in this section. To test if the data follow a distribution other than the
normal distribution or the lognormal distribution, apply the chi-square test discussed in Section
4.2.7 or consult a statistician.
There are many methods available for verifying the assumption of normality ranging from
simple to complex. This section discusses methods based on graphs, sample moments (kurtosis
and skewness), sample ranges, the Shapiro-Wilk test and closely related tests, and goodness-of-fit
tests. Discussions for the simplest tests contain step-by-step directions and examples based on the
data in Table 4-1. These tests are summarized in Table 4-2. This section ends with a comparison
of the tests to help the analyst select a test for normality.
Table 4-1. Data for Examples in Section 4.2
15.63
11.00
11.75
10.45
13.18
10.37
10.54
11.55
11.01
10.23
X=11.57
s= 1.677
Table 4-2. Tests for Normality
Test
Shapiro Wilk W
Test
Filliben's Statistic
Coefficient of
Variation Test
Skewness and
Kurtosis Tests
Geary's Test
Studentized
Range Test
Chi-Square Test
Lilliefors
Kolmogorov-
Smirnoff Test
Section
4.2.2
4.2.3
4.2.4
4.2.5
4.2.6
4.2.6
4.2.7
4.2.7
Sample
Size
< 50
< 100
Any
>50
>50
< 1000
Largea
>50
Recommended Use
Highly recommended.
Highly recommended.
Only use to quickly discard
an assumption of
normality.
Useful for large sample
sizes.
Useful when tables for
other tests are not
available.
Highly recommended
(with some conditions).
Useful for grouped data
and when the comparison
distribution is known.
Useful when tables for
other tests are not
available.
Data-
QUEST
Yes
Yes
Yes
Yes
Yes
Yes
No
No
a The necessary sample size depends on the number of groups formed when implementing this test. Each group
should contain at least 5 observations.
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The assumption of normality is very important as it is the basis for the majority of
statistical tests. A normal, or Gaussian, distribution is one of the most common probability
distributions in the analysis of environmental data. A normal distribution is a reasonable model of
the behavior of certain random phenomena and can often be used to approximate other probability
distributions. In addition, the Central Limit Theorem and other limit theorems state that as the
sample size gets large, some of the sample summary statistics (e.g., the sample mean) behave as if
they are a normally distributed variable. As a result, a common assumption associated with
parametric tests or statistical models is that the errors associated with data or a model follow a
normal distribution.
The graph of a normally distributed
random variable, a normal curve, is bell-
shaped (see Figure 4-1) with the highest point
located at the mean which is equal to the
median. A normal curve is symmetric about
the mean, hence the part to the left of the
mean is a mirror image of the part to the
right. In environmental data, random errors
occurring during the measurement process
may be normally distributed.
Normal Distribution
Lognormal Distribution
15
20
Figure 4-1. Graph of a Normal and Lognormal
Distribution
Environmental data commonly exhibit
frequency distributions that are non-negative
and skewed with heavy or long right tails. Several standard parametric probability models have
these properties, including the Weibull, gamma, and lognormal distributions. The lognormal
distribution (Figure 4-1) is a commonly used distribution for modeling environmental contaminant
data. The advantage to this distribution is that a simple (logarithmic) transformation will
transform a lognormal distribution into a normal distribution. Therefore, the methods for testing
for normality described in this section can be used to test for lognormality if a logarithmic
transformation has been used.
4.2.1 Graphical Methods
Graphical methods (Section 2.3) present detailed information about data sets that may not
be apparent from a test statistic. Histograms, stem-and-leaf plots, and normal probability plots
are some graphical methods that are useful for determining whether or not data follow a normal
curve. Both the histogram and stem-and-leaf plot of a normal distribution are bell-shaped. The
normal probability plot of a normal distribution follows a straight line. For non-normally
distributed data, there will be large deviations in the tails or middle of a normal probability plot.
Using a plot to decide if the data are normally distributed involves making a subjective
decision. For extremely non-normal data, it is easy to make this determination; however, in many
cases the decision is not straightforward. Therefore, formal test procedures are usually necessary
to test the assumption of normality.
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4.2.2 Shapiro-Wilk Test for Normality (the W test)
One of the most powerful tests for normality is the W test by Shapiro and Wilk. This test
is similar to computing a correlation between the quantiles of the standard normal distribution and
the ordered values of a data set. If the normal probability plot is approximately linear (i.e., the
data follow a normal curve), the test statistic will be relatively high. If the normal probability plot
contains significant curves, the test statistic will be relatively low.
The W test is recommended in several EPA guidance documents and in many statistical
texts. Tables of critical values for sample sizes up to 50 have been developed for determining the
significance of the test statistic. However, this test is difficult to compute by hand since it requires
two different sets of tabled values and a large number of summations and multiplications.
Therefore, directions for implementing this test are not given in this document, but the test is
contained in the Data Quality Assessment Statistical Toolbox (QA/G-9D) (EPA, 1996).
4.2.3 Extensions of the Shapiro-Wilk Test (Filliben's Statistic)
Because the W test may only be used for sample sizes less than or equal to 50, several
related tests have been proposed. D'Agostino's test for sample sizes between 50 and 1000 and
Royston's test for sample sizes up to 2000 are two such tests that approximate some of the key
quantities or parameters of the W test.
Another test related to the W test is the Filliben statistic, also called the probability plot
correlation coefficient. This test measures the linearity of the points on the normal probability
plot. Similar to the W test, if the normal probability plot is approximately linear (i.e., the data
follow a normal curve), the correlation coefficient will be relatively high. If the normal probability
plot contains significant curves (i.e., the data do not follow a normal curve), the correlation
coefficient will be relatively low. Although easier to compute that the W test, the Filliben statistic
is still difficult to compute by hand. Therefore, directions for implementing this test are not given
in this guidance; however, it is contained in the software, Data Quality Assessment Statistical
Toolbox (QA/G-9D) (EPA, 1996).
4.2.4 Coefficient of Variation
The coefficient of variation (CV) may be used to quickly determine whether or not the
data follow a normal curve by comparing the sample CV to 1. The use of the CV is only valid for
some environmental applications if the data represent a non-negative characteristic such as
contaminant concentrations. If the CV is greater than 1, the data should not be modeled with a
normal curve. However, this method should not be used to conclude the opposite, i.e., do not
conclude that the data can be modeled with a normal curve if the CV is less than 1. This test is to
be used only in conjunction with other statistical tests or when graphical representations of the
data indicate extreme departures from normality. Directions and an example of this method are
contained in Box 4-1.
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Box 4-1: Directions for the Coefficient of Variation Test for
Environmental Data and an Example
Directions
STEP1: Calculate the coefficient of variation (CV): CV = s IX =
«-l;=i
i "
-
«;=!
STEP 2: If CV > 1 .0, conclude that the data are not normally distributed. Otherwise, the test is
inconclusive.
The following example demonstrates using the coefficient of variation to determine that the data in Table
4-1 should not be modeled using a normal curve.
STEP1: Calculate the coefficient of variation (CV): CV = 4 = = 0.145
X 11.571
STEP 2: Since 0.145 > 1.0, the test is inconclusive.
4.2.5 Coefficient of Skewness/Coefficient of Kurtosis Tests
The degree of symmetry (or asymmetry) displayed by a data set is measured by the
coefficient of skewness (g3). The coefficient of kurtosis, g4, measures the degree of flatness of a
probability distribution near its center. Several test methods have been proposed using these
coefficients to test for normality. One method tests for normality by adjusting the coefficients of
skewness and kurtosis to approximate a standard normal distribution for sample sizes greater than
50.
Two other tests based on these coefficients include a combined test based on a chi-squared
(/2) distribution and Fisher's cumulant test. Fisher's cumulant test computes the exact sampling
distribution of g3 and g4; therefore, it is more powerful than previous methods which assume that
the distributions of the two coefficients are normal. Fisher's cumulant test requires a table of
critical values, and these tests require a sample size of greater than 50. Tests based on skewness
and kurtosis are rarely used as they are less powerful than many alternatives.
4.2.6 Range Tests
Almost 100% of the area of a normal curve lies within ±5 standard deviations from the
mean and tests for normality have been developed based on this fact. Two such tests, which are
both simple to apply, are the studentized range test and Geary's test. Both of these tests use a
ratio of an estimate of the sample range to the sample standard deviation. Very large and very
small values of the ratio then imply that the data are not well modeled by a normal curve.
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a. The studentized range test (or w/s test). This test compares the range of the
sample to the sample standard deviation. Tables of critical values for sample sizes up to 1000
(Table A-2 of Appendix A) are available for determining whether the absolute value of this ratio is
significantly large. Directions for implementing this method are given in Box 4-2 along with an
example. The studentized range test does not perform well if the data are asymmetric and if the
tails of the data are heavier than the normal distribution. In addition, this test may be sensitive to
extreme values. Unfortunately, lognormally distributed data, which are common in environmental
applications, have these characteristics. If the data appear to be lognormally distributed, then this
test should not be used. In most cases, the studentized range test performs as well as the Shapiro-
Wilk test and is much easier to apply.
Box 4-2: Directions for Studentized Range Test
and an Example
Directions
STEP 1: Calculate sample range (w) and sample standard deviation (s) using Section 2.2.3.
W (n) ~ (I)
STEP 2: Compare — = —^ = to the critical values given in Table A-2 (labeled a and b).
s s
If w/s falls outside the two critical values then the data do not follow a normal curve.
Example
The following example demonstrates the use of the studentized range test to determine if the data from Table 4-
1 can be modeled using a normal curve.
STEP 1: w = X(n)-X(1)= 15.63-10.23 = 5.40 and s = 1.677.
STEP 2: w/s = 5.4/1.677 = 3.22. The critical values given in Table A-2 are 2.51 and 3.875. Since 3.22 falls
between these values, the assumption of normality is not rejected.
b. Geary's Test. Geary's test uses the ratio of the mean deviation of the sample to
the sample standard deviation. This ratio is then adjusted to approximate a standard normal
distribution. Directions for implementing this method are given in Box 4-3 and an example is
given in Box 4-4. This test does not perform as well as the Shapiro-Wilk test or the studentized
range test. However, since Geary's test statistic is based on the normal distribution, critical values
for all possible sample sizes are available.
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Box 4-3: Directions for Geary's Test
STEP 1: Calculate the sample mean x, the sample sum of squares (SSS), and the sum of absolute
deviations (SAD):
(b^
_ sss = £Xf - ——- and SAD = T^x,~x\
« ;=1 ' ;=1 n i=l '
SAD
STEP 2: Calculate Geary's test statistic a =
/n(SSS)
a - o7979
STEPS: Test "a" for significance by computing Z= : . Here 0.7979 and 0.2123 are
0.2123/y«
constants used to achieve normality.
STEP 4: Use Table A-1 of Appendix A to find the critical value z.,_a such that 100(1-a)% of the normal
distribution is below z^. For example, if a = 0.05, then z^ = 1.645. Declare "a" to be
sufficiently small or large (i.e., conclude the data are not normally distributed) if Zi > Z.,_a.
Box 4-4: Example of Geary's Test
The following example demonstrates the use of Geary's test to determine if the data from Table 4-1 can
be modeled using a normal curve.
_ i « « _
STEP1: X= -YX.= 11.571, SAD = Y\X.-X\ = 11.694, and
n,,\ ,= i
SSS = - — - = 1364.178 - 1338.88 = 25.298
STEP 2: a = = _ = Q
^10(25.298)
STEPS: z = a735 - a7979 = -Q.934
0.2123/^10
STEP 4: Since Zi > 1.64 (5% significance level), there is not enough information to
conclude that the data do not follow a normal distribution.
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4.2.7 Goodness-of-Fit Tests
Goodness-of-fit tests are used to test whether data follow a specific distribution, i.e., how
"good" a specified distribution fits the data. In verifying assumptions of normality, one would
compare the data to a normal distribution with a specified mean and variance.
a. Chi-square Test. One classic goodness-of-fit test is the chi-square test which
involves breaking the data into groups and comparing these groups to the expected groups from
the known distribution. There are no fixed methods for selecting these groups and this test also
requires a large sample size since at least 5 observations per group are required to implement this
test. In addition, the chi-square test does not have the power of the Shapiro-Wilk test or some of
the other tests mentioned above.
b. Tests Based on the Empirical Distribution Function. The cumulative
distribution function, denoted by F(x), and the empirical distribution function of the data for a
given sample of size n are defined in Section 2.3.7.4. Since empirical distribution functions
estimate the true F(x) underlying a set of data, and as the cumulative distribution function for a
given type of distribution [e.g., a normal distribution (see Section 2.4) with given mean and
standard deviation] can be computed, a goodness of fit test can be performed using the empirical
distribution function. If the empirical distribution function is "not close to" the given cumulative
distribution function, then there is evidence that the data do not come from the distribution having
that cumulative distribution function.
Various methods have been used to measure the discrepancy between the sample empirical
distribution function and the theoretical cumulative distribution function. These measures are
referred to as empirical distribution function statistics. The best known empirical distribution
function statistic is the Kolmogorov-Smirnov (K-S) statistic. The K-S approach is appropriate if
the sample size exceeds 50 and if F(x) represents a specific distribution with known parameters
(e.g., a normal distribution with mean 100 and variance 30). A modification to the test, called the
Lilliefors K-S test, is appropriate (for n>50) for testing that the data are normally distributed
when the F(x) is based on an estimated mean and variance.
Unlike the K-S type statistics, most empirical distribution function statistics are based on
integrated or average values between the empirical distribution function and cumulative
distribution functions. The two most powerful are the Cramer-von Mises and Anderson-Darling
statistics. Extensive simulations show that the Anderson-Darling empirical distribution function
statistic is just as good as any, including the Shapiro-Wilk statistic, when testing for normality.
However, the Shapiro-Wilk test is applicable only for the case of a normal-distribution cumulative
distribution function, while the Anderson-Darling method is more general.
Most goodness-of-fit tests are difficult to perform manually and are usually included in
standard statistical software. The application of goodness-of-fit tests to non-normal data is
beyond the scope of this guidance and consultation with a statistician recommended.
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4.2.8 Recommendations
Analysts can perform tests for normality with samples as small as 3. However, the tests
lack statistical power for small sample size. Therefore, for small sample sizes, it is recommended
that a nonparametric statistical test (i.e., one that does not assume a distributional form of the
data) be selected during Step 3 of the DQA in order to avoid incorrectly assuming the data are
normally distributed when there is simply not enough information to test this assumption.
If the sample size is less than 50, then this guidance recommends using the Shapiro-Wilk
W test, wherever practicable. The Shapiro-Wilk W test is one of most powerful tests for
normality and it is recommended in several EPA guidance as the preferred test when the sample
size is less than 50. This test is difficult to implement by hand but can be applied easily using the
Data Quality Assessment Statistical Toolbox (QA/G-9D) (EPA, 1996). If the Shapiro-Wilk W
test is not feasible, then this guidance recommends using either Filliben's statistic or the
studentized range test. Filliben's statistic performs similarly to the Shapiro-Wilk test. The
studentized range is a simple test to perform; however, it is not applicable for non-symmetric data
with large tails. If the data are not highly skewed and the tails are not significantly large
(compared to a normal distribution), the studentized range provides a simple and powerful test
that can be calculated by hand.
If the sample size is greater than 50, this guidance recommends using either the Filliben's
statistic or the studentized range test. However, if critical values for these tests (for the specific
sample size) are not available, then this guidance recommends implementing either Geary's test or
the Lilliefors Kolmogorov-Smirnoff test. Geary's test is easy to apply and uses standard normal
tables similar to Table A-l of Appendix A and widely available in standard textbooks. Lilliefors
Kolmogorov-Smirnoff is more statistically powerful but is also more difficult to apply and uses
specialized tables not readily available.
4.3 TESTS FOR TRENDS
4.3.1 Introduction
This section presents statistical tools for detecting and estimating trends in environmental
data. The detection and estimation of temporal or spatial trends are important for many
environmental studies or monitoring programs. In cases where temporal or spatial patterns are
strong, simple procedures such as time plots or linear regression over time can reveal trends. In
more complex situations, sophisticated statistical models and procedures may be needed. For
example, the detection of trends may be complicated by the overlaying of long- and short-term
trends, cyclical effects (e.g., seasonal or weekly systematic variations), autocorrelations, or
impulses or jumps (e.g., due to interventions or procedural changes).
The graphical representations of Chapter 2 are recommended as the first step to identify
possible trends. A plot of the data versus time is recommended for temporal data, as it may reveal
long-term trends and may also show other major types of trends, such as cycles or impulses. A
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posting plot is recommended for spatial data to reveal spatial trends such as areas of high
concentration or areas that were inaccessible.
For most of the statistical tools presented below, the focus is on monotonic long-term
trends (i.e., a trend that is exclusively increasing or decreasing, but not both), as well as other
sources of systematic variation, such as seasonality. The investigations of trend in this section are
limited to one-dimensional domains, e.g., trends in a pollutant concentration over time. The
current edition of this document does not address spatial trends (with 2- and 3-dimensional
domains) and trends over space and time (with 3- and 4-dimensional domains), which may involve
sophisticated geostatistical techniques such as kriging and require the assistance of a statistician.
Section 4.3.2 discusses estimating and testing for trends using regression techniques. Section
4.3.3 discusses more robust trend estimation procedures, and Section 4.3.4 discusses hypothesis
tests for detecting trends under several types of situations.
4.3.2 Regression-Based Methods for Estimating and Testing for Trends
4.3.2.1 Estimating a Trend Using the Slope of the Regression Line
The classic procedures for assessing linear trends involve regression. Linear regression is
a commonly used procedure in which calculations are performed on a data set containing pairs of
observations (X;, Y;), so as to obtain the slope and intercept of a line that "best fits" the data. For
temporal trends, the X; values represent time and the Y; values represent the observations, such as
contaminant concentrations. An estimate of the magnitude of trend can be obtained by
performing a regression of the data versus time (or some function of the data versus some
function of time) and using the slope of the regression line as the measure of the strength of the
trend.
Regression procedures are easy to apply; most scientific calculators will accept data
entered as pairs and will calculate the slope and intercept of the best fitting line, as well as the
correlation coefficient r (see Section 2.2.4). However, regression entails several limitations and
assumptions. First of all, simple linear regression (the most commonly used method) is designed
to detect linear relationships between two variables; other types of regression models are
generally needed to detect non-linear relationships such as cyclical or non-monotonic trends.
Regression is very sensitive to extreme values (outliers), and presents difficulties in handling data
below the detection limit, which are commonly encountered in environmental studies. Regression
also relies on two key assumptions: normally distributed errors, and constant variance. It may be
difficult or burdensome to verify these assumptions in practice, so the accuracy of the slope
estimate may be suspect. Moreover, the analyst must ensure that time plots of the data show no
cyclical patterns, outlier tests show no extreme data values, and data validation reports indicate
that nearly all the measurements were above detection limits. Because of these drawbacks,
regression is not recommended as a general tool for estimating and detecting trends, although it
may be useful as an informal, quick, and easy screening tool for identifying strong linear trends.
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4.3.2.2 Testing for Trends Using Regression Methods
The limitations and assumptions associated with estimating trends based on linear
regression methods apply also to other regression-based statistical tests for detecting trends.
Nonetheless, for situations in which regression methods can be applied appropriately, there is a
solid body of literature on hypothesis testing using the concepts of statistical linear models as a
basis for inferring the existence of temporal trends. The methodology is complex and beyond the
scope of this document.
For simple linear regression, the statistical test of whether the slope is significantly
different from zero is equivalent to testing if the correlation coefficient is significantly different
from zero. Directions for this test are given in Box 4-5 along with an example. This test assumes
a linear relation between Y and X with independent normally distributed errors and constant
variance across all X and Y values. Censored values (e.g., below the detection limit) and outliers
may invalidate the tests.
Box 4-5: Directions for the Test for a Correlation Coefficient
and an Example
Directions
STEP 1: Calculate the correlation coefficient, r (Section 2.2.4).
r
STEP 2: Calculate the t-value t =
\
1 - r2
n - 2
STEP 3: Use Table A-1 of Appendix A to find the critical value t^ such that 100(1-a/2)% of the t
distribution with n - 2 degrees of freedom is below t.,_,.,. For example, if a = 0.10 and n = 17,
then n-2 = 15andt1.a/2= 1.753. Conclude that the correlation is significantly different from
zero if
't' > W-
Example: Consider the following data set (in ppb): for Sample 1, arsenic (X) is 4.0 and lead (Y) is 8.0;
for Sample 2, arsenic is 3.0 and lead is 7.0; for Sample 3, arsenic is 2.0 and lead is 7.0; and for Sample
4, arsenic is 1.0 and lead is 6.0.
STEP 1: In Section 2.2.4, the correlation coefficient r for this data was calculated to be 0.949.
0 949
STEP 2: t = - = 4.26
\
1 - 0.9492
4-2
STEP 3: Using Table A-1 of Appendix A, t^ = 2.920 fora 10% level of significance and 4-2 = 2
degrees of freedom. Therefore, there appears to be a significant correlation between the two
variables lead and arsenic.
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4.3.3 General Trend Estimation Methods
4.3.3.1 Sen's Slope Estimator
Sen's Slope Estimate is a nonparametric alternative for estimating a slope. This approach
involves computing slopes for all the pairs of ordinal time points and then using the median of
these slopes as an estimate of the overall slope. As such, it is insensitive to outliers and can
handle a moderate number of values below the detection limit and missing values. Assume that
there are n time points (or n periods of time), and let Xj denote the data value for the ith time
point. If there are no missing data, there will be n(n-l)/2 possible pairs of time points (i, j) in
which i > j. The slope for such a pair is called a pairwise slope, by, and is computed as by = (Xj -
Xj) / (i - j). Sen's slope estimator is then the median of the n(n-l)/2 pairwise slopes.
If there is no underlying trend, then a given Xj is as likely to be above another Xj as it is
below. Hence, if there is no underlying trend, there would be an approximately equal number of
positive and negative slopes, and thus the median would be near zero. Due to the number of
calculations required, Sen's estimator is rarely calculated by hand and directions are not given in
this document.
4.3.3.2 Seasonal Kendall Slope Estimator
If the data exhibit cyclic trends, then Sen's slope estimator can be modified to account for
the cycles. For example, if data are available for each month for a number of years, 12 separate
sets of slopes would be determined (one for each month of the year); similarly, if daily
observations exhibit weekly cycles, seven sets of slopes would be determined, one for each day of
the week. In these estimates, the above pairwise slope is calculated for each time period and the
median of all of the slopes is an estimator of the slope for a long-term trend. This is known as the
seasonal Kendall slope estimator. Because of the number of calculations required, this estimator
is rarely calculated by hand.
4.3.4 Hypothesis Tests for Detecting Trends
Most of the trend tests treated in this section involve the Mann-Kendall test or extensions
of it. The Mann-Kendall test does not assume any particular distributional form and
accommodates trace values or values below the detection limit by assigning them a common
value. The test can also be modified to deal with multiple observations per time period and
generalized to deal with multiple sampling locations and seasonality.
4.3.4.1 One Observation per Time Period for One Sampling Location
The Mann-Kendall test involves computing a statistic S, which is the difference between
the number of pairwise slopes (described in 4.3.3.1) that are positive minus the number that are
negative. If S is a large positive value, then there is evidence of an increasing trend in the data. If
S is a large negative value, then there is evidence of a decreasing trend in the data. The null
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hypothesis or baseline condition for this test is that there is no temporal trend in the data values,
i.e., "H0: no trend". The alternative condition or hypothesis will usually be either "HA: upward
trend" or "H4: downward trend."
The basic Mann-Kendall trend test involves listing the observations in temporal order, and
computing all differences that may be formed between measurements and earlier measurements, as
depicted in Box 4-6. The test statistic is the difference between the number of strictly positive
differences and the number of strictly negative differences. If there is an underlying upward trend,
then these differences will tend to be positive and a sufficiently large value of the test statistic will
suggest the presence of an upward trend. Differences of zero are not included in the test statistic
(and should be avoided, if possible, by recording data to sufficient accuracy). The steps for
conducting the Mann-Kendall test for small sample sizes (i.e., less than 10) are contained in Box
4-7 and an example is contained in Box 4-8.
Box 4-6: "Upper Triangular" Data for Basic Mann-Kendall Trend Test
with a Single Measurement at Each Time Point
Data Table
Original Time
Measurement
X,
X2
xn_2
Xn.!
Original Time
Measurement
X,
X2
xn_2
Xn.!
NOTE: Xy-Yk=
discarded.
t., t2 t3 t4 ... tn_! tn (time from earliest to latest)
X1 X2 X3 X4 ... Xn-1 Xn (actual values recorded)
X3-X2 X4-X2 . . . Xn_!-X2 Xn-X2
Xn-rXn-2 Xn-Xn_2
Xn-Xn_.|
After performing the subtractions this table converts to:
t! t2 t3 t4 ... tn_., tn # Of + # Of -
X., X2 X3 X4 ... Xn_! Xn Differences Differences
(>0) (<0)
'21 '31 '41 • • • '(n-1)1 'n1
'32 '42 • • • '(n-1)2 'n2
Y Y
' (n-1)(n-2) ' n(n-2)
Yn(n-1)
0 do not contribute to either total and are Total # >0 Total # <0
where Yik = sign (XrXk) = + if X; - Xk > 0
= 0 ifXi-Xk = 0
= - ifXi-Xk<0
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Box 4-7: Directions for the Mann-Kendall Trend Test for Small Sample Sizes
If the sample size is less than 10 and there is only one datum per time period, the Mann-Kendall Trend Test for
small sample sizes may be used.
STEP 1: List the data in the order collected overtime: X.,, X2, ..., Xn, where X, is the datum at time t,. Assign
a value of DL/2 to values reported as below the detection limit (DL). Construct a "Data Matrix"
similar to the top half of Box 4-6.
STEP 2: Compute the sign of all possible differences as shown in the bottom portion of Box 4-6.
STEP 3: Compute the Mann-Kendall statistic S, which is the number of positive signs minus the number of
negative signs in the triangular table: S = (number of + signs) - (number of - signs).
STEP 4: Use Table A-11 of Appendix A to determine the probability p using the sample size n and the
absolute value of the statistic S. For example, if n=5 and S=8, p=0.042.
STEP 5: For testing the null hypothesis of no trend against H., (upward trend), reject H0 if S > 0 and if p < a.
For testing the null hypothesis of no trend against H2 (downward trend), reject H0 if S < 0 and if p <
a.
Box 4-8: An Example of Mann-Kendall Trend Test for Small Sample Sizes
Consider 5 measurements ordered by the time of their collection: 5, 6, 11, 8, and 10. This data will be used to
test the null hypothesis, H0: no trend, versus the alternative hypothesis H., of an upward trend at an a = 0.05
significance level.
STEP 1: The data listed in order by time are: 5, 6, 11, 8, 10.
STEP 2: A triangular table (see Box 4-6) was used to construct the possible differences. The sum of signs of
the differences across the rows are shown in the columns 7 and 8.
Time 1 2 3 4 5 No. of + No. of
Data 5 6 11 8 10 Signs
Signs
5 + + + + 40
6 + + +30
11 - - 0 2
8 + 1 Q
8 2
STEP 3: Using the table above, 5 = 8-2 = 6.
STEP 4: From Table A-11 of Appendix A for n = 5 and S = 6, p = 0.117.
STEP 5: Since S > 0 but p = 0.117 < 0.05, the null hypothesis is not rejected. Therefore, there is not enough
evidence to conclude that there is an increasing trend in the data.
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For sample sizes greater than 10, a normal approximation to the Mann-Kendall test is
quite accurate. Directions for this approximation are contained in Box 4-9 and an example is
given in Box 4-10. Tied observations (i.e., when two or more measurements are equal) degrade
the statistical power and should be avoided, if possible, by recording the data to sufficient
accuracy.
4.3.4.2 Multiple Observations per Time Period for One Sampling Location
Often, more than one sample is collected for each time period. There are two ways to
deal with multiple observations per time period. One method is to compute a summary statistic,
such as the median, for each time period and to apply one of the Mann-Kendall trend tests of
Section 4.3.4.1 to the summary statistic. Therefore, instead of using the individual data points in
the triangular table, the summary statistic would be used. Then the steps given in Box 4-7 and
Box 4-9 could be applied to the summary statistics.
An alternative approach is to consider all the multiple observations within a given time
period as being essentially equal (i.e., tied) values within that period. The S statistic is computed
as before with n being the total of all observations. The variance of the S statistic (previously
calculated in step 2) is changed to:
VAR(S) = —
18
w +5) -
p
p=\ q=\ p= 1 q= 1
9n(n-l)(n-2) 2n(n-l)
Box 4-9: Directions for the Mann-Kendall Procedure Using Normal Approximation
If the sample size is 10 or more, a normal approximation to the Mann-Kendall procedure may be used.
STEP 1 : Complete steps 1 , 2, and 3 of Box 4-7.
STEP 2: Calculate the variance of S: V(S) = n(n~V(2n + 5
18
If ties occur, let g represent the number of tied groups and wp represent the number of data points in
the pth group. The variance of S is: V(S) = — [n(n- l)(2« + 5) - 2^ w (w - l)(2w +5)]
18 p=\
STEP 4: Calculate Z = if S > 0, Z = 0 if S = 0, or Z = if S < 0.
STEP 5: Use Table A-1 of Appendix A to find the critical value z^_a such that 100(1-a)% of the normal
distribution is below z^_a. For example, if a=0.05 then 2^=1.645.
STEP 6: For testing the hypothesis, H0 (no trend) against 1) H., (an upward trend) - reject H0 if Z > z^_a, or 2)
H2 (a downward trend) - reject H0 if Z < 0 and the absolute value of Z > z^_a.
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Box 4-10: An Example of Mann-Kendall Trend Test by Normal Approximation
A test for an upward trend with a=.05 will be based on the 1 1
STEP 1:
Week
Data
10
10
10
5
10
20
18
17
15
24
STEP 2:
STEP 3:
STEP 4:
STEP 5:
STEP 6:
Using Box 4-6, a triangular table was constructed
if the difference is zero, a "+" sign if the difference
negative.
1234567
10 10 10 5 10 20 18
0 0 - 0 + +
0 - 0 + +
0 + +
+ + +
+ +
-
S = (sum of + signs) - (sum of- signs) = 35 - 13 =
There are several observations tied at 10 and 15.
this formula, g=2, t.,=4 for tied values of 1 0, and t2
V(S) = — [11(11- l)(2(ll) + 5) - [4(4-
18
S-l 22-1
^ i nr^r* ^ i** no17* itiwr*" f — —
[V(Sj\* (155.33)
From Table A-1 of Appendix A, z1_05=1 .645.
H! is the alternative of interest. Therefore, since 1
weekly measurements shown below.
of the possible differences. A zero has been used
is positive, and a "-" sign if the difference is
8 9 10 11 No. of
17 15 24 15 + Signs
+ + + + 6
+ + + + 6
+ + + + 6
+ + + + 7
+ + + + 6
+ - 1
+ - 1
+ - 1
+ 0 1
0
35
22
Thus, the formula for tied values will be
=2 for tied values of 15.
-l)(2(4) + 5) + 2(2-l)(2(2) + 5)]]
20
•'•" - 1 (,C\Z.
v> 12.46
.605 is not greater than 1.645, H0 is not
No. of
- Signs
1
1
1
0
0
4
3
2
0
1
13
used. In
= 155.33
rejected.
Therefore, there is not enough evidence to determine that there is an upward trend.
where g represents the number of tied groups, wp represents the number of data points in the p*
group, h is the number of time periods which contain multiple data, and uq is the sample size in the
q* time period.
The preceding variance formula assumes that the data are not correlated. If correlation
within single time periods is suspected, it is preferable to use a summary statistic (e.g., the
median) for each time period and then apply either Box 4-7 or Box 4-9 to the summary statistics.
4.3.4.3 Multiple Sampling Locations with Multiple Observations
The preceding methods involve a single sampling location (station). However,
environmental data often consist of sets of data collected at several sampling locations (see Box
4-11). For example, data are often systematically collected at several fixed sites on a lake or
river, or within a region or basin. The data collection plan (or experimental design) must be
systematic in the sense that approximately the same sampling times should be used at all locations.
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In this situation, it is desirable to express the results by an overall regional summary statement
across all sampling locations. However, there must be consistency in behavioral characteristics
across sites over time in order for a single summary statement to be valid across all sampling
locations. A useful plot to assess the consistency requirement is a single time plot (Section
2.3.8.1) of the measurements from all stations where a different symbol is used to represent each
station.
Box 4-11: Data for Multiple Times and Multiple Stations
Let i = 1, 2, ..., n represent time, k = 1,2, ..., K represent sampling locations, and Xik represent the
measurement at time i for location k. This data can be summarized in matrix form, as shown below.
Stations
1
2
Time
X,
X21
Xn1
81
V(S1)
Z1
X12
X22
Xn, '.
S2
V(S2)
Z2
• • X1K
• • X2K
• • XnK
SK
V(SK)
ZK
where Sk = Mann-Kendall statistic for station k (see STEP 3, Box 4-7),
V(Sk) = variance for S statistic for station k (see STEP 2, Box 4-9), and
If the stations exhibit approximately steady trends in the same direction (upward or
downward), with comparable slopes, then a single summary statement across stations is valid and
this implies two relevant sets of hypotheses should be investigated:
Comparability of stations. H0: Similar dynamics affect all K stations vs. HA: At least
two stations exhibit different dynamics.
Testing for overall monotonic trend. H0*: Contaminant levels do not change over time
vs. HA': There is an increasing (or decreasing) trend consistently exhibited across all
stations.
Therefore, the analyst must first test for homogeneity of stations, and then, if homogeneity is
confirmed, test for an overall monotonic trend.
Ideally, the stations in Box 4-11 should have equal numbers. However, the numbers of
observations at the stations can differ slightly, because of isolated missing values, but the overall
time periods spanned must be similar. This guidance recommends that for less than 3 time
periods, an equal
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number of observations (a balanced design) is required. For 4 or more time periods, up to 1
missing value per sampling location may be tolerated.
a. One Observation per Time Period. When only one measurement is taken for
each time period for each station, a generalization of the Mann-Kendall statistic can be used to
test the above hypotheses. This procedure is described in Box 4-12.
Box 4-12: Testing for Comparability of Stations and an Overall Monotonic Trend
Let i = 1, 2, ..., n represent time, k = 1, 2, ..., K represent sampling locations, and Xik represent the
measurement at time i for location k. Let a represent the significance level for testing homogeneity and a*
represent the significance level for testing for an overall trend.
STEP 1: Calculate the Mann-Kendall statistic Sk and its variance V(Sk) for each of the K stations using the
methods of Section 4.3.4.1, Box 4-9.
STEP 2: For each of the K stations, calculate Zk = Sk/JV(Sk).
_ K
STEPS: Calculate the average Z = ^Zk/K.
k=\
K _
STEP 4: Calculate the homogeneity chi-square statistic 5^ = ^ Zk - K Z .
k=\
STEP 5: Using a chi-squared table (Table A-8 of Appendix A), find the critical value for x2 with (K-1 ) degrees
of freedom at an a significance level. For example, for a significance level of 5% and 5 degrees of
freedom, x2(5) = 11.07, i.e., 11.07 is the cut point which puts 5% of the probability in the upper tail of
a chi-square variable with 5 degrees of freedom.
STEP 6: If x2h X2(K-i), tne stations are not homogeneous (i.e., different dynamics at different stations) at the
significance level a. Therefore, individual a*-level Mann-Kendall tests should be conducted at each
station using the methods presented in Section 4.3.4.1.
STEP 7: Using a chi-squared table (Table A-8 of Appendix A), find the critical value for x2 with 1 degree of
freedom at an a significance level. If
then reject H0* and conclude that there is a significant (upward or downward) monotonic trend
across all stations at significance level a*. The signs of the Sk indicate whether increasing or
decreasing trends are present. If
K Z2 < X^1} ,
there is not significant evidence at the a' level of a monotonic trend across all stations. That is, the
stations appear approximately stable over time.
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b. Multiple Observations per Time Period. If multiple measurements are taken at
some times and station, then the previous approaches are still applicable. However, the variance
of the statistic Sk must be calculated using the equation for calculating V(S) given in Section
4.3.4.2. Note that Sk is computed for each station, so n, wp, g, h, and uq are all station-specific.
4.3.4.4 One Observation for One Station with Multiple Seasons
Temporal data are often collected over extended periods of time. Within the time
variable, data may exhibit periodic cycles, which are patterns in the data that repeat over time
(e.g., the data may rise and fall regularly over the months in a year or the hours in a day). For
example, temperature and humidity may change with the season or month, and may affect
environmental measurements. (For more information on seasonal cycles, see Section 2.3.8). In
the following discussion, the term season represents one time point in the periodic cycle, such as a
month within a year or an hour within a day.
If seasonal cycles are anticipated, then two approaches for testing for trends are the
seasonal Kendall test and Sen's test for trends. The seasonal Kendall test may be used for large
sample sizes, and Sen's test for trends may be used for small sample sizes. If different seasons
manifest similar slopes (rates of change) but possibly different intercepts, then the Mann-Kendall
technique of Section 4.3.4.3 is applicable, replacing time by year and replacing station by season.
The seasonal Kendall test, which is an extension of the Mann-Kendall test, involves
calculating the Mann-Kendall test statistic, S, and its variance separately for each "season" (e.g.,
month of the year, day of the week). The sum of the S's and the sum of their variances are then
used to form an overall test statistic that is assumed to be approximately normally distributed for
larger size samples.
For data at a single site, collected at multiple seasons within multiple years, the techniques
of Section 4.3.4.3 can be applied to test for homogeneity of time trends across seasons. The
methodology follows Boxes 4-11 and 4-12 exactly except that "station" is replaced by "season"
and the inferences refer to seasons.
4.3.5 A Discussion on Tests for Trends
This section discusses some further considerations for choosing among the many tests for
trends. All of the nonparametric trend tests and estimates use ordinal time (ranks) rather than
cardinal time (actual time values, such as month, day or hour) and this restricts the interpretation
of measured trends. All of the Mann-Kendall Trend Tests presented are based on certain pairwise
differences in measurements at different time points. The only information about these differences
that is used in the Mann-Kendall calculations is their signs (i.e., whether they are positive or
negative) and therefore are generalizations of the sign test. Mann-Kendall calculations are
relatively easy and simply involve counting the number of cases in which X; + j exceeds X; and the
number of cases in which X; exceeds Xj+j. Information about magnitudes of these differences is
not used by the Mann-Kendall methods and this can adversely affect the statistical power when
only limited amounts of data are available.
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There are, however, nonparametric methods based on ranks that takes such magnitudes
into account and still retains the benefit of robustness to outliers. These procedures can be
thought of as replacing the data by their ranks and then conducting parametric analyses. These
include the Wilcoxon rank sum test and its many generalizations. These methods are more
resistant to outliers than parametric methods; a point can be no more extreme than the smallest or
largest value.
Rank-based methods, which make fuller use of the information in the data than the Mann-
Kendall methods, are not as robust with respect to outliers as the sign and the Mann-Kendall
tests. They are, however, more statistically powerful than the sign test and the Mann-Kendall
methods; the Wilcoxon test being a case in point. If the data are random samples from normal
distributions with equal variances, then the sign test requires approximately 1.225 times as many
observations as the Wilcoxon rank sum test to achieve a given power at a given significance level.
This kind of tradeoff between power and robustness exemplifies the analyst's evaluation process
leading to the selection of the best statistical procedure for the current situation. Further
statistical tests will be developed in future editions of this guidance.
4.3.6 Testing for Trends in Sequences of Data
There are cases where it is desirable to see if a long sequence (for example, readings from
a monitoring station) could be considered random variation or correlated in some way, that is, if
consecutive results are attributable to random chance. An everyday example would be to
determine if a basketball player exhibited "hot streaks" during the season when shooting a basket
from the free-throw line. One test to make this determination is the Wald-Wolfowitz test. This
test can only be used if the data are binary, i.e., there are only two potential values. For example,
the data could either be 'Yes/No', '0/1', or 'black/white'. Directions for the Wald-Wofowitz test
are given in Box 4-13 and an example in Box 4-14.
4.4 OUTLIERS
4.4.1 Background
Outliers are measurements that are extremely large or small relative to the rest of the data
and, therefore, are suspected of misrepresenting the population from which they were collected.
Outliers may result from transcription errors, data-coding errors, or measurement system
problems such as instrument breakdown. However, outliers may also represent true extreme
values of a distribution (for instance, hot spots) and indicate more variability in the population
than was expected. Not removing true outliers and removing false outliers both lead to a
distortion of estimates of population parameters.
Statistical outlier tests give the analyst probabilistic evidence that an extreme value
(potential outlier) does not "fit" with the distribution of the remainder of the data and is therefore
a statistical outlier. These tests should only be used to identify data points that require further
investigation. The
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Box 4-13: Directions for the Wald-Wolfowitz Runs Test
Consider a sequence of two values and let n denote the number of observations of one value and m denote the
number of observations of the other value. Note that if is customary for n < m (i.e., n denotes the value that
occurs the least amount of times. This test is used to test the null hypothesis that the sequence is random
against the alternative hypothesis that the data in the sequence are correlated or may come from different
populations.
STEP 1: List the data in the order collected and identify which will be the 'n' values, and which will be the 'm'
values.
STEP 2: Bracket the sequences within the series. A sequence is a group of consecutive values. For
example, consider the data AAABAABBBBBBBABB. The following are sequences in the data
{AAA} {B} {AA} {BBBBBB} {A} {BB}
In the example above, the smallest sequence is has one data value and the largest sequence has 6.
STEP 3: Count the number of sequences for the 'n' values and call it T. For the example sequence, the 'n'
values are 'A' since there are 6 A's and 9 B's, and T = 3: {AAA}, {AA}, and {A}.
STEP 4: If T is less than the critical value from Table A-12 of Appendix A for the specified significance level a,
then reject the null hypothesis that the sequence is random in favor of the alternative that the data
are correlated amongst themselves or possibly came from different distributions. Otherwise,
conclude the sequence is random. In the example above, 3 < 6 (where 6 is the critical value from
Table A-12 using n=6, m=9, and a = 0.01) so the null hypothesis that the sequence is random is
rejected.
tests alone cannot determine whether a statistical outlier should be discarded or corrected within a
data set; this decision should be based on judgmental or scientific grounds.
There are 5 steps involved in treating extreme values or outliers:
1. Identify extreme values that may be potential outliers;
2. Apply statistical test;
3. Scientifically review statistical outliers and decide on their disposition;
4. Conduct data analyses with and without statistical outliers; and
5. Document the entire process.
Potential outliers may be identified through the graphical representations of Chapter 2 (step 1
above). Graphs such as the box and whisker plot, ranked data plot, normal probability plot, and
time plot can all be used to identify observations that are much larger or smaller than the rest of
the data. If potential outliers are identified, the next step is to apply one of the statistical tests
described in the following sections. Section 4.4.2 provides recommendations on selecting a
statistical test for outliers.
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Box 4-14: An Example of the Wald-Wolfowitz Runs Test
This is a set of monitoring data from the main discharge station at a chemical manufacturing plant. The permit
states that the discharge should have a pH of 7.0 and should never be less than 5.0. So the plant manager has
decided to use a pH of 6.0 to an indicate potential problems. In a four-week period the following values were
recorded:
6.5 6.6 6.4 6.2 5.9 5.8 5.9 6.2 6.2 6.3 6.6 6.6 6.7 6.4
6.2 6.3 6.2 5.8 5.9 5.8 6.1 5.9 6.0 6.2 6.3 6.2
STEP 1: Since the plant manager has decided that a pH of 6.0 will indicate trouble the data have been
replaced with a binary indicator. If the value is greater than 6.0, the value will be replaced by a 1;
otherwise the value will be replaced by a 0. So the data are now:
11110001111111111000100111
As there are 8 values of'O'and 19 values of '1', n = 8 and m = 19.
STEP 2: The bracketed sequence is: {1 1 1 1} {0 0 0} {1 1 1 1 1 1 1 1 1 1} {0 0 0} {1} {0 0 } {1 1 1}
STEP 3: T = 3: {000}, {000}, and {00}
STEP 4: Since 3 < 9 (where 9 is the critical value from Table A-12 using a = 0.05) so the null hypothesis that
the sequence is random is rejected.
If a data point is found to be an outlier, the analyst may either: 1) correct the data point;
2) discard the data point from analysis; or 3) use the data point in all analyses. This decision
should be based on scientific reasoning in addition to the results of the statistical test. For
instance, data points containing transcription errors should be corrected, whereas data points
collected while an instrument was malfunctioning may be discarded. One should never discard an
outlier based solely on a statistical test. Instead, the decision to discard an outlier should be based
on some scientific or quality assurance basis. Discarding an outlier from a data set should be done
with extreme caution, particularly for environmental data sets, which often contain legitimate
extreme values. If an outlier is discarded from the data set, all statistical analysis of the data
should be applied to both the full and truncated data set so that the effect of discarding
observations may be assessed. If scientific reasoning does not explain the outlier, it should not be
discarded from the data set.
If any data points are found to be statistical outliers through the use of a statistical test,
this information will need to be documented along with the analysis of the data set, regardless of
whether any data points are discarded. If no data points are discarded, document the
identification of any "statistical" outliers by documenting the statistical test performed and the
possible scientific reasons investigated. If any data points are discarded, document each data
point, the statistical test performed, the scientific reason for discarding each data point, and the
effect on the analysis of deleting the data points. This information is critical for effective peer
review.
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4.4.2 Selection of a Statistical Test
There are several statistical tests for determining whether or not one or more observations
are statistical outliers. Step by step directions for implementing some of these tests are described
in Sections 4.4.3 through 4.4.6. Section 4.4.7 describes statistical tests for multivariate outliers.
If the data are normally distributed, this guidance recommends Rosner's test when the
sample size is greater than 25 and the Extreme Value test when the sample size is less than 25. If
only one outlier is suspected, then the Discordance test may be substituted for either of these
tests. If the data are not normally distributed, or if the data cannot be transformed so that the
transformed data are normally distributed, then the analyst should either apply a nonparametric
test (such as Walsh's test) or consult a statistician. A summary of this information is contained in
Table 4-3.
Table 4-3. Recommendations for Selecting a Statistical Test for Outliers
Sample
Size
n < 25
n < 50
n> 25
n > 50
Test
Extreme Value Test
Discordance Test
Rosner's Test
Walsh's Test
Section
4.4.3
4.4.4
4.4.5
4.4.6
Assumes
Normality
Yes
Yes
Yes
No
Multiple
Outliers
No/Yes
No
Yes
Yes
4.4.3 Extreme Value Test (Dixon's Test)
Dixon's Extreme Value test can be used to test for statistical outliers when the sample size
is less than or equal to 25. This test considers both extreme values that are much smaller than the
rest of the data (case 1) and extreme values that are much larger than the rest of the data (case 2).
This test assumes that the data without the suspected outlier are normally distributed; therefore, it
is necessary to perform a test for normality on the data without the suspected outlier before
applying this test. If the data are not normally distributed, either transform the data, apply a
different test, or consult a statistician. Directions for the Extreme Value test are contained in Box
4-15; an example of this test is contained in Box 4-16.
This guidance recommends using this test when only one outlier is suspected in the data.
If more than one outlier is suspected, the Extreme Value test may lead to masking where two or
more outliers close in value "hide" one another. Therefore, if the analyst decides to use the
Extreme Value test for multiple outliers, apply the test to the least extreme value first.
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Box 4-15: Directions for the Extreme Value Test
(Dixon's Test)
STEP 1 : Let X( 1(, X( 2 ),..., X( n ) represent the data ordered from smallest to largest. Check that the data
without the suspect outlier are normally distributed, using one of the methods of Section 4.2. If
normality fails, either transform the data or apply a different outlier test.
STEP 2: X< 1 ; is a Potential Outlier (case 1): Compute the test statistic C, where
c = Xm ~ XW for3,n,7, C = X(3) ~ X(l) for 11, n, 13,
X(n) ~ X(l) X(n-l) ~ X(l)
X - X X - X
C = — £) - ^L- for 8 < n < 10, C = — £> - ~ X(n~2) for 11. n. 13,
X(n) ~ X(\) X(n) ~ X(2)
C = X(n) ~ X("~l) for8. n. 10, C = *<"> ~ X(n~2) for 14 , n < 25
X(n) ~ X(2) X(n) (3)
STEP 5: If C exceeds the critical value from Table A-3 of Appendix A for the specified significance level a, X(
n) is an outlier and should be further investigated.
Box 4-16: An Example of the Extreme Value Test
(Dixon's Test)
The data in order of magnitude from smallest to largest are: 82.39, 86.62, 91.72, 98.37, 103.46, 104.93,
105.52, 108.21, 113.23, and 150.55 ppm. Because the largest value (150.55) is much larger than the other
values, it is suspected that this data point might be an outlier which is Case 2 in Box 4-15.
STEP 1: A normal probability plot of the data shows that there is no reason to suspect that the data (without
the extreme value) are not normally distributed. The studentized range test (Section 4.2.6) also
shows that there is no reason to suspect that the data are not normally distributed. Therefore, the
Extreme Value test may be used to determine if the largest data value is an outlier.
STEp4. c = x(n) ~ Vi) = 150.55 - 113.23 = 37.32 = Q 5g4
X(n} - X(2) 150.55 - 86.62 63.93
STEP 5: Since C = 0.584 > 0.477 (from Table A-3 of Appendix A with n=10), there is evidence that X(n) is an
outlier at a 5% significance level and should be further investigated.
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4.4.4 Discordance Test
The Discordance test can be used to test if one extreme value is an outlier. This test
considers two cases: 1) where the extreme value (potential outlier) is the smallest value of the
data set, and 2) where the extreme value (potential outlier) is the largest value of the data set.
The Discordance test assumes that the data are normally distributed; therefore, it is necessary to
perform a test for normality before applying this test. If the data are not normally distributed
either transform the data, apply a different test, or consult a statistician. Note that the test
assumes that the data without the outlier are normally distributed; therefore, the test for normality
should be performed without the suspected outlier. Directions and an example of the Discordance
test are contained in Box 4-17 and Box 4-18.
Box 4-17: Directions for the Discordance Test
STEP 1: Let X( 1}, X( 2),..., X( n} represent the data ordered from smallest to largest. Check that the data
without the suspect outlier are normally distributed, using one of the methods of Section 4.2. If
normality fails, either transform the data or apply a different outlier test.
STEP 2: Compute the sample mean, x (Section 2.2.2), and the sample standard deviation, s (Section 2.2.3).
If the minimum value X(1) is a suspected outlier, perform Steps 3 and 4. If the maximum value X(n) is
a suspected outlier, perform Steps 5 and 6.
~~ (i1
STEP 3: IfX,!; is a Potential Outlier (case 1): Compute the test statistic D = =
s
STEP 4: If D exceeds the critical value from Table A-4, X(., (is an outlier and should be further investigated.
X - X
STEPS: If X;n) is a Potential Outlier (case 2): Compute the test statistic D = —^
s
STEP 6: If D exceeds the critical value from Table A-4, X(n) is an outlier and should be further investigated.
Box 4-18: An Example of the Discordance Test
The ordered data are 82.39, 86.62, 91.72, 98.37, 103.46, 104.93, 105.52, 108.21, 113.23, and 150.55 ppm.
Because the largest value of this data set (150.55) is much larger than the rest, it may be an outlier.
STEP 1: A normal probability plot of the data shows that there is no reason to suspect that the data (without
the extreme value) are not normally distributed. The studentized range test (Section 4.2.6) also
shows that there is no reason to suspect that the data are not normally distributed. Therefore, the
Discordance test may be used to determine if the largest data value is an outlier.
STEP 2: x = 104.5 ppm and s = 18.922 ppm.
STEP 5: D - *<"> " * - 15°-55 " 104-5° = 2.43
s 18.92
STEP 6: Since D = 2.43 > 2.176 (from Table A-4 of Appendix A with n = 10), there is evidence that X(n) is an
outlier at a 5% significance level and should be further investigated.
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4.4.5 Rosner's Test
A parametric test developed by Rosner can be used to detect up to 10 outliers for sample
sizes of 25 or more. This test assumes that the data are normally distributed; therefore, it is
necessary to perform a test for normality before applying this test. If the data are not normally
distributed either transform the data, apply a different test, or consult a statistician. Note that the
test assumes that the data without the outlier are normally distributed; therefore, the test for
normality may be performed without the suspected outlier. Directions for Rosner's test are
contained in Box 4-19 and an example is contained in Box 4-20.
Rosner's test is not as easy to apply as the preceding tests. To apply Rosner's test, first
determine an upper limit r0 on the number of outliers (r0 < 10), then order the r0 extreme values
from most extreme to least extreme. Rosner's test statistic is then based on the sample mean and
sample
Box 4-19: Directions for Rosner's Test for Outliers
STEP 1: LetX.,, X2, . . . , Xn represent the ordered data points. By inspection, identify the maximum number
of possible outliers, r0. Check that the data are normally distributed, using one of the methods of
Section 4.2.
STEP 2: Compute the sample mean x, and the sample standard deviation, s, for a]i the data. Label these
values x(0) and s(0), respectively. Determine the observation farthest from x(0) and label this
observation y( ° '. Delete y( ° ' from the data and compute the sample mean, labeled x( 1 ', and the
sample standard deviation, labeled s( 1 '. Then determine the observation farthest from x( 1 ' and
label this observation y(1 '. Delete y(1 ' and compute x(2) and s(2). Continue this process until r0
extreme values have been eliminated.
In summary, after the above process the analyst should have
[X(°\ s<0>,/°>]; [XW, s]; ..., [X(r°~l\ s(r°~ l\ y(r«~ l)} where
(l) l
X(l) = — — jc., s(l) = [— — £fr.-x(0)2]1/2, and y'1' is the farthest value fromx'1'.
n - ij, i J n- ij, i J
(Note, the above formulas for x( ' ' and s( ' ' assume that the data have been renumbered after each
observation is deleted.)
(r-l) _ ^(r-1)
STEPS: To test if there are V outliers in the data, compute: Rr = — - and compare Rr
to Ar in Table A-5 of Appendix A. If Rr > \, conclude that there are r outliers.
First, test if there are r0 outliers (compare R to A ). If not, test if there are r0-1 outliers
'o-1 'o-1
(compare .ft to A ). If not, test if there are r0-2 outliers, and continue, until either it is
'o- 1 'o-1
determined that there are a certain number of outliers or that there are no outliers at all.
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Box 4-20: An Example of Rosner's Test for Outliers
STEP 1: Consider the following 32 data points (in ppm) listed in order from smallest to largest: 2.07, 40.55,
84.15, 88.41, 98.84, 100.54, 115.37, 121.19, 122.08, 125.84, 129.47, 131.90, 149.06, 163.89,
166.77, 171.91, 178.23, 181.64, 185.47, 187.64, 193.73, 199.74, 209.43, 213.29, 223.14, 225.12,
232.72, 233.21, 239.97, 251.12, 275.36, and 395.67.
A normal probability plot of the data shows that there is no reason to suspect that the data (without
the suspect outliers) are not normally distributed. In addition, this graph identified four potential
outliers: 2.07, 40.55, 275.36, and 395.67. Therefore, Rosner's test will be applied to see if there are
4 or fewer (r0 = 4) outliers.
STEP 2: First the sample mean and sample standard deviation were computed for the entire data set (x(0) and
s(0)). Using subtraction, it was found that 395.67 was the farthest data p_pint from x(0), so y(0) =
395.67. Then 395.67 was deleted from the data and the sample mean, x(1), and the sample standard
deviation, s(1), were computed. Using subtraction, it was found that 2.07 was the farthest value from
x(1). This value was then dropped from the data and the process was repeated again on 40.55 to
yield x(2), s(2), and y(2) and x(3), s(3), and y(3). These values are summarized below.
i x('' s(''
~0 169.923 75.133
1 162.640 63.872
2 167.993 57.460
3 172.387 53.099
STEP 3: To apply Rosner's test, it is first necessary to test if there are 4 outliers by computing
|275.36 - 172.387
5(3) 53.099
and comparing R4 to A4 in Table A-5 of Appendix A with n = 32. Since R4 = 1.939 2 A4 = 2.89, there
are not 4 outliers in the data set. Therefore, it will next be tested if there are 3 outliers by computing
I (2) _ x(2) |4055 _ 167.9931
- L.
s (2) 57.460
218
and comparing R3 to A3 in Table A-5 with n = 32. Since R3 = 2.218 2 A3 = 2.91, there are not 3
outliers in the data set. Therefore, it will next be tested if there are 2 outliers by computing
R, =
|2.07 - 162.640
2 sm 63.872
and comparing R2 to A2 in Table A-5 with n = 32. Since R2 = 2.514 2 A3 = 2.92, there are not 2
outliers in the data set. Therefore, it will next be tested if there is 1 outlier by computing
|395.67 - 169.923
s
(0) 75.133
and comparing R1 to A., in Table A-5 with n = 32. Since R1 = 3.005 > A.,= 2.94, there is evidence at a
5% significance level that there is 1 outlier in the data set. Therefore, observation 355.67 is a
statistical outlier and should be further investigated.
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standard deviation computed without the r = r0 extreme values. If this test statistic is greater than
the critical value given in Table A-5 of Appendix A, there are r0 outliers. Otherwise, the test is
performed again without the r = r0 - 1 extreme values. This process is repeated until either
Rosner's test statistic is greater than the critical value or r = 0.
4.4.6 Walsh's Test
A nonparametric test was developed by Walsh to detect multiple outliers in a data set.
This test requires a large sample size: n > 220 for a significance level of a = 0.05, and n > 60 for a
significance level of a = 0.10. However, since the test is a nonparametric test, it may be used
whenever the data are not normally distributed. Directions for the test by Walsh for large sample
sizes are given in Box 4-21.
Box 4-21: Directions for Walsh's Test for Large Sample Sizes
Let X(.,), X( 2), . . . , X( n) represent the data ordered from smallest to largest. If n < 60, do not apply this
test. If 60 < n < 220, then a = 0.10. If n > 220, then a = 0.05.
STEP 1: Identify the number of possible outliers, r. Note that r can equal 1.
STEP 2: Compute c = [fin], k = r + c, b2 = 1 /a, and a = 1 + bv(c~b ^c ~ l\
c-b2- 1
where [ ] indicates rounding the value to the largest possible integer (i.e., 3.24 becomes 4).
STEP 3: The r smallest points are outliers (with a a% level of significance) if
STEP 4: The r largest points are outliers (with a a% level of significance) if
X(n+l-r) ~ (^+a)X(n-r) + aX(n+l-k)>®
STEP 5: If both of the inequalities are true, then both small and large outliers are indicated.
4.4.7 Multivariate Outliers
Multivariate analysis, such as factor analysis and principal components analysis, involves
the analysis of several variables simultaneously. Outliers in multivariate analysis are then values
that are extreme in relationship to either one or more variables. As the number of variables
increases, identifying potential outliers using graphical representations becomes more difficult. In
addition, special procedures are required to test for multivariate outliers. Details of these
procedures are beyond the scope of this guidance. However, procedures for testing for
multivariate outliers are contained in statistical textbooks on multivariate analysis.
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4.5 TESTS FOR DISPERSIONS
Many statistical tests make assumptions on the dispersion (as measured by variance) of
data; this section considers some of the most commonly used statistical tests for variance
assumptions. Section 4.5.1 contains the methodology for constructing a confidence interval for a
single variance estimate from a sample. Section 4.5.2 deals with the equality of two variances, a
key assumption for the validity of a two-sample t-test. Section 4.5.3 describes Bartlett's test and
Section 4.5.4 describes Levene's test. These two tests verify the assumption that two or more
variances are equal, a requirement for a standard two-sample t-test, for example. The analyst
should be aware that many statistical tests only require the assumption of approximate equality
and that many of these tests remain valid unless gross inequality in variances is determined.
4.5.1 Confidence Intervals for a Single Variance
This section discusses confidence intervals for a single variance or standard deviation for
analysts interested in the precision of variance estimates. This information may be necessary for
performing a sensitivity analysis of the statistical test or analysis method. The method described
in Box 4-22 can be used to find a two-sided 100(l-a)% confidence interval. The upper end point
of a two-sided 100(l-a)% confidence interval is a 100(l-a/2)% upper confidence limit, and the
lower end point of a two-sided 100(l-a)% confidence interval is a 100(l-a/2)% lower confidence
limit. For example, the upper end point of a 90% confidence interval is a 95% upper confidence
limit and the lower end point is a 95% lower confidence limit. Since the standard deviation is the
square root of the variance, a confidence interval for the variance can be converted to a
confidence interval for the standard deviation by taking the square roots of the endpoints of the
interval. This confidence interval assumes that the data constitute a random sample from a
normally distributed population and can be highly sensitive to outliers and to departures from
normality.
4.5.2 The F-Test for the Equality of Two Variances
An F-test may be used to test whether the true underlying variances of two populations
are equal. Usually the F-test is employed as a preliminary test, before conducting the two-sample
t-test for the equality of two means. The assumptions underlying the F-test are that the two
samples are independent random samples from two underlying normal populations. The F-test for
equality of variances is highly sensitive to departures from normality. Directions for implementing
an F-test with an example are given in Box 4-23.
4.5.3 Bartlett's Test for the Equality of Two or More Variances
Bartlett's test is a means of testing whether two or more population variances of normal
distributions are equal. In the case of only two variances, Bartlett's test is equivalent to the F-test.
Often in practice unequal variances and non-normality occur together and Bartlett's test is itself
sensitive
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Box 4-22: Directions for Constructing Confidence Intervals and
Confidence Limits for the Sample Variance and Standard Deviation with an Example
Directions: Let X.,, X2, . . . , Xn represent the n data points.
STEP 1: Calculate the sample variance s2 (Section 2.2.3).
STEP 2: For a 100(1-a)% two-sided confidence interval, use Table A-8 of Appendix A to find the cutoffs L and
: ( 1 - a/2 ) '
U such that L = x2a/2 and U = x2< 1.a/2) with (n-1) degrees of freedom (dof).
(n-l}s2 (n-l}s2
STEPS: A 100(1-a)% confidence interval forthe true underlying variance is: - — to —.
_/_j LJ
i 9 I
A 100(1-a)% confidence interval forthe true standard deviation is: —to '
L M u
Example: Ten samples were analyzed for lead: 46.4, 46.1, 45.8, 47, 46.1, 45.9, 45.8, 46.9, 45.2, 46 ppb.
STEP 1: Using Section 2.2.3, s2 = 0.286.
STEP 2: Using Table A-8 of Appendix A and 9 dof, L = x205/2 = X2025 = 19-02 and U = x2(i-.05/2) = X2.975 = 2.70.
STEP 3: A 95% confidence interval forthe variance is: ^ 1)0-286 fo (10-1)0.286 ^ Q^5 to Q g54
19.02 2.70
A 95% confidence interval forthe standard deviation is: A/0.135 = .368 to t/0.954 = .977.
Box 4-23: Directions for Calculating an F-Test to Compare
Two Variances with an Example
Directions: Let X.,, X2, . . . , Xm represent the m data points from population 1 and Y.,, Y2, . . . , Yn
represent the n data points from population 2. To perform an F-test, proceed as follows.
STEP 1: Calculate the sample variances sx2 (for the X's) and sY2 (for the Y's ) (Section 2.2.3).
STEP 2: Calculate the variance ratios Fx = sx2/sY2 and FY = sY2/sx2. Let F equal the larger of these two
values. If F = Fx, then let k = m -1 and q = n -1. If F = Fy, then let k = n -1 and q = m -1.
STEP 3: Using Table A-9 of Appendix A of the F distribution, find the cutoff U = f^k, q). If F > U,
conclude that the variances of the two populations are not the same.
Example: Manganese concentrations were collected from 2 wells. The data are Well X: 50, 73, 244,
202 ppm; and Well Y: 272, 171, 32, 250, 53 ppm. An F-test will be used to test if the variances are
equal.
STEP1: For Well X, sx2 = 9076. For Well Y, sY2 = 12125.
STEP 2: Fx = sx2/sY2 = 9076 /12125 = 0.749. FY = sY2/sx2 = 12125 / 9076 = 1.336. Since, FY > Fx, F =
FY = 1.336, k = 5 -1 = 4 and q = 4 - 1 = 3.
STEP 3: Using Table A-9 of Appendix A of the F distribution with a = 0.05, L = f,..^ 4, 3) = 15.1.
Since 1.336 < 15.1, there is no evidence that the variability of the two wells is different.
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to departures from normality. With long-tailed distributions, the test too often rejects equality
(homogeneity) of the variances.
Bartletfs test requires the calculation of the variance for each sample, then calculation of a
statistic associated with the logarithm of these variances. This statistic is compared to tables and
if it exceeds the tabulated value, the conclusion is that the variances differ as a complete set. It
does not mean that one is significantly different from the others, nor that one or more are larger
(smaller) than the rest. It simply implies the variances are unequal as a group. Directions for
Bartlett's test are given in Box 4-24 and an example is given in Box 4-25.
4.5.4 Levene's Test for the Equality of Two or More Variances
Levene's test provides an alternative to Bartlett's test for homogeneity of variance (testing
for differences among the dispersions of several groups). Levene's test is less sensitive to
departures from normality than Bartlett's test and has greater power than Bartlett's for non-normal
data. In addition, Levene's test has power nearly as great as Bartlett's test for normally distributed
data. However, Levene's test is more difficult to apply than Bartlett's test since it involves
applying an analysis of variance (ANOVA) to the absolute deviations from the group means.
Directions and an example of Levene's test are contained in Box 4-26 and Box 4-27, respectively.
Box 4-24: Directions for Bartlett's Test
Consider k groups with a sample size of n, for each group. Let N represent the total number of samples,
i.e., let N = n., + n2 + . . . + nk. For example, consider two wells where 4 samples have been taken from
well 1 and 3 samples have been taken from well 2. In this case, k = 2, n., = 4, n2 = 3, and N = 4 + 3 = 7.
STEP 1: For each of the k groups, calculate the sample variances, s2 (Section 2.2.3).
1 k
STEP 2: Compute the pooled variance across groups: s = /iX- ~ I)-?,
(N-k) ,= i
k
STEPS: Compute the test statistic: TS = (N - k) ln(s^2) - Y^n,~ *) ln(5f)
;= 1
where "In" stands for natural logarithms.
STEP 4: Using a chi-squared table (Table A-8 of Appendix A), find the critical value for x2 with (k-1)
degrees of freedom at a predetermined significance level. For example, for a significance
level of 5% and 5 degrees of freedom, x2 = 11.1. If the calculated value (TS) is greater than
the tabulated value, conclude that the variances are not equal at that significance level.
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Box 4-25: An Example of Bartlett
Manganese concentrations were collected from 6 wells over a 4 month
following table. Before analyzing the data, it is important to determine
Bartlett's test will be used to make this determination.
STEP
STEP
STEP
STEP
1 : For each of the 6 wells, the sample means and variances
bottom rows of the table below.
Sampling Date Well 1 Well 2
January 1 50
February 1 73
March 1 244 46
April 1 202 77
n;(N=17) 4 2
X; 142.25 61.50
s2 9076.37 480.49
o- o 2 1 Yin 1 IT 2
p (N-k) fff ' ' (17-6)
3:
TS = (17-6) ln(751837.27) - , (4-
Well 3
272
171
32
53
4
132
12455
[(4-1)9076 -
l)ln(9076) H
's Test
period. The data are shown in the
if the variances of the six wells are equal.
were calculated. These
Well 4
34
3940
2
1987
7628243
,-,(3
Well 5
48
54
2
51.00
17.98
are shown
Well 6
68
991
54
3
371.00
288348
in the
751837.27
- ... + (3- l)ln(288348) , =
4: The critical x2 value with 6-1=5 degrees of freedom at the 5% significance level
Table A-8 of Appendix A). Since 43.16 is larger than 11.1, it is concluded that the
(s2, . . . , s|) are not homogeneous at the 5% significance level.
43.16
is 11.1 (from
six variances
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Box 4-26: Directions for Levene's Test
Consider k groups with a sample size of n, for the ith group. Let N represent the total number of samples, i.e.,
let N = n., + n2 + . . . + nk. For example, consider two wells where 4 samples have been taken from well 1 and 3
samples have been taken from well 2. In this case, k = 2, n., = 4, n2 = 3, and N = 4 + 3 = 7.
STEP 1: For each of the k groups, calculate the group mean, x , (Section 2.2.2), i.e., calculate:
_ 1 "l _ 1 "2 1 "k
x\ = — T$ir X2= — Y$2j> •••' xk= — Yfkj-
STEP 2: Compute the absolute residuals z.. = JC.. - X where Xy represents the jth value of the i
group. For each of the k groups, calculate the means, zh of these residuals, i.e., calculate:
" "
1 k "' 1 k
= — / / z .. = — / n.z..
\r *• — ' *• — ^ (/ AT -^ — ^ I I
Also calculate the overall mean residual as z =
-\T '-^1 '-^1 II -\T
i= 1
STEP 3: Compute the following sums of squares for the absolute residuals:
k "i - £ -2 _
oo _ V^V~^2 _ z oo _ V^• / _ Z _
TOTAL / > / »n ~5 ^^GROUPS 2-J ~Tf ^^ERROR ~ ^^TOTAL ' ^^GROUPS-
1)
STEP 4: Compute / =
STEP 5: Using Table A-9 of Appendix A, find the critical value of the F-distribution with (k-1) numerator
degrees of freedom, (N-k) denominator degrees of freedom, and a desired level of significance (a).
For example, if a = 0.05, the numerator degrees of freedom is 5, and the denominator degrees of
freedom is 18, then using Table A-9, F = 2.77. Iff is greater than F, reject the assumptions of equal
variances.
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Box 4-27: An Example of Levene's Test
Four months of data on arsenic concentration were collected from six wells at a Superfund site. This data set is
shown in the table below. Before analyzing this data, it is important to determine if the variances of the six wells
are equal. Levene's test will be used to make this determination.
STEP 1 : The group mean for each well (x ,) is shown in the last row of the table below.
STEP 2:
STEP 3:
STEP 4:
STEP 5:
Arsenic Concentration (ppm)
Month Well 1 Well 2 Well 3 Well 4 Well 5 Well 6
1 22.90 2.00 2.0 7.84 24.90 0.34
2 3.09 1.25 109.4 9.30 1.30 4.78
3 35.70 7.80 4.5 25.90 0.75 2.85
4 4.18 52.00 2.5 2.00 27.00 1.20
Group Means x .,=16.47 x X3=29.6 x X5=13.49 x
2=15.76 4=11.26 6=2.29
To compute the absolute residuals zy in each well, the value 16.47 will be subtracted from Well 1
data, 15.76 from Well 2 data, 29.6 from Well 3 data, 11.26 from Well 4 data, 13.49 from Well 5
data, and 2.29 from Well 6 data. The resulting values are shown in the following table with the new
well means (z~) and the total mean z.
Residual Arsenic Concentration (ppm)
Month Well 1 Well 2 Well 3 Well 4 Well 5 Well 6
1 6.43 13.76 27.6 3.42 11.41 1.95
2 13.38 14.51 79.8 1.96 12.19 2.49
3 19.23 7.96 25.1 14.64 12.74 0.56
4 12.29 36.24 27.1 9.26 13.51 1.09
Residual Means z.,=12.83 z2=18.12 z3=39.9 z4=7.32 z5=12.46 z6=1.52
Total Residual Mean z = (1/6)(12.83 + 18.12 + 39.9 + 7.32 + 12.46+ 1.52) = 15.36
The sum of squares are: SSTOML = 6300.89, SS^^s = 3522.90, and SSERROR = 2777.99.
, ^WELLS' (k~V 3522.97(6-1) ^ ^
SSERRORl(N-k) 2777.997(24-6)
Using Table A-9 of Appendix A, the F statistic for 5 and 1 8 degrees of freedom with a = 0.05 is 2.77.
Since f=4.56 exceeds F05=2.77, the assumption of equal variances should be rejected.
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4.6 TRANSFORMATIONS
Most statistical tests and procedures contain assumptions about the data to which they will
be applied. For example, some common assumptions are that the data are normally distributed;
variance components of a statistical model are additive; two independent data sets have equal
variance; and a data set has no trends over time or space. If the data do not satisfy such
assumptions, then the results of a statistical procedure or test may be biased or incorrect.
Fortunately, data that do not satisfy statistical assumptions may often be converted or transformed
mathematically into a form that allows standard statistical tests to perform adequately.
4.6.1 Types of Data Transformations
Any mathematical function that is applied to every point in a data set is called a
transformation. Some commonly used transformations include:
Logarithmic (Log X or Ln X): This transformation may be used when the original
measurement data follow a lognormal distribution or when the variance at each level of the
data is proportional to the square of the mean of the data points at that level. For
example, if the variance of data collected around 50 ppm is approximately 250, but the
variance of data collected around 100 ppm is approximately 1000, then a logarithmic
transformation may be useful. This situation is often characterized by having a constant
coefficient of variation (ratio of standard deviation to mean) over all possible data values.
The logarithmic base (for example, either natural or base 10) needs to be consistent
throughout the analysis. If some of the original values are zero, it is customary to add a
small quantity to make the data value non-zero as the logarithm of zero does not exist.
The size of the small quantity depends on the magnitude of the non-zero data and the
consequences of potentially erroneous inference from the resulting transformed data. As a
working point, a value of one tenth the smallest non-zero value could be selected. It does
not matter whether a natural (In) or base 10 (log) transformation is used because the two
transformations are related by the expression ln(X) = 2.303 log(X). Directions for
applying a logarithmic transformation with an example are given in Box 4-28.
Square Root (x): This transformation may be used when dealing with small whole
numbers, such as bacteriological counts, or the occurrence of rare events, such as
violations of a standard over the course of a year. The underlying assumption is that the
original data follow a Poisson-like distribution in which case the mean and variance of the
data are equal. It should be noted that the square root transformation overcorrects when
very small values and zeros appear in the original data. In these cases, \/X+ 1 is often
used as a transformation.
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Box 4-28: Directions for Transforming Data and an Example
Let X.,, X2, . . . , Xn represent the n data points. To apply a transformation, simply apply the transforming
function to each data point. When a transformation is implemented to make the data satisfy some
statistical assumption, it will need to be verified that the transformed data satisfy this assumption.
Example: Transforming Loqnormal Data
A logarithmic transformation is particularly useful for pollution data. Pollution data are often skewed,
thus the log-transformed data will tend to be symmetric. Consider the data set shown below with 15 data
points. The frequency plot of this data (below) shows that the data are possibly lognormally distributed.
If any analysis performed with this data assumes normality, then the data may be logarithmically
transformed to achieve normality. The transformed data are shown in column 2. A frequency plot of the
transformed data (below) shows that the transformed data appear to be normally distributed.
Observed
X
0.22
3.48
6.67
2.53
1.11
0.33
1.64
1.37
« 6
? 5
O
•5 3
Transformed
ln(X)
-1.51
1.25
1.90
0.93
0.10
-1.11
0.50
- 0.31
Observed
X
0.47
0.67
0.75
0.60
0.99
0.90
0.26
Transformed
- ln(X)
-0.76
-0.40
-0.29
-0.51
-0.01
-0.11
-1.35
2 3
Observed Values
«6
O
"S *i
o
•5 3
2
-101
Transformed Values
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Inverse Sine (Arcsine X): This transformation may be used for binomial proportions
based on count data to achieve stability in variance. The resulting transformed data are
expressed in radians (angular degrees). Special tables must be used to transform the
proportions into degrees.
Box-Cox Transformations: This transformation is a complex power transformation that
takes the original data and raises each data observation to the power lambda (A). A
logarithmic transformation is a special case of the Box-Cox transformation. The rationale
is to find A such that the transformed data have the best possible additive model for the
variance structure, the errors are normally distributed, and the variance is as constant as
possible over all possible concentration values. The Maximum Likelihood technique is
used to find A such that the residual error from fitting the theorized model is minimized.
In practice, the exact value of A is often rounded to a convenient value for ease in
interpretation (for example, A = -1.1 would be rounded to -1 as it would then have the
interpretation of a reciprocal transform). One of the drawbacks of the Box-Cox
transformation is the difficulty in physically interpreting the transformed data.
4.6.2 Reasons for Transforming Data
By transforming the data, assumptions that are not satisfied in the original data can be
satisfied by the transformed data. For instance, a right-skewed distribution can be transformed to
be approximately Gaussian (normal) by using a logarithmic or square-root transformation. Then
the normal-theory procedures can be applied to the transformed data. If data are lognormally
distributed, then apply procedures to logarithms of the data. However, selecting the correct
transformation may be difficult. If standard transformations do not apply, it is suggested that the
data user consult a statistician.
Another important use of transformations is in the interpretation of data collected under
conditions leading to an Analysis of Variance (ANOVA). Some of the key assumptions needed
for analysis (for example, additivity of variance components) may only be satisfied if the data are
transformed suitably. The selection of a suitable transformation depends on the structure of the
data collection design; however, the interpretation of the transformed data remains an issue.
While transformations are useful for dealing with data that do not satisfy statistical
assumptions, they can also be used for various other purposes. For example, transformations are
useful for consolidating data that may be spread out or that have several extreme values. In
addition, transformations can be used to derive a linear relationship between two variables, so that
linear regression analysis can be applied. They can also be used to efficiently estimate quantities
such as the mean and variance of a lognormal distribution. Transformations may also make the
analysis of data easier by changing the scale into one that is more familiar or easier to work with.
Once the data have been transformed, all statistical analysis must be performed on the
transformed data. No attempt should be made to transform the data back to the original form
because this can lead to biased estimates. For example, estimating quantities such as means,
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variances, confidence limits, and regression coefficients in the transformed scale typically leads to
biased estimates when transformed back into original scale. However, it may be difficult to
understand or apply results of statistical analysis expressed in the transformed scale. Therefore, if
the transformed data do not give noticeable benefits to the analysis, it is better to use the original
data. There is no point in working with transformed data unless it adds value to the analysis.
4.7 VALUES BELOW DETECTION LIMITS
Data generated from chemical analysis may fall below the detection limit (DL) of the
analytical procedure. These measurement data are generally described as not detected, or
nondetects, (rather than as zero or not present) and the appropriate limit of detection is usually
reported. In cases where measurement data are described as not detected, the concentration of
the chemical is unknown although it lies somewhere between zero and the detection limit. Data
that includes both detected and non-detected results are called censored data in the statistical
literature.
There are a variety of ways to evaluate data that include values below the detection limit.
However, there are no general procedures that are applicable in all cases. Some general
guidelines are presented in Table 4-4. Although these guidelines are usually adequate, they should
be implemented cautiously.
Table 4-4. Guidelines for Analyzing Data with Nondetects
Percentage of
Nondetects
< 15%
15% -50%
> 50% - 90%
Section
4.7.1
4.7.2
4.7.3
Statistical Analysis
Method
Replace nondetects with
DL/2, DL, or a very small
number.
Trimmed mean, Cohen's
adjustment, Winsorized
mean and standard deviation.
Use tests for proportions
(Section 3. 2.2)
All of the suggested procedures for analyzing data with nondetects depend on the amount
of data below the detection limit. For relatively small amounts below detection limit values,
replacing the nondetects with a small number and proceeding with the usual analysis may be
satisfactory. For moderate amounts of data below the detection limit, a more detailed adjustment
is appropriate. In situations where relatively large amounts of data below the detection limit exist,
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one may need only to consider whether the chemical was detected as above some level or not.
The interpretation of small, moderate, and large amounts of data below the DL is subjective.
Table 4-4 provides percentages to assist the user in evaluating their particular situation.
However, it should be recognized that these percentages are not hard and fast rules, but should be
based on judgement.
In addition to the percentage of samples below the detection limit, sample size influences
which procedures should be used to evaluate the data. For example, the case where 1 sample out
of 4 is not detected should be treated differently from the case where 25 samples out of 100 are
not detected. Therefore, this guidance suggests that the data analyst consult a statistician for the
most appropriate way to evaluate data containing values below the detection level.
4.7.1 Less than 15% Nondetects - Substitution Methods
If a small proportion of the observations are not detected, these may be replaced with a
small number, usually the detection limit divided by 2 (DL/2), and the usual analysis performed.
As a guideline, if 15% or fewer of the values are not detected, replace them with the method
detection limit divided by two and proceed with the appropriate analysis using these modified
values. If simple substitution of values below the detection limit is proposed when more than
15% of the values are reported as not detected, consider using nonparametric methods or a test of
proportions to analyze the data. If a more accurate method is to be considered, see Cohen's
Method (Section 4.7.2.1).
4.7.2 Between 15-50% Nondetects
4.7.2.1 Cohen's Method
Cohen's method provides adjusted estimates of the sample mean and standard deviation
that accounts for data below the detection level. The adjusted estimates are based on the
statistical technique of maximum likelihood estimation of the mean and variance so that the fact
that the nondetects are below the limit of detection but may not be zero is accounted for. The
adjusted mean and standard deviation can then be used in the parametric tests described in
Chapter 3 (e.g., the one sample t-test of Section 3.2.1). However, if more than 50% of the
observations are not detected, Cohen's method should not be used. In addition, this method
requires that the data without the nondetects be normally distributed and the detection limit is
always the same. Directions for Cohen's method are contained in Box 4-29; an example is given
in Box 4-30.
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Box 4-29: Directions for Cohen's Method
Let X.,, X2, . . . , Xn represent the n data points with the first m values representing the data points above the
detection limit (DL). Thus, there are (n-m) data points are below the DL.
STEP 1: Compute the sample mean xd from the data above the detection limit:
m ,=
STEP 2: Compute the sample variance s2dfrom the data above the detection limit:
m- 1
s
STEP 3: Compute h = ^—^ and y = ——
" (Xd-DL)2
STEP 4: Use h and y in Table A-10 of Appendix A to determine A. For example, if h = 0.4 and y = 0.30, then
A = 0.6713. If the exact value of h and y do not appear in the table, use double linear interpolation
(Box 4-31) to estimate A.
STEP 5: Estimate the corrected sample mean, x, and sample variance, s2, to account for the data below the
detection limit, as follows: X = Xd - l(Xd - DL) and s2 = s] + l(Xd - DL)2.
Box 4-30: An Example of Cohen's Method
Sulfate concentrations were measured for 24 data points. The detection limit was 1,450 mg/L and 3 of the 24
values were below the detection level. The 24 values are 1850, 1760, < 1450 (ND), 1710, 1575, 1475, 1780,
1790, 1780, < 1450 (ND), 1790, 1800, < 1450 (ND), 1800, 1840, 1820, 1860, 1780, 1760, 1800, 1900, 1770,
1790, 1780 mg/L. Cohen's Method will be used to adjust the sample mean for use in a t-test to determine if the
mean is greater than 1600 mg/L.
STEP 1: The sample mean of the m = 21 values above the detection level is Xd = 1771.9
STEP 2: The sample variance of the 21 quantified values is s2d= 8593.69.
STEP 3: h = (24 - 21)/24 = 0.125 and Y = 8593.697(1771.9 - 1450)2 = 0.083
STEP 4: Table A-10 of Appendix A was used for h = 0.125 and y = 0.083 to find the value of A. Since the
table does not contain these entries exactly, double linear interpolation was used to estimate A =
0.149839 (see Box 4-31).
STEP 5: The adjusted sample mean and variance are then estimated as follows:
X= 1771.9 - 0.149839(1771.9-1450) = 1723.67 and
s2 = 8593.69 + 0.149839(1771.9-1450)2 = 24119.95
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Box 4-31: Double Linear Interpolation
The details of the double linear interpolation are provided to assist in the use of Table A-10 of Appendix
A. The desired value for A corresponds to y = 0.083 and, h = 0.125 from Box 4-30, Step 3. The values
from Table A-10 for interpolatation are:
Y h = 0.10 h = 0.15
0.05 0.11431 0.17925
0.10 0.11804 0.18479
There are 0.05 units between 0.10 and 0.15 on the h-scale and 0.025 units between 0.10 and 0.125.
Therefore, the value of interest lies (0.025/0.05)100% = 50% of the distance along the interval between
0.10 and 0.15. To linearly interpolate between tabulated values on the h axis for y = 0.05, the range
between the values must be calculated, 0.17925 -0.11431 = 0.06494; the value that is 50% of the
distance along the range must be computed, 0.06494 x 0.50 = 0.03247; and then that value must be
added to the lower point on the tabulated values, 0.11431 + 0.03247 = 0.14678. Similarly for y = 0.10,
0.18479 - 0.11804 = 0.06675, 0.06675 x 0.50 = 0.033375, and 0.11804 + 0.033375 = 0.151415.
On the Y-axis there are 0.033 units between 0.05 and 0.083 and there are 0.05 units between 0.05 and
0.10. The value of interest (0.083) lies (0.033/0.05 x 100) = 66% of the distance along the interval
between 0.05 and 0.10, so 0.151415 - 0.14678 = 0.004635, 0.004635 * 0.66 = 0.003059. Therefore,
A = 0.14678 + 0.003059 = 0.149839.
4.7.2.2 Trimmed Mean
Trimming discards the data in the tails of a data set in order to develop an unbiased
estimate of the population mean. For environmental data, nondetects usually occur in the left tail
of the data so trimming the data can be used to adjust the data set to account for nondetects when
estimating a mean. Developing a 100p% trimmed mean involves trimming p% of the data in both
the lower and the upper tail. Note that p must be between 0 and .5 since p represents the portion
deleted in both the upper and the lower tail. After np of the largest values and np of the smallest
values are trimmed, there are n(l-2p) data values remaining. Therefore, the proportion trimmed
is dependent on the total sample size (n) since a reasonable amount of samples must remain for
analysis. For approximately symmetric distributions, a 25% trimmed mean (the midmean) is a
good estimator of the population mean. However, environmental data are often skewed (non-
symmetric) and in these cases a 15% trimmed mean performance may be a good estimator of the
population mean. It is also possible to trim the data only to replace the nondetects. For example,
if 3% of the data are below the detection limit, a 3% trimmed mean could be used to estimate the
population mean. Directions for developing a trimmed mean are contained in Box 4-32 and an
example is given in Box 4-33. A trimmed variance is rarely calculated and is of limited use.
4.7.2.3 Winsorized Mean and Standard Deviation
Winsorizing replaces data in the tails of a data set with the next most extreme data value.
For environmental data, nondetects usually occur in the left tail of the data. Therefore,
winsorizing can be used to adjust the data set to account for nondetects. The mean and standard
deviation can then be computed on the new data set. Directions for winsorizing data (and
revising the sample size) are contained in Box 4-34 and an example is given in Box 4-35
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4.7.2.4 Atchison's Method
Previous adjustments to the mean and variance assumed that the data values really were
present but could not be recorded or "seen" as they were below the detection limit. In other
words, if the detection limit had been substantially lower, the data values would have been
recorded. There are however, cases where the data values are below the detection limit because
they are actually zero, the contaminant or chemical of concern being entirely absent. Such data
sets are actually a mixture - partly the assumed distribution (for example, a normal distribution)
and partly a number of zero values. Aitchison's Method is used in this situation to adjust the
mean and variance for the zero values.
Box 4-32: Directions for Developing a Trimmed Mean
LetX.,, X2, . . . , Xn represent the n data points. To develop a 100p% trimmed mean (0 < p < 0.5):
STEP 1 : Let t represent the integer part of the product np. For example, if p = .25 and n = 1 7,
np = (.25)(17) = 4.25, so t = 4.
STEP 2: Delete the t smallest values of the data set and the t largest values of the data set.
_ i n-2t
STEPS: Compute the arithmetic mean of the remaining n - 2t values: X =
n - 2t ,,
This value is the estimate of the population mean.
Box 4-33: An Example of the Trimmed Mean
Sulfate concentrations were measured for 24 data points. The detection limit was 1,450 mg/L and 3 of
the 24 values were below this limit. The 24 values listed in order from smallest to largest are: < 1450
(ND), < 1450 (ND), < 1450 (ND), 1475, 1575, 1710, 1760, 1760, 1770, 1780, 1780, 1780, 1780, 1790,
1790, 1790, 1800, 1800, 1800, 1820, 1840, 1850, 1860, 1900 mg/L. A 15% trimmed mean will be used
to develop an estimate of the population mean that accounts for the 3 nondetects.
STEP 1: Since np = (24)(.15) = 3.6, t = 3.
STEP 2: The 3 smallest values of the data set and the 3 largest values of the data set were deleted.
The new data set is: 1475, 1575, 1710, 1760, 1760, 1770, 1780, 1780, 1780, 1780, 1790,
1790, 1790, 1800, 1800, 1800, 1820, 1840 mg/L.
STEP 3: Compute the arithmetic mean of the remaining n-2t values:
X= i (1475 + ... + 1840) = 1755.56
24 - (2)(3)
Therefore, the 15% trimmed mean is 1755.56 mg/L.
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Box 4-34: Directions for Developing a Winsorized
Mean and Standard Deviation
Let X.,, X2, . . . , Xn represent the n data points and m represent the number of data points above the
detection limit (DL), and hence n-m below the DL.
STEP 1: List the data in order from smallest to largest, including nondetects. Label these points X,.,}, X,
2)l. . ., X(n) (so that X(1) is the smallest, X(2) is the second smallest, and X(n) is the largest).
STEP 2: Replace the n-m nondetects with X, m +.,} and replace the n-m largest values with X, n _ m}.
STEP 3: Using the revised data set, compute the sample mean, x, and the sample standard deviation,
s:
X = - yXt and s
n
i= 1
sf) - nx2
n-1
STEP 4: The Winsorized mean x w is equal to x. The Winsorized standard deviation is
s(n- 1)
s =
(2m-n- 1)
Box 4-35: An Example of a Winsorized
Mean and Standard Deviation
Sulfate concentrations were measured for 24 data points. The detection limit was 1,450 mg/L and 3 of
the 24 values were below the detection level. The 24 values listed in order from smallest to largest are: <
1450 (ND), < 1450 (ND), < 1450 (ND), 1475, 1575, 1710, 1760, 1760, 1770, 1780, 1780, 1780, 1780,
1790, 1790, 1790, 1800, 1800, 1800, 1820, 1840, 1850, 1860, 1900 mg/L.
STEP 1: The data above are already listed from smallest to largest. There are n=24 samples, 21 above
DL, and n-m=3 nondetects.
STEP 2: The 3 nondetects were replaced with X, 4 ( and the 3 largest values were replaced with X, 21 >.
The resulting data set is: 1475, 1475, 1475, 1475, 1575, 1710, 1760, 1760, 1770, 1780, 1780,
1780, 1780, 1790, 1790, 1790, 1800, 1800, 1800, 1820, 1840, 1840, 1840, 1840 mg/L
STEP 3: For the new data set, x = 1731 mg/L and s = 128.52 mg/L.
STEP 4: The Winsorized mean xw = 1731 mg/L. The Winsorized sample standard deviation is:
s __ 128.52(24-1) = n388
2(21) -24-1
Aitchison's method for adjusting the mean and variance of the above the detection level
values works quite well provided the percentage of non-detects is between 15-50% of the total
number of values. Care must be taken when using Aitchison's adjustment to the mean and
standard deviation as the mean is reduced and variance increased. With such an effect it may
become very difficult to use the adjusted data for tests of hypotheses or for predicative purposes.
As a diagnostic tool, the relevance of Aitchison' adjustment can lead to an evaluation of the data
to determine if two populations are being sampled simultaneously: one population being
represented by a normal distribution, the other being simply blanks. In some circumstances, for
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example investigating a hazardous site, it may be possible to relate the position of the sample
through a Posting Plot (Section 2.3.9) and determine if the target population has not been
adequately stratified. Directions for Aitchison's method are contained in Box 4-36; an example
(with a comparison to Cohen's method) is contained in Box 4-37.
Box 4-36: 11 Directions for Aitchison's Method to Adjust Means and Variances
Let X.,, X2, . .,Xm, . . . , Xn represent the data points where the first m values are above the detection limit (DL)
and the remaining (n-m) data points are below the DL
STEP 1: Using the data above the detection level, compute the sample mean,
1 \ m
Xd = — y Xj and the sample variance, sd = ——
m-l
STEP 2: Estimate the corrected sample mean, X = —Xd
n
2 m-l ^ , m(n-m)—2
j — ^ d d
and the sample variance n — i n(n — i)
Box 4-37: An Example of Aitchison's Method
The following data consist of 10 Methylene Chloride samples: 1.9,1.3, <1, 2.0, 1.9, <1, <1, <1, 1.6, and 1.7.
There are 7 values above the detection limit and 3 below, so m = 7 and n - m=3. Aitchison's method will be
used to estimate the mean and sample variance of this data.
STEP1: Xd=~2^ Xt =-(1.9 + 1.3 + 2.0 + 1.9 + 1.6 + 1.6 + 1.7) = 1.714
and s2d = -^ - ^ - = 0.05809
d 7-1
STEP 2: The corrected sample mean is then X = —(1.714) = 1.2
7 _ i
and the sample variance s2 = - (0 05809) H -- — (1 714) = 0 7242
10-1 3(3-1)
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4.7.2.5
Selecting Between Atchison's Method or Cohen's Method
To determine if a data set is better adjusted by Cohen's method or Aitchison's method, a
simple graphical procedure using Normal Probability Paper (Section 2.3) can be used. Examples
for this procedures are given in Box 4-38 and an example is contained in Box 4-39.
Box 4-38: Directions for Selecting Between
Cohen's Method or Aitchison's Method
LetX.,, X2, . .,Xm, . . . , Xn represent the data points with the first m values are above the detection limit (DL) and
the remaining (n-m) data points are below the DL.
STEP 1: Use Box 2-19 to construct a Normal Probability Plot of all the data but only plot the values belonging
to those above the detection level. This is called the Censored Plot.
STEP 2: Use Box 2-19 to construct a Normal Probability Plot of only those values above the detection level.
This called the Detects only Plot.
STEP 3: If the Censored Plot is more linear than the Detects Only Plot, use Cohen's Method to estimate the
sample mean and variance. If the Detects Only Plot is more linear than the Censored Plot, then use
Aitchison's Method to estimate the sample mean and variance.
Box 4-39: Example of Determining Between
Cohen's Method and Aitchison's Method
In this example, 10 readings of Chlorobenzene were obtained from a monitoring well and submitted for
consideration for a permit: < 1, 1.9, 1.4, 1.5, < 1, 1.2, <1, 1.3, 1.9, 2.1 ppm. The data can be thought to be
independent readings.
Step 1: Using the directions in Box 2-19 the following is the
Censored Plot:
STEP 2: Using the directions in Box 2-19 the following is the
Detects only Plot:
STEP 3: Since the Censored Plots is more linear than the Detects Only Plot, Cohen's Method should be used
to estimate the sample mean and variance.
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4.7.3 Greater than 50% Nondetects - Test of Proportions
If more than 50% of the data are below the detection limit but at least 10% of the
observations are quantified, tests of proportions may be used to test hypotheses using the data.
Thus, if the parameter of interest is a mean, consider switching the parameter of interest to some
percentile greater than the percent of data below the detection limit. For example, if 67% of the
data are below the DL, consider switching the parameter of interest to the 75th percentile. Then
the method described in 3.2.2 can be applied to test the hypothesis concerning the 75th percentile.
It is important to note that the tests of proportions may not be applicable for composite samples.
In this case, the data analyst should consult a statistician before proceeding with analysis.
If very few quantified values are found, a method based on the Poisson distribution may be
used as an alternative approach. However, with a large proportion of nondetects in the data, the
data analyst should consult with a statistician before proceeding with analysis.
4.7.4 Recommendations
If the number of sample observations is small (n<20), maximum likelihood methods can
produce biased results since it is difficult to assure that the underlying distribution appropriate and
the solutions to the likelihood equation for the parameters of interest are statistically consistent
only if the number of samples is large. Additionally, most methods will yield estimated parameters
with large estimation variance, which reduces the power to detect import differences from
standards or between populations. While these methods can be applied to small data sets, the user
should be cautioned that they will only be effective in detecting large departures from the null
hypothesis.
If the degree of censoring (the percentage of data below the detection limit) is relatively
low, reasonably good estimates of means, variances and upper percentiles can be obtained.
However, if the rate of censoring is very high (greater than 50%) then little can be done
statistically except to focus on some upper quantile of the contaminant distribution, or on some
proportion of measurements above a certain critical level that is at or above the censoring limit.
When the numerical standard is at or below one of the censoring levels and a one-sample
test is used, the most useful statistical method is to test whether the proportion of a population is
above (below) the standard is too large, or to test whether and upper quantile of the population
distribution is above the numerical standard. Table 4-5 gives some recommendation on which
statistical parameter to use when censoring is present in data sets for different sizes of the
coefficient of variation (Section 2.2.3).
When comparing two data sets with different censoring levels (i.e., different detection
limits), it is recommended that all data be censored at the highest censoring value present and a
non parametric test such as the Wilcoxon Rank Sum Test (Section 3.3.3.1) used to compare the
two data sets. There is a corresponding loss of statistical power but this can be minimized
through the use of large samples.
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Table 4-5. Guidelines for Recommended Parameters for Different
Coefficient of Variations and Censoring
Assumed Coefficient
of Variation (CV)
Large: CV> 1.5
Medium: 0.530%)
Upper Percentile
Upper Percentile
Median
4.8 INDEPENDENCE
The assumption of independence of data is key to the validity of the false rejection and
false acceptance error rates associated with the selected statistical test. When data are truly
independent between themselves, the correlation between data points is by definition zero and the
selected statistical tests work with the desired chosen decision error rates (given appropriate the
assumptions have been satisfied). When correlation (usually positive) exists, the effectiveness of
statistical tests is diminished. Environmental data are particularly susceptible to correlation
problems due to the fact that such environmental data are collected under a spatial pattern (for
example a grid) or sequentially over time (for example, daily readings from a monitoring station).
The reason non-independence is an issue for statistical testing situations is that if
observations are positively correlated over time or space, then the effective sample size for a test
tends to be smaller than the actual sample size - i.e., each additional observation does not provide
as much "new" information because its value is partially determined by (or a function of) the value
of adjacent observations. This smaller effective sample size means that the degrees of freedom for
the test statistic is less, or equivalently, the test is not as powerful as originally thought. In
addition to affecting the false acceptance error rate, applying the usual tests to correlated data
tends to result in a test whose actual significance level (false rejection error rate) is larger than the
nominal error rate.
When observations are correlated, estimates of the variance that are used in test statistic
formulas are often understated. For example, consider the mean of a series of n temporally-
ordered observations. If these observations are independent, then the variance of the mean is
o2/n, where a2 is the variance of individual observations (see Section 2.2.3). However, if the
observations are not independent and the correlation between successive observations is p (for
example, the correlation between the first and second observation is p, between first and third
observations is p2, between first and fourth observations is p3, etc.), then the variance of the mean
increases to
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VAR(X) = o\l + q\where q=~
n k=\
which will tend to be larger than o2/n if the correlations (on average) are positive. If one conducts
a t-test at the significance level, using the usual formula for the estimated variance (Box 2-3), then
the actual significance level can be approximately double what was expected even for relatively
low values of p.
One of the most effective ways to determine statistical independence is through use of the
Rank von Neumann Test. Directions for this test are given in Box 4-40 and an example in
contained in Box 4-41. Compared to other tests of statistical independence, the rank von
Neumann test has been shown to be more powerful over a wide variety of cases. It is also a
reasonable test when the data really follow a Normal distribution. In that case, the efficiency of
the test is always close to 90 percent when compared to the von Neumann ratio computed on the
original data instead of the ranks. This means that very little effectiveness is lost by always using
the ranks in place of the original concentrations; the rank von Neumann ration should still
correctly detect non-independent data.
Box 4-40: Directions for the Rank von Neumann Test
LetX.,, X2, . . . , Xn represent the data values collected in sequence over equally spaced periods of time.
Step 1. Order the data measurements from smallest to largest and assign a unique rank (H) to each
measurement (See Box 3-20 Then list the observations and their corresponding ranks in the order that
sampling occurred(i.e., by sampling event or time order.)
Step 2. Using the list of ranks, rh for the sampling periods i=1, 2, ..., n, compute the rank von
Neumann ratio:
n(n2 -1)/12
Step 3: Use Table A-15 of Appendix A to determine the lower critical point of the rank von Neumann ration
using the sample size, n, and the desired significance level, a. If the computed ratio, v, is smaller than
this critical point, conclude that the data series is strongly auto correlated. If not, the data may be
mildly correlated, but there is no statistically significant evidence to reject the hypothesis of
independence. Therefore, the data should be regarded as independent in subsequent statistical testing.
Note: if the rank von Neumann ratio test indicates significant evidence of dependence in the data, a
statistician should be consulted before further analysis is performed.
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Th
St
St
St
Box 4-41: An Example of the Rank von Neumann Test
le following are hourly readings from a discharge monitor: hourly readings from a discharge monitor.
fime:
Reading
Rank
ep 1: Tht
ep2: v
ep 3: Us
the
12:00
6.5
7
3 ranks
n
I
/' =
13:00
6.6
8.5
14:00
6.7
10
are displayed
(r- ~ r- ,
2 ' l~l
15:00
6.4
5.5
16:00
6.3
3.5
17:00
6.4
5.5
18:00
6.2
1.5
in the table above and the time
)2
(8.5- 7)2 + (10-8
19:00
6.2
1.5
periods
5)2 + .
20:00
6.3
3.5
21:00
6.6
8.5
22:00 23:00
6.8 6.9
11 12
24:00
7.0
13
were labeled 1 through 13.
..+ (13- 12)2 _
«O2-1)/12 13(132-1)/12
ng Table A-15 of Appendix A with a = 0.05 , the lower critical point is 1.17.
hypothesis that the data are independent must be rejected.
- U.t/J
Since v = 0.473 < 1.17,
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CHAPTER 5
STEP 5: DRAW CONCLUSIONS FROM THE DATA
THE DATA QUALITY ASSESSMENT PROCESS
Review DQOs and Sampling Design
Conduct Preliminary Data Review
Select the Statistical Test
Verify the Assumptions
Draw Conclusions From the Data
DRAW CONCLUSIONS FROM THE DATA
Purpose
Conduct the hypothesis test and interpret the results
in the context of the data user's objectives.
Activities
• Perform the Statistical Hypothesis Test
• Draw Study Conclusions.
• Evaluate Performance of the Sampling Design
Tools
• Issues in hypothesis testing related to understanding
and communicating the test results
Step 5: Draw Conclusions from the Data
! Perform the calculations for the statistical hypothesis test.
P Perform the calculations and document them clearly.
P If anomalies or outliers are present in the data set, perform the calculations
with and without the questionable data.
! Evaluate the statistical test results and draw conclusions.
P If the null hypothesis is rejected, then draw the conclusions and document the
analysis.
P If the null hypothesis is not rejected, verify whether the tolerable limits on
false acceptance decision errors have been satisfied. If so, draw conclusions
and document the analysis; if not, determine corrective actions, if any.
P Interpret the results of the test.
! Evaluate the performance of the sampling design if the design is to be used
again.
P Evaluate the statistical power of the design over the full range of parameter
values; consult a statistician as necessary.
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List of Boxes
Box 5-1: Checking Adequacy of Sample Size for a One-
Sample t-Test for Simple Random Sampling 5-5
Box 5-2: Example of Power Calculations for the One-Sample Test of a Single Proportion . . 5 - 6
Box 5-3: Example of a Comparison of Two Variances
which is Statistically but not Practically Significant 5-9
Box 5-4: Example of a Comparison of Two Biases 5-10
List of Figures
Page
Figure 5-1. Illustration of Unbiased versus Biased Power Curves 5-11
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CHAPTER 5
STEP 5: DRAW CONCLUSIONS FROM THE DATA
5.1 OVERVIEW AND ACTIVITIES
In this final step of the DQA, the analyst performs the statistical hypothesis test and draws
conclusions that address the data user's objectives. This step represents the culmination of the
planning, implementation, and assessment phases of the data operations. The data user's planning
objectives will have been reviewed (or developed retrospectively) and the sampling design
examined in Step 1. Reports on the implementation of the sampling scheme will have been
reviewed and a preliminary picture of the sampling results developed in Step 2. In light of the
information gained in Step 2, the statistical test will have been selected in Step 3. To ensure that
the chosen statistical methods are valid, the key underlying assumptions of the statistical test will
have been verified in Step 4. Consequently, all of the activities conducted up to this point should
ensure that the calculations performed on the data set and the conclusions drawn here in Step 5
address the data user's needs in a scientifically defensible manner. This chapter describes the main
activities that should be conducted during this step. The actual procedures for implementing
some commonly used statistical tests are described in Step 3, Select the Statistical Test.
5.1.1 Perform the Statistical Hypothesis Test
The goal of this activity is to conduct the statistical hypothesis test. Step-by-step
directions for several commonly used statistical tests are described in Chapter 3. The calculations
for the test should be clearly documented and easily verifiable. In addition, the documentation of
the results of the test should be understandable so that the results can be communicated
effectively to those who may hold a stake in the resulting decision. If computer software is used
to perform the calculations, ensure that the procedures are adequately documented, particularly if
algorithms have been developed and coded specifically for the project.
The analyst should always exercise best professional judgment when performing the
calculations. For instance, if outliers or anomalies are present in the data set, the calculations
should be performed both with and without the questionable data to see what effect they may
have on the results.
5.1.2 Draw Study Conclusions
The goal of this activity is to translate the results of the statistical hypothesis test so that
the data user may draw a conclusion from the data. The results of the statistical hypothesis test
will be either:
(a) reject the null hypothesis, in which case the analyst is concerned about a possible
false rejection decision error; or
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(b) fail to reject the null hypothesis, in which case the analyst is concerned about a
possible false acceptance decision error.
In case (a), the data have provided the evidence needed to reject the null hypothesis, so
the decision can be made with sufficient confidence and without further analysis. This is because
the statistical test based on the classical hypothesis testing philosophy, which is the approach
described in prior chapters, inherently controls the false rejection decision error rate within the
data user's tolerable limits, provided that the underlying assumptions of the test have been verified
correctly.
In case (b), the data do not provide sufficient evidence to reject the null hypothesis, and
the data must be analyzed further to determine whether the data user's tolerable limits on false
acceptance decision errors have been satisfied. One of two possible conditions may prevail:
(1) The data do not support rejecting the null hypothesis and the false acceptance
decision error limits were satisfied. In this case, the conclusion is drawn in favor
of the null hypothesis, since the probability of committing a false acceptance
decision error is believed to be sufficiently small in the context of the current study
(see Section 5.2).
(2) The data do not support rejecting the null hypothesis, and the false acceptance
decision error limits were not satisfied. In this case, the statistical test was not
powerful enough to satisfy the data user's performance criteria. The data user may
choose to tolerate a higher false acceptance decision error rate than previously
specified and draw the conclusion in favor of the null hypothesis, or instead take
some form of corrective action, such as obtaining additional data before drawing a
conclusion and making a decision.
When the test fails to reject the null hypothesis, the most thorough procedure for verifying
whether the false acceptance decision error limits have been satisfied is to compute the estimated
power of the statistical test, using the variability observed in the data. Computing the power of
the statistical test across the full range of possible parameter values can be complicated and
usually requires specialized software. Power calculations are also necessary for evaluating the
performance of a sampling design. Thus, power calculations will be discussed further in Section
5.1.3.
A simpler method can be used for checking the performance of the statistical test. Using
an estimate of variance obtained from the actual data or upper 95% confidence limit on variance,
the sample size required to satisfy the data user's objectives can be calculated retrospectively. If
this theoretical sample size is less than or equal to the number of samples actually taken, then the
test is sufficiently powerful. If the required number of samples is greater than the number actually
collected, then additional samples would be required to satisfy the data user's performance criteria
for the statistical test. An example of this method is contained in Box 5-1. The equations
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Box 5-1: Checking Adequacy of Sample Size for a One-
Sample t-Test for Simple Random Sampling
In Box 3-1, the one-sample t-test was used to test the hypothesis H0: u < 95 ppm vs. HA: u > 95 ppm. DQOs
specified that the test should limit the false rejection error rate to 5% and the false acceptance error rate to 20%
if the true mean were 105 ppm. A random sample of size n = 9 had sample mean x = 99.38 ppm and standard
deviation s = 10.41 ppm. The null hypothesis was not rejected. Assuming that the true value of the standard
deviation was equal to its sample estimate 10.41 ppm, it was found that a sample size of 9 would be required,
which validated the sample size of 9 which had actually been used.
The distribution of the sample standard deviation is skewed with a long right tail. It follows that the chances
are greater than 50% that the sample standard deviation will underestimate the true standard deviation. In
such a case it makes sense to build in some conservatism, for example, by using an upper 90% confidence
limit for o in Step 5 of Box 3-12. Using Box 4-22 and n -1 = 8 degrees of freedom, it is found that L = 3.49, so
that an upper 90% confidence limit for the true standard deviation is
sj[(n - 1)/Z] = 10.41^/8/3.49 = 15.76
Using this value for s in Step 5 of Box 3-1 leads to the sample size estimate of 17. Hence, a sample size of at
least 17 should be used to be 90% sure of achieving the DQOs. Since it is generally desirable to avoid the
need for additional sampling, it is advisable to conservatively estimate sample size in the first place. In cases
where DQOs depend on a variance estimate, this conservatism is achieved by intentionally overestimating the
variance.
required to perform these calculations have been provided in the detailed step-by-step instructions
for each hypothesis test procedure in Chapter 3.
5.1.3 Evaluate Performance of the Sampling Design
If the sampling design is to be used again, either in a later phase of the current study or in
a similar study, the analyst will be interested in evaluating the overall performance of the design.
To evaluate the sampling design, the analyst performs a statistical power analysis that describes
the estimated power of the statistical test over the range of possible parameter values. The power
of a statistical test is the probability of rejecting the null hypothesis when the null hypothesis is
false. The estimated power is computed for all parameter values under the alternative hypothesis
to create a power curve. A power analysis helps the analyst evaluate the adequacy of the
sampling design when the true parameter value lies in the vicinity of the action level (which may
not have been the outcome of the current study). In this manner, the analyst may determine how
well a statistical test performed and compare this performance with that of other tests.
The calculations required to perform a power analysis can be relatively complicated,
depending on the complexity of the sampling design and statistical test selected. Box 5-2
illustrates power calculations for a test of a single proportion, which is one of the simpler cases.
A further discussion of power curves (performance curves) is contained in the Guidance for Data
Quality Objectives (QA/G-4) (EPA 1994).
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Box 5-2: Example of Power Calculations for the One-Sample Test of a Single Proportion
This box illustrates power calculations for the test of H0: P > .20 vs. HA: P < .20, with a false rejection error rate
of 5% when P=.20 presented in Boxes 3-10 and 3-11 The power of the test will be calculated assuming P., =
.15 and before any data are available. Since nP., and n(1-P.,) both exceed 4, the sample size is large enough
for the normal approximation, and the test can be carried out as in steps 3 and 4 of Box 3-10
STEP 1: Determine the general conditions for rejection of the null hypothesis. In this case, the null
hypothesis is rejected if the sample proportion is sufficiently smaller than P0. (Clearly, a sample
proportion above P0 cannot cast doubt on H0.) By steps 3 and 4 of Box 3-10 and 3-3 H0 is rejected
if
P + -5/n ~ Po ^
Here p is the sample proportion, Q0 = 1 - P0, n is the sample size, and z^_a is the critical value such
that 100(1-a)% of the standard normal distribution is below z^_a. This inequality is true if
p + .5/n < P0 - z^JP^JTt.
STEP 2: Determine the specific conditions for rejection of the null hypothesis if P., (=1-Q.,) is the true value of
the proportion P. The same operations as are used in step 3 of Box 3-10 are performed on both
sides of the above inequality. However, P0 is replaced by P., since it is assumed that P., is the true
proportion. These operations make the normal approximation applicable. Hence, rejection occurs if
p + .5/n - P1 P0 - P1 - Zl_tt^P0Q0/n _ 20 - .15 - ,..-,.vv.^v.uy, „. = _Q
STEP 3: Find the probability of rejection if P., is the true proportion. By the same reasoning that led to the
test in steps 3 and 4 of Boxes 3-10 and 3-11 the quantity on the left-hand side of the above
inequality is a standard normal variable. Hence the power at P., = .15 (i.e., the probability of
rejection of H0 when .15 is the true proportion) is the probability that a standard normal variable is
less than -0.55. In this case, the probability is approximately 0.3 (using the last line from Table A-1
of Appendix A) which is fairly small.
5.2 INTERPRETING AND COMMUNICATING THE TEST RESULTS
Sometimes difficulties may arise in interpreting or explaining the results of a statistical test.
One reason for such difficulties may stem from inconsistencies in terminology; another may be due
to a lack of understanding of some of the basic notions underlying hypothesis tests. As an
example, in explaining the results to a data user, an analyst may use different terminology than
that appearing in this guidance. For instance, rather than saying that the null hypothesis was or
was not rejected, analysts may report the result of a test by saying that their computer output
shows a p-value of 0.12. What does this mean? Similar problems of interpretation may occur
when the data user attempts to understand the practical significance of the test results or to
explain the test results to others. The following paragraphs touch on some of the philosophical
issues related to hypothesis testing which may help in understanding and communicating the test
results.
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5.2.1 Interpretation of p-Values
The classical approach for hypothesis tests is to prespecify the significance level of the
test, i.e., the Type I decision error rate a. This rate is used to define the decision rule associated
with the hypothesis test. For instance, in testing whether the population mean |i exceeds a
threshold level (e.g., 100 ppm), the test statistic may depend on x, an estimate of |i. Obtaining an
estimate X that is greater than 100 ppm may occur simply by chance even if the true mean |i is less
than or equal to 100; however, if X is "much larger" than 100 ppm, then there is only a small
chance that the null hypothesis H0 (|i < 100 ppm) is true. Hence the decision rule might take the
form "reject H0 if x exceeds 100 + C", where C is a positive quantity that depends on a (and on
the variability of x). If this condition is met, then the result of the statistical test is reported as
"reject H0"; otherwise, the result is reported as "do not reject H0."
An alternative way of reporting the result of a statistical test is to report its p-value, which
is defined as the probability, assuming the null hypothesis to be true, of observing a test result at
least as extreme as that found in the sample. Many statistical software packages report p-values,
rather than adopting the classical approach of using a prespecified false rejection error rate. In the
above example, for instance, the p-value would be the probability of observing a sample mean as
large as X (or larger) if in fact the true mean was equal to 100 ppm. Obviously, in making a
decision based on the p-value, one should reject H0 when p is small and not reject it if p is large.
Thus the relationship between p-values and the classical hypothesis testing approach is that one
rejects H0 if the p-value associated with the test result is less than a. If the data user had chosen
the false rejection error rate as 0.05 a priori and the analyst reported a p-value of 0.12, then the
data user would report the result as "do not reject the null hypothesis;" if the p-value had been
reported as 0.03, then that person would report the result as "reject the null hypothesis." An
advantage of reporting p-values is that they provide a measure of the strength of evidence for or
against the null hypothesis, which allows data users to establish their own false rejection error
rates. The significance level can be interpreted as that p-value (a) that divides "do not reject H0"
from "reject H0."
5.2.2 "Accepting" vs. "Failing to Reject" the Null Hypothesis
As noted in the paragraphs above, the classical approach to hypothesis testing results in
one of two conclusions: "reject H0" (called a significant result) or "do not reject H0" (a
nonsignificant result). In the latter case one might be tempted to equate "do not reject H0" with
"accept H0." This terminology is not recommended, however, because of the philosophy
underlying the classical testing procedure. This philosophy places the burden of proof on the
alternative hypothesis, that is, the null hypothesis is rejected only if the evidence furnished by the
data convinces us that the alternative hypothesis is the more likely state of nature. If a
nonsignificant result is obtained, it provides evidence that the null hypothesis could sufficiently
account for the observed data, but it does not imply that the hypothesis is the only hypothesis that
could be supported by the data. In other words, a highly nonsignificant result (e.g., a p-value of
0.80) may indicate that the null hypothesis provides a reasonable model for explaining the data,
but it does not necessarily imply that the null hypothesis is true. It may, for example, simply
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indicate that the sample size was not large enough to establish convincingly that the alternative
hypothesis was more likely. When the phrase "accept H0" is encountered, it must be considered
as "accepted with the preceding caveats."
5.2.3 Statistical Significance vs. Practical Significance
There is an important distinction between these two concepts. Statistical significance
simply refers to the result of the hypothesis test: Was the null hypothesis rejected? The likelihood
of achieving a statistically significant result depends on the true value of the population parameter
being tested (for example, |i), how much that value deviates from the value hypothesized under
the null hypothesis (for example, |i0), and on the sample size. This dependence on (|i - |i0) is
depicted by the power curve associated with the test (Section 5.1.3). A steep power curve can be
achieved by using a large sample size; this means that there will be a high likelihood of detecting
even a small difference. On the other hand, if small sample sizes are used, the power curve will be
less steep, meaning that only a very large difference between ji and |i0 will be detectable with high
probability. Hence, suppose one obtains a statistically significant result but has no knowledge of
the power of the test. Then it is possible, in the case of the steep power curve, that one may be
declaring significance (claiming ji > |i0, for example) when the actual difference, from a practical
standpoint, may be inconsequential. Or, in the case of the slowly increasing power curve, one
may not find a significant result even though a "large" difference between ji and |i0 exists. Neither
of these situations is desirable: in the former case, there has been an excess of resources
expended, whereas in the latter case, a false acceptance error is likely and has occurred.
But how large a difference between the parameter and the null value is of real importance?
This relates to the concept of practical significance. Ideally, this question is asked and answered
as part of the DQO process during the planning phase of the study. Knowing the magnitude of
the difference that is regarded as being of practical significance is important during the design
stage because this allows one, to the extent that prior information permits, to determine a
sampling plan of type and size that will make the magnitude of that difference commensurate with
a difference that can be detected with high probability. From a purely statistical design
perspective, this can be considered to be main purpose of the DQO process. With such planning,
the likelihood of encountering either of the undesirable situations mentioned in the prior
paragraph can be reduced. Box 5-3 contains an example of a statistically significant but fairly
inconsequential difference.
5.2.4 Impact of Bias on Test Results
Bias is defined as the difference between the expected value of a statistic and a population
parameter. It is relevant when the statistic of interest (e.g., a sample average x) is to be used as
an estimate of the parameter (e.g., the population mean ji). For example, the population
parameter of interest may be the average concentration of dioxin within the given bounds of a
hazardous waste site, and the statistic might be the sample average as obtained from a random
sample of points within those bounds. The expected value of a statistic can be interpreted as
supposing one repeatedly implemented the particular sampling design a very large number of
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times and calculated the statistic of interest in each case. The average of the statistic's values
would then be regarded as its expected value. Let E denote the expected value of x and denote
the relationship between the expected value and the parameter, |i, as E = |i + b where b is the
bias. For instance, if the bias occurred due to incomplete recovery of an analyte (and no
adjustment is made), then b = (R-100)|o/100, where R denotes the percent recovery. Bias may
also occur for other reasons, such as lack of coverage of the entire target population (e.g., if only
the drums within a storage site that are easily accessible are eligible for inclusion in the sample,
then inferences to the entire group of drums may be biased). Moreover, in cases of incomplete
coverage, the magnitude and direction of the bias may be unknown. An example involving
comparison of the biases of two measurement methods is contained in Box 5-4.
Box 5-3: Example of a Comparison of Two Variances
which is Statistically but not Practically Significant
The quality control (QC) program associated with a measurement system provides important information on
performance and also yields data which should be taken into account in some statistical analyses. The QC
program should include QC check samples, i.e., samples of known composition and concentration which are
run at regular frequencies. The term precision refers to the consistency of a measurement method in repeated
applications under fixed conditions and is usually equated with a standard deviation. The appropriate standard
deviation is one which results from applying the system to the same sample over a long period of time.
This example concerns two methods for measuring ozone in ambient air, an approved method and a new
candidate method. Both methods are used once per week on a weekly basis for three months. Based on 13
analyses with each method of the mid-range QC check sample at 100 ppb, the null hypothesis of the equality
of the two variances will be tested with a false rejection error rate of 5% or less. (If the variances are equal,
then the standard deviations are equal.) Method 1 had a sample mean of 80 ppb and a standard deviation of 4
ppb. Method 2 had a mean of 90 ppb and a standard deviation of 8 ppb. The Shapiro-Wilks test did not reject
the assumption of normality for either method. Applying the F-test of Box 4-23, the F ratio is 82/42 = 2. Using
12 degrees of freedom for both the numerator and denominator, the F ratio must exceed 3.28 in order to reject
the hypothesis of equal variances (Table A-9 of Appendix A). Since 4 > 3.28, the hypothesis of equal variances
is rejected, and it is concluded that method 1 is significantly more precise than method 2.
In an industrialized urban environment, the true ozone levels at a fixed location and time of day are known to
vary over a period of months with a coefficient of variation of at least 100%. This means that the ratio of the
standard deviation (SD) to the mean at a given location is at least 1. For a mean of 100 ppb, the standard
deviation over time for true ozone values at the location would be at least 100 ppb. Relative to this degree of
variability, a difference between measurement error standard deviations of 4 or 8 ppb is negligible. The overall
variance, incorporating the true process variability and measurement error, is obtained by adding the individual
variances. For instance, if measurement error standard deviation is 8 ppb, then the total variance is (100
ppb)(100 ppb) + (8 ppb)(8 ppb). Taking the square root of the variance gives a corresponding total standard
deviation of 100.32 ppb. For a measurement error standard deviation of 4 ppb, the total standard deviation
would be 100.08 ppb. From a practical standpoint, the difference in precision between the two methods is
insignificant for the given application, despite the finding that there is a statistically significant difference
between the variances of the two methods.
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Box 5-4: Example of a Comparison of Two Biases
This example is a continuation of the ozone measurement comparison described in Box 5-3. Let x and
sx denote the sajriple mean and standard deviation of measurement method 1 applied to the QC check
sample, andjet Y and SY denote the sample mean and standard deviation of method 2. Then x = 80 ppb,
sx = 4 ppb, Y = 90 ppb and SY = 8 ppb. The estimated biases are x - T = 80 -100 = -20 ppb for method
1, and Y - T = 90-100 = 10 ppb for method 2, since 100 ppb is the true value T. That is, method 1 seems
to underestimate by 20 ppb, and method 2 seems to underestimate by 10 ppb. Let U., and u2 be the
underlying mean concentrations for measurement methods 1 and 2 applied to the QC check sample.
These means correspond to the average results which would obtain by applying each method a large
number of times to the QC check sample, over a long period of time.
A two-sample t-test (Boxes 3-14 and 3-16) can be used to test for a significant difference between these
two biases. In this case, a two-tailed test of the null hypothesis H0: U., - u2 = 0 against the alternative HA:
U., - u2t 0 is appropriate, because there is no a priori reason (in advance of data collection) to suspect
that one measurement method is superior to the other. (In general, hypotheses should not be tailored to
data.) Note that the difference between the two biases is the same as the difference (p., - u2) between the
two underlying means of the measurement methods. The test will be done to limit the false rejection
error rate to 5% if the two means are equal.
STEP 1: x = 80 ppb, sx = 4 ppb, Y = 90 ppb, SY = 8 ppb.
STEP 2: From Box 5-3, it is known that the methods have significantly different variances, so that
Sattherthwaite's t-test should be used. Therefore,
JNE
2
SX ^
m
2
. SY
n
42 82
— + — = 2.48
13 13
STEP 3: / =
sx
m
4
SX
m2(m - 1)
SY
n
2
4
SY
n2 (n - 1)
13
44
132 12
13
2
84
132 12
= 17.65.
STEP 4:
Rounding down to the nearest integer gives f = 17. For a two-tailed test, the critical value is t
1-0/2 = t.975 = 2.110, from Table A-1 of Appendix A.
t =
X - Y
80 - 90
2.48
= -4.032
STEP 5: For a two-tailed test, compare *t* with t^ = 2.11. Since 4.032 > 2.11, reject the null
hypothesis and conclude that there is a significant difference between the two method biases,
in favor of method 2.
This box illustrates a situation involving two measurement methods where one method is more precise,
but also more biased, than the other. If no adjustment for bias is made, then for many purposes, the
less biased, more variable method is preferable. However, proper bias adjustment can make both
methods unbiased, so that the more precise method becomes the preferred method. Such adjustments
can be based on QC check sample results, if the QC check samples are regarded as representative of
environmental samples involving sufficiently similar analytes and matrices.
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1
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True Value of the Parameter
In the context of hypothesis testing, the impact of bias can be quite severe in some
circumstances. This can be illustrated by comparing the power curve of a test when bias is not
present with a power curve for the same test when bias is present. The basic influence of bias is
to shift the former "no bias" curve to
the right or left, depending on the
direction of the bias. If the bias is
constant, then the second curve will be
an exact translation of the former
curve; if not, there will be a change in
the shape of the second curve in
addition to the translation. If the
existence of the bias is unknown, then
the former power curve will be
regarded as the curve that determines
the properties of the test when in fact Figure 5_L illustration of Unbiased versus Biased
the second curve will be the one that Power Curves
actually represents the test's power.
For example, in Figure 5-1 when the true value of the parameter is 120, the "no bias" power is
0.72 but the true power (the biased power) is only 0.4, a substantial difference. Since bias is
not impacted by changing the sample size, while the precision of estimates and the power of tests
increases with sample size, the relative importance of bias becomes more pronounced when the
sample size increases (i.e., when one makes the power curve steeper). Similarly, if the same
magnitude of bias exists for two different sites, then the impact on testing errors will be more
severe for the site having the smaller inherent variability in the characteristic of interest (i.e., when
bias represents a larger portion of total variability).
To minimize the effects of bias: identify and document sources of potential bias; adopt
measurement procedures (including specimen collection, handling, and analysis procedures) that
minimize the potential for bias; make a concerted effort to quantify bias whenever possible; and
make appropriate compensation for bias when possible.
5.2.5 Quantity vs. Quality of Data
The above conclusions imply that, if compensation for bias cannot be made and if
statistically-based decisions are to be made, then there will be situations in which serious
consideration should be given to using an imprecise (and perhaps relatively inexpensive) chemical
method having negligible bias as compared to using a very precise method that has even a
moderate degree of bias. The tradeoff favoring the imprecise method is especially relevant when
the inherent variability in the population is very large relative to the random measurement error.
For example, suppose a mean concentration for a given spatial area (site) is of interest and
that the coefficient of variation (CV) characterizing the site's variability is 100%. Let method A
denote an imprecise method, with measurement-error CV of 40%, and let method B denote a
highly precise method, with measurement-error CV of 5%. The overall variability, or total
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variability, can essentially be regarded as the sum of the spatial variability and the measurement
variability. These are obtained from the individual CVs in the form of variances. As CV equals
standard deviation divided by mean, it follows that the site standard deviation is then the CV times
the mean. Thus, for the site, the variance is l.OO2 x mean2; for method A, the variance is 0.402 x
mean2; and for method B, the variance is 0.052 x mean2. The overall variability when using
method A is then (l.OO2 x mean2) + (0.402 x mean2) = 1.16 x mean2, and when using method B,
the variance is (l.OO2 x mean2) + (0.052 x mean2) = 1.0025 mean2. It follows that the overall CV
when using each method is then (1.077 x mean) / mean = 107.7% for method A, and (1.001 x
mean) / mean = 100.1% for method B.
Now consider a sample of 25 specimens from the site. The precision of the sample mean
can then be characterized by the relative standard error (RSE) of the mean (which for the simple
random sample situation is simply the overall CV divided by the square root of the sample size).
For Method A, RSE = 21.54%; for method B, RSE = 20.02%. Now suppose that the imprecise
method (Method A) is unbiased, while the precise method (Method B) has a 10% bias (e.g., an
analyte percent recovery of 90%). An overall measure of error that reflects how well the sample
mean estimates the site mean is the relative root mean squared error (RRMSE):
RRMSE = \I(RB)2 + (RSE)2
where RB denotes the relative bias (RB = 0 for Method A since it is unbiased and RB = ±10% for
Method B since it is biased) and RSE is as defined above. The overall error in the estimation of
the population mean (the RRMSE) would then be 21.54% for Method A and 22.38% for Method
B. If the relative bias for Method B was 15% rather than 10%, then the RRMSE for Method A
would be 21.54% and the RRMSE for Method B would be 25.02%, so the method difference is
even more pronounced. While the above illustration is portrayed in terms of estimation of a mean
based on a simple random sample, the basic concepts apply more generally.
This example serves to illustrate that a method that may be considered preferable from a
chemical point of view [e.g., 85 or 90% recovery, 5% relative standard deviation (RSD)] may not
perform as well in a statistical application as a method with less bias and greater imprecision (e.g.,
zero bias, 40% RSD), especially when the inherent site variability is large relative to the
measurement-error RSD.
5.2.6 "Proof of Safety" vs. "Proof of Hazard"
Because of the basic hypothesis testing philosophy, the null hypothesis is generally
specified in terms of the status quo (e.g., no change or action will take place if null hypothesis is
not rejected). Also, since the classical approach exercises direct control over the false rejection
error rate, this rate is generally associated with the error of most concern (for further discussion
of this point, see Section 1.2). One difficulty, therefore, may be obtaining a consensus on which
error should be of most concern. It is not unlikely that the Agency's viewpoint in this regard will
differ from the viewpoint of the regulated party. In using this philosophy, the Agency's ideal
approach is not only to set up the direction of the hypothesis in such a way that controlling the
EPA QA/G-9 Final
QAOO Version 5-12 July 2000
-------
false rejection error protects the health and environment but also to set it up in a way that
encourages quality (high precision and accuracy) and minimizes expenditure of resources in
situations where decisions are relatively "easy" (e.g., all observations are far from the threshold
level of interest).
In some cases, how one formulates the hypothesis testing problem can lead to very
different sampling requirements. For instance, following remediation activities at a hazardous
waste site, one may seek to answer "Is the site clean?" Suppose one attempts to address this
question by comparing a mean level from samples taken after the remediation with a threshold
level (chosen to reflect "safety"). If the threshold level is near background levels that might have
existed in the absence of the contamination, then it may be very difficult (i.e., require enormous
sample sizes) to "prove" that the site is "safe." This is because the concentrations resulting from
even a highly efficient remediation under such circumstances would not be expected to deviate
greatly from such a threshold. A better approach for dealing with this problem may be to
compare the remediated site with a reference ("uncontaminated") site, assuming that such a site
can be determined.
To avoid excessive expense in collecting and analyzing samples for a contaminant,
compromises will sometimes be necessary. For instance, suppose that a significance level of 0.05
is to be used; however, the affordable sample size may be expected to yield a test with power of
only 0.40 at some specified parameter value chosen to have practical significance (see Section
5.2.3). One possible way that compromise may be made in such a situation is to relax the
significance level, for instance, using a = 0.10, 0.15, or 0.20. By relaxing this false rejection rate,
a higher power (i.e., a lower false acceptance rate P) can be achieved. An argument can be made,
for example, that one should develop sampling plans and determine sample sizes in such a way
that both the false rejection and false acceptance errors are treated simultaneously and in a
balanced manner (for example, designing to achieve a = P = 0.15) instead of using the traditional
approach of fixing the false rejection error rate at 0.05 or 0.01 and letting P be determined by the
sample size. This approach of treating the false rejection and false acceptance errors
simultaneously is taken in the DQO Process and it is recommended that several different scenarios
of a and P be investigated before a decision on specific values for a and P are selected.
EPA QA/G-9 Final
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This page is intentionally blank.
EPA QA/G-9 Final
QAOO Version 5-14 July 2000
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APPENDIX A
STATISTICAL TABLES
EPA QA/G-9 Final
QAOO Version A - 1 July 2000
-------
LIST OF TABLES
Table No. Page
A-l: CRITICAL VALUES OF STUDENT'S t DISTRIBUTION A - 3
A-2: CRITICAL VALUES FOR THE STUDENTIZED RANGE TEST A - 4
A-3: CRITICAL VALUES FOR THE EXTREME VALUE TEST (DIXONS TEST) A - 5
A-4: CRITICAL VALUES FOR DISCORDANCE TEST A - 6
A-5: APPROXIMATE CRITICAL VALUES Ar FOR ROSNERS TEST A - 7
A-6: QUANTILES OF THE WILCOXON SIGNED RANKS TEST A - 9
A-7: CRITICAL VALUES FOR THE RANK-SUM TEST A - 10
A-8: PERCENTILES OF THE CHI-SQUARE DISTRIBUTION A - 12
A-9: PERCENTILES OF THE F DISTRIBUTION A - 13
A-10: VALUES OF THE PARAMETER FOR COHENS ESTIMATES
ADJUSTING FOR NONDETECTED VALUES A - 18
A-l 1: PROBABILITIES FOR THE SMALL-SAMPLE
MANN-KENDALL TEST FOR TREND A - 19
A-12. QUANTILES FOR THE WALD-WOLFOWITZ TEST FOR RUNS A - 20
A-13. MODIFIED QUANTILE TEST CRITICAL NUMBERS A - 23
A-14. DUNNETT'S TEST (ONE TAILED) A - 27
A-15. APPROXIMATE a-LEVEL CRITICAL POINTS FOR
RANK VON NEUMANN RATIO TEST A - 29
EPA QA/G-9 Final
QAOO Version A - 2 July 2000
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TABLE A-l: CRITICAL VALUES OF STUDENT'S t DISTRIBUTION
Degrees of
Freedom
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
60
120
00
1-
.70
0.727
0.617
0.584
0.569
0.559
0.553
0.549
0.546
0.543
0.542
0.540
0.539
0.538
0.537
0.536
0.535
0.534
0.534
0.533
0.533
0.532
0.532
0.532
0.531
0.531
0.531
0.531
0.530
0.530
0.530
0.529
0.527
0.526
0.524
.75
1.000
0.816
0.765
0.741
0.727
0.718
0.711
0.706
0.703
0.700
0.697
0.695
0.694
0.692
0.691
0.690
0.689
0.688
0.6880
.687
0.686
0.686
0.685
0.685
0.684
0.684
0.684
0.683
0.683
0.683
0.681
0.679
0.677
0.674
.80
1.376
1.061
0.978
0.941
0.920
0.906
0.896
0.889
0.883
0.879
0.876
0.873
0.870
0.868
0.866
0.865
0.863
0.862
0.861
0.860
0.859
0.858
0.858
0.857
0.856
0.856
0.855
0.855
0.854
0.854
0.851
0.848
0.845
0.842
.85
1.963
1.386
1.250
1.190
1.156
1.134
1.119
1.108
1.100
1.093
1.088
1.083
1.079
1.076
1.074
1.071
1.069
1.067
1.066
1.064
1.063
1.061
1.060
1.059
1.058
1.058
1.057
1.056
1.055
1.055
1.050
1.046
1.041
1.036
.90
3.078
1.886
1.638
1.533
1.476
1.440
1.415
1.397
1.383
1.372
1.363
1.356
1.350
1.345
1.34
1.337
1.333
1.330
1.328
1.325
1.323
1.321
1.319
1.318
1.316
1.315
1.314
1.313
1.311
1.310
1.303
1.296
1.289
1.282
.95
6.314
2.920
2.353
2.132
2.015
1.943
1.895
1.860
1.833
1.812
1.796
1.782
1.771
1.761
1.753
1.746
1.740
1.734
1.729
1.725
1.721
1.717
1.714
1.711
1.708
1.706
1.703
1.701
1.699
1.697
1.684
1.671
1.658
1.645
.975
12.706
4.303
3.182
2.776
2.571
2.447
2.365
2.306
2.262
2.228
2.201
2.179
2.160
2.145
2.131
2.120
2.110
2.101
2.093
2.086
2.080
2.074
2.069
2.064
2.060
2.056
2.052
2.048
2.045
2.042
2.021
2.000
1.980
1.960
.99
31.821
6.965
4.541
3.747
3.365
3.143
2.998
2.896
2.821
2.764
2.718
2.681
2.650
2.624
2.602
2.583
2.567
2.552
2.539
2.528
2.518
2.508
2.500
2.492
2.485
2.479
2.473
2.467
2.462
2.457
2.423
2.390
2.358
2.326
.995
63.657
9.925
5.841
4.604
4.032
3.707
3.499
3.355
3.250
3.169
3.106
3.055
3.012
2.977
2.947
2.921
2.898
2.878
2.861
2.845
2.831
2.819
2.807
2.797
2.787
2.779
2.771
2.763
2.756
2.750
2.704
2.660
2.617
2.576
Note: The last row of the table (°
= zng, = 1.645.
degrees of freedom) gives the critical values fora standard normal distribution (z), e.g., t.
EPA QA/G-9
QAOO Version
A-3
Final
July 2000
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TABLE A-2: CRITICAL VALUES FOR THE STUDENTIZED RANGE TEST
n
o
J
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
150
200
500
1000
a
1.737
1.87
2.02
2.15
2.26
2.35
2.44
2.51
2.58
2.64
2.70
2.75
2.80
2.84
2.88
2.92
2.96
2.99
3.15
3.27
3.38
3.47
3.55
3.62
3.69
3.75
3.80
3.85
3.90
3.94
3.99
4.02
4.06
4.10
4.38
4.59
5.13
5.57
0.01
b
2.000
2.445
2.803
3.095
3.338
3.543
3.720
3.875
4.012
4.134
4.244
4.34
4.44
4.52
4.60
4.67
4.74
4.80
5.06
5.26
5.42
5.56
5.67
5.77
5.86
5.94
6.01
6.07
6.13
6.18
6.23
6.27
6.32
6.36
6.64
6.84
7.42
7.80
Level of Significance «
0.05
a
1.758
1.98
2.15
2.28
2.40
2.50
2.59
2.67
2.74
2.80
2.86
2.92
2.97
3.01
3.06
3.10
3.14
3.18
3.34
3.47
3.58
3.67
3.75
3.83
3.90
3.96
4.01
4.06
4.11
4.16
4.20
4.24
4.27
4.31
4.59
4.78
5.47
5.79
b
1.999
2.429
2.753
3.012
3.222
3.399
3.552
3.685
3.80
3.91
4.00
4.09
4.17
4.24
4.31
4.37
4.43
4.49
4.71
4.89
5.04
5.16
5.26
5.35
5.43
5.51
5.57
5.63
5.68
5.73
5.78
5.82
5.86
5.90
6.18
6.39
6.94
7.33
a
1.782
2.04
2.22
2.37
2.49
2.59
2.68
2.76
2.84
2.90
2.96
3.02
3.07
3.12
3.17
3.21
3.25
3.29
3.45
3.59
3.70
3.79
3.88
3.95
4.02
4.08
4.14
4.19
4.24
4.28
4.33
4.36
4.40
4.44
4.72
4.90
5.49
5.92
0.10
b
1.997
2.409
2.712
2.949
3.143
3.308
3.449
3.57
3.68
3.78
3.87
3.95
4.02
4.09
4.15
4.21
4.27
4.32
4.53
4.70
4.84
4.96
5.06
5.14
5.22
5.29
5.35
5.41
5.46
5.51
5.56
5.60
5.64
5.68
5.96
6.15
6.72
7.11
EPA QA/G-9
QAOO Version
A-4
Final
July 2000
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TABLE A-3: CRITICAL VALUES FOR THE EXTREME VALUE TEST
(DIXON'S TEST)
n
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Level of Significance «
0.10
0.886
0.679
0.557
0.482
0.434
0.479
0.441
0.409
0.517
0.490
0.467
0.492
0.472
0.454
0.438
0.424
0.412
0.401
0.391
0.382
0.374
0.367
0.360
0.05
0.941
0.765
0.642
0.560
0.507
0.554
0.512
0.477
0.576
0.546
0.521
0.546
0.525
0.507
0.490
0.475
0.462
0.450
0.440
0.430
0.421
0.413
0.406
0.01
0.988
0.889
0.780
0.698
0.637
0.683
0.635
0.597
0.679
0.642
0.615
0.641
0.616
0.595
0.577
0.561
0.547
0.535
0.524
0.514
0.505
0.497
0.489
EPA QA/G-9
QAOO Version
A-5
Final
July 2000
-------
TABLE A-4: CRITICAL VALUES FOR DISCORDANCE TEST
n
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Level of Significance «
0.01
1.155
1.492
1.749
1.944
2.097
2.221
2.323
2.410
2.485
2.550
2.607
2.659
2.705
2.747
2.785
2.821
2.854
2.884
2.912
2.939
2.963
2.987
3.009
3.029
3.049
3.068
3.085
3.103
3.119
3.135
0.05
1.153
1.463
1.672
1.822
1.938
2.032
2.110
2.176
2.234
2.285
2.331
2.371
2.409
2.443
2.475
2.504
2.532
2.557
2.580
2.603
2.624
2.644
2.663
2.681
2.698
2.714
2.730
2.745
2.759
2.773
n
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Level of Significance «
0.01
3.150
3.164
3.178
3.191
3.204
3.216
3.228
3.240
3.251
3.261
3.271
3.282
3.292
3.302
3.310
3.319
3.329
3.336
0.05
2.786
2.799
2.811
2.823
2.835
2.846
2.857
2.866
2.877
2.887
2.896
2.905
2.914
2.923
2.931
2.940
2.948
2.956
EPA QA/G-9
QAOO Version
A-6
Final
July 2000
-------
TABLE A-5: APPROXIMATE CRITICAL VALUES Ar FOR ROSNER'S TEST
n
25
26
27
28
29
30
31
r
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
a
0.05
2.82
2.80
2.78
2.76
2.73
2.59
2.84
2.82
2.80
2.78
2.76
2.62
2.86
2.84
2.82
2.80
2.78
2.65
2.88
2.86
2.84
2.82
2.80
2.68
2.89
2.88
2.86
2.84
2.82
2.71
2.91
2.89
2.88
2.86
2.84
2.73
2.92
2.91
2.89
2.88
2.86
2.76
0.01
3.14
3.11
3.09
3.06
3.03
2.85
3.16
3.14
3.11
3.09
3.06
2.89
3.18
3.16
3.14
3.11
3.09
2.93
3.20
3.18
3.16
3.14
3.11
2.97
3.22
3.20
3.18
3.16
3.14
3.00
3.24
3.22
3.20
3.18
3.16
3.03
3.25
3.24
3.22
3.20
3.18
3.06
n
32
33
34
35
36
37
38
r
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
a
0.05
2.94
2.92
2.91
2.89
2.88
2.78
2.95
2.94
2.92
2.91
2.89
2.80
2.97
2.95
2.94
2.92
2.91
2.82
2.98
2.97
2.95
2.94
2.92
2.84
2.99
2.98
2.97
2.95
2.94
2.86
3.00
2.99
2.98
2.97
2.95
2.88
3.01
3.00
2.99
2.98
2.97
2.91
0.01
3.27
3.25
3.24
3.22
3.20
3.09
3.29
3.27
3.25
3.24
3.22
3.11
3.30
3.29
3.27
3.25
3.24
3.14
3.32
3.30
3.29
3.27
3.25
3.16
3.33
3.32
3.30
3.29
3.27
3.18
3.34
3.33
3.32
3.30
3.29
3.20
3.36
3.34
3.33
3.32
3.30
3.22
n
39
40
41
42
43
44
45
r
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
a
0.05
3.03
3.01
3.00
2.99
2.98
2.91
3.04
3.03
3.01
3.00
2.99
2.92
3.05
3.04
3.03
3.01
3.00
2.94
3.06
3.05
3.04
3.03
3.01
2.95
3.07
3.06
3.05
3.04
3.03
2.97
3.08
3.07
3.06
3.05
3.04
2.98
3.09
3.08
3.07
3.06
3.05
2.99
0.01
3.37
3.36
3.34
3.33
3.32
3.24
3.38
3.37
3.36
3.34
3.33
3.25
3.39
3.38
3.37
3.36
3.34
3.27
3.40
3.39
3.38
3.37
3.36
3.29
3.41
3.40
3.39
3.38
3.37
3.30
3.43
3.41
3.40
3.39
3.38
3.32
3.44
3.43
3.41
3.40
3.39
3.33
EPA QA/G-9
QAOO Version
A-7
Final
July 2000
-------
TABLE A-5: APPROXIMATE CRITICAL VALUES Ar FOR ROSNER'S TEST
n
46
47
48
49
50
60
r
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
a
0.05
3.09
3.09
3.08
3.07
3.06
3.00
3.10
3.09
3.09
3.08
3.07
3.01
3.11
3.10
3.09
3.09
3.08
3.03
3.12
3.11
3.10
3.09
3.09
3.04
3.13
3.12
3.11
3.10
3.09
3.05
3.20
3.19
3.19
3.18
3.17
3.14
0.01
3.45
3.44
3.43
3.41
3.40
3.34
3.46
3.45
3.44
3.43
3.41
3.36
3.46
3.46
3.45
3.44
3.43
3.37
3.47
3.46
3.46
3.45
3.44
3.38
3.48
3.47
3.46
3.46
3.45
3.39
3.56
3.55
3.55
3.54
3.53
3.49
n
70
80
90
100
150
200
r
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
1
2
3
4
5
10
a
0.05
3.26
3.25
3.25
3.24
3.24
3.21
3.31
3.30
3.30
3.29
3.29
3.26
3.35
3.34
3.34
3.34
3.33
3.31
3.38
3.38
3.38
3.37
3.37
3.35
3.52
3.51
3.51
3.51
3.51
3.50
3.61
3.60
3.60
3.60
3.60
3.59
0.01
3.62
3.62
3.61
3.60
3.60
3.57
3.67
3.67
3.66
3.66
3.65
3.63
3.72
3.71
3.71
3.70
3.70
3.68
3.75
3.75
3.75
3.74
3.74
3.72
3.89
3.89
3.89
3.88
3.88
3.87
3.98
3.98
3.97
3.97
3.97
3.96
n
250
300
350
400
450
500
r
1
5
10
1
5
10
1
5
10
1
5
10
1
5
10
1
5
10
a
0.05
3.67
3.67
3.66
3.72
3.72
3.71
3.77
3.76
3.76
3.80
3.80
3.80
3.84
3.83
3.83
3.86
3.86
3.86
0.01
4.04
4.04
4.03
4.09
4.09
4.09
4.14
4.13
4.13
4.17
4.17
4.16
4.20
4.20
4.20
4.23
4.23
4.22
EPA QA/G-9
QAOO Version
A-8
Final
July 2000
-------
TABLE A-6: QUANTILES OF THE WILCOXON SIGNED RANKS TEST
n
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
w.01
0
0
0
1
2
4
6
8
10
13
16
20
24
28
33
38
44
W.05
0
1
3
4
6
9
11
14
18
22
26
31
36
42
48
54
61
WJO
1
3
4
6
9
11
15
18
22
27
32
37
43
49
56
63
70
w.20
3
4
6
9
12
15
19
23
28
33
39
45
51
58
66
74
82
EPA QA/G-9
QAOO Version
A-9
Final
July 2000
-------
TABLE A-7: CRITICAL VALUES FOR THE RANK-SUM TEST
n
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
a
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
0.05
0.10
m
2
0
0
0
1
0
1
1
2
1
2
1
2
2
3
2
3
2
4
2
4
3
5
3
5
4
5
4
6
4
6
4
7
3
0
1
1
2
1
2
2
3
3
4
3
5
4
6
5
6
5
7
6
8
6
9
7
10
8
11
8
11
9
12
10
13
4
0
1
1
2
2
4
3
5
4
6
5
7
6
8
7
10
8
11
9
12
10
13
11
14
12
16
13
17
15
18
16
19
5
1
2
2
3
3
5
5
6
6
8
7
9
9
11
10
13
12
14
13
16
14
18
16
19
17
21
19
23
20
24
21
26
6
1
2
3
4
4
6
6
8
8
10
9
12
111
4
13
16
15
18
17
20
18
22
20
24
22
26
24
28
26
30
27
32
7
1
2
3
5
5
7
7
9
9
12
12
14
14
17
16
19
18
22
20
24
22
27
25
29
27
32
29
34
31
37
34
39
8
2
3
4
6
6
8
9
11
11
14
14
17
16
20
19
23
21
25
24
28
27
31
29
34
32
37
34
40
37
43
40
46
9
2
3
5
6
7
10
10
13
13
16
16
19
19
23
22
26
25
29
28
32
31
36
34
39
37
42
40
46
43
49
46
53
10
2
4
5
7
8
11
12
14
15
18
18
22
21
25
25
29
28
33
32
37
35
40
38
44
42
48
45
52
49
55
52
59
11
2
4
6
8
9
12
13
16
17
20
20
24
24
28
28
32
32
37
35
41
39
45
43
49
47
53
51
58
55
62
58
66
12
3
5
6
9
10
13
14
18
18
22
22
27
27
31
31
36
35
40
39
45
43
50
48
54
52
59
56
64
61
68
65
73
13
3
5
7
10
11
14
16
19
20
24
25
29
29
34
34
39
38
44
43
49
48
54
52
59
57
64
62
69
66
75
71
80
14
4
5
8
11
12
16
17
21
22
26
27
32
32
37
37
42
42
48
47
53
52
59
57
64
62
70
67
75
72
81
78
86
15
4
6
8
11
13
17
19
23
24
28
29
34
34
40
40
46
45
52
51
58
56
64
62
69
67
75
73
81
78
87
84
93
16
4
6
9
12
15
18
20
24
26
30
31
37
37
43
43
49
49
55
55
62
61
68
66
75
72
81
78
87
84
94
90
100
17
4
7
10
13
16
19
21
26
27
32
34
39
40
46
46
53
52
59
58
66
65
73
71
80
78
86
84
93
90
100
97
107
18
5
7
10
14
17
21
23
28
29
35
36
42
42
49
49
56
56
63
62
70
69
78
76
85
83
92
89
99
96
107
103
114
19
5
8
11
15
18
22
24
29
31
37
38
44
45
52
52
59
59
67
66
74
73
82
81
90
88
98
95
105
102
113
110
121
20
5
8
12
16
19
23
26
31
33
39
40
47
48
55
55
63
63
71
70
79
78
87
85
95
93
103
101
111
108
120
116
128
EPA QA/G-9
QAOO Version
A- 10
Final
July 2000
-------
TABLE A-7: CRITICAL VALUES FOR THE RANK-SUM TEST
n
18
19
20
a
0.05
0.10
0.05
0.10
0.05
n 11)
m
2
5
1
5
8
5
8
3
10
14
11
15
12
ifi
4
17
21
18
22
19
9^
5
23
28
24
29
26
^1
6
29
35
31
37
33
^Q
7
36
42
38
44
40
47
8
42
49
45
52
48
ss
9
49
56
52
59
55
«
10
56
63
59
67
63
71
11
62
70
66
74
70
7Q
12
69
78
73
82
78
87
13
76
85
81
90
85
QS
14
83
92
88
98
93
im
15
89
99
95
105
101
1 1 1
16
96
107
102
113
108
190
17
103
114
110
121
116
198
18
110
121
117
129
124
^^,£,
19
117
129
124
136
131
144
20
124
136
131
144
139
1S9
EPA QA/G-9
QAOO Version
A- 11
Final
July 2000
-------
TABLE A-8: PERCENTILES OF THE CHI-SQUARE DISTRIBUTION
V
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
50
60
70
80
90
100
1-a
.005
0.04393
0.0100
0.072
0.207
0.412
0.676
0.989
1.34
1.73
2.16
2.60
3.07
3.57
4.07
4.60
5.14
5.70
6.26
6.84
7.43
8.03
8.64
9.26
9.89
10.52
11.16
11.81
12.46
13.12
13.79
20.71
27.99
35.53
43.28
51.17
59.20
67.33
.010
0.03157
0.0201
0.115
0.297
0.554
0.872
1.24
1.65
2.09
2.56
3.05
3.57
4.11
4.66
5.23
5.81
6.41
7.01
7.63
8.26
8.90
9.54
10.20
10.86
11.52
12.20
12.88
13.56
14.26
14.95
22.16
29.71
37.48
45.44
53.54
61.75
70.06
.025
0.03982
0.0506
0.216
0.484
0.831
1.24
1.69
2.18
2.70
3.25
3.82
4.40
5.01
5.63
6.26
6.91
7.56
8.23
8.91
9.59
10.28
10.98
11.69
12.40
13.12
13.84
14.57
15.31
16.05
16.79
24.43
32.36
40.48
48.76
57.15
65.65
74.22
.050
0.02393
0.103
0.352
0.711
1.145
1.64
2.17
2.73
3.33
3.94
3.57
5.23
5.89
6.57
7.26
7.96
8.67
9.39
10.12
10.85
11.59
12.34
13.09
13.85
14.61
15.38
16.15
16.93
17.71
18.49
26.51
34.76
43.19
51.74
60.39
69.13
77.93
.100
0.0158
0.211
0.584
1.064
1.61
2.20
2.83
3.49
4.17
4.87
5.58
6.30
7.04
7.79
8.55
9.31
10.09
10.86
11.65
12.44
13.24
14.04
14.85
15.66
16.47
17.29
18.11
18.94
19.77
20.60
29.05
37.69
46.46
53.33
64.28
73.29
82.36
.900
2.71
4.61
6.25
7.78
9.24
10.64
12.02
13.36
14.68
15.99
17.28
18.55
19.81
21.06
22.31
23.54
24.77
25.99
27.20
28.41
29.62
30.81
32.01
33.20
34.38
35.56
36.74
37.92
39.09
40.26
51.81
63.17
74.40
85.53
96.58
107.6
118.5
.950
3.84
5.99
7.81
9.49
11.07
12.59
14.07
15.51
16.92
18.31
19.68
21.03
22.36
23.68
25.00
26.30
27.59
28.87
30.14
31.41
32.67
33.92
35.17
36.42
37.65
38.89
40.11
41.34
42.56
43.77
55.76
67.50
79.08
90.53
101.9
113.1
124.3
.975
5.02
7.38
9.35
11.14
12.83
14.45
16.01
17.53
19.02
20.48
21.92
23.34
24.74
26.12
27.49
28.85
30.19
31.53
32.85
34.17
35.48
36.78
38.08
39.36
40.65
41.92
43.19
44.46
45.72
46.98
59.34
71.42
83.30
95.02
106.6
118.1
129.6
.990
6.63
9.21
11.34
13.28
15.09
16.81
18.48
20.09
21.67
23.21
24.73
26.22
27.69
29.14
30.58
32.00
33.41
34.81
36.19
37.57
38.93
40.29
41.64
42.98
44.31
45.64
46.96
48.28
49.59
50.89
63.69
76.15
88.38
100.4
112.3
124.1
135.8
.995
7.88
10.60
12.84
14.86
16.75
18.55
20.28
21.96
23.59
25.19
26.76
28.30
29.82
31.32
32.80
34.27
35.72
37.16
38.58
40.00
41.40
42.80
44.18
45.56
46.93
48.29
49.64
50.99
52.34
53.67
66.77
79.49
91.95
104.2
116.3
128.3
140.2
EPA QA/G-9
QAOO Version
A- 12
Final
July 2000
-------
TABLE A-9: PERCENTILES OF THE F DISTRIBUTION
Degrees
Freedom
for
Denom-
inator
1 .50
.90
.95
.975
.99
2 .50
.90
.95
.975
.99
3 .50
.90
.95
.975
.99
4 .50
.90
.95
.975
.99
.999
Degrees of Freedom for Numerator
1
1.00
39.9
161
648
4052
0.667
8.53
18.5
38.5
98.5
0.585
5.54
10.1
17.4
34.1
0.549
4.54
7.71
12.2
21.2
74.1
2
1.50
49.5
200
800
5000
1.00
9.00
19.0
39.0
99.0
0.881
5.46
9.55
16.0
30.8
0.828
4.32
6.94
10.6
18.0
61.2
3
1.71
53.6
216
864
5403
1.13
9.16
19.2
39.2
99.2
1.00
5.39
9.28
15.4
29.5
0.941
4.19
6.59
9.98
16.7
56.2
4
1.82
55.8
225
900
5625
1.21
9.24
19.2
39.2
99.2
1.06
5.34
9.12
15.1
28.7
1.00
4.11
6.39
9.60
16.0
53.4
5
1.89
57.2
230
922
5764
1.25
9.29
19.3
39.3
99.3
1.10
5.31
9.01
14.9
28.2
1.04
4.05
6.26
9.36
15.5
51.7
6
1.94
58.2
234
937
5859
1.28
9.33
19.3
39.3
99.3
1.13
5.28
8.94
14.7
27.9
1.06
4.01
6.16
9.20
15.2
50.5
7
1.98
58.9
237
948
5928
1.30
9.35
19.4
39.4
99.4
1.15
5.27
8.89
14.6
27.7
1.08
3.98
6.09
9.07
15.0
49.7
8
2.00
59.4
239
957
5981
1.32
9.37
19.4
39.4
99.4
1.16
5.25
8.85
14.5
27.5
1.09
3.95
6.04
8.98
14.8
49.0
9
2.03
59.9
241
963
6022
1.33
9.38
19.4
39.4
99.4
1.17
5.24
8.81
14.5
27.3
1.10
3.94
6.00
8.90
14.7
48.5
10
2.04
60.2
242
969
6056
1.34
9.39
19.4
39.4
99.4
1.18
5.23
8.79
14.4
27.2
1.11
3.92
5.96
8.84
14.5
48.1
12
2.07
60.7
244
977
6106
1.36
9.41
19.4
39.4
99.4
1.20
5.22
8.74
14.3
27.1
1.13
3.90
5.91
8.75
14.4
47.4
15
2.09
61.2
246
985
6157
1.38
9.42
19.4
39.4
99.4
1.21
5.20
8.70
14.3
26.9
1.14
3.87
5.86
8.66
14.2
46.8
20
2.12
61.7
248
993
6209
1.39
9.44
19.4
39.4
99.4
1.23
5.18
8.66
14.2
26.7
1.15
3.84
5.80
8.56
14.0
46.1
24
2.13
62.0
249
997
6235
1.40
9.45
19.5
39.5
99.5
1.23
5.18
8.64
14.1
26.6
1.16
3.83
5.77
8.51
13.9
45.8
30
2.15
62.3
250
1001
6261
1.41
9.46
19.5
39.5
99.5
1.24
5.17
8.62
14.1
26.5
1.16
3.82
5.75
8.46
13.8
45.4
60
2.17
62.8
252
1010
6313
1.43
9.47
19.5
39.5
99.5
1.25
5.15
8.57
14.0
26.3
1.18
3.79
5.69
8.36
13.7
44.7
120
2.18
63.1
253
1014
6339
1.43
9.48
19.5
39.5
99.5
1.26
5.14
8.55
13.9
26.2
1.18
3.78
5.66
8.31
13.6
44.4
CO
2.20
63.3
254
1018
6366
1.44
9.49
19.5
39.5
99.5
1.27
5.13
8.53
13.9
26.1
1.19
3.76
5.63
8.26
13.5
44.1
EPA QA/G-9
QAOO Version
A- 13
Final
July 2000
-------
TABLE A-9: PERCENTILES OF THE F DISTRIBUTION
Degrees
Freedom
for
Denom-
inator
5 .50
.90
.95
.975
.99
.999
6 .50
.90
.95
.975
.99
.999
7 .50
.90
.95
.975
.99
.999
8 .50
.90
.95
.975
.99
.999
Degrees of Freedom for Numerator
1
0.528
4.06
6.61
10.0
16.3
47.2
0.515
3.78
5.99
8.81
22.8
35.5
0.506
3.59
5.59
8.07
12.2
29.2
0.499
3.46
5.32
7.57
11.3
25.4
2
0.799
3.78
5.79
8.43
13.3
37.1
0.780
3.46
5.14
7.26
10.9
27.0
.0767
3.26
4.74
6.54
9.55
21.7
0.757
3.11
4.46
6.06
8.65
18.5
3
0.907
3.62
5.41
7.76
12.1
33.2
0.886
3.29
4.76
6.60
9.78
23.7
0.871
3.07
4.35
5.89
8.45
18.8
0.860
2.92
4.07
5.42
7.59
15.8
4
0.965
3.52
5.19
7.39
11.4
31.1
0.942
3.18
4.53
6.23
9.15
21.9
0.926
2.96
4.12
5.52
7.85
17.2
0.915
2.81
3.84
5.05
7.01
14.4
5
1.00
3.45
5.05
7.15
11.0
29.8
0.977
3.11
4.39
5.99
8.75
20.8
0.960
2.88
3.97
5.29
7.46
16.2
0.948
2.73
3.69
4.82
6.63
13.5
6
1.02
3.40
4.95
6.98
10.7
28.8
1.00
3.05
4.28
5.82
8.47
20.0
0.983
2.83
3.87
5.12
7.19
15.5
0.971
2.67
3.58
4.65
6.37
12.9
7
1.04
3.37
4.88
6.85
10.5
28.2
1.02
3.01
4.21
5.70
8.26
19.5
1.00
2.78
3.79
4.99
6.99
15.0
0.988
2.62
3.50
4.53
6.18
12.4
8
1.05
3.34
4.82
6.76
10.3
27.6
1.03
2.98
4.15
5.60
8.10
19.0
1.01
2.75
3.73
4.90
6.84
14.6
1.00
2.59
3.44
4.43
6.03
12.0
9
1.06
3.32
4.77
6.68
10.2
27.2
1.04
2.96
4.10
5.52
7.98
18.7
1.02
2.72
3.68
4.82
6.72
14.5
1.01
2.56
3.39
4.36
5.91
11.8
10
1.07
3.39
4.74
6.62
10.1
26.9
1.05
2.94
4.06
5.46
7.87
18.4
1.03
2.70
3.64
4.76
6.62
14.1
1.02
2.54
3.35
4.30
5.81
11.5
12
1.09
3.27
4.68
6.52
9.89
26.4
1.06
2.90
4.00
5.37
7.72
18.0
1.04
2.67
3.57
4.67
6.47
13.7
1.03
2.50
3.28
4.20
5.67
11.2
15
1.10
3.24
4.62
6.43
9.72
25.9
1.07
2.87
3.94
5.27
7.56
17.6
1.05
2.63
3.51
4.57
6.31
13.3
1.04
2.46
3.22
4.10
5.52
10.8
20
1.11
3.21
4.56
6.33
9.55
25.4
1.08
2.84
3.87
5.17
7.40
17.1
1.07
2.59
3.44
4.47
6.16
12.9
1.05
2.42
3.15
4.00
5.36
10.5
24
1.12
3.19
4.53
6.28
9.47
25.1
1.09
2.82
3.84
5.12
7.31
16.9
1.07
2.58
3.41
4.42
6.07
12.7
1.06
2.40
3.12
3.95
5.28
10.3
30
1.12
3.17
4.50
6.23
9.38
24.9
1.10
2.80
3.81
5.07
7.23
16.7
1.08
2.56
3.38
4.36
5.99
12.5
1.07
2.38
3.08
3.89
5.20
10.1
60
1.14
3.14
4.43
6.12
9.20
24.3
1.11
2.76
3.74
4.96
7.06
16.2
1.09
2.51
3.30
4.25
5.82
12.1
1.08
2.34
3.01
3.78
5.03
9.73
120
1.14
3.12
4.40
6.07
9.11
24.1
1.12
2.74
3.70
4.90
6.97
16.0
1.10
2.49
3.27
4.20
5.74
11.9
1.08
2.32
2.97
3.73
4.95
9.53
CO
1.15
3.11
4.37
6.02
9.02
23.8
1.12
2.72
3.67
4.85
6.88
15.7
1.10
2.47
3.23
4.14
5.65
11.7
1.09
2.29
2.93
3.67
4.86
9.33
EPA QA/G-9
QAOO Version
A- 14
Final
July 2000
-------
TABLE A-9: PERCENTILES OF THE F DISTRIBUTION
Degrees
Freedom
for
Denom-
inator
9 .50
.90
.95
.975
.99
.999
10 .50
.90
.95
.975
.99
.999
12 .50
.90
.95
.975
.99
.999
15 .50
.90
.95
.975
.99
.999
Degrees of Freedom for Numerator
1
0.494
3.36
5.12
7.21
10.6
22.9
0.490
3.29
4.96
6.94
10.0
21.0
0.484
3.18
4.75
6.55
9.33
18.6
0.478
3.07
4.54
6.20
8.68
16.6
2
0.749
3.01
4.26
5.71
8.02
16.4
0.743
2.92
4.10
5.46
7.56
14.9
0.735
2.81
3.89
5.10
6.93
13.0
0.726
2.70
3.68
4.77
6.36
11.3
3
0.852
2.81
3.86
5.08
6.99
13.9
0.845
2.73
3.71
4.83
6.55
12.6
0.835
2.61
3.49
4.47
5.95
10.8
0.826
2.49
3.29
4.15
5.42
9.34
4
0.906
2.69
3.63
4.72
6.42
12.6
0.899
2.61
3.48
4.47
5.99
11.3
0.888
2.48
3.26
4.12
5.41
9.63
0.878
2.36
3.06
3.80
4.89
8.25
5
0.939
2.61
3.48
4.48
6.06
11.7
0.932
2.52
3.33
4.24
5.64
10.5
0.921
2.39
3.11
3.89
5.06
8.89
0.911
2.27
2.90
3.58
4.56
7.57
6
0.962
2.55
3.37
4.32
5.80
11.1
0.954
2.46
3.22
4.07
5.39
9.93
0.943
2.33
3.00
3.73
4.82
8.38
0.933
2.21
2.79
3.41
4.32
7.09
7
0.978
2.51
3.29
4.20
5.61
10.7
0.971
2.41
3.14
3.95
5.20
9.52
0.959
2.28
2.91
3.61
4.64
8.00
0.949
2.16
2.71
3.29
4.14
6.74
8
0.990
2.47
3.23
4.10
5.47
10.4
0.983
2.38
3.07
3.85
5.06
9.20
0.972
2.24
2.85
3.51
4.50
7.71
0.960
2.12
2.64
3.20
4.00
6.47
9
1.00
2.44
3.18
4.03
5.35
10.1
0.992
2.35
3.02
3.78
4.94
8.96
0.981
2.21
2.80
3.44
4.39
7.48
0.970
2.09
2.59
3.12
3.89
6.26
10
1.01
2.42
3.14
3.96
5.26
9.89
1.00
2.32
2.98
3.72
4.85
8.75
0.989
2.19
2.75
3.37
4.30
7.29
0.977
2.06
2.54
3.06
3.80
6.08
12
1.01
2.38
3.07
3.87
5.11
9.57
1.01
2.28
2.91
3.62
4.71
8.45
1.00
2.15
2.69
3.28
4.16
7.00
0.989
2.02
2.48
2.96
3.67
5.81
15
1.03
2.34
3.01
3.77
4.96
9.24
1.02
2.24
2.84
3.52
4.56
8.13
1.01
2.10
2.62
3.18
4.01
6.71
1.00
1.97
2.40
2.86
3.52
5.54
20
1.04
2.30
2.94
3.67
4.81
8.90
1.03
2.20
2.77
3.42
4.41
7.80
1.02
2.06
2.54
3.07
3.86
6.40
1.01
1.92
2.33
2.76
3.37
5.25
24
1.05
2.28
2.90
3.61
4.73
8.72
1.04
2.18
2.74
3.37
4.33
7.64
1.03
2.04
2.51
3.02
3.78
6.25
1.02
1.90
2.29
2.70
3.29
5.10
30
1.05
2.25
2.86
3.56
4.65
8.55
1.05
2.16
2.70
3.31
4.25
7.47
1.03
2.01
2.47
2.96
3.70
6.09
1.02
1.87
2.25
2.64
3.21
4.95
60
1.07
2.21
2.79
3.45
4.48
8.19
1.06
2.11
2.62
3.20
4.08
7.12
1.05
1.96
2.38
2.85
3.54
5.76
1.03
1.82
2.16
2.52
3.05
4.64
120
1.07
2.18
2.75
3.39
4.40
8.00
1.06
2.08
2.58
3.14
4.00
6.94
1.05
1.93
2.34
2.79
3.45
5.59
1.04
1.79
2.11
2.46
2.96
4.48
CO
1.08
2.16
2.71
3.33
4.31
7.81
1.07
2.06
2.54
3.08
3.91
6.76
1.06
1.90
2.30
2.72
3.36
5.42
1.05
1.76
2.07
2.40
2.87
4.31
EPA QA/G-9
QAOO Version
A- 15
Final
July 2000
-------
TABLE A-9: PERCENTILES OF THE F DISTRIBUTION
Degrees
Freedom
for
Denom-
inator
20 .50
.90
.95
.975
.99
.999
24 .50
.90
.95
.975
.99
.999
30 .50
.90
.95
.975
.99
.999
60 .50
.90
.95
.975
.99
.999
Degrees of Freedom for Numerator
1
0.472
2.97
4.35
5.87
8.10
14.8
0.469
2.93
4.26
5.72
7.82
14.0
0.466
2.88
4.17
5.57
7.56
13.3
0.461
2.79
4.00
5.29
7.08
12.0
2
0.718
2.59
3.49
4.46
5.85
9.95
0.714
2.54
3.40
4.32
6.66
9.34
0.709
2.49
3.32
4.18
5.39
8.77
0.701
2.39
3.15
3.93
4.98
7.77
3
0.816
2.38
3.10
3.86
4.94
8.10
0.812
2.33
3.01
3.72
4.72
7.55
0.807
2.28
2.92
3.59
4.51
7.05
0.798
2.18
2.76
3.34
4.13
6.17
4
0.868
2.25
2.87
3.51
4.43
7.10
0.863
2.19
2.78
3.38
4.22
6.59
0.858
2.14
2.69
3.25
4.02
6.12
0.849
2.04
2.53
3.01
3.65
5.31
5
0.900
2.16
2.71
3.29
4.10
6.46
0.895
2.10
2.62
3.15
3.90
5.98
0.890
2.05
2.53
3.03
3.70
5.53
0.880
1.95
2.37
2.79
3.34
4.76
6
0.922
2.09
2.60
3.13
3.87
6.02
0.917
2.04
2.51
2.99
3.67
5.55
0.912
1.98
2.42
2.87
3.47
5.12
0.901
1.87
2.25
2.63
3.12
4.37
7
0.938
2.04
2.51
3.01
3.70
5.69
0.932
1.98
2.42
2.87
3.50
5.23
0.927
1.93
2.33
2.75
3.30
4.82
0.917
1.82
2.17
2.51
2.95
4.09
8
0.950
2.00
2.45
2.91
3.56
5.44
0.944
1.94
2.36
2.78
3.36
4.99
0.939
1.88
2.27
2.65
3.17
4.58
0.928
1.77
2.10
2.41
2.82
3.86
9
0.959
1.96
2.39
2.84
3.46
5.24
0.953
1.91
2.30
2.70
3.26
4.80
0.948
1.85
2.21
2.57
3.07
4.39
0.937
1.74
2.04
2.33
2.72
3.69
10
0.966
1.94
2.35
2.77
3.37
5.08
0.961
1.88
2.25
2.64
3.17
4.64
0.955
1.82
2.16
2.51
2.98
4.24
0.945
1.71
1.99
2.27
2.63
3.54
12
0.977
1.89
2.28
2.68
3.23
4.82
0.972
1.83
2.18
2.54
3.03
4.39
0.966
1.77
2.09
2.41
2.84
4.00
0.956
1.66
1.92
2.17
2.50
3.32
15
0.989
1.84
2.20
2.57
3.09
4.56
0.983
1.78
2.11
2.44
2.89
4.14
0.978
1.72
2.01
2.31
2.70
3.75
0.967
1.60
1.84
2.06
2.35
3.08
20
1.00
1.79
2.12
2.46
2.94
4.29
0.994
1.73
2.03
2.33
2.74
3.87
0.989
1.62
1.93
2.20
2.55
3.49
0.978
1.54
1.75
1.94
2.20
2.83
24
1.01
1.77
2.08
2.41
2.86
4.15
1.00
1.70
1.98
2.27
2.66
3.74
0.994
1.64
1.89
2.14
2.47
3.36
0.983
1.51
1.70
1.88
2.12
2.69
30
1.01
1.74
2.04
2.35
2.78
4.00
1.01
1.67
1.94
2.21
2.58
3.59
1.00
1.61
1.84
2.07
2.39
3.22
0.989
1.48
1.65
1.82
2.03
2.55
60
1.02
1.68
1.95
2.22
2.61
3.70
1.02
1.61
1.84
2.08
2.40
3.29
1.01
1.54
1.74
1.94
2.21
2.92
1.00
1.40
1.53
1.67
1.84
2.25
120
1.03
1.64
1.90
2.16
2.52
3.54
1.02
1.57
1.79
2.01
2.31
3.14
1.02
1.50
1.68
1.87
2.11
2.76
1.01
1.35
1.47
1.58
1.73
2.08
CO
1.03
1.61
1.84
2.09
2.42
3.38
1.03
1.53
1.73
1.94
2.21
2.97
1.02
1.46
1.62
1.79
2.01
2.59
1.01
1.29
1.39
1.48
1.60
1.89
EPA QA/G-9
QAOO Version
A- 16
Final
July 2000
-------
TABLE A-9: PERCENTILES OF THE F DISTRIBUTION
Degrees
Freedom
for
Denom-
inator
20.90
.95
.975
.99
.999
•*> .90
.95
.975
.99
.999
Degrees of Freedom for Numerator
1
2.75
3.92
5.15
6.85
11.4
2.71
3.84
5.02
6.63
10.8
2
2.35
3.07
3.80
4.79
7.32
2.30
3.00
3.69
4.61
6.91
3
2.13
2.68
3.23
3.95
5.78
2.08
2.60
3.12
3.78
5.42
4
1.99
2.45
2.89
3.48
4.95
1.94
2.37
2.79
3.32
4.62
5
1.90
2.29
2.67
3.17
4.42
1.85
2.21
2.57
3.02
4.10
6
1.82
2.18
2.52
2.96
4.04
1.77
2.10
22.41
2.80
3.74
7
1.77
2.09
2.39
2.79
3.77
1.72
2.01
2.29
2.64
3.47
8
1.72
2.02
2.30
2.66
3.55
1.67
1.94
2.19
2.51
3.27
9
1.68
1.96
2.22
2.56
3.38
1.63
1.88
2.11
2.41
3.10
10
1.65
1.91
2.16
2.47
3.24
1.60
1.83
2.05
2.32
2.96
12
1.60
1.83
2.05
2.34
3.02
1.55
1.75
1.94
2.18
2.74
15
1.55
1.75
1.95
2.19
2.78
1.49
1.67
1.83
2.04
2.51
20
1.48
1.66
1.82
2.03
2.53
1.42
1.57
1.71
1.88
2.27
24
1.45
1.61
1.76
1.95
2.40
1.38
1.52
1.64
1.79
2.13
30
1.41
1.55
1.69
1.86
2.26
1.34
1.46
1.57
1.70
1.99
60
1.32
1.43
1.53
1.66
1.95
1.24
1.32
1.39
1.47
1.66
120
1.26
1.35
1.43
1.53
1.77
1.17
1.22
1.27
1.32
1.45
00
1.19
1.25
1.31
1.38
1.54
1.00
1.00
1.00
1.00
1.00
EPA QA/G-9
QAOO Version
A- 17
Final
July 2000
-------
TABLE A-10: VALUES OF THE PARAMETER A FOR COHEN'S ESTIMATES
ADJUSTING FOR NONDETECTED VALUES
Y
.00
.05
.10
.15
.20
.25
.30
.35
.40
.45
.50
.55
.60
.65
.70
.75
.80
.85
.90
.95
1.00
.01
.010100
.010551
.010950
.011310
.011642
.011952
.012243
.012520
.012784
.013036
.013279
.013513
.013739
.013958
.014171
.014378
.014579
.014773
.014967
.015154
.015338
.02
.020400
.021294
.022082
.022798
.023459
.024076
.024658
.025211
.025738
.026243
.026728
.027196
.027849
.028087
.028513
.029927
.029330
.029723
.030107
.030483
.030850
.03
.030902
.032225
.033398
.034466
.035453
.036377
.037249
.038077
.038866
.039624
.040352
.041054
.041733
.042391
.043030
.043652
.044258
.044848
.045425
.045989
.046540
.04
.041583
.043350
.044902
.046318
.047829
.048858
.050018
.051120
.052173
.053182
.054153
.055089
.055995
.056874
.057726
.058556
.059364
.060153
.060923
.061676
.062413
.05
.052507
.054670
.056596
.058356
.059990
.061522
.062969
.064345
.065660
.066921
.068135
.069306
.070439
.071538
.072505
.073643
.074655
.075642
.075606
.077549
.078471
.06
.063625
.066159
.068483
.070586
.072539
.074372
.076106
.077736
.079332
.080845
.082301
.083708
.085068
.086388
.087670
.088917
.090133
.091319
.092477
.093611
.094720
h
.07
.074953
.077909
.080563
.083009
.085280
.087413
.089433
.091355
.093193
.094958
.096657
.098298
.099887
.10143
.10292
.10438
.10580
.10719
.10854
.10987
.11116
.08
.08649
.08983
.09285
.09563
.09822
.10065
.10295
.10515
.10725
.10926
.11121
.11208
.11490
.11666
.11837
.12004
.12167
.12225
.12480
.12632
.12780
.09
.09824
.10197
.10534
.10845
.11135
.11408
.11667
.11914
.12150
.12377
.12595
.12806
.13011
.13209
.13402
.13590
.13775
.13952
.14126
.14297
.14465
.10
.11020
.11431
.11804
.12148
.12469
.12772
.13059
.13333
.13595
.13847
.14090
.14325
.14552
.14773
.14987
.15196
.15400
.15599
.15793
.15983
.16170
.15
.17342
.17925
.18479
.18985
.19460
.19910
.20338
.20747
.21129
.21517
.21882
.22225
.22578
.22910
.23234
.23550
.23858
.24158
.24452
.24740
.25022
.20
.24268
.25033
.25741
.26405
.27031
.27626
.28193
.28737
.29250
.29765
.30253
.30725
.31184
.31630
.32065
.32489
.32903
.33307
.33703
.34091
.34471
Y
.00
.05
.10
.15
.20
.25
.30
.35
.40
.45
.50
.55
.60
.65
.70
.75
.80
.85
.90
.95
1.00
.25
.31862
.32793
.33662
.34480
.35255
.35993
.36700
.37379
.38033
.38665
.39276
.39679
.40447
.41008
.41555
.42090
.42612
.43122
.43622
.44112
.44592
.30
.4021
.4130
.4233
.4330
.4422
.4510
.4595
.4676
.4735
.4831
.4904
.4976
.5045
.5114
.5180
.5245
.5308
.5370
.5430
.5490
.5548
.35
.4941
.5066
.5184
.5296
.5403
.5506
.5604
.5699
.5791
.5880
.5967
.6061
.6133
.6213
.6291
.6367
.6441
.6515
.6586
.6656
.6724
.40
.5961
.6101
.6234
.6361
.6483
.6600
.6713
.6821
.6927
.7029
.7129
.7225
.7320
.7412
.7502
.7590
.7676
.7781
.7844
.7925
.8005
.45
.7096
.7252
.7400
.7542
.7673
.7810
.7937
.8060
.8179
.8295
.8408
.8517
.8625
.8729
.8832
.8932
.9031
.9127
.9222
.9314
.9406
.50
.8388
.8540
.8703
.8860
.9012
.9158
.9300
.9437
.9570
.9700
.9826
.9950
1.007
1.019
1.030
1.042
1.053
1.064
1.074
1.085
1.095
h
.55
.9808
.9994
1.017
1.035
1.051
1.067
1.083
1.098
1.113
1.127
1.141
1.155
1.169
1.182
1.195
1.207
1.220
1.232
1.244
1.255
1.287
.60
1.145
1.166
1.185
1.204
1.222
1.240
1.257
1.274
1.290
1.306
1.321
1.337
1.351
1.368
1.380
1.394
1.408
1.422
1.435
1.448
1.461
.65
1.336
1.358
1.379
1.400
1.419
1.439
1.457
1.475
1.494
1.511
1.528
1.545
1.561
1.577
1.593
1.608
1.624
1.639
1.653
1.668
1.882
.70
1.561
1.585
1.608
1.630
1.651
1.672
1.693
1.713
1.732
1.751
1.770
1.788
1.806
1.824
1.841
1.851
1.875
1.892
1.908
1.924
1.940
.80
2.176
2.203
2.229
2.255
2.280
2.305
2.329
2.353
2.376
2.399
2.421
2.443
2.465
2.486
2.507
2.528
2.548
2.568
2.588
2.607
2.626
.90
3.283
3.314
3.345
3.376
3.405
3.435
3.464
3.492
3.520
3.547
3.575
3.601
3.628
3.654
3.679
3.705
3.730
3.754
3.779
3.803
3.827
EPA QA/G-9
QAOO Version
A- 18
Final
July 2000
-------
TABLE A-ll: PROBABILITIES FOR THE SMALL-SAMPLE
MANN-KENDALL TEST FOR TREND
s
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
n
458 9
0.625 0.592 0.548 0.540
0.375 0.408 0.452 0.460
0.167 0.242 0.360 0.381
0.042 0.117 0.274 0.306
0.042 0.199 0.238
0.0083 0.138 0.179
0.089 0.130
0.054 0.090
0.031 0.060
0.016 0.038
0.0071 0.022
0.0028 0.012
0.00087 0.0063
0.00019 0.0029
0.000025 0.0012
0.00043
0.00012
0.000025
0.0000028
S
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
n
67 10
0.500 0.500 0.500
0.360 0.386 0.431
0.235 0.281 0.364
0.136 0.191 0.300
0.068 0.119 0.242
0.028 0.068 0.190
0.0083 0.035 0.146
0.0014 0.015 0.108
0.0054 0.078
0.0014 0.054
0.00020 0.036
0.023
0.014
0.0083
0.0046
0.0023
0.0011
0.00047
0.00018
0.000058
0.000015
0.0000028
0.00000028
EPA QA/G-9
QAOO Version
A- 19
Final
July 2000
-------
TABLE A-12. QUANTILES FOR THE WALD-WOLFOWITZ TEST FOR RUNS
n
4
in
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
-
-
-
-
-
-
-
3
3
3
3
3
4
4
4
4
4
w
" 0.05
-
-
3
3
3
3
4
4
4
4
5
5
5
5
5
5
5
w
"0.10
3
3
4
4
4
4
5
5
5
5
6
6
6
6
6
6
6
n
5
in
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
3
3
3
3
3
4
4
4
4
5
5
5
5
5
5
5
w
" 0.05
4
4
4
4
4
4
5
5
5
6
6
6
6
6
6
6
w
"0.10
4
4
5
5
5
5
6
6
6
6
7
7
7
7
7
7
n
6
m
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
3
4
4
4
4
5
5
5
5
5
6
6
6
6
6
w
" 0.05
4
5
5
5
6
6
6
7
7
7
7
7
7
7
7
w
"0.10
5
6
6
6
7
7
7
8
8
8
8
8
8
8
8
n
7
m
8
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
4
4
4
5
5
5
5
6
6
6
7
7
7
w
" 0.05
5
5
6
6
6
7
7
7
7
8
8
8
8
Wo.!
0
6
6
7
7
7
7
8
8
8
8
9
9
9
EPA QA/G-9
QAOO Version
A-20
Final
July 2000
-------
TABLE A-12. QUANTILES FOR THE WALD-WOLFOWITZ TEST FOR RUNS
n
8
in
8
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
5
5
5
6
6
6
6
6
7
7
7
7
7
w
" 0.05
6
6
7
7
7
7
8
8
8
8
9
9
9
w
"0.10
6
7
7
8
8
8
8
9
9
9
10
10
10
n
9
in
9
10
11
12
13
14
15
16
17
18
19
20
w,.01
5
6
6
6
7
7
7
7
8
8
8
8
w
" 0.05
6
7
7
7
8
8
9
9
9
9
10
10
w
"0.10
7
8
8
8
9
9
9
10
10
10
11
11
n
10
m
10
11
12
13
14
15
16
17
18
19
20
w,.01
7
7
7
7
8
8
8
8
9
9
9
w
" 0.05
8
8
8
9
9
9
9
10
10
11
11
w
"0.10
9
9
9
10
10
10
11
11
11
12
12
n
11
m
11
12
13
14
15
16
17
18
19
20
w,.01
7
7
7
8
8
8
9
9
9
9
w
" 0.05
8
8
9
9
9
10
10
10
11
11
Wo.!
0
9
9
10
10
10
11
11
11
12
12
12
12
13
14
15
16
17
7
8
8
8
9
9
9
10
10
10
11
11
10
10
10
11
12
12
13
13
14
15
16
17
18
9
9
10
10
10
10
10
11
11
11
11
11
11
12
12
12
12
13
14
14
15
16
17
18
19
9
9
10
10
10
11
11
11
11
12
12
13
12
12
13
13
13
14
15
15
16
17
18
19
20
8
9
10
10
11
12
10
11
12
12
13
14
11
12
13
14
15
16
EPA QA/G-9
QAOO Version
A-21
Final
July 2000
-------
TABLE A-12. QUANTILES FOR THE WALD-WOLFOWITZ TEST FOR RUNS
n
16
in
18
19
20
16
17
18
19
20
w,.01
9
10
10
10
11
11
12
13
w
" 0.05
11
12
12
12
13
13
14
14
w
"0.10
12
13
13
13
14
14
15
16
n
in
19
20
w,.01
10
11
w
" 0.05
12
12
w
"0.10
13
13
17
17
18
19
20
11
11
12
12
13
13
14
14
14
15
16
16
n
m
20
w,.01
11
w
" 0.05
13
w
"0.10
14
18
18
19
20
13
13
14
14
14
15
16
16
16
n
m
w,.01
w
" 0.05
Wo.!
0
19
19
20
14
14
16
16
17
17
20
20
143
16
17
When n or m is greater than 20 the Wp quantile is given by:
I , 2mn \ 7rl 2mn(2mn -m- n)
= \-\ -- + LP - -^— - -
m + n
where Zp is the appropriate quantile from the standard normal (see last row of Table A-1).
EPA QA/G-9
QAOO Version
A-22
Final
July 2000
-------
TABLE A-13. MODIFIED QUANTILE TEST CRITICAL NUMBERS
LEVEL OF SIGNIFICANCE ( ) APPROXIMATELY 0.10
m = number of measurements population 2
n = number of measurements population 1
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
5
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
6
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
7
4
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
8
4
4
3
3
3
2
2
2
2
2
2
2
2
2
2
2
90
5
4
4
3
3
3
2
2
2
2
2
2
2
2
2
2
10
5
4
4
4
3
3
3
2
2
2
2
2
2
2
2
2
11
5
4
4
4
4
3
3
3
2
2
2
2
2
2
2
2
12
6
5
4
4
4
3
3
3
3
2
2
2
2
2
2
2
13
6
5
5
4
4
4
3
3
3
3
2
2
2
2
2
2
14
7
6
5
5
4
4
4
3
3
3
3
2
2
2
2
2
15
7
6
6
5
5
4
4
4
3
3
3
3
2
2
2
2
16
7
7
6
5
5
4
4
4
4
3
3
3
3
2
2
2
17
8
7
6
5
5
4
4
4
4
4
3
3
3
3
2
2
18
8
8
7
6
5
5
5
4
4
4
4
3
3
3
3
2
19
8
8
7
6
5
5
5
5
4
4
4
4
3
3
3
3
20
8
8
7
6
6
5
5
5
4
4
4
4
3
3
3
3
EPA QA/G-9
QAOO Version
A-23
Final
July 2000
-------
TABLE A-13. MODIFIED QUANTILE TEST CRITICAL NUMBERS (CONTINUED)
LEVEL OF SIGNIFICANCE ( ) APPROXIMATELY 0.10
m = number of measurements population 2
n = number of measurements population 1
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
30
4
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
35
4
4
3
3
3
3
3
3
3
2
2
2
2
2
2
2
40
5
4
4
4
3
3
3
3
3
3
2
2
2
2
2
2
45
5
5
4
4
4
3
3
3
3
3
3
2
2
2
2
2
50
5
5
4
4
4
4
3
3
3
3
3
3
3
3
2
2
55
6
5
5
4
4
4
4
3
3
3
3
3
3
3
3
3
60
6
6
5
5
4
4
4
4
3
3
3
3
3
3
3
3
65
7
6
5
5
4
4
4
4
4
3
3
3
3
3
3
3
70
7
6
6
5
5
4
4
4
4
4
3
3
3
3
3
3
75
8
7
6
5
5
5
4
4
4
4
4
3
3
3
3
3
80
8
7
6
6
5
5
4
4
4
4
4
4
3
3
3
3
85
8
7
6
6
5
5
5
4
4
4
4
4
4
3
3
3
90
9
8
7
6
6
5
5
5
4
4
4
4
4
4
3
3
95
9
8
7
6
6
5
5
5
5
4
4
4
4
4
3
3
100
10
8
7
7
6
6
5
5
5
4
4
4
4
4
4
4
EPA QA/G-9
QAOO Version
A-24
Final
July 2000
-------
TABLE A-13. MODIFIED QUANTILE TEST CRITICAL NUMBERS (CONTINUED)
LEVEL OF SIGNIFICANCE (<*) APPROXIMATELY 0.05
m = number of measurements population 2
n = number of measurements population 1
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
5
4
4
3
3
3
3
3
3
3
2
2
2
2
2
2
2
6
4
4
4
3
3
3
3
3
3
3
2
2
2
2
2
2
7
5
4
4
4
3
3
3
3
3
3
3
2
2
2
2
2
8
5
5
4
4
4
3
3
3
3
3
3
3
2
2
2
2
90
6
5
5
4
4
4
3
3
3
3
3
3
3
2
2
2
10
6
5
5
5
4
4
4
3
3
3
3
3
3
3
2
2
11
6
5
5
5
5
4
4
4
3
3
3
3
3
3
3
2
12
7
6
5
5
5
4
4
4
4
3
3
3
3
3
3
3
13
7
6
6
5
5
5
4
4
4
4
3
3
3
3
3
3
14
8
7
6
6
5
5
5
4
4
4
4
3
3
3
3
3
15
8
7
7
6
6
5
5
5
4
4
4
4
3
3
3
3
16
8
8
7
6
6
5
5
5
5
4
4
4
4
3
3
3
17
9
8
7
6
6
6
5
5
5
5
4
4
4
4
3
3
18
9
9
8
7
6
6
6
5
5
5
5
4
4
4
4
3
19
10
9
8
7
6
6
6
6
5
5
5
5
4
4
4
4
20
10
9
8
7
6
6
6
6
5
5
5
5
4
4
4
4
EPA QA/G-9
QAOO Version
A-25
Final
July 2000
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TABLE A-13. MODIFIED QUANTILE TEST CRITICAL NUMBERS (CONTINUED)
LEVEL OF SIGNIFICANCE (<*) APPROXIMATELY 0.05
m = number of measurements population 2
n = number of measurements population 1
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
4
4
3
3
3
3
3
3
2
2
2
2
2
2
2
2
30
4
4
4
4
4
3
3
3
3
3
3
3
2
2
2
2
35
5
5
4
4
4
4
3
3
3
3
3
3
3
2
2
2
40
6
5
5
4
4
4
4
3
3
3
3
3
3
3
2
2
45
7
6
5
5
4
4
4
4
3
3
3
3
3
3
3
2
50
7
6
6
5
5
4
4
4
4
3
3
3
3
3
3
3
55
8
7
6
5
5
5
4
4
4
4
3
3
3
3
3
3
60
8
7
6
6
5
5
5
4
4
4
4
3
3
3
3
3
65
8
8
7
6
6
5
5
5
4
4
4
4
3
3
3
3
70
9
8
7
7
6
5
5
5
5
4
4
4
4
3
3
3
75
9
9
8
7
6
6
5
5
5
5
5
4
4
4
3
3
80
10
9
8
7
7
6
6
5
5
5
5
5
4
4
4
3
85
10
9
8
8
7
6
6
6
5
5
5
5
5
4
4
4
90
11
10
9
8
7
7
6
6
5
5
5
5
5
5
4
4
95
11
10
9
8
8
7
6
6
6
5
5
5
5
5
5
4
100
12
11
10
9
8
7
7
6
6
6
5
5
5
5
5
5
EPA QA/G-9
QAOO Version
A-26
Final
July 2000
-------
TABLE A-14. DUNNETT'S TEST (ONE TAILED)
TOTAL NUMBER OF INVESTIGATED GROUPS (K - 1)
Degrees of Freedom
2
3
4
5
6
7
8
9
10
12
16
a
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
2
3.80
2.54
2.94
2.13
2.61
1.96
2.44
1.87
2.34
1.82
2.27
1.78
2.22
1.75
2.18
1.73
2.15
1.71
2.11
1.69
2.06
1.66
3
4.34
2.92
3.28
2.41
2.88
2.20
2.68
2.09
2.56
2.02
2.48
1.98
2.42
1.94
2.37
1.92
2.34
1.90
2.29
1.87
2.23
1.83
4
4.71
3.20
3.52
2.61
3.08
2.37
2.85
2.24
2.71
2.17
2.82
2.11
2.55
2.08
2.50
2.05
2.47
2.02
2.41
1.99
2.34
1.95
5
5.08
3.40
3.70
2.76
3.22
2.50
2.98
2.36
2.83
2.27
2.73
2.22
2.66
2.17
2.60
2.14
2.56
2.12
2.50
2.08
2.43
2.04
6
5.24
3.57
3.85
2.87
3.34
2.60
3.08
2.45
2.92
2.36
2.81
2.30
2.74
2.25
2.68
2.22
2.64
2.19
2.58
2.16
2.50
2.11
7
5.43
3.71
3.97
2.97
3.44
2.68
3.16
2.53
3.00
2.43
2.89
2.37
2.81
2.32
2.75
2.28
2.70
2.26
2.64
2.22
2.56
2.17
8
5.60
3.83
4.08
3.06
3.52
2.75
3.24
2.59
3.06
2.49
2.95
2.42
2.87
2.38
2.81
2.34
2.76
2.31
2.69
2.27
2.61
2.22
9
5.75
3.94
4.17
3.13
3.59
2.82
3.30
2.65
3.12
2.54
3.00
2.47
2.92
2.42
2.86
2.39
2.81
2.35
2.74
2.31
2.65
2.26
10
5.88
4.03
4.25
3.20
3.66
2.87
3.36
2.70
3.17
2.59
3.05
2.52
2.96
2.47
2.90
2.43
2.85
2.40
2.78
2.35
2.69
2.30
12
6.11
4.19
4.39
3.31
3.77
2.97
3.45
2.78
3.26
2.67
3.13
2.59
3.04
2.54
2.97
2.50
2.92
2.46
2.84
2.42
2.75
2.36
14
6.29
4.32
4.51
3.41
3.86
3.05
3.53
2.86
3.33
2.74
3.20
2.66
3.11
2.60
3.04
2.56
2.98
2.52
2.90
2.47
2.81
2.41
16
6.45
4.44
4.61
3.49
3.94
3.11
3.60
2.92
3.48
2.79
3.26
2.71
3.16
2.65
3.09
2.61
3.03
2.57
2.95
2.52
2.85
2.46
EPA QA/G-9
QAOO Version
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Final
July 2000
-------
TABLE A-14. DUNNETT'S TEST (ONE TAILED) (CONTINUED)
TOTAL NUMBER OF INVESTIGATED GROUPS (K - 1)
Degrees of Freedom
20
24
30
40
50
60
70
80
90
100
120
00
a
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
.05
.10
2
2.03
1.64
2.01
1.63
1.99
1.62
1.97
1.61
1.96
1.61
1.95
1.60
1.95
1.60
1.94
1.60
1.94
1.60
1.93
1.59
1.93
1.59
1.92
1.58
3
2.19
1.81
2.17
2.80
2.15
1.79
2.13
1.77
2.11
1.77
2.10
1.76
2.10
1.76
2.10
1.76
2.09
1.76
2.08
1.75
2.08
1.75
2.06
1.73
4
2.30
1.93
2.28
1.91
2.25
1.90
2.23
1.88
2.22
1.88
2.21
1.87
2.21
1.87
2.20
1.87
2.20
1.86
2.18
1.85
2.18
1.85
2.16
1.84
5
2.39
2.01
2.36
2.00
2.34
1.98
2.31
1.96
2.29
1.96
2.28
1.95
2.28
1.95
2.28
1.95
2.27
1.94
2.27
1.93
2.26
1.93
2.23
1.92
6
2.46
2.08
2.43
2.06
2.40
2.05
2.37
2.03
2.32
2.02
2.34
2.01
2.34
2.01
2.34
2.01
2.33
2.00
2.33
1.99
2.32
1.99
2.29
1.98
7
2.51
2.14
2.48
2.12
2.45
2.10
2.42
2.08
2.41
2.07
2.40
2.06
2.40
2.06
2.39
2.06
2.39
2.06
2.38
2.05
2.37
2.05
2.34
2.03
8
2.56
2.19
2.53
2.17
2.50
2.15
2.47
2.13
2.45
2.12
2.44
2.11
2.44
2.11
2.43
2.10
2.43
2.10
2.42
2.09
2.41
2.09
2.38
2.07
9
2.60
2.23
2.57
2.21
2.54
2.19
2.51
2.17
2.49
2.16
2.48
2.15
2.48
2.15
2.47
2.15
2.47
2.14
2.46
2.14
2.45
2.13
2.42
2.11
10
2.64
2.26
2.60
2.24
2.57
2.22
2.54
2.20
2.52
2.19
2.51
2.18
2.51
2.18
2.50
2.18
2.50
2.17
2.49
2.17
2.48
2.16
2.45
2.14
12
2.70
2.33
2.66
2.30
2.63
2.28
2.60
2.26
2.58
2.25
2.57
2.24
2.56
2.24
2.55
2.23
2.55
2.23
2.54
2.22
2.53
2.22
2.50
2.20
14
2.75
2.38
2.72
2.35
2.68
2.33
2.65
2.31
2.63
2.30
2.61
2.29
2.61
2.29
2.60
2.28
2.60
2.28
2.59
2.27
2.58
2.27
2.55
2.24
16
2.80
2.42
2.76
2.40
2.72
2.37
2.69
2.35
2.67
2.34
2.65
2.33
2.65
2.33
2.64
2.32
2.63
2.31
2.63
2.31
2.62
2.31
2.58
2.28
EPA QA/G-9
QQAOO Version
A-28
Final
May 2000
-------
TABLE A-15. APPROXIMATE a-LEVEL CRITICAL POINTS FOR
RANK VON NEUMANN RATIO TEST
n/a
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
32
34
36
38
40
42
44
46
48
50
55
60
65
70
75
80
85
90
95
100
.050
0.70
0.80
0.86
0.93
0.98
1.04
1.08
1.11
1.14
1.17
1.19
1.21
1.24
1.26
1.27
1.29
1.31
1.32
1.33
1.35
1.36
1.37
1.38
1.39
1.40
1.41
1.43
1.45
1.46
1.48
1.49
1.50
1.51
1.52
1.53
1.54
1.56
1.58
1.60
1.61
1.62
1.64
1.65
1.66
1.66
1.67
.100
0.60
0.97
1.11
1.14
1.18
1.23
1.26
1.29
1.32
1.34
1.36
1.38
1.40
1.41
1.43
1.44
1.45
1.46
1.48
1.49
1.50
1.51
1.51
1.52
1.53
1.54
1.55
1.57
1.58
1.59
1.60
1.61
1.62
1.63
1.63
1.64
1.66
1.67
1.68
1.70
1.71
1.71
1.72
1.73
1.74
1.74
EPA QA/G-9
QAOO Version
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Final
July 2000
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This page is intentionally blank.
EPA QA/G-9 Final
QAOO Version A - 30 July 2000
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APPENDIX B
REFERENCES
EPA QA/G-9 Final
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APPENDIX B: REFERENCES
This appendix provides references for the topics and procedures described in this
document. The references are broken into three groups: Primary, Basic Statistics Textbooks, and
Secondary. This classification does not refer in any way to the subject matter content but to the
relevance to the intended audience for this document, ease in understanding statistical concepts
and methodologies, and accessability to the non-statistical community. Primary references are
those thought to be of particular benefit as hands-on material, where the degree of sophistication
demanded by the writer seldom requires extensive training in statistics; most of these references
should be on an environmental statistician's bookshelf. Users of this document are encouraged to
send recommendations on additional references to the address listed in the Foreword.
Some sections within the chapters reference materials found in most introductory statistics
books. This document uses Walpole and Myers (1985), Freedman, Pisani, Purves, and Adhakari
(1991), Mendenhall (1987), and Dixon and Massey (1983). Table B-l (at the end of this
appendix) lists specific chapters in these books where topics contained in this guidance may be
found. This list could be extended much further by use of other basic textbooks; this is
acknowledged by the simple statement that further information is available from introductory text
books.
Some important books specific to the analysis of environmental data include: Gilbert
(1987), an excellent all-round handbook having strength in sampling, estimation, and hot-spot
detection; Gibbons (1994), a book specifically concentrating on the application of statistics to
groundwater problems with emphasis on method detection limits, censored data, and the detection
of outliers; and Madansky (1988), a slightly more theoretical volume with important chapters on
the testing for Normality, transformations, and testing for independence. In addition, Ott (1995)
describes modeling, probabilistic processes, and the Lognormal distribution of contaminants, and
Berthouex and Brown (1994) provide an engineering approach to problems including estimation,
experimental design and the fitting of models.
B.I CHAPTER 1
Chapter 1 establishes the framework of qualitative and quantitative criteria against which
the data that has been collected will be assessed. The most important feature of this chapter is the
concept of the test of hypotheses framework which is described in any introductory textbook. A
non-technical exposition of hypothesis testing is also to be found in U.S. EPA (1994a, 1994b)
which provides guidance on planning for environmental data collection. An application of the
DQO Process to geostatistical error management may be found in Myers (1997).
A full discussion of sampling methods with the attendant theory are to be found in Gilbert
(1987) and a shorter discussion may be found in U.S. EPA (1989). Cochran (1966) and Kish
(1965) also provide more advanced theoretical concepts but may require the assistance of a
statistician for full comprehension. More sophisticated sampling designs such as composite
EPA QA/G-9 Final
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sampling, adaptive sampling, and ranked set sampling, will be discussed in future Agency
guidance.
B.2 CHAPTER 2
Standard statistical quantities and graphical representations are discussed in most
introductory statistics books. In addition, Berthouex & Brown (1994) and Madansky (1988) both
contain thorough discussions on the subject. There are also several textbooks devoted exclusively
to graphical representations, including Cleveland (1993), which may contain the most applicable
methods for environmental data, Tufte (1983), and Chambers, Cleveland, Kleiner and Tukey
(1983).
Two EPA sources for temporal data that keep theoretical discussions to a minimum are
U.S. EPA (1992a) and U.S. EPA (1992b). For a more complete discussion on temporal data,
specifically time series analysis, see Box and Jenkins (1970), Wei (1990), or Ostrum (1978).
These more complete references provide both theory and practice; however, the assistance of a
statistician may be needed to adapt the methodologies for immediate use. Theoretical discussions
of spatial data may be found in Journel and Huijbregts (1978), Cressie (1993), and Ripley (1981).
B.3 CHAPTER 3
The hypothesis tests covered in this edition of the guidance are well known and straight-
forward; basic statistics texts cover these subjects. Besides basic statistical text books, Berthouex
& Brown (1994), Hardin and Gilbert (1993), and U.S. EPA (1989, 1994c) may be useful to the
reader. In addition, there are some statistics books devoted specifically to hypothesis testing, for
example, see Lehmann (1991). These books may be too theoretical for most practitioners, and
their application to environmental situations may not be obvious.
The statement in this document that the sign test requires approximately 1.225 times as
many observations as the Wilcoxon rank sum test to achieve a given power at a given significance
level is attributable to Lehmann (1975).
B.4 CHAPTER 4
This chapter is essentially a compendium of statistical tests drawn mostly from the primary
references and basic statistics textbooks. Gilbert (1987) and Madansky (1988) have an excellent
collection of techniques and U.S. EPA (1992a) contains techniques specific to water problems.
For Normality (Section 4.2), Madansky (1988) has an excellent discussion on tests as does
Shapiro (1986). For trend testing (Section 4.3), Gilbert (1987) has an excellent discussion on
statistical tests and U.S. EPA (1992b) provides adjustments for trends and seasonality in the
calculation of descriptive statistics.
EPA QA/G-9 Final
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There are several very good textbooks devoted to the treatment of outliers (Section 4.4).
Two authoritative texts are Barnett and Lewis (1978) and Hawkins (1980). Additional
information is also to be found in Beckman and Cook (1983) and Tietjen and Moore (1972).
Several useful software programs are available on the statistical market including U.S. EPA's
GEO-EASE and Scout, both developed by the Environmental Monitoring Systems Laboratory,
Las Vegas, Nevada and described in U.S. EPA (1991) and U.S. EPA (1993b), respectively.
Tests for dispersion (Section 4.5) are described in the basic textbooks and examples are to
be found in U.S. EPA (1992a). Transformation of data (Section 4.6) is a sensitive topic and
thorough discussions may be found in Gilbert (1987), and Dixon and Massey (1983). Equally
sensitive is the analysis of data where some values are recorded as non-detected (Section 4.7);
Gibbons (1994) and U.S. EPA (1992a) have relevant discussions and examples.
B.5 CHAPTER 5
Chapter 5 discusses some of the philosophical issues related to hypothesis testing which
may help in understanding and communicating the test results. Although there are no specific
references for this chapter, many topics (e.g., the use of p-values) are discussed in introductory
textbooks. Future editions of this guidance will be expanded by incorporating practical
experiences from the environmental community into this chapter.
B.6 LIST OF REFERENCES
B.6.1 Primary References
Berthouex, P.M., and L.C. Brown, 1994. Statistics for Environmental Engineers. Lewis, Boca
Raton, FL.
Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitor ing. John Wiley,
New York, NY.
Gibbons, R. D., 1994. Statistical Methods for Groundwater Monitoring. John Wiley, New
York, NY.
Madansky, A., 1988. Prescriptions for Working Statisticians. Springer-Verlag, New York, NY.
Ott, W.R., 1995. Environmental Statistics and Data Analysis. Lewis, Boca Raton, FL.
U.S. Environmental Protection Agency, 1996. The Data Quality Evaluation Statistical Toolbox
(DataQUEST) Software, EPA QA/G-9D. Office of Research and Development.
U.S. Environmental Protection Agency, 1994a. Guidance for the Data Quality Objectives
Process (EPA QA/G4). EPA/600/R-96/055. Office of Research and Development.
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U.S. Environmental Protection Agency, 1994b. The Data Quality Objectives Decision Error
Feasibility Trials (DEFT) Software (EPA QA/G-4D). EPA/600/R-96/056. Office of
Research and Development.
U.S. Environmental Protection Agency , 1992a. Guidance Document on the Statistical Analysis
of Ground-Water Monitoring Data at RCRA Facilities. EPA/530/R-93/003. Office of
Solid Waste. (NTIS: PB89-151026)
B.6.2 Basic Statistics Textbooks
Dixon, W.J., and FJ. Massey, Jr., 1983. Introduction to Statistical Analysis (Fourth Edition).
McGraw-Hill, New York, NY.
Freedman, D., R. Pisani, R. Purves, and A. Adhikari, 1991. Statistics. W.W. Norton & Co.,
New York, NY.
Mendenhall, W., 1987. Introduction to Probability and Statistics (Seventh Edition). PWS-Kent,
Boston, MA.
Walpole, R., and R. Myers, 1985. Probability and Statistics for Engineers and Scientists (Third
Ed.). MacMillan, New York, NY.
B.6.3 Secondary References
Aitchison, J., 1955. On the distribution of a positive random variable having a discrete probability
mass at the origin. Journal of American Statistical Association 50(272):901-8
Barnett, V., and T. Lewis, 1978. Outliers in Statistical Data. John Wiley, New York, NY.
Beckman, R.J., and R.D. Cook, 1983. Outlier s, Technometrics 25:119-149.
Box[, G.E.P., and G.M. Jenkins, 1970. Time Series Analysis, Forecasting, and Control.
Holden-Day, San Francisco, CA.
Chambers, J.M, W.S. Cleveland, B. Kleiner, and P.A. Tukey, 1983. Graphical Methods for Data
Analysis. Wadsworth & Brooks/Cole Publishing Co., Pacific Grove, CA.
Chen, L., 1995. Testing the mean of skewed distributions, Journal of the American Statistical
Association 90:767-772.
Cleveland, W.S., 1993. Visualizing Data. Hobart Press, Summit, NJ.
Cochran, W. G., 1966. Sampling Techniques (Third Edition). John Wiley, New York, NY.
EPA QA/G-9 Final
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Cohen, A.C., Jr. 1959. Simplified estimators for the normal distribution when samples are singly
censored or truncated, Technometrics 1:217-237.
Conover, W.J., 1980. PracticalNonparametric Statistics (Second Edition). John Wiley, New
York, NY.
Cressie, N., 1993. Statistics for Spatial Data. John Wiley, New York, NY.
D'Agostino, R.B., 1971. An omnibus test of normality for moderate and large size samples,
Biometrika 58:341-348.
David, H.A., H.O. Hartley, and E.S. Pearson, 1954. The distribution of the ratio, in a single
normal sample, of range to standard deviation, Biometrika 48:41-55.
Dixon, W.J., 1953. Processing data for outliers, Biometrika 9:74-79.
Filliben, J.J., 1975. The probability plot correlation coefficient test for normality, Technometrics
17:111-117.
Geary, R.C., 1947. Testing for normality, Biometrika 34:209-242.
Geary, R.C., 1935. The ratio of the mean deviation to the standard deviation as a test of
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Hardin, J.W., and R.O. Gilbert, 1993. Comparing Statistical Tests for Detecting Soil
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8989, UC-630, Pacific Northwest Laboratory, Richland, WA.
Hawkins, D.M., 1980. Identification of Outliers. Chapman and Hall, New York, NY.
Hochberg, Y., and A. Tamhane, 1987. Multiple Comparison Procedures. John Wiley, New
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Journel, A.G., and C.J. Huijbregts, 1978. Mining Geostatistics. Academic Press, London.
Kish, L., 1965. Survey Sampling. John Wiley, New York, NY.
Kleiner, B., and J.A. Hartigan, 1981. Representing points in many dimensions by trees and
castles, Journal of the American Statistical Association 76:260.
EPA QA/G-9 Final
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Lehmann, E.L., 1991. Testing Statistical Hypotheses. Wadsworth & Brooks/Cole Publishing
Co., Pacific Grove, CA.
Lehmann, E.L., 1975. Nonparametrics: Statistical Methods Based on Ranks. Holden-Day, Inc.,
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United States Office of Research and EPA/600/R-98/018
Environmental Protection Development February 1998
Agency Washington, B.C. 20460
EPA GUIDANCE FOR
QUALITY ASSURANCE
PROJECT PLANS
EPA QA/G-5
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FOREWORD
The U.S. Environmental Protection Agency (EPA) has developed the Quality Assurance Project
Plan (QAPP) as an important tool for project managers and planners to document the type and quality of
data needed for environmental decisions and to use as the blueprint for collecting and assessing those
data from environmental programs. The development, review, approval, and implementation of the
QAPP is part of the mandatory Agency-wide Quality System that requires all organizations performing
work for EPA to develop and operate management processes and structures for ensuring that data or
information collected are of the needed and expected quality for their desired use. The QAPP is an
integral part of the fundamental principles of quality management that form the foundation of the
Agency's Quality System and the requirements for a QAPP are contained in EPA QA/R-5, EPA
Requirements for Quality Assurance Project Plans for Environmental Data Operations.
This document is one of the U.S. Environmental Protection Agency Quality System Series
requirements and guidance documents. These documents describe the EPA policies and procedures for
planning, implementing, and assessing the effectiveness of the Quality System. Requirements
documents (identified as EPA/R-x) establish criteria and mandatory specifications for quality assurance
(QA) and quality control (QC) activities. Guidance documents (identified as EPA QA/G-x) provide
suggestions and recommendations of a nonmandatory nature for using the various components of the
Quality System. This guidance document contains advice and recommendations on how to meet the
requirements of EPA QA/R-5. In addition to this guidance document on writing a QAPP, other EPA
documents are available to assist the QAPP writer; these are discussed in Appendix A. Effective use of
this document assumes that appropriate management systems for QA and QC have been established by
the implementing organization and are operational. For requirements and guidance on the structure of
this management system, refer to Appendix A.
Questions regarding this document or other documents from the Quality System Series may be
directed to:
U.S. EPA
Quality Staff (2811R)
Office of Environmental Information
401 M Street, SW
Washington, DC 20460
Phone: (202)564-6830
Fax: (202) 565-2441
All requirements and guidance documents are available on the EPA's Quality Staff website:
http://www.epa.gov/quality
EPA QA/G-5 1 QA98 (1/00)
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TABLE OF CONTENTS
CHAPTER I. INTRODUCTION 1
OVERVIEW 1
PURPOSE OF QA PLANNING 2
CHAPTER II. QAPP REQUIREMENTS 3
EPA POLICY ON QAPPS 3
QAPP GROUPS AND ELEMENTS 3
QAPP RESPONSIBILITIES 5
CHAPTER III. QAPP ELEMENTS 7
A. PROJECT MANAGEMENT 7
Al Title and Approval Sheet 7
A2 Table of Contents and Document Control Format 7
A3 Distribution List 8
A4 Project/Task Organization 8
A5 Problem Definition/Background 10
A6 Project/Task Description and Schedule 11
A7 Quality Objectives and Criteria for Measurement Data 12
A8 Special Training Requirements/Certification 13
A9 Documentation and Records 14
B. MEASUREMENT/DATA ACQUISITION 17
Bl Sampling Process Design (Experimental Design) 17
B2 Sampling Methods Requirements 19
B3 Sample Handling and Custody Requirements 23
B4 Analytical Methods Requirements 28
B5 Quality Control Requirements 30
B6 Instrument/Equipment Testing, Inspection, and Maintenance Requirements . . 32
B7 Instrument Calibration and Frequency 33
B8 Inspection/Acceptance Requirements for Supplies and Consumables 35
B9 Data Acquisition Requirements (Non-Direct Measurements) 37
BIO Data Management 38
C. ASSESSMENT/OVERSIGHT 41
Cl Assessments and Response Actions 41
C2 Reports to Management 44
D. DATA VALIDATION AND USABILITY 45
Dl Data Review, Validation, and Verification Requirements 45
D2 Validation and Verification Methods 47
D3 Reconciliation with Data Quality Objectives 48
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CHAPTER IV. QAPP REVISIONS AND RELATED GUIDANCE 49
QAPP REVISIONS 49
COMPARISON WITH PREVIOUS GUIDANCE (QAMS 005/80) 49
APPENDIX A. CROSSWALKS BETWEEN QUALITY ASSURANCE DOCUMENTS A-l
AA1. Relationship Between E4 and EPA Quality System A-l
AA2. Crosswalk Between QA/R-5 and QAMS-005/80 A-3
AA3. Crosswalk Between EPA QA/R-5 and ISO 9000 A-4
AA4. Crosswalk Between the DQO Process and the QAPP A-5
AA5. EPA Quality Assurance Documents A-7
APPENDIX B. GLOSSARY OF QUALITY ASSURANCE AND RELATED TERMS B-l
APPENDIX C. CHECKLISTS USEFUL IN QUALITY ASSURANCE REVIEW C-l
AC1. Sample Handling, Preparation, and Analysis Checklist C-l
AC2. QAPP Review Checklist C-5
ACS. Chain-of-Custody Checklist C-8
APPENDIX D. DATA QUALITY INDICATORS D-l
ADI. Principal DQIs: PARCC D-l
AD2. Other Data Quality Indicators D-5
APPENDIX E. QUALITY CONTROL TERMS E-l
AE1. Quality Control Operations E-l
AE2. Quality Control Requirements in Existing Programs E-3
APPENDIX F. SOFTWARE FOR THE DEVELOPMENT AND PREPARATION
OF A QUALITY ASSURANCE PROJECT PLAN F-l
AF1. Overview of Potential Need for Software in QAPP Preparation F-l
AF2. Existing Software F-3
AF3. Software Availability and Sources F-5
APPENDIX G. ISSUES IN DATA MANAGEMENT G-l
AG1. Introduction G-l
AG2. Regulatory and Policy Framework G-l
AG3. QA Planning for Information Systems G-3
AG4. References G-14
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LIST OF FIGURES
Figure 1. QA Planning and the Data Life Cycle 2
Figure 2. An Example of a Table of Contents and a Distribution List 9
Figure 3. An Example of a Project Organization Chart 10
Figure 4. The DQO Process 12
Figure 5. An Example of a Sample Log Sheet 25
Figure 6. An Example of a Sample Label 26
Figure 7. An Example of a Custody Seal 26
Figure 8. An Example of a Chain-Of-Custody Record 27
Figure 9. Example of a Record for Consumables 36
Figure 10. Example of Inspection/Acceptance Testing Requirements 36
Figure 11. Example of a Log for Tracking Supplies and Consumables 36
Figure AA1. Relationships Among EPA Quality System Documents at the Program Level A-9
Figure AA2. Relationships Among EPA Quality System Documents at the Project Level A-10
Figure ADI. Measurement Bias and Random Measurement Uncertainties. Shots at a Target D-3
LIST OF TABLES
Table 1. Project Quality Control Checks 31
Table AA1. Numbering System for EPA's Quality System Documents A-7
Table AA2. Quality System Documents A-8
Table AD 1. Principal Types of Error D-6
Table AE1. Comparison of QC Terms E-6
Table AE2. QC Requirements for Programs E-13
Table AE3. QC Requirements for Methods E-16
Table API. Software Available to Meet QAPP Development Needs F-4
Table AG1. Project Scope and Risks G-5
Table AG2. Software Development Life Cycle G-7
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LIST OF ACRONYMS
ACS American Chemical Society
ADQ Audit of Data Quality
CFR Code of Federal Regulations
DQA Data Quality Assessment
DQI Data Quality Indicator
DQO Data Quality Objective
EPA Environmental Protection Agency
ISO International Organization for Standardization
MSR Management Systems Review
NIST National Institute of Standards and Technology
OSHA Occupational Safety and Health Administration
PARCC Precision, Accuracy, Representativeness, Comparability, and Completeness
PE Performance Evaluation
QA Quality Assurance
QAD Quality Assurance Division
QAMS Quality Assurance Management Staff (now QAD)
QAPP Quality Assurance Project Plan
QC Quality Control
RCRA Resource Conservation and Recovery Act
SOP Standard Operating Procedure
SRM Standard Reference Material
TSA Technical Systems Audit
EPA QA/G-5
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CHAPTER I
INTRODUCTION
OVERVIEW
This document presents detailed guidance on how to develop a Quality Assurance Project Plan
(QAPP) for environmental data operations performed by or for the U.S. Environmental Protection
Agency (EPA). This guidance discusses how to address and implement the specifications in
Requirements for QA Project Plans for Environmental Data Operations (EPA QA/R-5).
The QAPP is the critical planning document for any environmental data collection operation
because it documents how quality assurance (QA) and quality control (QC) activities will be
implemented during the life cycle of a program, project, or task. The QAPP is the blueprint for
identifying how the quality system of the organization performing the work is reflected in a particular
project and in associated technical goals. QA is a system of management activities designed to ensure
that the data produced by the operation will be of the type and quality needed and expected by the data
user. QA is acknowledged to be a management function emphasizing systems and policies, and it aids
the collection of data of needed and expected quality appropriate to support management decisions in a
resource-efficient manner.
In order to obtain environmental data for decision making, a project should be conducted in three
phases: planning, implementation, and assessment. The first phase involves the development of Data
Quality Objectives (DQOs) using the DQO Process or a similar structural systematic planning process.
The DQOs provide statements about the expectations and requirements of the data user (such as the
decision maker). In the second phase, the QAPP translates these requirements into measurement
performance specifications and QA/QC procedures for the data suppliers to provide the information
needed to satisfy the data user's needs. This guidance links the results of the DQO Process with the
QAPP to complete documentation of the planning process. Once the data have been collected and
validated in accordance with the elements of the QAPP, the data should be evaluated to determine
whether the DQOs have been satisfied. In the assessment phase, the Data Quality Assessment (DQA)
Process applies statistical tools to determine whether the data meet the assumptions made during
planning and whether the total error in the data is small enough to support a decision within tolerable
decision error rates expressed by the decision maker. Plans for data validation and DQA are discussed in
the final sections of the QAPP. Thus, the activities addressed and documented in the QAPP cover the
entire project life cycle, integrating elements of the planning, implementation, and assessment phases.
A QAPP is composed of four sections of project-related information called "groups," which are
subdivided into specific detailed "elements." The degree to which each QAPP element should be
addressed will be dependent on the specific project and can range from "not applicable" to extensive
documentation. This document provides a discussion and background of the elements of a QAPP that
will typically be necessary. There is no Agency-wide template for QAPP format; however, QAD
encourages organizational consistency in the presentation and content of the elements contained within
the QAPP. The final decision on the specific need for these elements for project-specific QAPPs will be
made by the overseeing or sponsoring EPA organization(s). The Agency encourages the specific
tailoring of implementation documents within the EPA's general QA framework on a project-specific
basis.
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PLANNING
Data Quality Objectives Process
Quality Assurance Project Plan Development
IMPLEMENTATION
Field Data Collection and Associated
Quality Assurance/Quality Control Activities
ASSESSMENT
Data Validation
Data Quality Assessment
QA PLANNING FOR
DATA COLLECTION
Data Quality Objectives Process
i
r OUTPUTS ^
/Data ,
Quality /
Objectives //
i
r
// Data /
/ Collection /
Design /
, INPUTS 1
r
Quality Assurance Project Plan
Development
^
r
Quality Assurance
Project Plan
^
Figure 1. QA Planning and the Data Life Cycle.
PURPOSE OF QA PLANNING
The EPA Quality System is a structured and documented management system describing the
policies, objectives, principles, organization, responsibilities, accountability, and implementation plan of
an organization for ensuring quality in its work processes, products, and services. The Agency's Quality
System is described in EPA QA/G-0, The EPA Quality System.
EPA policy requires that all projects involving the generation, acquisition, and use of
environmental data be planned and documented and have an Agency-approved QAPP prior to the start of
data collection. The primary purpose of the QAPP is to provide an overview of the project, describe the
need for the measurements, and define QA/QC activities to be applied to the project, all within a single
document. The QAPP should be detailed enough to provide a clear description of every aspect of the
project and include information for every member of the project staff, including samplers, lab staff, and
data reviewers. The QAPP facilitates communication among clients, data users, project staff,
management, and external reviewers. Effective implementation of the QAPP assists project managers in
keeping projects on schedule and within the resource budget. Agency QA policy is described in the
Quality Manual and EPA QA/R-1, EPA Quality System Requirements for Environmental Programs.
EPA QA/G-5
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CHAPTER II
QAPP REQUIREMENTS
EPA POLICY ON QAPPS
It is EPA's internal policy requirement1 that the collection of environmental data by or for the
Agency be supported by a QA program, or quality system. The authority for this requirement for work
done for EPA through extramural agreements may be found in 48 CFR, Chapter 15, Part 1546 for
contractors, and 40 CFR, Parts 30, 31, and 35 for financial assistance recipients, and may be included in
negotiated interagency agreements and consent agreements in enforcement actions.
A key component of this mandatory quality system is the development, review, approval, and
implementation of the QAPP. A QAPP must address all of the elements contained in QA/R-5 unless
otherwise specified by the EPA QA Manager responsible for the data collection. The format of the
QAPP is decided by the QA approving authority prior to preparation of the QAPP.
The QAPP is the logical product of the planning process for any data collection, as it documents
how QA and QC activities will be planned and implemented. To be complete, the QAPP must meet
certain specifications for detail and coverage, but the extent of detail is dependent on the type of project,
the data to be collected, and the decisions to be made. Overall, the QAPP must provide sufficient detail
to demonstrate that:
the project's technical and quality objectives are identified and agreed upon,
• the intended measurements or data acquisition methods are consistent with project
objectives,
• the assessment procedures are sufficient for determining if data of the type and quality
needed and expected are obtained, and
• any potential limitations on the use of the data can be identified and documented.
Documents prepared prior to the QAPP (e.g., standard operating procedures [SOPs], test plans, and
sampling plans) can be appended or, in some cases, incorporated by reference.
QAPP GROUPS AND ELEMENTS
The elements of a QAPP are categorized into "groups" according to their function.
Specifications for each element are found in EPA Requirements for Quality Assurance Project Plans
(EPA QA/R-5). Summaries of each requirement of the elements from that document are contained in a
box at the beginning of each specific element description. The elements of a QAPP are:
Group A: Project Management
This group of QAPP elements covers the general areas of project management, project history
and objectives, and roles and responsibilities of the participants. The following 9 elements ensure that
'EPA Order 5360.1, Policy and Program Requirements to Implement the Mandatory Quality Assurance
Program, was issued originally in April 1984 and will be revised in 1998.
EPA QA/G-5 3 QA98
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the project's goals are clearly stated, that all participants understand the goals and the approach to be
used, and that project planning is documented:
Al Title and Approval Sheet
A2 Table of Contents and Document Control Format
A3 Distribution List
A4 Project/Task Organization and Schedule
A5 Problem Definition/Background
A6 Project/Task Description
A7 Quality Objectives and Criteria for Measurement Data
A8 Special Training Requirements/Certification
A9 Documentation and Records
Group B: Measurement/Data Acquisition
This group of QAPP elements covers all of the aspects of measurement system design and
implementation, ensuring that appropriate methods for sampling, analysis, data handling, and QC are
employed and will be thoroughly documented:
Bl Sampling Process Design (Experimental Design)
B2 Sampling Methods Requirements
B3 Sample Handling and Custody Requirements
B4 Analytical Methods Requirements
B5 Quality Control Requirements
B6 Instrument/Equipment Testing, Inspection, and Maintenance Requirements
B7 Instrument Calibration and Frequency
B8 Inspection/Acceptance Requirements for Supplies and Consumables
B9 Data Acquisition Requirements (Non-Direct Measurements)
BIO Data Management
Group C: Assessment/Oversight
The purpose of assessment is to ensure that the QAPP is implemented as prescribed. This group
of QAPP elements addresses the activities for assessing the effectiveness of the implementation of the
project and the associated QA/QC activities:
C1 Assessments and Response Actions
C2 Reports to Management
Group D: Data Validation and Usability
Implementation of Group D elements ensures that the individual data elements conform to the
specified criteria, thus enabling reconciliation with the project's objectives. This group of elements
covers the QA activities that occur after the data collection phase of the project has been completed:
Dl Data Review, Validation, and Verification Requirements
D2 Validation and Verification Methods
D3 Reconciliation with Data Quality Objectives
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QAPP RESPONSIBILITIES
QAPPs may be prepared by EPA organizations and by groups outside EPA including contractors,
assistance agreement holders, or other Federal agencies under interagency agreements. Generally, all
QAPPs prepared by non-EPA organizations must be approved by EPA for implementation. Writing a
QAPP is often a collaborative effort within an organization, or among organizations, and depends on the
technical expertise, writing skills, knowledge of the project, and availability of the staff. Organizations
are encouraged to involve technical project staff and the QA Manager or the QA Officer in this effort to
ensure that the QAPP has adequate detail and coverage.
None of the environmental data collection work addressed by the QAPP may be started until the
initial QAPP has been approved by the EPA Project Officer and the EPA QA Manager and then
distributed to project personnel except under circumstances requiring immediate action to protect human
health and the environment or to operations conducted under police power. In some cases, EPA may
grant conditional or partial approval to a QAPP to permit some work to begin while noncritical
deficiencies in it are being resolved. However, the QA Manager should be consulted to determine the
length of time and nature of the work that may continue and the type of work that may be performed
under a conditionally approved QAPP. Some organizations have defined and outlined these terms as:
• Approval: No remaining identified deficiencies exist in the QAPP and the project may
commence.
• Partial Approval: Some activities identified in the QAPP still contain critical
deficiencies while other activities are acceptable. If the acceptable activities are not
contingent upon the completion of the activities with the deficiencies, a partial approval
may be granted to allow those activities to proceed. Work will continue to resolve the
portions of the QAPP that contain deficiencies.
• Conditional Approval: Approval of the QAPP or portions thereof will be granted upon
agreement to implement specific conditions, specific language, etc. by entities required
to approve the QAPP in order to expedite the initiation of field work. In most situations,
the conditional approval is upgraded to final approval upon receipt, review, and sign off
by all entities of the revised/additional QAPP pages.
The organizational group performing the work is responsible for implementing the approved
QAPP. This responsibility includes ensuring that all personnel involved in the work have copies of or
access to the approved QAPP along with all other necessary planning documents. In addition, the group
must ensure that these personnel understand their requirements prior to the start of data generation
activities.
Moreover, organizations are responsible for keeping the QAPP current when changes to
technical aspects of the project change. QAPPs must be revised to incorporate such changes and the
QAPP must be re-examined to determine the impact of the changes. Any revisions to the QAPP must be
re-approved and distributed to all participants in the project.
EPA QA/G-5 5 QA98
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EPA QA/G-5
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CHAPTER III
QAPP ELEMENTS
A PROJECT MANAGEMENT
The following project management elements address the procedural aspects of project
development and what to include in the QAPP project background, task description, and quality
objectives elements. Summaries from R-5 are contained in the text box following the title of each
element.
Al TITLE AND APPROVAL SHEET
Include title of plan; name of the organization(s); and names, titles, signatures of appropriate
approving officials, and their approval dates.
The title and approval sheet includes the title of the QAPP; the name(s) of the organization(s)
implementing the project; and the names, titles, and signatures, and the signature dates of the appropriate
approving officials. The approving officials typically include: the organization's Technical Project
Manager, the organization's Quality Assurance Officer or Manager, the EPA (or other funding agency)
Technical Project Manager/Project Officer, Laboratory Directors, Laboratory QA Officers, the EPA (or
other funding agency) Quality Assurance Officer or Manager, and other key staff, such as the QA Officer
of the prime contractor when a QAPP is prepared by a subcontractor organization.
The purpose of the approval sheet is to enable officials to document their approval of the QAPP.
The title page (along with the organization chart) also identifies the key project officials for the work.
The title and approval sheet should also indicate the date of the revision and a document number, if
appropriate.
A2 TABLE OF CONTENTS AND DOCUMENT CONTROL FORMAT
List sections, figures, tables, references, and appendices.
The table of contents lists all the elements, references, and appendices contained in a QAPP,
including a list of tables and a list of figures that are used in the text. The major headings for most
QAPPs should closely follow the list of required elements; an example is shown in Figure 2. While the
exact format of the QAPP does not have to follow the sequence given here, it is generally more
convenient to do so, and it provides a standard format to the QAPP reviewer. Moreover, consistency in
the format makes the document more familiar to users, who can expect to find a specific item in the same
place in every QAPP.
The table of contents of the QAPP may include a document control component. This information
should appear in the upper right-hand corner of each page of the QAPP when document control format is
desired. For example:
EPA QA/G-5 7 QA98
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Project No. or Name
Element or Section No.
Revision No.
Revision Date
Section/Element Page of.
This component, together with the distribution list (see element A3), facilitates control of the
document to help ensure that the most current QAPP is in use by all project participants. Each revision
of the QAPP should have a different revision number and date.
A3 DISTRIBUTION LIST
List all the individuals and their organizations who will receive copies of the approved QAPP
and any subsequent revisions. Include all persons who are responsible for implementation
(including managers), the QA managers, and representatives of all groups involved.
All the persons and document files designated to receive copies of the QAPP, and any planned
future revisions, need to be listed in the QAPP. This list, together with the document control
information, will help the project manager ensure that all key personnel in the implementation of the
QAPP have up-to-date copies of the plan. A typical distribution list appears in Figure 2.
A4 PROJECT/TASK ORGANIZATION
Identify the individuals or organizations participating in the project and discuss their specific
roles and responsibilities. Include principal data users, the decision makers, the project QA
manager, and all persons responsible for implementation.
Ensure that the project QA manager is independent of the unit generating the data.
Provide a concise organization chart showing the relationships and the lines of communication
among all project participants; other data users who are outside of the organization generating
the data; and any subcontractor relationships relevant to environmental data operations.
A4.1 Purpose/Background
The purpose of the project organization is to provide EPA and other involved parties with a clear
understanding of the role that each party plays in the investigation or study and to provide the lines of
authority and reporting for the project.
A4.2 Roles and Responsibilities
The specific roles, activities, and responsibilities of participants, as well as the internal lines of
authority and communication within and between organizations, should be detailed. The position of the
QA Manager or QA Officer should be described. Include the principal data users, the decision maker,
project manager, QA manager, and all persons responsible for implementation of the QAPP. Also
included should be the person responsible for maintaining the QAPP and any individual approving
EPA QA/G-5 8 QA98
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CONTENTS
Section
List of Tables iv
List of Figures v
A Project Management 1
1 Project/Task Organization 1
2 Problem Definition/Background 3
3 Project/Task Description 4
4 Data Quality Objectives 7
4.1 Project Quality Objectives 7
4.2 Measurement Performance Criteria 8
5 Documentation and Records 10
B Measurement Data Acquisition 11
6 Sampling Process Design 11
7 Analytical Methods Requirements 13
7.1 Organics 13
7.2 Inorganics 14
7.3 Process Control Monitoring 15
8 Quality Control Requirements 16
8.1 Field QC Requirements 16
8.2 Laboratory QC Requirements 17
9 Instrument Calibration and Frequency 19
10 Data Acquisition Requirements 20
11 Data Management 22
C Assessment/Oversight 23
12 Assessment and Response Actions 23
12.1 Technical Systems Audits 23
12.2 Performance Evaluation Audits 23
13 Reports to Management 24
D Data Validation and Usability 24
14 Data Review, Validation, and Verification Requirements 24
15 Reconciliation with Data Quality Objectives 26
15.1 Assessment of Measurement Performance 26
15.2 Data Quality Assessment 27
Distribution List
N. Wentworth, EPA/ORD (Work Assignment Manager)*
B. Waldron, EPA/ORD (QA Manager)
J. Warren, State University (Principal Investigator)
T. Dixon, State University (QA Officer)
G. Johnson, State University (Field Activities)
F. Haeberer, State University (Laboratory Activities)
B. Odom, State University (Data Management)
E. Renard, ABC Laboratories (Subcontractor Laboratory)
P. Lafornara, ABC Laboratories (QA Manager Subcontractor Laboratory)
indicates approving authority
Figure 2. An Example of a Table of Contents and a Distribution List
EPA QA/G-5 9 QA98
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deliverables other than the project manager. A concise chart showing the project organization, the lines
of responsibility, and the lines of communication should be presented; an example is given in Figure 3.
For complex projects, it may be useful to include more than one chart—one for the overall project (with
at least the primary contact) and others for each organization. Where direct contact between project
managers and data users does not occur, such as between a project consultant for a potentially
responsible party and the EPA risk assessment staff, the organization chart should show the route by
which information is exchanged.
EPA Work Assic
*N. We
2O2-56
Office of Researc
nment Manager
ntwo rth
n & Development
Principal Investigator
J. Warren
202-564-6876
State University
Engineering Department
EPA QA Manager
B. Waldron
2O2-2564-683O
ce of Research & Development
communication only
Project QA Officer
T. Dixon, post doctoral fellow
State University
Chemistry Department
Fie,d Activities Laboratory Activities ^^OdorT^'
G. Johnson, graduate student h" Haebe_rel". graduate assistant professor
91 9-541 -761 2 2O2 564 6872 202-564-6881
State University State Unlvefs^ M ^^ Un^ersi^
Engineering Department Chemistry Department Mathemat.cs Departmen
*approving authority
Subcontractor
ABC Laboratories
(GC/MS Analyses Only)
Laboratory Manager
E. Renard
908-321-4355
t QA Manager
P. Lafornara
9O8-9O6-6988
Figure 3. An Example of a Project Organization Chart
AS PROBLEM DEFINITION/BACKGROUND
State the specific problem to be solved or decision to be made and include sufficient
background information to provide a historical and scientific perspective for this particular
project.
A5.1 Purpose/Background
The background information provided in this element will place the problem in historical
perspective, giving readers and users of the QAPP a sense of the project's purpose and position relative
to other project and program phases and initiatives.
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A5.2 Problem Statement and Background
This discussion must include enough information about the problem, the past history, any
previous work or data, and any other regulatory or legal context to allow a technically trained reader to
make sense of the project objectives and activities. This discussion should include:
a description of the problem as currently understood, indicating its importance and
programmatic, regulatory, or research context;
• a summary of existing information on the problem, including any conflicts or
uncertainties that are to be resolved by the project;
a discussion of initial ideas or approaches for resolving the problem there were
considered before selecting the approach described in element A6, "Project/Task
Description"; and
• the identification of the principal data user or decision maker (if know).
Note that the problem statement is the first step of the DQO Process and the decision specification is the
second step of the DQO Process.
A6 PROJECT/TASK DESCRIPTION AND SCHEDULE
Provide a description of the work to be performed and the schedule for implementation.
Include measurements that will be made during the course of the project; applicable technical,
regulatory, or program-specific quality standards, criteria, or objectives; any special personnel
and equipment requirements; assessment tools needed; a schedule for work to be performed;
and project and quality records required, including types of reports needed.
A6.1 Purpose/Background
The purpose of the project/task description element is to provide the participants with a
background understanding of the project and the types of activities to be conducted, including the
measurements that will be taken and the associated QA/QC goals, procedures, and timetables for
collecting the measurements.
A6.2 Description of the Work to be Performed
(1) Measurements that are expected during the course of the project. Describe the
characteristic or property to be studied and the measurement processes and techniques
that will be used to collect data.
(2) Applicable technical quality standards or criteria. Cite any relevant regulatory
standards or criteria pertinent to the project. For example, if environmental data are
collected to test for compliance with a permit limit standard, the standard should be cited
and the numerical limits should be given in the QAPP. The DQO Process refers to these
limits as "action levels," because the type of action taken by the decision maker will
depend on whether the measured levels exceed the limit (Step 5 of the DQO Process).
(3) Any special personnel and equipment requirements that may indicate the
complexity of the project. Describe any special personnel or equipment required for
the specific type of work being planned or measurements being taken.
EPA QA/G-5 11 QA98
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(4) The assessment techniques needed for the project. The degree of quality assessment
activity for a project will depend on the project's complexity, duration, and objectives. A
discussion of the timing of each planned assessment and a brief outline of the roles of the
different parties to be involved should be included.
(5) A schedule for the work performed. The anticipated start and completion dates for the
project should be given. In addition, this discussion should include an approximate
schedule of important project milestones, such as the start of environmental
measurement activities.
(6) Project and quality records required, including the types of reports needed. An
indication of the most important records should be given.
A7 QUALITY OBJECTIVES AND CRITERIA FOR MEASUREMENT DATA
Describe the project quality objectives and measurement performance criteria.
A7.1 Purpose/Background
The purpose of this element is to document the DQOs of the project and to establish performance
criteria for the mandatory systematic planning process and measurement system that will be employed in
generating the data.
A7.2 Specifying Quality Objectives
This element of the QAPP should discuss the
desired quality of the final results of the study to ensure that
the data user's needs are met. The Agency strongly
recommends using the DQO Process (see Figure 4), a
systematic procedure for planning data collection activities,
to ensure that the right type, quality, and quantity of data are
collected to satisfy the data user's needs. DQOs are
qualitative and quantitative statements that:
clarify the intended use of the data,
• define the type of data needed to support the
decision,
• identify the conditions under which the data
should be collected, and
• specify tolerable limits on the probability of
making a decision error due to uncertainty
in the data.
Data Quality Indicators (DQIs) can be evolved from DQOs
for a sampling activity through the use of the DQO Process _,. A _,, _.„„ _
/A A• T\\ c A u +u + f+u nr\r\ Figure 4. The DQO Process
(Appendix D). figure 4 shows the seven steps or the DQO
Process, which is explained in detail in EPA QA/G-4, Guidance for the Data Quality Objectives Process.
1. State the Problem
J,
2. Identify the Decision
^,
3. Identify Inputs to the Decision
J,
4. Define the Study Boundaries
^,
5. Develop a Decision Rule
J,
6. Specify Limits on Decision Errors
1 ¥
7. Optimize the Design for Obtaining Data
EPA QA/G-5
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Appendix A.4 provides a crosswalk between the requirements of the QAPP and the DQO outputs. The
QAPP should include a reference for a full discussion of the proposed DQOs.
For exploratory research, sometimes the goal is to develop questions that may be answered by
subsequent work. Therefore, researchers may modify activities advocated in QA/G-4 to define decision
errors (see EPA QA/G-4R, Data Quality Objectives for Researchers).
A7.3 Specifying Measurement Performance Criteria
While the quality objectives state what the data user's needs are, they do not provide sufficient
information about how these needs can be satisfied. The specialists who will participate in generating
the data need to know the measurement performance criteria that must be satisfied to achieve the overall
quality objectives. One of the most important features of the QAPP is that it links the data user's quality
objectives to verifiable measurement performance criteria. Although the level of rigor with which this is
done and documented will vary widely, this linkage represents an important advancement in the
implementation of QA. Once the measurement performance criteria have been established, sampling and
analytical methods criteria can be specified under the elements contained in Group B.
A8 SPECIAL TRAINING REQUIREMENTS/CERTIFICATION
Identify and describe any specialized training or certification requirements and discuss how
such training will be provided and how the necessary skills will be assured and documented.
A8.1 Purpose/Background
The purpose of this element is to ensure that any specialized training requirements necessary to
complete the projects are known and furnished and the procedures are described in sufficient detail to
ensure that specific training skills can be verified, documented, and updated as necessary.
A8.2 Training
Requirements for specialized training for nonroutine field sampling techniques, field analyses,
laboratory analyses, or data validation should be specified. Depending on the nature of the
environmental data operation, the QAPP may need to address compliance with specifically mandated
training requirements. For example, contractors or employees working at a Superfund site need
specialized training as mandated by the Occupational Safety and Health (OSF£A) regulations. If
hazardous materials are moved offsite, compliance with the training requirements for shipping hazardous
materials as mandated by the Department of Transportation (DOT) in association with the International
Air Transportation Association may be necessary. This element of the QAPP should show that the
management and project teams are aware of specific health and safety needs as well as any other
organizational safety plans.
A8.3 Certification
Usually, the organizations participating in the project that are responsible for conducting training
and health and safety programs are also responsible for ensuring certification. Training and certification
should be planned well in advance for necessary personnel prior to the implementation of the project.
EPAQA/G-5 13 QA98
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All certificates or documentation representing completion of specialized training should be maintained in
personnel files.
A9 DOCUMENTATION AND RECORDS
Itemize the information and records that must be included in the data report package and
specify the desired reporting format for hard copy and electronic forms, when used.
Identify any other records and documents applicable to the project, such as audit reports,
interim progress reports, and final reports, that will be produced.
Specify or reference all applicable requirements for the final disposition of records and
documents, including location and length of retention period.
A9.1 Purpose/Background
This element defines which records are critical to the project and what information needs to be
included in reports, as well as the data reporting format and the document control procedures to be used.
Specification of the proper reporting format, compatible with data validation, will facilitate clear, direct
communication of the investigation.
A9.2 Information Included in the Reporting Packages
The selection of which records to include in a data reporting package must be determined based
on how the data will be used. Different "levels of effort" require different supporting QA/QC
documentation. For example, organizations conducting basic research have different reporting
requirements from organizations collecting data in support of litigation or in compliance with permits.
When possible, field and laboratory records should be integrated to provide a continuous reporting track.
The following are examples of different records that may be included in the data reporting package.
A9.2.1 Field Operation Records
The information contained in these records documents overall field operations and generally
consists of the following:
• Sample collection records. These records show that the proper sampling protocol was
performed in the field. At a minimum, this documentation should include the names of
the persons conducting the activity, sample number, sample collection points, maps and
diagrams, equipment/method used, climatic conditions, and unusual observations.
Bound field notebooks are generally used to record raw data and make references to
prescribed procedures and changes in planned activities. They should be formatted to
include pre-numbered pages with date and signature lines.
• Chain-of-custody records. Chain-of-custody records document the progression of
samples as they travel from the original sampling location to the laboratory and finally to
their disposal area. (See Appendix C for an example of a chain-of-custody checklist.)
EPA QA/G-5 14 QA98
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• QC sample records. These records document the generation of QC samples, such as
field, trip, and equipment rinsate blanks and duplicate samples. They also include
documentation on sample integrity and preservation and include calibration and
standards' traceability documentation capable of providing a reproducible reference
point. Quality control sample records should contain information on the frequency,
conditions, level of standards, and instrument calibration history.
• General field procedures. General field procedures record the procedures used in the
field to collect data and outline potential areas of difficulty in gathering specimens.
• Corrective action reports. Corrective action reports show what methods were used in
cases where general field practices or other standard procedures were violated and
include the methods used to resolve noncompliance.
If applicable, to show regulatory compliance in disposing of waste generated during the data operation,
procedures manifest and testing contracts should be included in the field procedures section.
A9.2.2 Laboratory Records
The following list describes some of the laboratory-specific records that should be compiled if
available and appropriate:
Sample Data. These records contain the times that samples were analyzed to verify that
they met the holding times prescribed in the analytical methods. Included should be the
overall number of samples, sample location information, any deviations from the SOPs,
time of day, and date. Corrective action procedures to replace samples violating the
protocol also should be noted.
• Sample Management Records. Sample management records document sample receipt,
handling and storage, and scheduling of analyses. The records verify that the chain-of-
custody and proper preservation were maintained, reflect any anomalies in the samples
(such as receipt of damaged samples), note proper log-in of samples into the laboratory,
and address procedures used to ensure that holding time requirements were met.
Test Methods. Unless analyses are performed exactly as prescribed by SOPs, this
documentation will describe how the analyses were carried out in the laboratory. This
includes sample preparation and analysis, instrument standardization, detection and
reporting limits, and test-specific QC criteria. Documentation demonstrating laboratory
proficiency with each method used could be included.
* QA/QC Reports. These reports will include the general QC records, such as initial
demonstration of capability, instrument calibration, routine monitoring of analytical
performance, calibration verification, etc. Project-specific information from the QA/QC
checks such as blanks (field, reagent, rinsate, and method), spikes (matrix, matrix spike
replicate, analysis matrix spike, and surrogate spike), calibration check samples (zero
check, span check, and mid-range check), replicates, splits, and so on should be included
in these reports to facilitate data quality analysis.
EPAQA/G-5 15 QA98
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A9.2.3 Data Handling Records
These records document protocols used in data reduction, verification, and validation. Data
reduction addresses data transformation operations such as converting raw data into reportable quantities
and units, use of significant figures, recording of extreme values, blank corrections, etc. Data
verification ensures the accuracy of data transcription and calculations, if necessary, by checking a set of
computer calculations manually. Data validation ensures that QC criteria have been met.
A9.3 Data Reporting Package Format and Documentation Control
The format of all data reporting packages must be consistent with the requirements and
procedures used for data validation and data assessment described in Sections B, C, and D of the QAPP.
All individual records that represent actions taken to achieve the objective of the data operation and the
performance of specific QA functions are potential components of the final data reporting package. This
element should discuss how these various components will be assembled to represent a concise and
accurate record of all activities impacting data quality. The discussion should detail the recording
medium for the project, guidelines for hand-recorded data (e.g., using indelible ink), procedures for
correcting data (e.g., single line drawn through errors and initialed by the responsible person), and
documentation control. Procedures for making revisions to technical documents should be clearly
specified and the lines of authority indicated.
A9.4 Data Reporting Package Archiving and Retrieval
The length of storage for the data reporting package may be governed by regulatory
requirements, organizational policy, or contractual project requirements. This element of the QAPP
should note the governing authority for storage of, access to, and final disposal of all records.
A9.5 References
Kanare, Howard M. 1985. Writing the Laboratory Notebook. Washington, DC: American Chemical Society.
U.S. Environmental Protection Agency. 1993. Guidance on Evaluation, Resolution, and Documentation of Analytical Problems
Associated-with Compliance Monitoring. EPA/821/B-93/001.
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B MEASUREMENT/DATA ACQUISITION
Bl SAMPLING PROCESS DESIGN (EXPERIMENTAL DESIGN)
Describe the experimental design or data collection design for the project.
Classify all measurements as critical or non-critical.
Bl.l Purpose/Background
The purpose of this element is to describe all the relevant components of the experimental
design; define the key parameters to be estimated; indicate the number and type of samples expected;
and describe where, when, and how samples are to be taken. The level of detail should be sufficient that
a person knowledgeable in this area could understand how and why the samples will be collected. This
element provides the main opportunity for QAPP reviewers to ensure that the "right" samples will be
taken. Strategies such as stratification, compositing, and clustering should be discussed, and diagrams or
maps showing sampling points should be included. Most of this information should be available as
outputs from the final steps of the planning (DQO) process.
In addition to describing the design, this element of the QAPP should discuss the following:
a schedule for project sampling activities,
• a rationale for the design (in terms of meeting DQOs),
the sampling design assumptions,
• the procedures for locating and selecting environmental samples,
a classification of measurements as critical or noncritical, and
• the validation of any nonstandard sampling/measurement methods.
Elements B1.2 through B1.8 address these subjects.
B1.2 Scheduled Project Activities, Including Measurement Activities
This element should give anticipated start and completion dates for the project as well as
anticipated dates of major milestones, such as the following:
schedule of sampling events;
• schedule for analytical services by offsite laboratories;
schedule for phases of sequential sampling (or testing), if applicable;
• schedule of test or trial runs; and
schedule for peer review activities.
The use of bar charts showing time frames of various QAPP activities to identify both potential
bottlenecks and the need for concurrent activities is recommended.
B1.3 Rationale for the Design
The objectives for an environmental study should be formulated in the planning stage of any
investigation. The requirements and the rationale of the design for the collection of data are derived
EPAQA/G-5 17 QA98
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from the quantitative outputs of the DQO Process. The type of design used to collect data depends
heavily on the key characteristic being investigated. For example, if the purpose of the study is to
estimate overall average contamination at a site or location, the characteristic (or parameter) of interest
would be the mean level of contamination. This information is identified in Step 5 of the DQO Process.
The relationship of this parameter to any decision that has to be made from the data collected is obtained
from Steps 2 and 3 of the DQO Process (see Figure 4).
The potential range of values for the parameter of interest should be considered during
development of the data collection methodology and can be greatly influenced by knowledge of potential
ranges in expected concentrations. For example, the number of composite samples needed per unit area
is directly related to the variability in potential contaminant levels expected in that area.
The choice between a probability-based (statistical) data collection design or a nonrandom
(judgmental) data collection methodology depends on the ultimate use of the data being collected. This
information is specified in Steps 5 and 6 of the DQO Process. Adherence to the data collection design
chosen in Step 7 of the DQO Process directly affects the magnitude of potential decision error rates
(false positive rate and false negative rate) established in Step 6 of the DQO Process. Any procedures for
coping with unanticipated data collection design changes also should be briefly discussed.
B1.4 Design Assumptions
The planning process usually recommends a specific data collection method (Step 7 of the DQO
Process), but the effectiveness of this methodology rests firmly on assumptions made to establish the
data collection design. Typical assumptions include the homogeneity of the medium to be sampled (for
example, sludge, fine silt, or wastewater effluent), the independence in the collection of individual
samples (for example, four separate samples rather than four aliquots derived from a single sample), and
the stability of the conditions during sample collection (for example, the effects of a rainstorm during
collection of wastewater from an industrial plant). The assumptions should have been considered during
the DQO Process and should be summarized together with a contingency plan to account for exceptions
to the proposed sampling plan. An important part of the contingency plan is documenting the procedures
to be adopted in reporting deviations or anomalies observed after the data collection has been completed.
Examples include an extreme lack of homogeneity within a physical sample or the presence of analytes
that were not mentioned in the original sampling plan. Chapter 1 of EPA QA/G-9 provides an overview
of sampling plans and the assumptions needed for their implementation. EPA QA/G-5S provides
guidance on the construction of sampling plans to meet the requirements generated by the DQO Process.
B1.5 Procedures for Locating and Selecting Environmental Samples
The most appropriate plan for a particular sampling application will depend on: the practicality
and feasibility (e.g., determining specific sampling locations) of the plan, the key characteristic (the
parameter established in Step 5 of the DQO Process) to be estimated, and the implementation resource
requirements (e.g., the costs of sample collection, transportation, and analysis).
This element of the QAPP should also describe the frequency of sampling and specific sample
locations (e.g., sample port locations and traverses for emissions source testing, well installation designs
for groundwater investigations) and sampling materials. When decisions on the number and location of
samples will be made in the field, the QAPP should describe how these decisions will be driven whether
by actual observations or by field screening data. When locational data are to be collected, stored, and
transmitted, the methodology used must be described (or referenced) and include the following:
EPAQA/G-5 18 QA98
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procedures for rinding prescribed sample locations,
• contingencies for cases where prescribed locations are inaccessible,
location bias and its assessment, and
• procedures for reporting deviations from the sampling plan.
When appropriate, a map of the sample locations should be provided and locational map
coordinates supplied. EPA QA/G-5S provides nonmandatory guidance on the practicality of
constructing sampling plans and references to alternative sampling procedures.
B1.6 Classification of Measurements as Critical or Noncritical
All measurements should be classified as critical (i.e., required to achieve project objectives or
limits on decision errors, Step 6 of the DQO Process) or noncritical (for informational purposes only or
needed to provide background information). Critical measurements will undergo closer scrutiny during
the data gathering and review processes and will have first claim on limited budget resources. It is also
possible to include the expected number of samples to be tested by each procedure and the acceptance
criteria for QC checks (as described in element B5, "Quality Control Requirements").
B1.7 Validation of Any Nonstandard Methods
For nonstandard sampling methods, sample matrices, or other unusual situations, appropriate
method validation study information may be needed to confirm the performance of the method for the
particular matrix. The purpose of this validation information is to assess the potential impact on the
representativeness of the data generated. For example, if qualitative data are needed from a modified
method, rigorous validation may not be necessary. Such validation studies may include round-robin
studies performed by EPA or by other organizations. If previous validation studies are not available,
some level of single-user validation study or ruggedness study should be performed during the project
and included as part of the project's final report. This element of the QAPP should clearly reference any
available validation study information.
B2 SAMPLING METHODS REQUIREMENTS
Describe the procedures for collecting samples and identify the sampling methods and
equipment. Include any implementation requirements, support facilities, sample preservation
requirements, and materials needed. Describe the process for preparing and decontaminating
sampling equipment, including disposing decontamination by-products; selecting and
preparing sample containers, sample volumes, preservation methods, and maximum holding
times for sampling and/or analysis.
Describe specific performance requirements for the method. Address what to do when a
failure in the sampling occurs, who is responsible for corrective action, and how the
effectiveness of the corrective action shall be determined and documented.
B2.1 Purpose/Background
Environmental samples should reflect the target population and parameters of interest. As with
all other considerations involving environmental measurements, sampling methods should be chosen
with respect to the intended application of the data. Just as methods of analysis vary in accordance with
EPAQA/G-5 19 QA98
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project needs, sampling methods can also vary according to these requirements. Different sampling
methods have different operational characteristics, such as cost, difficulty, and necessary equipment. In
addition, the sampling method can materially affect the representativeness, comparability, bias, and
precision of the final analytical result.
In the area of environmental sampling, there exists a great variety of sample types. It is beyond
the scope of this document to provide detailed advice for each sampling situation and sample type.
Nevertheless, it is possible to define certain common elements that are pertinent to many sampling
situations with discrete samples (see EPA QA/G-5S).
If a separate sampling and analysis plan is required or created for the project, it should be
included as an appendix to the QAPP. The QAPP should simply refer to the appropriate portions of the
sampling and analysis plan for the pertinent information and not reiterate information.
B2.2 Describe the Sample Collection, Preparation, and Decontamination Procedures
(1) Select and describe appropriate sampling methods from the appropriate compendia of methods.
For each parameter within each sampling situation, identify appropriate sampling methods from
applicable EPA regulations, compendia of methods, or other sources of methods that have been
approved by EPA. When EPA-sanctioned procedures are available, they will usually be
selected. When EPA-sanctioned procedures are not available, standard procedures from other
organizations and disciplines may be used. A complete description of non-EPA methods should
be provided in (or attached to) the QAPP. Procedures for sample homogenization of nonaqueous
matrices may be described in part (2) as a technique for assuring sample representativeness. In
addition, the QAPP should specify the type of sample to be collected (e.g., grab, composite,
depth-integrated, flow- weighted) together with the method of sample preservation.
(2) Discuss sampling methods' requirements. Each medium or contaminant matrix has its own
characteristics that define the method performance and the type of material to be sampled.
Investigators should address the following:
• actual sampling locations,
choice of sampling method/collection,
• delineation of a properly shaped sample,
inclusion of all particles within the volume sampled, and
• subsampling to reduce the representative field sample into a representative laboratory
aliquot.
Having identified appropriate and applicable methods, it is necessary to include the
requirements for each method in the QAPP. If there is more than one acceptable sampling
method applicable to a particular situation, it may be necessary to choose one from among them.
DQOs should be considered in choosing these methods to ensure that: a) the sample accurately
represents the portion of the environment to be characterized, b) the sample is of sufficient
volume to support the planned chemical analysis, and c) the sample remains stable during
shipping and handling.
(3) Describe the decontamination procedures and materials. Decontamination is primarily
applicable in situations of sample acquisition from solid, semi-solid, or liquid media, but it
should be addressed, if applicable, for continuous monitors as well. The investigator must
EPA QA/G-5 20 QA98
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consider the appropriateness of the decontamination procedures for the project at hand. For
example, if contaminants are present in the environmental matrix at the 1% level, it is probably
unnecessary to clean sampling equipment to parts-per-billion (ppb) levels. Conversely, if ppb-
level detection is required, rigorous decontamination or the use of disposable equipment is
required. Decontamination by-products must be disposed of according to EPA policies and the
applicable rules and regulations that would pertain to a particular situation, such as the
regulations of OSHA, the Nuclear Regulatory Commission (NRC), and State and local
governments.
B2.3 Identify Support Facilities for Sampling Methods
Support facilities vary widely in their analysis capabilities, from percentage-level accuracy to
ppb-level accuracy. The investigator must ascertain that the capabilities of the support facilities are
commensurate with the requirements of the sampling plan established in Step 7 of the DQO Process.
B2.4 Describe Sampling/Measurement System Failure Response and Corrective Action Process
This section should address issues of responsibility for the quality of the data, the methods for
making changes and corrections, the criteria for deciding on a new sample location, and how these
changes will be documented. This section should describe what will be done if there are serious flaws
with the implementation of the sampling methodology and how these flaws will be corrected. For
example, if part of the complete set of samples is found to be inadmissable, how replacement samples
will be obtained and how these new samples will be integrated into the total set of data should be
described.
B2.5 Describe Sampling Equipment, Preservation, and Holding Time Requirements
This section includes the requirements needed to prevent sample contamination (disposable
samplers or samplers capable of appropriate decontamination), the physical volume of the material to be
collected (the size of composite samples, core material, or the volume of water needed for analysis), the
protection of physical specimens to prevent contamination from outside sources, the temperature
preservation requirements, and the permissible holding times to ensure against degradation of sample
integrity.
B2.6 References
Publications useful in assisting the development of sampling methods include:
Solid and Hazardous Waste Sampling
U.S. Environmental Protection Agency. 1986. Test Methods for Evaluating SolidWaste (SW-846). 3rd Ed., Chapter 9.
U.S. Environmental Protection Agency. 1985. Characterization of Hazardous Waste Sites - A Methods Manual. Vol. I, Site
Investigations. EPA-600/4-84-075. Environmental Monitoring Systems Laboratory. Las Vegas, NV.
U.S. Environmental Protection Agency. 1984. Characterization of Hazardous Waste Sites - A Methods Manual. Vol. II,
Available Sampling Methods. EPA-600/4-84-076. Environmental Monitoring Systems Laboratory. Las Vegas, NV.
U.S. Environmental Protection Agency. 1987. A Compendium of Superfund Field Operations Methods. NTIS PB88-181557.
EPA/540/P-87/001. Washington, DC.
EPAQA/G-5 21 QA98
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Ambient Air Sampling
U.S. Environmental Protection Agency. 1994. Quality Assurance Handbook for Air Pollution Measurement Systems. Vol. I,
Principles. EPA 600/9-76-005. Section 1.4.8 and Appendix M.S.6.
U.S. Environmental Protection Agency. 1994. Quality Assurance Handbook for Air Pollution Measurement Systems. Vol. II,
EPA 600/R-94-038b. Sections 2.0.1 and 2.0.2 and individual methods.
U.S. Environmental Protection Agency. 1984. Compendium of Methods for the Determination of Toxic Organic Compounds in
Ambient Air. EPA/600-4-84-41. Environmental Monitoring Systems Laboratory. Research Triangle Park, NC.
Supplement: EPA-600-4-87-006. September 1986.
Source Testing (Air)
U.S. Environmental Protection Agency. 1994. Quality Assurance Handbook for Air Pollution Measurement Systems. Vol. Ill,
EPA 600/R-94-038c. Section 3.0 and individual methods.
Water/ Ground Water
U.S. Environmental Protection Agency. Handbook: Ground Water. Cincinnati, OH. EPA/625/6-87/016. March 1987.
U.S. Environmental Protection Agency. RCRA Ground Water Monitoring Technical Enforcement Guidance Document.
Washington, DC. 1986.
U.S. Environmental Protection Agency. Standard Methods for the Examination of Water and Waste-water. 16th ed.
Washington, DC. 1985.
Acid Precipitation
U.S. Environmental Protection Agency. 1994. Quality Assurance Handbook for Air Pollution Measurement Systems. Vol. V,
EPA 600/94-038e.
Meteorological Measurements
U.S. Environmental Protection Agency. 1989. Quality Assurance Handbook for Air Pollution Measurement Systems. Vol. IV,
EPA 600/4-90-003.
Radioactive Materials and Mixed Waste
U.S. Department of Energy. 1989. Radioactive-Hazardous Mixed Waste Sampling and Analysis: Addendum to SW-846.
Soils and Sediments
U.S. Environmental Protection Agency. 1985. Sediment Sampling Quality Assurance User's Guide. NTIS PB85-233542.
EPA/600/4-85/048. Environmental Monitoring Systems Laboratory. Las Vegas, NV.
U.S. Environmental Protection Agency. 1989. Soil Sampling Quality Assurance User's Guide. EPA/600/8-89/046.
Environmental Monitoring Systems Laboratory. Las Vegas, NV.
Earth, D.S., and T.H.Starks. 1985. Sediment Sampling Quality Assurance User's Guide. EPA/600-4-85/048. Prepared for
Environmental Monitoring and Support Laboratory. Las Vegas, NV.
Statistics, Geostatistics, and Sampling Theory
Myers, J.C. 1997. Geostatistical Error Measurement. New York: Van Nostrand Reinhold.
Pitard, F.F. 1989. Pierre Gy's Sampling Theory and Sampling Practice. Vollandll. Boca Raton, FL: CRC Press.
EPA QA/G-5 22 QA98
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Miscellaneous
American Chemical Society Joint Board/Council Committee on Environmental Improvement. 1990. Practical Guide for
Environmental Sampling and Analysis, Section II. Environmental Analysis. Washington, DC.
ASTM Committee D-34. 1986. Standard Practices for Sampling Wastes from Pipes and Other Point Discharges. Document
No. D34.01-001R7.
Keith, L. 1990. EPA's Sampling and Analysis Methods Database Manual. Austin, TX: Radian Corp.
Keith,L. 1991. Environmental Sampling and Analysis: A Practical Guide. Chelsea, MI: Lewis Publishers, Inc.
B3 SAMPLE HANDLING AND CUSTODY REQUIREMENTS
Describe the requirements and provisions for sample handling and custody in the field,
laboratory, and transport, taking into account the nature of the samples, the maximum
allowable sample holding times before extraction or analysis, and available shipping options
and schedules.
Include examples of sample labels, custody forms, and sample custody logs.
B3.1 Purpose/Background
This element of the QAPP should describe all procedures that are necessary for ensuring that:
(1) samples are collected, transferred, stored, and analyzed by authorized personnel;
(2) sample integrity is maintained during all phases of sample handling and analyses; and
(3) an accurate written record is maintained of sample handling and treatment from the time
of its collection through laboratory procedures to disposal.
Proper sample custody minimizes accidents by assigning responsibility for all stages of sample handling
and ensures that problems will be detected and documented if they occur. A sample is in custody if it is
in actual physical possession or it is in a secured area that is restricted to authorized personnel. The level
of custody necessary is dependent upon the project's DQOs. While enforcement actions necessitate
stringent custody procedures, custody in other types of situations (i.e., academic research) may be
primarily concerned only with the tracking of sample collection, handling, and analysis.
Sample custody procedures are necessary to prove that the sample data correspond to the sample
collected, if data are intended to be legally defensible in court as evidence. In a number of situations, a
complete, detailed, unbroken chain of custody will allow the documentation and data to substitute for the
physical evidence of the samples (which are often hazardous waste) in a civil courtroom. Some statutes
or criminal violations may still necessitate that the physical evidence of sample containers be presented
along with the custody and data documentation.
An outline of the scope of sample custody-starting from the planning of sample collection, field
sampling, sample analysis to sample disposal—should also be included. This discussion should further
stress the completion of sample custody procedures, which include the transfer of sample custody from
field personnel to lab, sample custody within the analytical lab during sample preparation and analysis,
and datastorage.
EPA QA/G-5 23 QA98
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B3.2 Sample Custody Procedure
The QAPP should discuss the sample custody procedure at a level commensurate with the
intended use of the data. This discussion should include the following:
(1) List the names and responsibilities of all sample custodians in the field and laboratories.
(2) Give a description and example of the sample numbering system.
(3) Define acceptable conditions and plans for maintaining sample integrity in the field prior
to and during shipment to the laboratory (e.g., proper temperature and preservatives).
(4) Give examples of forms and labels used to maintain sample custody and document
sample handling in the field and during shipping. An example of a sample log sheet is
given in Figure 5; an example sample label is given in Figure 6.
(5) Describe the method of sealing shipping containers with chain-of-custody seals. An
example of a seal is given in Figure 7.
(6) Describe procedures that will be used to maintain the chain of custody and document
sample handling during transfer from the field to the laboratory, within the laboratory,
and among contractors. An example of a chain-of-custody record is given in Figure 8.
(7) Provide for the archiving of all shipping documents and associated paperwork.
(8) Discuss procedures that will ensure sample security at all times.
(9) Describe procedures for within-laboratory chain-of-custody together with verification of
the printed name, signature, and initials of the personnel responsible for custody of
samples, extracts, or digests during analysis at the laboratory. Finally, document
disposal or consumption of samples should also be described. A chain-of-custody
checklist is included in Appendix C to aid in managing this element.
Minor documentation of chain-of-custody procedures is generally applicable when:
• Samples are generated and immediately tested within a facility or site; and
• Continuous rather than discrete or integrated samples are subjected to real- or near real-
time analysis (e.g., continuous monitoring).
The discussion should be as specific as possible about the details of sample storage, transportation, and
delivery to the receiving analytical facility.
EPA QA/G-5 24 QA98
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EPA QA/G-5
25
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(Name of Sampling Organization)
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EPA QA/G-5
26
QA98
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STATION
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Figure 8. An Example of a Chain-of-Custody Record
EPA QA/G-5
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B4 ANALYTICAL METHODS REQUIREMENTS
Identify the analytical methods and equipment required, including sub-sampling or extraction
methods, laboratory decontamination procedures and materials (such as the case of hazardous
or radioactive samples), waste disposal requirements (if any), and specific performance
requirements for the method.
Identify analytical methods by number, date, and regulatory citation (as appropriate). If a
method allows the user to select from various options, then the method citations should state
exactly which options are being selected. For non-standard methods, such as unusual sample
matrices and situations, appropriate method performance study information is needed to
confirm the performance of the method for the particular matrix. If previous performance
studies are not available, they must be developed during the project and included as part of the
project results.
Address what to do when a failure in the analytical system occurs, who is responsible for
corrective action, and how the effectiveness of the corrective action shall be determined and
documented.
Specify the laboratory turnaround time needed, if important to the project schedule. Specify
whether a field sampling and/or laboratory analysis case narrative is required to provide a
complete description of any difficulties encountered during sampling or analysis.
B4.1 Purpose/Background
The choice of analytical methods will be influenced by the performance criteria, Data Quality
Objectives, and possible regulatory criteria. If appropriate, a citation of analytical procedures may be
sufficient if the analytical method is a complete SOP. For other methods, it may suffice to reference a
procedure (i.e., from Test Methods for Evaluating Solid Waste, SW-846) and further supplement it with
the particular options/variations being used by the lab, the detection limits actually achieved, the
calibration standards and concentrations used, etc. If the procedure is unique or an adaption of a
"standard" method, complete analytical and sample preparation procedures will need to be attached to
the QAPP.
Specific monitoring methods and requirements to demonstrate compliance traditionally were
specified in the applicable regulations and/or permits. However, this approach is being replaced by the
Performance-Based Measurement System (PBMS). PBMS is a process in which data quality needs,
mandates, or limitations of a program or project are specified and serve as a criterion for selecting
appropriate methods. The regulated body selects the most cost-effective methods that meet the criteria
specified in the PBMS. Under the PBMS framework, the performance of the method employed is
emphasized rather than the specific technique or procedure used in the analysis. Equally stressed in this
system is the requirement that the performance of the method be documented and certified by the
laboratory that appropriate QA/QC procedures have been conducted to verify the performance. PBMS
applies to physical, chemical, and biological techniques of analysis performed in the field as well as in the
laboratory. PBMS does not apply to the method-defined parameters.
EPA QA/G-5 28 QA98
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The QAPP should also address the issue of the quality of analytical data as indicated by the
data's ability to meet the QC acceptance criteria. This section should describe what should be done if the
calibration check samples exceed the control limits due to mechanical failure of the instrumentation, a
drift in the calibration curve occurs, or if a reagent blank indicates contamination. This section should
also indicate the authorities responsible for the quality of the data, the protocols for making changes and
implementing corrective actions, and the methods for reporting the data and its limitations.
Laboratory contamination from the processing of hazardous materials such as toxic or
radioactive samples for analysis and their ultimate disposal should be a considered during the planning
stages for selection of analysis methods. Safe handling requirements for project samples in the
laboratory with appropriate decontamination and waste disposal procedures should also be described.
B4.2 Subsampling
If subsampling is required, the procedures should be described in this QAPP element, and the
full text of the subsampling operating procedures should be appended to the QAPP. Because
subsampling may involve more than one stage, it is imperative that the procedures be documented fully
so that the results of the analysis can be evaluated properly.
B4.3 Preparation of the Samples
Preparation procedures should be described and standard methods cited and used where possible.
Step-by-step operating procedures for the preparation of the project samples should be listed in an
appendix. The sampling containers, methods of preservation, holding times, holding conditions, number
and types of all QA/QC samples to be collected, percent recovery, and names of the laboratories that will
perform the analyses need to be specifically referenced.
B4.4 Analytical Methods
The citation of an analytical method may not always be sufficient to fully characterize a method
because the analysis of a sample may require deviation from a standard method and selection from the
range of options in the method. The SOP for each analytical method should be cited or attached to the
QAPP, and all deviations or alternative selections should be detailed in the QAPP.
The matrix containing the subject analytes often dictates the sampling and analytical methods.
Gaseous analytes often must be concentrated on a trap in order to collect a measurable quantity. If the
matrix is a liquid or a solid, the analytes usually must be separated from it using various methods of
extraction. Sometimes the analyte is firmly linked by chemical bonds to other elements and must be
subjected to digestion methods to be freed for analysis.
Often the selected analytical methods may be presented conveniently in one or several tables
describing the matrix, the analytes to be measured, the analysis methods, the type, the precision/accuracy
data, the performance acceptance criteria, the calibration criteria, and etc. Appendix C contains a
checklist of many important components to consider when selecting analytical methods.
B4.5 References
Greenberg, A.E., L.S. Clescer, and A. D. Eaton, eds. 1992. Standard Methods for the Examination of Water and Waste-water.
18th ed. American Public Health Association. Water Environment Federation.
EPA QA/G-5 29 QA98
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U.S. Environmental Protection Agency. 1996. Quality Control: Variability in Protocols. EPA/600/9-91/034. Risk Reduction
Engineering Laboratory. U.S. EPA. Cincinnati, OH.
U.S. Environmental Protection Agency. Test Methods for Evaluating Solid Waste. SW-846. Chapter 2, "Choosing the
Correct Procedure."
B5 QUALITY CONTROL REQUIREMENTS
Identify required measurement QC checks for both the field and the laboratory. State the
frequency of analysis for each type of QC check, and the spike compounds sources and levels.
State or reference the required control limits for each QC check and corrective action required
when control limits are exceeded and how the effectiveness of the corrective action shall be
determined and documented.
Describe or reference the procedures to be used to calculate each of the QC statistics.
B5.1 Purpose/Background
QC is "the overall system of technical activities that measures the attributes and performance of
a process, item, or service against defined standards to verify that they meet the stated requirements
established by the customer." QC is both corrective and proactive in establishing techniques to prevent
the generation of unacceptable data, and so the policy for corrective action should be outlined. This
element will rely on information developed in section A7, "Quality Objectives and Criteria for
Measurement Data," which establishes measurement performance criteria.
B5.2 QC Procedures
This element documents any QC checks not defined in other QAPP elements and should
reference other elements that contain this information where possible. Most of the QC acceptance limits
of EPA methods are based on the results of interlaboratory studies. Because of improvements in
measurement methodology and continual improvement efforts in individual laboratories, these
acceptance limits may not be stringent enough for some projects. In some cases, acceptance limits are
based on intralaboratory studies (which often result in narrower acceptance limits than those based on
interlaboratory limits), and consultation with an expert may be necessary. Other elements of the QAPP
that contain related sampling and analytical QC requirements include:
Sampling Process Design (Bl), which identifies the planned field QC samples as well
as procedures for QC sample preparation and handling;
• Sampling Methods Requirements (B2), which includes requirements for determining if
the collected samples accurately represent the population of interest;
Sample Handling and Custody Requirements (B3), which discusses any QC devices
employed to ensure samples are not tampered with (e.g., custody seals) or subjected to
other unacceptable conditions during transport;
• Analytical Methods Requirements (B4), which includes information on the
subsampling methods and information on the preparation of QC samples in the sample
matrix (e.g., splits, spikes, and replicates); and
EPA QA/G-5 30 QA98
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• Instrument Calibration and Frequency (B7), which defines prescribed criteria for
triggering recalibration (e.g., failed calibration checks).
Table 1 lists QC checks often included in QAPPs. The need for the specific check depends on
the project objectives.
Table 1. Project Quality Control Checks
QC Check
Information Provided
Blanks
field blank
reagent blank
rinsate blank
method blank
transport and field handling bias
contaminated reagent
contaminated equipment
response of entire laboratory analytical system
Spikes
matrix spike
matrix spike replicate
analysis matrix spike
surrogate spike
analytical (preparation + analysis) bias
analytical bias and precision
instrumental bias
analytical bias
Calibration Check Samples
zero check
span check
mid-range check
calibration drift and memory effects
calibration drift and memory effects
calibration drift and memory effects
Replicates, splits, etc.
collocated samples
field replicates
field splits
laboratory splits
laboratory replicates
analysis replicates
sampling + measurement precision
precision of all steps after acquisition
shipping + interlaboratory precision
interlaboratory precision
analytical precision
instrument precision
Many QC checks result in measurement data that are used to compute statistical indicators of data
quality. For example, a series of dilute solutions may be measured repeatedly to produce an estimate of
the instrument detection limit. The formulas for calculating such Data Quality Indicators (DQIs) should
be provided or referenced in the text. This element should also prescribe any limits that define
acceptable data quality for these indicators (see also Appendix D, "Data Quality Indicators"). A QC
checklist should be used to discuss the relation of QC to the overall project objectives with respect to:
• the frequency and point in the measurement process in which the check sample is
introduced,
the traceability of the standards,
the matrix of the check sample,
the level or concentration of the analyte of interest,
the actions to be taken if a QC check identifies a failed or changed measurement system,
the formulas for estimating DQIs, and
• the procedures for documenting QC results, including control charts.
EPA QA/G-5
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Finally, this element should describe how the QC check data will be used to determine that
measurement performance is acceptable. This step can be accomplished by establishing QC "warning"
and "control" limits for the statistical data generated by the QC checks (see standard QC textbooks or
refer to EPA QA/G-5T for operational details).
Depending on the breadth of the potential audience for reviewing and implementing the QAPP, it
may be advantageous to separate the field QC from the laboratory QC requirements.
B6 INSTRUMENT/EQUIPMENT TESTING, INSPECTION, AND MAINTENANCE
REQUIREMENTS
Describe how inspections and acceptance testing of environmental sampling and measurement
systems and their components will be performed and documented.
Identify and discuss the procedure by which final acceptance will be performed by independent
personnel and/or by the EPA Project Officer.
Describe how deficiencies are to be resolved and when re-inspection will be performed.
Describe or reference how periodic preventive and corrective maintenance of measurement or
test equipment shall be performed. Identify the equipment and/or systems requiring periodic
maintenance. Discuss how the availability of critical spare parts, identified in the operating
guidance and/or design specifications of the systems, will be assured and maintained.
B6.1 Purpose/Background
The purpose of this element of the QAPP is to discuss the procedures used to verify that all
instruments and equipment are maintained in sound operating condition and are capable of operating at
acceptable performance levels.
B6.2 Testing, Inspection, and Maintenance
The procedures described should (1) reflect consideration of the possible effect of equipment
failure on overall data quality, including timely delivery of project results; (2) address any relevant site-
specific effects (e.g., environmental conditions); and (3) include procedures for assessing the equipment
status. This element should address the scheduling of routine calibration and maintenance activities, the
steps that will be taken to minimize instrument downtime, and the prescribed corrective action
procedures for addressing unacceptable inspection or assessment results. This element should also
include periodic maintenance procedures and describe the availability of spare parts and how an
inventory of these parts is monitored and maintained. The reader should be supplied with sufficient
information to review the adequacy of the instrument/equipment management program. Appending
SOPs containing this information to the QAPP and referencing the SOPs in the text are acceptable.
Inspection and testing procedures may employ reference materials, such as the National Institute
of Standards and Technology's (NIST's) Standard Reference Materials (SRMs), as well as QC standards
or an equipment certification program. The accuracy of calibration standards is important because all
data will be measured in reference to the standard used. The types of standards or special programs
should be noted in this element, including the inspection and acceptance testing criteria for all
EPA QA/G-5 32 QA98
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components. The acceptance limits for verifying the accuracy of all working standards against primary
grade standards should also be provided.
B7 INSTRUMENT CALIBRATION AND FREQUENCY
Identify all tools, gauges, instruments, and other sampling, measuring, and test equipment used
for data collection activities affecting quality that must be controlled and, at specified periods,
calibrated to maintain performance within specified limits.
Identify the certified equipment and/or standards used for calibration. Describe or reference
how calibration will be conducted using certified equipment and/or standards with known valid
relationships to nationally recognized performance standards. If no such nationally recognized
standards exist, document the basis for the calibration. Indicate how records of calibration
shall be maintained and be traceable to the instrument.
B7.1 Purpose/Background
This element of the QAPP concerns the calibration procedures that will be used for instrumental
analytical methods and other measurement methods that are used in environmental measurements. It is
necessary to distinguish between defining calibration as the checking of physical measurements against
accepted standards and as determining the relationship (function) of the response versus the
concentration. The American Chemical Society (ACS) limits the definition of the term calibration to the
checking of physical measurements against accepted standards, and uses the term standardization to
describe the determination of the response function.
B7.2 Identify the Instrumentation Requiring Calibration
The QAPP should identify any equipment or instrumentation that requires calibration to maintain
acceptable performance. While the primary focus of this element is on instruments of the measurement
system (sampling and measurement equipment), all methods require standardization to determine the
relationship between response and concentration.
B7.3 Document the Calibration Method that Will Be Used for Each Instrument
The QAPP must describe the calibration method for each instrument in enough detail for another
researcher to duplicate the calibration method. It may reference external documents such as EPA-
designated calibration procedures or SOPs providing that these documents can be easily obtained.
Nonstandard calibration methods or modified standard calibration methods should be fully documented
and justified.
Some instrumentation may be calibrated against other instrumentation or apparatus (e.g., NIST
thermometer), while other instruments are calibrated using standard materials traceable to national
reference standards. QAPP documentation for calibration apparatus and calibration standards are
addressed in B7.4 and B7.5.
Calibrations normally involve challenging the measurement system or a component of the
measurement system at a number of different levels over its operating range. The calibration may cover
a narrower range if accuracy in that range is critical, given the end use of the data. Single-point
EPA QA/G-5 33 QA98
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calibrations are of limited use, and two-point calibrations do not provide information on nonlinearity. If
single- or two-point calibrations are used for critical measurements, the potential shortcomings should be
carefully considered and discussed in the QAPP. Most EPA-approved analytical methods require
multipoint (three or more) calibrations that include zeros, or blanks, and higher levels so that unknowns
fall within the calibration range and are bracketed by calibration points. The number of calibration
points, the calibration range, and any replication (repeated measures at each level) should be given in the
QAPP.
The QAPP should describe how calibration data will be analyzed. The use of statistical QC
techniques to process data across multiple calibrations to detect gradual degradations in the measurement
system should be described. The QAPP should describe any corrective action that will be taken if
calibration (or calibration check) data fail to meet the acceptance criteria, including recalibration.
References to appended SOPs containing the calibration procedures are an acceptable alternative to
describing the calibration procedures within the text of the QAPP.
B7.4 Document the Calibration Apparatus
Some instruments are calibrated using calibration apparatus rather than calibration standards.
For example, an ozone generator is part of a system used to calibrate continuous ozone monitors.
Commercially available calibration apparatus should be listed together with the make (the manufacturer's
name), the model number, and the specific variable control settings that will be used during the
calibrations. A calibration apparatus that is not commercially available should be described in enough
detail for another researcher to duplicate the apparatus and follow the calibration procedure.
B7.5 Document the Calibration Standards
Most measurement systems are calibrated by processing materials that are of known and stable
composition. References describing these calibration standards should be included in the QAPP.
Calibration standards are normally traceable to national reference standards, and the traceability protocol
should be discussed. If the standards are not traceable, the QAPP must include a detailed description of
how the standards will be prepared. Any method used to verify the certified value of the standard
independently should be described.
B7.6 Document Calibration Frequency
The QAPP must describe how often each measurement method will be calibrated. It is desirable
that the calibration frequency be related to any known temporal variability (i.e., drift) of the
measurement system. The calibration procedure may involve less-frequent comprehensive calibrations
and more-frequent simple drift checks. The location of the record of calibration frequency and
maintenance should be referenced.
B7.7 References
American Chemical Society. 1980. "Calibration." Analytical Chemistry, Vol. 52, pps. 2,242-2,249.
Dieck, R.H. 1992. Measurement Uncertainty Methods and Applications. Research Triangle Park, NC: Instrument Society of
America.
Dux, J.P. 1986. Handbook of Quality Assurance for the Analytical Chemistry Laboratory. New York: VanNostrand
Reinhold.
EPA QA/G-5 34 QA98
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ILAC Task Force E. 1984. Guidelines for the Determination ofRecalibration Intervals of Testing Equipment Used in Testing
Laboratories. International Organization for Legal Metrology (OIML). International Document No. 10. 11 Rue
Twigot, Paris 95009, France.
Ku, H.H., ed. 1969. Precision Measurement and Calibration. Selected NBS Papers on Statistical Concepts and Procedures.
Special Publication 300. Vol.1. Gaithersburg, MD: National Bureau of Standards.
Liggett, W. 1986. "Tests of the Recalibration Period of a Drifting Instrument." In Oceans'86 Conference Record. Vol.3.
Monitoring Strategies Symposium. The Institute of Electrical and Electronics Engineers, Inc., Service Center.
Piscataway, NJ.
Pontius, P.E. 1974. Notes on the Fundamentals of Measurement as a Production Process. Publication No. NBSIR 74-545.
Gaithersburg, MD: National Bureau of Standards.
Taylor, J.T. 1987. Quality Assurance of Chemical Measurements. Boca Raton, FL: Lewis Publishers, Inc.
B8 INSPECTION/ACCEPTANCE REQUIREMENTS FOR SUPPLIES AND
CONSUMABLES
Describe how and by whom supplies and consumables shall be inspected and accepted for use in
the project. State acceptance criteria for such supplies and consumables.
B8.1 Purpose
The purpose of this element is to establish and document a system for inspecting and accepting
all supplies and consumables that may directly or indirectly affect the quality of the project or task. If
these requirements have been included under another section, it is sufficient to provide a reference.
B8.2 Identification of Critical Supplies and Consumables
Clearly identify and document all supplies and consumables that may directly or indirectly affect
the quality of the project or task. See Figures 9 and 10 for example documentation of
inspection/acceptance testing requirements. Typical examples include sample bottles, calibration gases,
reagents, hoses, materials for decontamination activities, deionized water, and potable water.
For each item identified, document the inspection or acceptance testing requirements or
specifications (e.g., concentration, purity, cell viability, activity, or source of procurement) in addition to
any requirements for certificates of purity or analysis.
B8.3 Establishing Acceptance Criteria
Acceptance criteria must be consistent with overall project technical and quality criteria (e.g.,
concentration must be within ± 2.5%, cell viability must be >90%). If special requirements are needed
for particular supplies or consumables, a clear agreement should be established with the supplier,
including the methods used for evaluation and the provisions for settling disparities.
B8.4 Inspection or Acceptance Testing Requirements and Procedures
Inspections or acceptance testing should be documented, including procedures to be followed,
individuals responsible, and frequency of evaluation. In addition, handling and storage conditions for
supplies and consumables should be documented.
EPA QA/G-5 35 QA98
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B8.5 Tracking and Quality Verification of Supplies and Consumables
Procedures should be established to ensure that inspections or acceptance testing of supplies and
consumables are adequately documented by permanent, dated, and signed records or logs that uniquely
identify the critical supplies or consumables, the date received, the date tested, the date to be retested (if
applicable), and the expiration date. These records should be kept by the responsible individual(s) (see
Figure 11 for an example log). In order to track supplies and consumables, labels with the information
on receipt and testing should be used.
These or similar procedures should be established to enable project personnel to (1) verify, prior
to use, that critical supplies and consumables meet specified project or task quality objectives; and
(2) ensure that supplies and consumables that have not been tested, have expired, or do not meet
acceptance criteria are not used for the project or task.
Unique identification no. (if not clearly shown)_
Date received
Date opened
Date tested (if performed)
Date to be retested (if applicable).
Expiration date
Figure 9. Example of a Record for Consumables
Critical
Supplies and
Consumables
Inspection/
Acceptance
Testing
Requirements
Acceptance
Criteria
Testing
Method
Frequency
Responsible
Individual
Handling/Storage
Conditions
Figure 10. Example of Inspection/Acceptance Testing Requirements
Critical Supplies
and Consumable
(Type, ID No.)
Date
Received
Meets Inspection/
Acceptance Criteria
(Y/N, Include Date)
Requires Retesting
(Y/N, If Yes, Include
Date)
Expiration
Date
Comments
Initials/Date
Figure 11. Example of a Log for Tracking Supplies and Consumables
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B9 DATA ACQUISITION REQUIREMENTS (NON-DIRECT MEASUREMENTS)
Identify any types of data needed for project implementation or decision making that are
obtained from non-measurement sources such as computer databases, programs, literature
files, and historical databases.
Define the acceptance criteria for the use of such data in the project and discuss any
limitations on the use of the data resulting from uncertainty in its quality.
Document the rationale for the original collection of data and indicate its relevance to this
project.
B9.1 Purpose/Background
This element of the QAPP should clearly identify the intended sources of previously collected
data and other information that will be used in this project. Information that is non-representative and
possibly biased and is used uncritically may lead to decision errors. The care and skepticism applied to
the generation of new data are also appropriate to the use of previously compiled data (for example, data
sources such as handbooks and computerized databases).
B9.2 Acquisition of Non-Direct Measurement Data
This element's criteria should be developed to support the objectives of element A7. Acceptance
criteria for each collection of data being considered for use in this project should be explicitly stated,
especially with respect to:
Representativeness. Were the data collected from a population that is sufficiently
similar to the population of interest and the population boundaries? How will potentially
confounding effects (for example, season, time of day, and cell type) be addressed so
that these effects do not unduly alter the summary information?
Bias. Are there characteristics of the data set that would shift the conclusions. For
example, has bias in analysis results been documented? Is there sufficient information to
estimate and correct bias?
• Precision. How is the spread in the results estimated? Does the estimate of variability
indicate that it is sufficiently small to meet the objectives of this project as stated in
element A7? See also Appendix D.
Qualifiers. Are the data evaluated in a manner that permits logical decisions on whether
or not the data are applicable to the current project? Is the system of qualifying or
flagging data adequately documented to allow the combination of data sets?
• Summarization. Is the data summarization process clear and sufficiently consistent
with the goals of this project? (See element D2 for further discussion.) Ideally,
observations and transformation equations are available so that their assumptions can be
evaluated against the objectives of the current project.
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This element should also include a discussion on limitations on the use of the data and the nature of the
uncertainty of the data.
BIO DATA MANAGEMENT
Describe the project data management scheme, tracing the path of the data from their
generation in the field or laboratory to their final use or storage. Describe or reference the
standard record-keeping procedures, document control system, and the approach used for
data storage and retrieval on electronic media.
Discuss the control mechanism for detecting and correcting errors and for preventing loss of
data during data reduction, data reporting, and data entry to forms, reports, and databases.
Provide examples of any forms or checklists to be used.
Identify and describe all data handling equipment and procedures to process, compile, and
analyze the data, including any required computer hardware and software. Address any
specific performance requirements and describe the procedures that will be followed to
demonstrate acceptability of the hardware/software configuration required.
Describe the process for assuring that applicable Agency information resource management
requirements and locational data requirements are satisfied. If other Agency data
management requirements are applicable, discuss how these requirements are addressed.
B10.1 Purpose/Background
This element should present an overview of all mathematical operations and analyses performed
on raw ("as-collected") data to change their form of expression, location, quantity, or dimensionality.
These operations include data recording, validation, transformation, transmittal, reduction, analysis,
management, storage, and retrieval. A diagram that illustrates the source(s) of the data, the processing
steps, the intermediate and final data files, and the reports produced may be helpful, particularly when
there are multiple data sources and data files. When appropriate, the data values should be subjected to
the same chain-of-custody requirements as outlined in element B3. Appendix G has further details.
B10.2 Data Recording
Any internal checks (including verification and validation checks) that will be used to ensure
data quality during data encoding in the data entry process should be identified together with the
mechanism for detailing and correcting recording errors. Examples of data entry forms and checklists
should be included.
B10.3 Data Validation
The details of the process of data validation and prespecified criteria should be documented in
this element of the QAPP. This element should address how the method, instrument, or system performs
the function it is intended to consistently, reliably, and accurately in generating the data. Part D of this
document addresses the overall project data validation, which is performed after the project has been
completed.
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B10.4 Data Transformation
Data transformation is the conversion of individual data point values into related values or
possibly symbols using conversion formulas (e.g., units conversion or logarithmic conversion) or a
system for replacement. The transformations can be reversible (e.g., as in the conversion of data points
using a formulas) or irreversible (e.g., when a symbol replaces actual values and the value is lost). The
procedures for all data transformations should be described and recorded in this element. The procedure
for converting calibration readings into an equation that will be applied to measurement readings should
be documented in the QAPP. Transformation and aberration of data for statistical analysis should be
outlined in element D3, "Reconciliation with Data Quality Objectives."
B10.5 Data Transmittal
Data transmittal occurs when data are transferred from one person or location to another or when
data are copied from one form to another. Some examples of data transmittal are copying raw data from
a notebook onto a data entry form for keying into a computer file and electronic transfer of data over a
telephone or computer network. The QAPP should describe each data transfer step and the procedures
that will be used to characterize data transmittal error rates and to minimize information loss in the
transmittal.
B10.6 Data Reduction
Data reduction includes all processes that change the number of data items. This process is
distinct from data transformation in that it entails an irreversible reduction in the size of the data set and
an associated loss of detail. For manual calculations, the QAPP should include an example in which
typical raw data are reduced. For automated data processing, the QAPP should clearly indicate how the
raw data are to be reduced with a well-defined audit trail, and reference to the specific software
documentation should be provided.
B10.7 Data Analysis
Data analysis sometimes involves comparing suitably reduced data with a conceptual model
(e.g., a dispersion model or an infectivity model). It frequently includes computation of summary
statistics, standard errors, confidence intervals, tests of hypotheses relative to model parameters, and
goodness-of-fit tests. This element should briefly outline the proposed methodology for data analysis
and a more detailed discussion should be included in the final report.
B10.8 Data Tracking
Data management includes tracking the status of data as they are collected, transmitted, and
processed. The QAPP should describe the established procedures for tracking the flow of data through
the data processing system.
B10.9 Data Storage and Retrieval
The QAPP should discuss data storage and retrieval including security and time of retention, and
it should document the complete control system. The QAPP should also discuss the performance
requirements of the data processing system, including provisions for the batch processing schedule and
the data storage facilities.
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C ASSESSMENT/OVERSIGHT
Cl ASSESSMENTS AND RESPONSE ACTIONS
Identify the number, frequency, and type of assessment activities needed for this project.
List and describe the assessments to be used in the project. Discuss the information expected
and the success criteria for each assessment proposed. List the approximate schedule of
activities, identify potential organizations and participants. Describe how and to whom the
results of the assessments shall be reported.
Define the scope of authority of the assessors, including stop work orders. Define explicitly the
unsatisfactory conditions under which the assessors are authorized to act and provide an
approximate schedule for the assessments to be performed.
Discuss how response actions to non-conforming conditions shall be addressed and by whom.
Identify who is responsible for implementing the response action and describe how response
actions shall be verified and documented.
Cl.l Purpose/Background
During the planning process, many options for sampling design (see EPA QA/G-5S, Guidance
on Sampling Design to Support QAPPs), sample handling, sample cleanup and analysis, and data
reduction are evaluated and chosen for the project. In order to ensure that the data collection is
conducted as planned, a process of evaluation and validation is necessary. This element of the QAPP
describes the internal and external checks necessary to ensure that:
all elements of the QAPP are correctly implemented as prescribed,
• the quality of the data generated by implementation of the QAPP is adequate, and
corrective actions, when needed, are implemented in a timely manner and their
effectiveness is confirmed.
Although any external assessments that are planned should be described in the QAPP, the most
important part of this element is documenting all planned internal assessments. Generally, internal
assessments are initiated or performed by the internal QA Officer so the activities described in this
element should be related to the responsibilities of the QA Officer as discussed in Section A4.
C1.2 Assessment Activities and Project Planning
The following is a description of various types of assessment activities available to managers in
evaluating the effectiveness of environmental program implementation.
Cl.2.1 Assessment of the Subsidiary Organizations
A. Management Systems Review (MSR). A form of management assessment, this process is
a qualitative assessment of a data collection operation or organization to establish
whether the prevailing quality management structure, policies, practices, and procedures
are adequate for ensuring that the type and quality of data needed are obtained. The
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MSR is used to ensure that sufficient management controls are in place and carried out
by the organization to adequately plan, implement, and assess the results of the project.
See the Guidance for the Management Systems Review Process (EPA QA/G-3).
B. Readiness reviews. A readiness review is a technical check to determine if all
components of the project are in place so that work can commence on a specific phase.
Cl.2.2 Assessment of Project Activities
A. Surveillance. Surveillance is the continual or frequent monitoring of the status of a
project and the analysis of records to ensure that specified requirements are being
fulfilled.
B. Technical Systems Audit (TSA). A TSA is a thorough and systematic onsite qualitative
audit, where facilities, equipment, personnel, training, procedures, and record keeping
are examined for conformance to the QAPP. The TSA is a powerful audit tool with
broad coverage that may reveal weaknesses in the management structure, policy,
practices, or procedures. The TSA is ideally conducted after work has commenced, but
before it has progressed very far, thus giving opportunity for corrective action.
C. Performance Evaluation (PE). A PE is a type of audit in which the quantitative data
generated by the measurement system are obtained independently and compared with
routinely obtained data to evaluate the proficiency of an analyst or laboratory. "Blind"
PE samples are those whose identity is unknown to those operating the measurement
system. Blind PEs often produce better performance assessments because they are
handled routinely and are not given the special treatment that undisguised PEs
sometimes receive. The QAPP should list the PEs that are planned, identifying:
the constituents to be measured,
• the target concentration ranges,
the timing/schedule for PE sample analysis, and
• the aspect of measurement quality to be assessed (e.g., bias, precision,
and detection limit).
A number of EPA regulations and EPA-sanctioned methods require the successful
accomplishment of PEs before the results of the test can be considered valid. PE
materials are now available from commercial sources and a number of EPA Program
Offices coordinate various interlaboratory studies and laboratory proficiency programs.
Participation in these or in the National Voluntary Laboratory Accreditation Program
(NVLAP, run by NIST) should be mentioned in the QAPP.
D. Audit of Data Quality (ADQ). An ADQ reveals how the data were handled, what
judgments were made, and whether uncorrected mistakes were made. Performed prior to
producing a project's final report, ADQs can often identify the means to correct
systematic data reduction errors.
E. Peer review. Peer review is not a TSA, nor strictly an internal QA function, as it may
encompass non-QA aspects of a project and is primarily designed for scientific review.
Whether a planning team chooses ADQs or peer reviews depends upon the nature of the
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project, the intended use of the data, the policies established by the sponsor of the
project, and overall the conformance to the Program Office or Region's peer-review
policies and procedures. Reviewers are chosen who have technical expertise comparable
to the project's performers but who are independent of the project. ADQs and peer
reviews ensure that the project activities:
were technically adequate,
• were competently performed,
were properly documented,
• satisfied established technical requirements, and
satisfied established QA requirements.
In addition, peer reviews assess the assumptions, calculations, extrapolations, alternative
interpretations, methods, acceptance criteria, and conclusions documented in the
project's report. Any plans for peer review should conform with the Agency's peer-
review policy and guidance. The names, titles, and positions of the peer reviewers
should be included in the final QAPP, as should their report findings, the QAPP authors'
documented responses to their findings, and reference to where responses to peer-review
comments may be located, if necessary.
F. Data Quality Assessment (DQA). DQA involves the application of statistical tools to
determine whether the data meet the assumptions that the DQOs and data collection
design were developed under and whether the total error in the data is tolerable.
Guidance for the Data Quality Assessment Process (EPA QA/G-9) provides
nonmandatory guidance for planning, implementing, and evaluating retrospective
assessments of the quality of the results from environmental data operations.
C1.3 Documentation of Assessments
The following material describes what should be documented in a QAPP after consideration of
the above issues and types of assessments.
Cl.3.1 Number. Frequency, and Types of Assessments
Depending upon the nature of the project, there may be more than one assessment. A schedule
of the number, frequencies, and types of assessments required should be given.
Cl.3.2 Assessment Personnel
The QAPP should specify the individuals, or at least the specific organizational units, who will
perform the assessments. Internal audits are usually performed by personnel who work for the
organization performing the project work but who are organizationally independent of the management
of the project. External audits are performed by personnel of organizations not connected with the
project but who are technically qualified and who understand the QA requirements of the project.
Cl.3.3 Schedule of Assessment Activities
A schedule of audit activities, together with relevant criteria for assessment, should be given to
the extent that it is known in advance of project activities.
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Cl.3.4 Reporting and Resolution of Issues
Audits, peer reviews, and other assessments often reveal findings of practice or procedure that do
not conform to the written QAPP. Because these issues must be addressed in a timely manner, the
protocol for resolving them should be given here together with the proposed actions to ensure that the
corrective actions were performed effectively. The person to whom the concerns should be addressed,
the decision making hierarchy, the schedule and format for oral and written reports, and the
responsibility for corrective action should all be discussed in this element. It also should explicitly define
the unsatisfactory conditions upon which the assessors are authorized to act and list the project personnel
who should receive assessment reports.
C2 REPORTS TO MANAGEMENT
Identify the frequency and distribution of reports issued to inform management of the status
of the project; results of performance evaluations and systems audits; results of periodic data
quality assessments; and significant quality assurance problems and recommended solutions.
Identify the preparer and the recipients of the reports, and the specific actions management is
expected to take as a result of the reports.
C2.1 Purpose/Background
Effective communication between all personnel is an integral part of a quality system. Planned
reports provide a structure for apprising management of the project schedule, the deviations from
approved QA and test plans, the impact of these deviations on data quality, and the potential
uncertainties in decisions based on the data. Verbal communication on deviations from QA plans should
be noted in summary form in element Dl of the QAPP.
C2.2 Frequency, Content, and Distribution of Reports
The QAPP should indicate the frequency, content, and distribution of the reports so that
management may anticipate events and move to ameliorate potentially adverse results. An important
benefit of the status reports is the opportunity to alert the management of data quality problems, propose
viable solutions, and procure additional resources. If program assessment (including the evaluation of
the technical systems, the measurement of performance, and the assessment of data) is not conducted on
a continual basis, the integrity of the data generated in the program may not meet the quality
requirements. These audit reports, submitted in a timely manner, will provide an opportunity to
implement corrective actions when most appropriate.
C2.3 Identify Responsible Organizations
It is important that the QAPP identify the personnel responsible for preparing the reports,
evaluating their impact, and implementing follow-up actions. It is necessary to understand how any
changes made in one area or procedure may affect another part of the project. Furthermore, the
documentation for all changes should be maintained and included in the reports to management. At the
end of a project, a report documenting the Data Quality Assessment findings to management should be
prepared.
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D DATA VALIDATION AND USABILITY
Dl DATA REVIEW, VALIDATION, AND VERIFICATION REQUIREMENTS
State the criteria used to review and validate data.
Provide examples of any forms or checklists to be used.
Identify any project-specific calculations required.
Dl.l Purpose/Background
The purpose of this element is to state the criteria for deciding the degree to which each data
item has met its quality specifications as described in Group B. Investigators should estimate the
potential effect that each deviation from a QAPP may have on the usability of the associated data item,
its contribution to the quality of the reduced and analyzed data, and its effect on the decision.
The process of data verification requires confirmation by examination or provision of objective
evidence that the requirements of these specified QC acceptance criteria are met. In design and
development, verification concerns the process of examining the result of a given activity to determine
conformance to the stated requirements for that activity. For example, have the data been collected
according to a specified method and have the collected data been faithfully recorded and transmitted?
Do the data fulfill specified data format and metadata requirements. The process of data verification
effectively ensures the accuracy of data using validated methods and protocols and is often based on
comparison with reference standards.
The process of data validation requires confirmation by examination and provision of objective
evidence that the particular requirements for a specific intended use have been fulfilled. In design and
development, validation concerns the process of examining a product or result to determine conformance
to user needs. For example, have the data and assessment methodology passed a peer review to evaluate
the adequacy of their accuracy and precision in assessing progress towards meeting the specific
commitment articulated in the objective or subobjective. The method validation process effectively
develops the QC acceptance criteria or specific performance criteria.
Each of the following areas of discussion should be included in the QAPP elements. The
discussion applies to situations in which a sample is separated from its native environment and
transported to a laboratory for analysis and data generation. However, these principles can be adapted to
other situations (for example, in-situ analysis or laboratory research).
D1.2 Sampling Design
How closely a measurement represents the actual environment at a given time and location is a
complex issue that is considered during development of element B1. See Guidance on Sampling Designs
to Support QAPPs (EPA QA/G-5S). Acceptable tolerances for each critical sample coordinate and the
action to be taken if the tolerances are exceeded should be specified in element Bl.
Each sample should be checked for conformity to the specifications, including type and location
(spatial and temporal). By noting the deviations in sufficient detail, subsequent data users will be able to
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determine the data's usability under scenarios different from those included in project planning. The
strength of conclusions that can be drawn from data (see Guidance Document for Data Quality
Assessment, EPA QA/G-9) has a direct connection to the sampling design and deviations from that
design. Where auxiliary variables are included in the overall data collection effort (for example,
microbiological nutrient characteristics or process conditions), they should be included in this evaluation.
D1.3 Sample Collection Procedures
Details of how a sample is separated from its native time/space location are important for
properly interpreting the measurement results. Element B2 provides these details, which include
sampling and ancillary equipment and procedures (including equipment decontamination). Acceptable
departures (for example, alternate equipment) from the QAPP, and the action to be taken if the
requirements cannot be satisfied, should be specified for each critical aspect. Validation activities should
note potentially unacceptable departures from the QAPP. Comments from field surveillance on
deviations from written sampling plans also should be noted.
D1.4 Sample Handling
Details of how a sample is physically treated and handled during relocation from its original site
to the actual measurement site are extremely important. Correct interpretation of the subsequent
measurement results requires that deviations from element B3 of the QAPP and the actions taken to
minimize or control the changes, be detailed. Data collection activities should indicate events that occur
during sample handling that may affect the integrity of the samples.
At a minimum, investigators should evaluate the sample containers and the preservation methods
used and ensure that they are appropriate to the nature of the sample and the type of data generated from
the sample. Checks on the identity of the sample (e.g., proper labeling and chain-of-custody records) as
well as proper physical/chemical storage conditions (e.g., chain-of-custody and storage records) should
be made to ensure that the sample continues to be representative of its native environment as it moves
through the analytical process.
D1.5 Analytical Procedures
Each sample should be verified to ensure that the procedures used to generate the data (as
identified in element B4 of the QAPP) were implemented as specified. Acceptance criteria should be
developed for important components of the procedures, along with suitable codes for characterizing each
sample's deviation from the procedure. Data validation activities should determine how seriously a
sample deviated beyond the acceptable limit so that the potential effects of the deviation can be
evaluated during DQA.
D1.6 Quality Control
Element B5 of the QAPP specifies the QC checks that are to be performed during sample
collection, handling, and analysis. These include analyses of check standards, blanks, spikes, and
replicates, which provide indications of the quality of data being produced by specified components of
the measurement process. For each specified QC check, the procedure, acceptance criteria, and
corrective action (and changes) should be specified. Data validation should document the corrective
actions that were taken, which samples were affected, and the potential effect of the actions on the
validity of the data.
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D1.7 Calibration
Element B7 addresses the calibration of instruments and equipment and the information that
should be presented to ensure that the calibrations:
• were performed within an acceptable time prior to generation of measurement data;
• were performed in the proper sequence;
• included the proper number of calibration points;
• were performed using standards that "bracketed" the range of reported measurement
results (otherwise, results falling outside the calibration range are flagged as such); and
had acceptable linearity checks and other checks to ensure that the measurement system
was stable when the calibration was performed.
When calibration problems are identified, any data produced between the suspect calibration event and
any subsequent recalibration should be flagged to alert data users.
D1.8 Data Reduction and Processing
Checks on data integrity evaluate the accuracy of "raw" data and include the comparison of
important events and the duplicate rekeying of data to identify data entry errors.
Data reduction is an irreversible process that involves a loss of detail in the data and may involve
averaging across time (for example, hourly or daily averages) or space (for example, compositing results
from samples thought to be physically equivalent). Since this summarizing process produces few values
to represent a group of many data points, its validity should be well-documented in the QAPP. Potential
data anomalies can be investigated by simple statistical analyses (see Guidance for Data Quality
Assessment, EPA QA/G-9).
The information generation step involves the synthesis of the results of previous operations and
the construction of tables and charts suitable for use in reports. How information generation is checked,
the requirements for the outcome, and how deviations from the requirements will be treated, should be
addressed in this element.
D2 VALIDATION AND VERIFICATION METHODS
Describe the process to be used for validating and verifying data, including the chain of
custody for data throughout the life cycle of the project or task.
Discuss how issues shall be resolved and identify the authorities for resolving such issues.
Describe how the results are conveyed to the data users.
Precisely define and interpret how validation issues differ from verification issues for this
project.
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D2.1 Purpose/Background
The purpose of this element is to describe, in detail, the process for validating (determining if
data satisfy QAPP-defmed user requirements) and verifying (ensuring that conclusions can be correctly
drawn) project data. The amount of data validated is directly related to the DQOs developed for the
project. The percentage validated for the specific project together with its rationale should be outlined or
referenced. The QAPP should have a clear definition of what is implied by "verification" and
"validation."
D2.2 Describe the Process for Validating and Verifying Data
The individuals responsible for data validation together with the lines of authority should be
shown on an organizational chart and may be indicated in the chart in element A7. The chart should
indicate who is responsible for each activity of the overall validation and verification processes.
The data to be validated should be compared to "actual" events using the criteria documented in
the QAPP. The data validation procedure for all environmental measurements should be documented in
the SOPs for specific data validation. Verification and validation issues are discussed at length in
Guidance on Environmental Verification and Validation, (EPA QA/G-8).
D3 RECONCILIATION WITH DATA QUALITY OBJECTIVES
Describe how the results obtained from the project or task will be reconciled with the
requirements defined by the data user or decision maker.
Outline the proposed methods to analyze the data and determine possible anomalies or
departures from assumptions established in the planning phase of data collection.
Describe how issues will be resolved and discuss how limitations on the use of the data will be
reported to decision makers.
D3.1 Purpose/Background
The purpose of element D3 is to outline and specify, if possible, the acceptable methods for
evaluating the results obtained from the project. This element includes scientific and statistical
evaluations of data to determine if the data are of the right type, quantity, and quality to support their
intended use.
D3.2 Reconciling Results with DQOs
The DQA process has been developed for cases where formal DQOs have been established.
Guidance for Data Quality Assessment (EPA QA/G-9) focuses on evaluating data for fitness in decision
making and also provides many graphical and statistical tools.
DQA is a key part of the assessment phase of the data life cycle, as shown in Figure 1. As the
part of the assessment phase that follows data validation and verification, DQA determines how well the
validated data can support their intended use. If an approach other than DQA has been selected, an
outline of the proposed activities should be included.
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CHAPTER IV
QAPP REVISIONS AND RELATED GUIDANCE
QAPP REVISIONS
During the course of environmental data collection, it is possible that changes will occur and
revisions to the QAPP will have to be made. Any changes to the technical procedures should be
evaluated by the EPA QA Officer and Project Officer to determine if they significantly affect the
technical and quality objectives of the project. If so, the QAPP should be revised and reapproved, and a
revised copy should be sent to all the persons on the distribution list.
COMPARISON WITH PREVIOUS GUIDANCE (QAMS-005/80)
EPA's previous guidance for preparing QAPPs, Interim Guidelines and Specifications for
Preparing Quality Assurance Project Plans (QAMS-005/80), was released in December 1980. The
evolution of EPA programs, changing needs, and changes to quality management practices have
mandated the preparation of a new guidance. The QAPPs that will be generated based on this guidance
will be slightly different from those in the past because:
• New QAPP specifications are given in the R-5 requirements document.
• Additional guidance documents from the Agency including Guidance for the Data
Quality Objectives Process (EPA QA/G-4), and Guidance for Data Quality Assessment
(EPA QA/G-9), are available on important quality management practices. These
guidance documents show how the DQO Process, the QAPP, and the DQA Process link
together in a coherent way (see Appendix A for a crosswalk between the DQOs and the
QAPP).
• The new guidance includes flexibility in the requirements and reporting format.
However, if an element of the QAPP is not applicable to a particular project, the
rationale for not addressing the element should be included.
• The elements of the QAPP are now organized in an order that corresponds to the
customary planning, implementation, and assessment phases of a project. They have
been categorized into four groups for ease of implementation:
• Project Management,
Measurement/Data Acquisition,
• Assessment/Oversight, and
Data Validation and Usability.
• There are more elements identified than in the previous QAMS-005/80 guidance and this
encourages flexibility in construction of defensible QAPPs.
A comparison between the requirements of QAMS-005/80 and the R-5 document is presented in
Appendix A, "Crosswalk Between EPA QA/R-5 and QAMS-005/80."
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APPENDIX A
CROSSWALKS BETWEEN QUALITY ASSURANCE DOCUMENTS
This appendix consists of five sections. The first section describes the relationship between the
systems requirements developed in ANSI/ASQC E4-1994 and the Environmental Protection Agency
(EPA) Quality System requirements. The second section provides a crosswalk between the requirements
document for Quality Assurance Project Plans (QAPPs), EPA QA/R-5, EPA Requirements for Quality
Assurance Project Plans for Environmental Data Operations, and its predecessor document, QAMS
005/80, Interim Guidelines and Specifications for Preparing Quality Assurance Project Plans. The third
section provides a crosswalk between QA/R-5 and the elements of International Organization for
Standardization (ISO) 9000. The fourth section is a crosswalk between the requirements of the QAPP
and the steps of the Data Quality Objectives (DQOs) Process. The final section describes the Agency's
QA documents at the program and project levels.
AA1. RELATIONSHIP BETWEEN E4 AND EPA QUALITY SYSTEM
EPA Order 5360.1 establishes a mandatory Agency-wide Quality System that applies to all
organizations, both internal and external, performing work for EPA. (The authority for the requirements
defined by the Order are contained in the applicable regulations for extramural agreements.) These
organizations must ensure that data collected for the characterization of environmental processes and
conditions are of the appropriate type and quality for their intended use and that environmental
technologies are designed, constructed, and operated according to defined expectations. All EPA
Regional, Office, and Laboratory quality systems established in accordance with these requirements shall
comply with ANSI/ASQC E4-1994, Specifications and Guidelines for Quality Systems for
Environmental Data Collection and Environmental Technology Programs, which conforms generally to
ISO 9000. In addition, EPA has developed two documents S EPA QA/R-1, EPA Quality Systems
Requirements for Environmental Programs, and EPA QA/R-2, EPA Requirements for Quality
Management Plans S that specify the requirements for developing, documenting, implementing, and
assessing a Quality System. This appendix describes these three Agency documents (Order 5360.1, EPA
QA/R-1, and EPA QA/R-2) in order to define their relationships and roles in laying the foundation for
EPA's Quality System.
ANSI/ASQC E4-1994 provides the basis for the preparation of a quality system for an
organization's environmental programs. The document provides the requisite management and technical
area elements necessary for developing and implementing a quality system. The document first describes
the quality management elements that are generally common to environmental problems, regardless of
their technical scope. The document then discusses the specifications and guidelines that apply to
project-specific environmental activities involving the generation, collection, analysis, evaluation, and
reporting of environmental data. Finally, the document contains the minimum specifications and
guidelines that apply to the design, construction, and operation of environmental technology.
EPA QA/R-1 provides the details on EPA quality management requirements to organizations
conducting environmental programs. This document states that"... all EPA organizations and all
organizations performing work for EPA shall develop and establish Quality Systems, as appropriate, that
conform to the American National Standard ANSI/ASQC E4-1994, Specifications and Guidelines for
Quality Systems for Environmental Data Collection and Environmental Technology Programs, and its
additions and supplements from the American National Standards Institute (ANSI) and the American
Society for Quality Control (ASQC)." R-l applies to all EPA programs and organizations, unless
explicitly exempted, that produce, acquire, or use environmental data depending on the purposes for
EPAQA/G-5 A-l QA98
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which the data will be used. This document also applies to systems, facilities, processes, and methods
for pollution control, waste treatment, waste remediation, and waste packaging and storage. Essentially,
R-l formally describes how EPA Order 5360.1 applies to extramural organizations.
EPA Requirements for Quality Management Plans, EPA QA/R-2, discusses the development,
review, approval, and implementation of the Quality Management Plan (QMP). The QMP is a means of
documenting how an organization will plan, implement, and assess the effectiveness of the management
processes and structures (required under R-l) that relate to the Quality System. R-2 describes the
program elements that should be part of a QMP. These requirements match the quality management
elements described in ANSI/ASQC E4-1994 that are generally common to environmental projects.
These elements include the following: (1) management and organization, (2) quality system and
description, (3) personnel qualifications and training, (4) procurement of items and services, (5)
documents and records, (6) computer hardware and software, (7) planning, (8) implementation of work
processes, (9) assessment and response, and (10) quality improvement.
The procedures, roles, and responsibilities for QAPPs are addressed in the organization's QMP.
In essence, the QMP establishes the nature of the requirements for QAPPs for work done by or for that
organization.
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AA2. CROSSWALK BETWEEN EPA QA/R-5 AND QAMS-005/80
QAMS-005/80 ELEMENTS
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
Title Page with Provision for Approval
Signatures
Table of Contents
Project Description
Project Organization and
Responsibility
QA Objectives for Measurement
Data (PARCC)
Sampling Procedures
Sample Custody
Calibration Procedures and Frequency
Analytical Procedures
Data Reduction, Validation, and
Reporting
Internal Quality Control Checks and
Frequency
Performance and Systems
Preventive Maintenance
Specific Routine Procedures
Measurement Parameters Involved
Corrective Action
QA Reports to Management
QA/R-5 ELEMENTS
Al
A2
A5
A6
A4
A9
A7
Bl
B2
A8
B3
B7
B4
Dl
D2
B9
BIO
B5
Cl
B6
B8
D3
Cl
A3
C2
Title and Approval Sheet
Table of Contents
Problem Definition/Background
Project/Task Description
Project/Task Organization
Documentation and Records
Quality Objectives and Criteria for Measurement
Data
Sampling Process Design
Sampling Methods Requirements
Special Training Requirements or Certification
Sample Handling and Custody Requirements
Instrument Calibration and Frequency
Analytical Methods Requirements
Data Review, Validation, and Verification
Requirements
Validation and Verification Methods
Data Acquisition Requirements
Data Quality Management
Quality Control Requirements
Assessments and Response Actions
Instrument/Equipment Testing, Procedures and
Schedules Inspection, and Maintenance
Requirements
Inspection/ Acceptance Requirements for Supplies
and Consumables
Reconciliation with Data Used to Assess
PARCC for Quality Objectives Measurement
Assessments and Response Actions
Distribution List
Reports to Management
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AA3. CROSSWALK BETWEEN EPA QA/R-5 AND ISO 9000
EPA QA/R-5 Elements
Al
A2
A3
A4
A5
A6
A7
A8
A9
Bl
B2
B3
B4
B5
B6
B7
B8
B9
BIO
Cl
C2
Dl
D2
D3
Title and Approval Sheet
Table of Contents
Distribution List
Project/Task Organization
Problem Definition/Background
Project/Task Description
Quality Objectives and Criteria for
Measurement Data
Special Training Requirements/Certification
Documentation and Records
Sampling Process Design
Sampling Methods Requirements
Sample Handling and Custody Requirements
Analytical Methods Requirements
Quality Control Requirements
Instrument/Equipment Testing, Inspection, and
Maintenance Requirements
Instrument Calibration and Frequency
Inspection/ Acceptance Requirements for
Supplies and Consumables
Data Acquisition Requirements
Data Quality Management
Assessments and Response Actions
Reports to Management
Data Review, Validation, and Verification
Requirements
Validation and Verification Methods
Reconciliation with User Requirements
ISO 9000 Elements
N/A
N/A
N/A
4
Management Responsibility
N/A
N/A
5
5.2
Quality System Principles
Structure of the Quality System
N/A
N/A
8
10
16
10
11
13
Quality in Specification and Design
Quality of Production
Handling and Post-Production Functions
Quality of Production
Control of Production
Control of Measuring and Test Equipment
N/A
9
11.2
Quality in Procurement
Material Control and Traceability
N/A
N/A
5.4
14
15
5.3
6
11.7
12
Auditing the Quality System
Nonconformity
Corrective Action
Documentation of the Quality System
Economics - Quality Related Costs
Control of Verification Status
Verification Status
N/A
7
Quality in Marketing
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AA4.
CROSSWALK BETWEEN THE DQO PROCESS AND THE QAPP
Elements
Requirements
DQO Overlap
PROJECT MANAGEMENT
Al Title and Approval
Sheet
A2 Table of Contents
A3 Distribution List
A4 Project/Task
Organization
A5 Problem
Definition/Backgro
und
A6 Project/Task
Description
A7 Data Quality
Objectives for
Measurement Data
A8 Special Training
Requirements/
Certification
A9 Documentation
and Record
Title and approval sheet.
Document control format.
Distribution list for the QAPP revisions and final
guidance.
Identify individuals or organizations participating
in the project and discuss their roles,
responsibilities and organization.
1 ) State the specific problem to be solved or the
decision to be made.
2) Identify the decision maker and the principal
customer for the results.
1) Hypothesis test, 2) expected measurements, 3)
ARARs or other appropriate standards, 4)
assessment tools (technical audits), 5) work
schedule and required reports.
Decision(s), population parameter of interest,
action level, summary statistics and acceptable
limits on decision errors. Also, scope of the
project (domain or geographical locale).
Identify special training that personnel will need.
Itemize the information and records that must be
included in a data report package, including
report format and requirements for storage, etc.
N/A
N/A
List the members of the scoping team.
Step 1: State the Problem.
Step 1 : State the Problem requires
definition of the DQO scoping or
planning team, which includes the
decision maker, technical staff, data
users, etc. This step also requires the
specification of each member's role and
responsibilities.
Step 1: State the Problem/Step 2:
Identify the Decision requires a
description of the problem. It also
identifies the decision makers who
could use the data.
Step 1 : State the Problem/Step 2:
Identify the Decision requires a work
schedule. Step 3: Identify the Inputs
requires the ARARs or standards and
expected measurements. Step 6:
Specify Limits on Decision Errors.
Step 1: State the Problem, Step 2:
Identify the Decision, Step 4: Define
the Boundaries, Step 5: Develop a
Decision Rule, Step 6: Specify Limits
on Decision Errors.
Step 3: Identify the Inputs to the
Decision.
Step 3: Identify the Inputs to the
Decision, Step 7: Optimize the Design
for Obtaining Data.
MEASUREMENT/DATA ACQUISITION
Bl Sampling Process
Designs
(Experimental
Design)
B2 Sampling Methods
Requirements
B3 Sample Handling
and Custody
Requirements
B4 Analytical
Methods
Requirements
B5 Quality Control
Requirements
Outline the experimental design, including
sampling design and rationale, sampling
frequencies, matrices, and measurement
parameter of interest.
Sample collection method and approach.
Describe the provisions for sample labeling,
shipment, chain-of-custody forms, procedures for
transferring and maintaining custody of samples.
Identify analytical method(s) and equipment for
the study, including method performance
requirements.
Describe routine (real-time) QC procedures that
should be associated with each sampling and
measurement technique. List required QC checks
and corrective action procedures.
Step 5: Develop a Decision Rule, Step
7: Optimize the Design for Obtaining
Data.
Step 7: Optimize the Design for
Obtaining Data.
Step 3: Identify the Inputs to the
Decision.
Step 3: Identify the Inputs to the
Decision, Step 7: Optimize the Design
for Obtaining Data.
Step 3: Identify the Inputs to the
Decision.
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Elements
B6 Instrument/Equip
ment Testing
Inspection and
Maintenance
Requirements
B7 Instrument
Calibration and
Frequency
B8 Inspection/Accepta
nee Requirements
for Supplies and
Consumables
B9 Data Acquisition
Requirements
(Non-direct
Measurements)
BIO Data Management
Requirements
Discuss how inspection and acceptance testing,
including the use of QC samples, must be
performed to ensure their intended use as
specified by the design.
Identify tools, gauges and instruments, and other
sampling or measurement devices that need
calibration. Describe how the calibration should
be done.
Define how and by whom the sampling supplies
and other consumables will be accepted for use in
the project.
Define the criteria for the use of non-
measurement data such as data that come from
databases or literature.
Outline the data management scheme including
the path and storage of the data and the data
record-keeping system. Identify all data handling
equipment and procedures that will be used to
process, compile, and analyze the data.
DQO Overlap
Step 3: Identify the Inputs to the
Decision.
Step 3: Identify the Inputs to the
Decision.
N/A
Step 1 : State the Problem, Step 7:
Optimize the Design for Obtaining
Data.
Step 3: Identify the Inputs to the
Decision, Step 7: Optimize the Design
for Obtaining Data.
ASSESSMENT/OVERSIGHT
Cl Assessments and
Response Actions
C2 Reports to
Management
Describe the assessment activities needed for this
project. These may include DQA, PE, TSA,
MSR/PR/RR
Identify the frequency, content, and distribution
of reports issued to keep management informed.
Step 5: Develop a Decision Rule, Step
6: Specify Limits on Decision Errors.
N/A
DATA VALIDATION AND USABILITY
Dl Data Review,
Validation, and
Verification
Requirements
D2 Validation and
Verification
Methods
D3 Reconciliation
With Data Quality
Objectives
State the criteria used to accept or reject the data
based on quality.
Describe the process to be used for validating and
verifying data, including the chain-of-custody for
data throughout the lifetime of the project.
Describe how results will be evaluated to
determine if DQOs have been satisfied.
Step 7: Optimize the Design for
Obtaining Data.
Step 3: Identify the Inputs to the
Decision.
Step 7: Optimize the Design for
Obtaining Data.
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A-6
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AA5. EPA QUALITY ASSURANCE DOCUMENTS
The Quality Assurance Division issues QA documents for use both internally (National Programs,
Centers, and Laboratories) and externally (state and local agencies, contractors, extramural agreement
holders, and nonprofit groups). The scopes of the documents span all aspects of QA and can be obtained
by writing QAD directly or by visiting the QAD Website:
http://es.epa.gov/ncerqa/qa/qa_docs.html
QAD documents fall into three categories: the EPA Quality Manual (for internal use);
Requirements documents (for external use, labeled 'R-xx'); and Guidance documents (for internal and
external use, labeled 'G-xx'). Requirements documents and the Quality Manual contain the Agency's
QA policies and Guidance documents contain nonmandatory guidance on how to achieve these QA
requirements.
Table Al shows the general numbering system for EPA's Quality System documents, and Table
A2 illustrates some specific documents available and under construction. The auxiliary letter on some of
the documents denotes specialized audiences or areas of interest. Figure Al shows the relationship
among the documents at the Policy and Program levels. Figure A2 demonstrates the sequence and
interrelationship of documents at the Program level.
Not all of the documents listed in Table A2 are available, as some are in various stages of
development and will not be finalized until late 1998. Consult the Website or contact QAD directly for
information on the current status and availability of all QAD documents.
Table AA1. Numbering System for EPA's Quality System Documents
1 = Quality System Policy and Quality Manual 6 = Standard Operating Procedures (SOPs)
2 = Quality Management Plans (QMPs) 7 = Technical Assessments (TAs)
3 = Management Systems Reviews (MSRs) 8 = Data Verification and Validation
4 = Data Quality Objectives (DQOs) 9 = Data Quality Assessment (DQA)
5 = Quality Assurance Project Plans (QAPPs) 10 = Training Issues
EPA QA/G-5 A-7 QA98
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Table AA2. Quality System Documents
Overview
QA/G-0
Program level
QA/R-1
QA/G-1
QA/R-2
QA/G-2
QA/G-2C
QA/G-2EA
QA/G-2F
QA/G-3
QA/G-10
Project level
QA/G-4
QA/G-4CS
QA/G-4D
QA/G-4HW
QA/G-4R
QA/R-5
QA/G-5
QA/G-5I
QA/G-5 S
QA/G-5T
QA/G-6
QA/G-7
QA/G-8
QA/G-9
QA/G-9D
EPA Quality System Description
EPA Quality Systems Requirements for Environmental Programs
Guidance for Developing Quality Systems for Environmental Data Operations
EPA Requirements for Quality Management Plans
Guidance for Preparing Quality Management Plans
Guide to Satisfying EPA Quality Assurance Requirements for Contracts
Guide to Implementing Quality Assurance in Extramural Agreements
Guide to Satisfying EPA Quality Assurance Requirements for Financial Assistance
Agreements
Guidance for the Management Systems Review Process
Guidance for Determining Quality Training Requirements for Environmental Data
Operations
Guidance for the Data Quality Objectives Process
The Data Quality Objectives Process: Case Studies
Data Quality Objectives Decision Errors Feasibility Trials (DEFT) Software
Guidance for the Data Quality Objectives Process for Hazardous Waste Sites
Guidance for the Data Quality Objectives for Researchers
EPA Requirements for Quality Assurance Project Plans
EPA Guidance for Quality Assurance Project Plans
Guidance for Data Quality Indicators
Guidance on Sampling Designs to Support Quality Assurance Project Plans
Guidance on Specialized Topics in Quality Assurance
Guidance for the Preparation of Standard Operating Procedures for Quality-Related
Operations
Guidance on Technical Assessments for Environmental Data Operations
Guidance on Environmental Data Verification and Validation
Guidance for Data Quality Assessment: Practical Methods for Data Analysis
Data Quality Evaluation Statistical Toolbox (DataQUEST).
EPA QA/G-5
A-8
QA98
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o
__i
o
Q_
Agency-
wide
Policies.
Requirements
and
procedures
EPA
Quality
Manual
Authority —
Internal
assessment
o
o
Q_
o
§
Quality
System
structure,
procedures,
standards
Quality
Management
Plan
(QMP)
(G-2)
(R-2)
Quality
Assurance
Annual
Report and
Work Plan
(QAARWP)
Ensures
resources for
implementation of
Quality System
procedures
Quality System
structure, procedures,
standards
Revisions to
QMP
Requirements,
procedures
Internal assessment of
Quality System
effectiveness
Assess-
ment of —
training
needs
Assessment
of Quality
System
effectiveness
Management
Systems Reviews
(MSRs)
(G-3)
Performance
measures
Organizational
responsibilities, Quality
System structure,
procedures, standards,
personnel
Ensure
adequacy of
knowledge,
skills
Assess conformity of
project components
to QMP
1
Performance
measures
Individual Projects
Figure AA1. Relationships Among EPA Quality System Documents at the Program Level
EPA QA/G-5 A-9 QA98
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PLANNING
IMPLEMENTATION
ASSESSMENT
INITIAL
PLANNING
DESIGN
IMPLEMENTATION
PLANNING
R-5
Requirements for
QA Project Plans
G-4/G-4R/G-4HW
Guidance on the
DQO Process
(decision making)
G-5
Guidance on QA
Project Plans
G-9
Guidance for Data
Quality Assessment
KEY
G-xx | EPA QA guidance document
^ primary outputs/inputs
^ background/reference information
Figure AA2. Relationship Among EPA Quality System Documents at the Project Level
EPAQA/G-5 A-10
QA98
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APPENDIX B
GLOSSARY OF QUALITY ASSURANCE AND RELATED TERMS
Acceptance criteria — Specified limits placed on characteristics of an item, process, or service defined
in requirements documents. (ASQC Definitions)
Accuracy — A measure of the closeness of an individual measurement or the average of a number of
measurements to the true value. Accuracy includes a combination of random error (precision) and
systematic error (bias) components that are due to sampling and analytical operations; the EPA
recommends using the terms "precision " and "bias ", rather than "accuracy," to convey the information
usually associated with accuracy. Refer to Appendix D, Data Quality Indicators for a more detailed
definition.
Activity — An all-inclusive term describing a specific set of operations of related tasks to be performed,
either serially or in parallel (e.g., research and development, field sampling, analytical operations,
equipment fabrication), that, in total, result in a product or service.
Assessment — The evaluation process used to measure the performance or effectiveness of a system and
its elements. As used here, assessment is an all-inclusive term used to denote any of the following: audit,
performance evaluation (PE), management systems review (MSR), peer review, inspection, or
surveillance.
Audit (quality) — A systematic and independent examination to determine whether quality activities
and related results comply with planned arrangements and whether these arrangements are implemented
effectively and are suitable to achieve objectives.
Audit of Data Quality (ADQ) — A qualitative and quantitative evaluation of the documentation and
procedures associated with environmental measurements to verify that the resulting data are of
acceptable quality.
Authenticate — The act of establishing an item as genuine, valid, or authoritative.
Bias — The systematic or persistent distortion of a measurement process, which causes errors in one
direction (i.e., the expected sample measurement is different from the sample's true value). Refer to
Appendix D, Data Quality Indicators, for a more detailed definition.
Blank — A sample subjected to the usual analytical or measurement process to establish a zero baseline
or background value. Sometimes used to adjust or correct routine analytical results. A sample that is
intended to contain none of the analytes of interest. A blank is used to detect contamination during
sample handling preparation and/or analysis.
Calibration — A comparison of a measurement standard, instrument, or item with a standard or
instrument of higher accuracy to detect and quantify inaccuracies and to report or eliminate those
inaccuracies by adjustments.
Calibration drift — The deviation in instrument response from a reference value over a period of time
before recalibration.
EPAQA/G-5 B-l QA98
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Certification — The process of testing and evaluation against specifications designed to document,
verify, and recognize the competence of a person, organization, or other entity to perform a function or
service, usually for a specified time.
Chain of custody — An unbroken trail of accountability that ensures the physical security of samples,
data, and records.
Characteristic — Any property or attribute of a datum, item, process, or service that is distinct,
describable, and/or measurable.
Check standard — A standard prepared independently of the calibration standards and analyzed exactly
like the samples. Check standard results are used to estimate analytical precision and to indicate the
presence of bias due to the calibration of the analytical system.
Collocated samples — Two or more portions collected at the same point in time and space so as to be
considered identical. These samples are also known as field replicates and should be identified as such.
Comparability — A measure of the confidence with which one data set or method can be compared to
another.
Completeness — A measure of the amount of valid data obtained from a measurement system compared
to the amount that was expected to be obtained under correct, normal conditions. Refer to Appendix D,
Data Quality Indicators, for a more detailed definition.
Confidence Interval — The numerical interval constructed around a point estimate of a population
parameter, combined with a probability statement (the confidence coefficient) linking it to the
population's true parameter value. If the same confidence interval construction technique and
assumptions are used to calculate future intervals, they will include the unknown population parameter
with the same specified probability.
Confidentiality procedure — A procedure used to protect confidential business information (including
proprietary data and personnel records) from unauthorized access.
Configuration — The functional, physical, and procedural characteristics of an item, experiment, or
document.
Conformance — An affirmative indication or judgment that a product or service has met the
requirements of the relevant specification, contract, or regulation; also, the state of meeting the
requirements.
Consensus standard — A standard established by a group representing a cross section of a particular
industry or trade, or a part thereof.
Contractor — Any organization or individual contracting to furnish services or items or to perform
work.
Corrective action — Any measures taken to rectify conditions adverse to quality and, where possible, to
preclude their recurrence.
EPA QA/G-5 B-2 QA98
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Data Quality Assessment (DQA) — The scientific and statistical evaluation of data to determine if data
obtained from environmental operations are of the right type, quality, and quantity to support their
intended use. The five steps of the DQA Process include: 1) reviewing the DQOs and sampling design,
2) conducting a preliminary data review, 3) selecting the statistical test, 4) verifying the assumptions of
the statistical test, and 5) drawing conclusions from the data.
Data Quality Indicators (DQIs) — The quantitative statistics and qualitative descriptors that are used to
interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are
bias, precision, accuracy (bias is preferred), comparability, completeness, representativeness.
Data Quality Objectives (DQOs) — The qualitative and quantitative statements derived from the DQO
Process that clarify study's technical and quality objectives, define the appropriate type of data, and
specify tolerable levels of potential decision errors that will be used as the basis for establishing the
quality and quantity of data needed to support decisions.
Data Quality Objectives (DQO) Process — A systematic strategic planning tool based on the scientific
method that identifies and defines the type, quality, and quantity of data needed to satisfy a specified use.
DQOs are the qualitative and quantitative outputs from the DQO Process.
Data reduction — The process of transforming the number of data items by arithmetic or statistical
calculations, standard curves, and concentration factors, and collating them into a more useful form.
Data reduction is irreversible and generally results in a reduced data set and an associated loss of detail.
Data usability — The process of ensuring or determining whether the quality of the data produced meets
the intended use of the data.
Deficiency — An unauthorized deviation from acceptable procedures or practices, or a defect in an item.
Demonstrated capability — The capability to meet a procurement's technical and quality specifications
through evidence presented by the supplier to substantiate its claims and in a manner defined by the
customer.
Design — The specifications, drawings, design criteria, and performance requirements. Also, the result
of deliberate planning, analysis, mathematical manipulations, and design processes.
Design change — Any revision or alteration of the technical requirements defined by approved and
issued design output documents and approved and issued changes thereto.
Design review — A documented evaluation by a team, including personnel such as the responsible
designers, the client for whom the work or product is being designed, and a quality assurance (QA)
representative but excluding the original designers, to determine if a proposed design will meet the
established design criteria and perform as expected when implemented.
Detection Limit (DL) — A measure of the capability of an analytical method to distinguish samples that
do not contain a specific analyte from samples that contain low concentrations of the analyte; the lowest
concentration or amount of the target analyte that can be determined to be different from zero by a single
measurement at a stated level of probability. DLs are analyte- and matrix-specific and may be
laboratory-dependent.
EPA QA/G-5 B-3 QA98
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Distribution — 1) The appointment of an environmental contaminant at a point over time, over an area,
or within a volume; 2) a probability function (density function, mass function, or distribution function)
used to describe a set of observations (statistical sample) or a population from which the observations are
generated.
Document control — The policies and procedures used by an organization to ensure that its documents
and their revisions are proposed, reviewed, approved for release, inventoried, distributed, archived,
stored, and retrieved in accordance with the organization's requirements.
Duplicate samples — Two samples taken from and representative of the same population and carried
through all steps of the sampling and analytical procedures in an identical manner. Duplicate samples are
used to assess variance of the total method, including sampling and analysis. See also collocated sample.
Environmental conditions — The description of a physical medium (e.g., air, water, soil, sediment) or a
biological system expressed in terms of its physical, chemical, radiological, or biological characteristics.
Environmental data — Any parameters or pieces of information collected or produced from
measurements, analyses, or models of environmental processes, conditions, and effects of pollutants on
human health and the ecology, including results from laboratory analyses or from experimental systems
representing such processes and conditions.
Environmental data operations — Any work performed to obtain, use, or report information pertaining
to environmental processes and conditions.
Environmental monitoring — The process of measuring or collecting environmental data.
Environmental processes — Any manufactured or natural processes that produce discharges to, or that
impact, the ambient environment.
Environmental programs — An all-inclusive term pertaining to any work or activities involving the
environment, including but not limited to: characterization of environmental processes and conditions;
environmental monitoring; environmental research and development; the design, construction, and
operation of environmental technologies; and laboratory operations on environmental samples.
Environmental technology — An all-inclusive term used to describe pollution control devices and
systems, waste treatment processes and storage facilities, and site remediation technologies and their
components that may be utilized to remove pollutants or contaminants from, or to prevent them from
entering, the environment. Examples include wet scrubbers (air), soil washing (soil), granulated
activated carbon unit (water), and filtration (air, water). Usually, this term applies to hardware-based
systems; however, it can also apply to methods or techniques used for pollution prevention, pollutant
reduction, or containment of contamination to prevent further movement of the contaminants, such as
capping, solidification or vitrification, and biological treatment.
Estimate — A characteristic from the sample from which inferences on parameters can be made.
Evidentiary records — Any records identified as part of litigation and subject to restricted access,
custody, use, and disposal.
EPA QA/G-5 B-4 QA98
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Expedited change — An abbreviated method of revising a document at the work location where the
document is used when the normal change process would cause unnecessary or intolerable delay in the
work.
Field blank — A blank used to provide information about contaminants that may be introduced during
sample collection, storage, and transport. A clean sample, carried to the sampling site, exposed to
sampling conditions, returned to the laboratory, and treated as an environmental sample.
Field (matrix) spike — A sample prepared at the sampling point (i.e., in the field) by adding a known
mass of the target analyte to a specified amount of the sample. Field matrix spikes are used, for example,
to determine the effect of the sample preservation, shipment, storage, and preparation on analyte recovery
efficiency (the analytical bias).
Field split samples — Two or more representative portions taken from the same sample and submitted
for analysis to different laboratories to estimate interlaboratory precision.
Financial assistance — The process by which funds are provided by one organization (usually
governmental) to another organization for the purpose of performing work or furnishing services or
items. Financial assistance mechanisms include grants, cooperative agreements, and governmental
interagency agreements.
Finding — An assessment conclusion that identifies a condition having a significant effect on an item or
activity. An assessment finding may be positive or negative, and is normally accompanied by specific
examples of the observed condition.
Goodness-of-fit test — The application of the chi square distribution in comparing the frequency
distribution of a statistic observed in a sample with the expected frequency distribution based on some
theoretical model.
Grade — The category or rank given to entities having the same functional use but different
requirements for quality.
Graded approach — The process of basing the level of application of managerial controls applied to an
item or work according to the intended use of the results and the degree of confidence needed in the
quality of the results. (See also Data Quality Objectives (DQO) Process.)
Guidance — A suggested practice that is not mandatory, intended as an aid or example in complying
with a standard or requirement.
Guideline — A suggested practice that is not mandatory in programs intended to comply with a standard.
Hazardous waste — Any waste material that satisfies the definition of hazardous waste given in 40 CFR
261, "Identification and Listing of Hazardous Waste."
Holding time — The period of time a sample may be stored prior to its required analysis. While
exceeding the holding time does not necessarily negate the veracity of analytical results, it causes the
qualifying or "flagging" of any data not meeting all of the specified acceptance criteria.
Identification error — The misidentification of an analyte. In this error type, the contaminant of
concern is unidentified and the measured concentration is incorrectly assigned to another contaminant.
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Independent assessment — An assessment performed by a qualified individual, group, or organization
that is not a part of the organization directly performing and accountable for the work being assessed.
Inspection — The examination or measurement of an item or activity to verify conformance to specific
requirements.
Internal standard — A standard added to a test portion of a sample in a known amount and carried
through the entire determination procedure as a reference for calibrating and controlling the precision
and bias of the applied analytical method.
Laboratory split samples — Two or more representative portions taken from the same sample and
analyzed by different laboratories to estimate the interlaboratory precision or variability and the data
comparability.
Limit of quantitation — The minimum concentration of an analyte or category of analytes in a specific
matrix that can be identified and quantified above the method detection limit and within specified limits
of precision and bias during routine analytical operating conditions.
Management — Those individuals directly responsible and accountable for planning, implementing, and
assessing work.
Management system — A structured, nontechnical system describing the policies, objectives,
principles, organizational authority, responsibilities, accountability, and implementation plan of an
organization for conducting work and producing items and services.
Management Systems Review (MSR) — The qualitative assessment of a data collection operation
and/or organization(s) to establish whether the prevailing quality management structure, policies,
practices, and procedures are adequate for ensuring that the type and quality of data needed are obtained.
Matrix spike — A sample prepared by adding a known mass of a target analyte to a specified amount of
matrix sample for which an independent estimate of the target analyte concentration is available. Spiked
samples are used, for example, to determine the effect of the matrix on a method's recovery efficiency.
Mean (arithmetic) — The sum of all the values of a set of measurements divided by the number of
values in the set; a measure of central tendency.
Mean squared error — A statistical term for variance added to the square of the bias.
Measurement and Testing Equipment (M&TE) — Tools, gauges, instruments, sampling devices, or
systems used to calibrate, measure, test, or inspect in order to control or acquire data to verify
conformance to specified requirements.
Memory effects error — The effect that a relatively high concentration sample has on the measurement
of a lower concentration sample of the same analyte when the higher concentration sample precedes the
lower concentration sample in the same analytical instrument.
Method — A body of procedures and techniques for performing an activity (e.g., sampling, chemical
analysis, quantification), systematically presented in the order in which they are to be executed.
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Method blank — A blank prepared to represent the sample matrix as closely as possible and analyzed
exactly like the calibration standards, samples, and quality control (QC) samples. Results of method
blanks provide an estimate of the within-batch variability of the blank response and an indication of bias
introduced by the analytical procedure.
Mid-range check — A standard used to establish whether the middle of a measurement method's
calibrated range is still within specifications.
Mixed waste — A hazardous waste material as defined by 40 CFR 261 Resource Conservation and
Recovery Act (RCRA) and mixed with radioactive waste subject to the requirements of the Atomic
Energy Act.
Must — When used in a sentence, a term denoting a requirement that has to be met.
Nonconformance — A deficiency in a characteristic, documentation, or procedure that renders the
quality of an item or activity unacceptable or indeterminate; nonfulfillment of a specified requirement.
Objective evidence — Any documented statement of fact, other information, or record, either
quantitative or qualitative, pertaining to the quality of an item or activity, based on observations,
measurements, or tests that can be verified.
Observation — An assessment conclusion that identifies a condition (either positive or negative) that
does not represent a significant impact on an item or activity. An observation may identify a condition
that has not yet caused a degradation of quality.
Organization — A company, corporation, firm, enterprise, or institution, or part thereof, whether
incorporated or not, public or private, that has its own functions and administration.
Organization structure — The responsibilities, authorities, and relationships, arranged in a pattern,
through which an organization performs its functions.
Outlier — An extreme observation that is shown to have a low probability of belonging to a specified
data population.
Parameter — A quantity, usually unknown, such as a mean or a standard deviation characterizing a
population. Commonly misused for "variable," "characteristic," or "property."
Peer review — A documented critical review of work generally beyond the state of the art or
characterized by the existence of potential uncertainty. Conducted by qualified individuals (or an
organization) who are independent of those who performed the work but collectively equivalent in
technical expertise (i.e., peers) to those who performed the original work. Peer reviews are conducted to
ensure that activities are technically adequate, competently performed, properly documented, and satisfy
established technical and quality requirements. An in-depth assessment of the assumptions, calculations,
extrapolations, alternate interpretations, methodology, acceptance criteria, and conclusions pertaining to
specific work and of the documentation that supports them. Peer reviews provide an evaluation of a
subject where quantitative methods of analysis or measures of success are unavailable or undefined, such
as in research and development.
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Performance Evaluation (PE) — A type of audit in which the quantitative data generated in a
measurement system are obtained independently and compared with routinely obtained data to evaluate
the proficiency of an analyst or laboratory.
Pollution prevention — An organized, comprehensive effort to systematically reduce or eliminate
pollutants or contaminants prior to their generation or their release or discharge into the environment.
Precision — A measure of mutual agreement among individual measurements of the same property,
usually under prescribed similar conditions expressed generally in terms of the standard deviation. Refer
to Appendix D, Data Quality Indicators, for a more detailed definition.
Procedure — A specified way to perform an activity.
Process — A set of interrelated resources and activities that transforms inputs into outputs. Examples of
processes include analysis, design, data collection, operation, fabrication, and calculation.
Project — An organized set of activities within a program.
Qualified data — Any data that have been modified or adjusted as part of statistical or mathematical
evaluation, data validation, or data verification operations.
Qualified services — An indication that suppliers providing services have been evaluated and
determined to meet the technical and quality requirements of the client as provided by approved
procurement documents and demonstrated by the supplier to the client's satisfaction.
Quality — The totality of features and characteristics of a product or service that bears on its ability to
meet the stated or implied needs and expectations of the user.
Quality Assurance (QA) — An integrated system of management activities involving planning,
implementation, assessment, reporting, and quality improvement to ensure that a process, item, or service
is of the type and quality needed and expected by the client.
Quality Assurance Program Description/Plan — See quality management plan.
Quality Assurance Project Plan (QAPP) — A formal document describing in comprehensive detail the
necessary quality assurance (QA), quality control (QC), and other technical activities that must be
implemented to ensure that the results of the work performed will satisfy the stated performance criteria.
The QAPP components are divided into four classes: 1) Project Management, 2) Measurement/Data
Acquisition, 3) Assessment/Oversight, and 4) Data Validation and Usability. Requirements for preparing
QAPPs can be found in EPA QA/R-5.
Quality Control (QC) — The overall system of technical activities that measures the attributes and
performance of a process, item, or service against defined standards to verify that they meet the stated
requirements established by the customer; operational techniques and activities that are used to fulfill
requirements for quality. The system of activities and checks used to ensure that measurement systems
are maintained within prescribed limits, providing protection against "out of control" conditions and
ensuring the results are of acceptable quality.
Quality control (QC) sample — An uncontaminated sample matrix spiked with known amounts of
analytes from a source independent of the calibration standards. Generally used to establish intra-
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laboratory or analyst-specific precision and bias or to assess the performance of all or a portion of the
measurement system.
Quality improvement — A management program for improving the quality of operations. Such
management programs generally entail a formal mechanism for encouraging worker recommendations
with timely management evaluation and feedback or implementation.
Quality management — That aspect of the overall management system of the organization that
determines and implements the quality policy. Quality management includes strategic planning,
allocation of resources, and other systematic activities (e.g., planning, implementation, and assessment)
pertaining to the quality system.
Quality Management Plan (QMP) — A formal document that describes the quality system in terms of
the organization's structure, the functional responsibilities of management and staff, the lines of
authority, and the required interfaces for those planning, implementing, and assessing all activities
conducted.
Quality system — A structured and documented management system describing the policies, objectives,
principles, organizational authority, responsibilities, accountability, and implementation plan of an
organization for ensuring quality in its work processes, products (items), and services. The quality
system provides the framework for planning, implementing, and assessing work performed by the
organization and for carrying out required quality assurance (QA) and quality control (QC).
Radioactive waste — Waste material containing, or contaminated by, radionuclides, subject to the
requirements of the Atomic Energy Act.
Readiness review — A systematic, documented review of the readiness for the start-up or continued use
of a facility, process, or activity. Readiness reviews are typically conducted before proceeding beyond
project milestones and prior to initiation of a major phase of work.
Record (quality) — A document that furnishes objective evidence of the quality of items or activities
and that has been verified and authenticated as technically complete and correct. Records may include
photographs, drawings, magnetic tape, and other data recording media.
Recovery — The act of determining whether or not the methodology measures all of the analyte
contained in a sample. Refer to Appendix D, Data Quality Indicators, for a more detailed definition.
Remediation — The process of reducing the concentration of a contaminant (or contaminants) in air,
water, or soil media to a level that poses an acceptable risk to human health.
Repeatability — The degree of agreement between independent test results produced by the same
analyst, using the same test method and equipment on random aliquots of the same sample within a short
time period.
Reporting limit — The lowest concentration or amount of the target analyte required to be reported
from a data collection project. Reporting limits are generally greater than detection limits and are usually
not associated with a probability level.
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Representativeness — A measure of the degree to which data accurately and precisely represent a
characteristic of a population, a parameter variation at a sampling point, a process condition, or an
environmental condition. See also Appendix D, Data Quality Indicators.
Reproducibility — The precision, usually expressed as variance, that measures the variability among the
results of measurements of the same sample at different laboratories.
Requirement — A formal statement of a need and the expected manner in which it is to be met.
Research (applied) — A process, the objective of which is to gain the knowledge or understanding
necessary for determining the means by which a recognized and specific need may be met.
Research (basic) — A process, the objective of which is to gain fuller knowledge or understanding of
the fundamental aspects of phenomena and of observable facts without specific applications toward
processes or products in mind.
Research development/demonstration — The systematic use of the knowledge and understanding
gained from research and directed toward the production of useful materials, devices, systems, or
methods, including prototypes and processes.
Round-robin study — A method validation study involving a predetermined number of laboratories or
analysts, all analyzing the same sample(s) by the same method. In a round-robin study, all results are
compared and used to develop summary statistics such as interlaboratory precision and method bias or
recovery efficiency.
Ruggedness study — The carefully ordered testing of an analytical method while making slight
variations in test conditions (as might be expected in routine use) to determine how such variations affect
test results. If a variation affects the results significantly, the method restrictions are tightened to
minimize this variability.
Scientific method — The principles and processes regarded as necessary for scientific investigation,
including rules for concept or hypothesis formulation, conduct of experiments, and validation of
hypotheses by analysis of observations.
Self-assessment — The assessments of work conducted by individuals, groups, or organizations directly
responsible for overseeing and/or performing the work.
Sensitivity — the capability of a method or instrument to discriminate between measurement responses
representing different levels of a variable of interest. Refer to Appendix D, Data Quality Indicators, for a
more detailed definition.
Service — The result generated by activities at the interface between the supplier and the customer, and
the supplier internal activities to meet customer needs. Such activities in environmental programs
include design, inspection, laboratory and/or field analysis, repair, and installation.
Shall — A term denoting a requirement that is mandatory whenever the criterion for conformance with
the specification permits no deviation. This term does not prohibit the use of alternative approaches or
methods for implementing the specification so long as the requirement is fulfilled.
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Significant condition — Any state, status, incident, or situation of an environmental process or
condition, or environmental technology in which the work being performed will be adversely affected
sufficiently to require corrective action to satisfy quality objectives or specifications and safety
requirements.
Software life cycle — The period of time that starts when a software product is conceived and ends
when the software product is no longer available for routine use. The software life cycle typically
includes a requirement phase, a design phase, an implementation phase, a test phase, an installation and
check-out phase, an operation and maintenance phase, and sometimes a retirement phase.
Source reduction — Any practice that reduces the quantity of hazardous substances, contaminants, or
pollutants.
Span check — A standard used to establish that a measurement method is not deviating from its
calibrated range.
Specification — A document stating requirements and referring to or including drawings or other
relevant documents. Specifications should indicate the means and criteria for determining conformance.
Spike — A substance that is added to an environmental sample to increase the concentration of target
analytes by known amounts; used to assess measurement accuracy (spike recovery). Spike duplicates
are used to assess measurement precision.
Split samples — Two or more representative portions taken from one sample in the field or in the
laboratory and analyzed by different analysts or laboratories. Split samples are quality control (QC)
samples that are used to assess analytical variability and comparability.
Standard deviation — A measure of the dispersion or imprecision of a sample or population distribution
expressed as the positive square root of the variance and has the same unit of measurement as the mean.
Standard Operating Procedure (SOP) — A written document that details the method for an operation,
analysis, or action with thoroughly prescribed techniques and steps and that is officially approved as the
method for performing certain routine or repetitive tasks.
Supplier — Any individual or organization furnishing items or services or performing work according to
a procurement document or a financial assistance agreement. An all-inclusive term used in place of any
of the following: vendor, seller, contractor, subcontractor, fabricator, or consultant.
Surrogate spike or analyte — A pure substance with properties that mimic the analyte of interest. It is
unlikely to be found in environmental samples and is added to them to establish that the analytical
meyhod has been performed properly.
Surveillance (quality) — Continual or frequent monitoring and verification of the status of an entity and
the analysis of records to ensure that specified requirements are being fulfilled.
Technical review — A documented critical review of work that has been performed within the state of
the art. The review is accomplished by one or more qualified reviewers who are independent of those
who performed the work but are collectively equivalent in technical expertise to those who performed the
original work. The review is an in-depth analysis and evaluation of documents, activities, material, data,
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or items that require technical verification or validation for applicability, correctness, adequacy,
completeness, and assurance that established requirements have been satisfied.
Technical Systems Audit (TSA) — A thorough, systematic, on-site qualitative audit of facilities,
equipment, personnel, training, procedures, record keeping, data validation, data management, and
reporting aspects of a system.
Traceability — The ability to trace the history, application, or location of an entity by means of recorded
identifications. In a calibration sense, traceability relates measuring equipment to national or
international standards, primary standards, basic physical constants or properties, or reference materials.
In a data collection sense, it relates calculations and data generated throughout the project back to the
requirements for the quality of the project.
Trip blank — A clean sample of a matrix that is taken to the sampling site and transported to the
laboratory for analysis without having been exposed to sampling procedures.
Validation — Confirmation by examination and provision of objective evidence that the particular
requirements for a specific intended use have been fulfilled. In design and development, validation
concerns the process of examining a product or result to determine conformance to user needs. See also
Appendix G, Data Management.
Variance (statistical) — A measure or dispersion of a sample or population distribution.
Verification — Confirmation by examination and provision of objective evidence that specified
requirements have been fulfilled. In design and development, verification concerns the process of
examining a result of a given activity to determine conformance to the stated requirements for that
activity.
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APPENDIX C
CHECKLISTS USEFUL IN QUALITY ASSURANCE REVIEW
This appendix contains three checklists:
AC.l Sample Handling, Preparation, and Analysis Checklist
AC.2 QAPP Review Checklist
AC.3 Chain-of-Custody Checklist
These three checklists were developed as tools for quality assurance (QA) managers to screen for
completeness of documentation. This appendix was not intended to be used or adapted for auditing
purposes. The items listed on the checklists are not ranked or identified to indicate which items are
trivial and which are of major importance. When using these checklists, it is extremely important to
ensure that a mechanism be established for assessing and addressing important comments or violations
during the data assessment (e.g., Data Quality Assessment [DQA]) stage.
AC1. SAMPLE HANDLING, PREPARATION, AND ANALYSIS CHECKLIST
This checklist covers most of the appropriate elements performed during the analysis of
environmental samples. Functions not appropriate for a specific analysis should be annotated.
Information on the collection and handling of samples should be completely documented to
allow the details of sample collection and handling to be re-created. All information should be entered
in ink at the time the information was generated in a permanently bound logbook. Errors should not be
erased or crossed-out but corrected by putting a line through the erroneous information and by entering,
initialing, and dating the correct information. Blank spaces should have an obliterating line drawn
through to prevent addition of information. Each set of information should have an identifying printed
name, signature, and initials.
Sample Handling
• Field Logs Documentation of events occurring during field sampling to
identify individual field samples.
• Sample Labels Links individual samples with the field log and the chain-of-
custody record.
• Chain-of-Custody Records Documentation of exchange and transportation of samples from
the field to final analysis.
• Sample Receipt Log Documentation of receipt of the laboratory or organization of
the entire set of individual samples for analysis.
Sample Preparation and Analysis
• Sample Preparation Log Documents the preparation of samples for a specific method.
• Sample Analysis Log Records information on the analysis of analytical results.
• Instrument Run Log Records analyses of calibration standards, field samples, and
quality control (QC) samples.
Chemical Standards
• Chemical Standard Receipt Log Records receipt of analytical standards and chemicals.
• Standards/Reagent Preparation Log Records of the preparation of internal standards, reagents,
spiking solutions, surrogate solutions, and reference materials.
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AC.l SAMPLE HANDLING, REPORTING, AND ANALYSIS CHECKLIST
Field Loss
ELEMENT
Project name/ID and location
Sampling personnel
Geological observations including map
Atmospheric conditions
Field measurements
Sample dates, times, and locations
Sample identifications present
Sample matrix identified
Sample descriptions (e.g., odors and colors)
Number of samples taken per location
Sampling method/equipment
Description of any QC samples
Any deviations from the sampling plan
Difficulties in sampling or unusual circumstances
COMMENT
Sample Labels
ELEMENT
Sample ID
Date and time of collection
Sampler's signature
Characteristic or parameter investigated
Preservative used
COMMENT
Chain of Custody Records
ELEMENT
Project name/ID and location
Sample custodian signatures verified and on file
Date and time of each transfer
Carrier ID number
Integrity of shipping container and seals verified
Standard Operating Procedures (SOPs) for receipt on file
Samples stored in same area
Holding time protocol verified
SOPs for sample preservation on file
Identification of proposed analytical method verified
Proposed analytical method documentation verified
QA Plan for proposed analytical method on file
COMMENT
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AC.l SAMPLE HANDLING, REPORTING, AND ANALYSIS CHECKLIST (CONTINUED)
Sample Receipt Los
ELEMENT
Date and time of receipt
Sample collection date
Client sample ID
Number of samples
Sample matrices
Requested analysis, including method number(s)
Signature of the sample custodian or designee
Sampling kit code (if applicable)
Sampling condition
Chain-of-custody violations and identities
COMMENT
SAMPLE PREPARATION AND ANALYSIS
Sample Preparation Loss
ELEMENT
Parameter/analyte of investigation
Method number
Date and time of preparation
Analyst's initials or signature
Initial sample volume or weight
Final sample volume
Concentration and amount of spiking solutions used
QC samples included with the sample batch
ID for reagents, standards, and spiking solutions used
COMMENT
Sample Analysis Loss
ELEMENT
Parameter analyte of investigation
Method number/reference
Date and time of analysis
Analyst's initials or signature
Laboratory sample ID
Sample aliquot
Dilution factors and final sample volumes (if applicable)
Absorbance values, peak heights, or initial concentrations reading
Final analyte concentration
Calibration data (if applicable)
Correlation coefficient (including parameters)
Calculations of key quantities available
Comments on interferences or unusual observations
QC information, including percent recovery
COMMENT
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AC.l SAMPLE HANDLING, REPORTING, AND ANALYSIS CHECKLIST (CONTINUED)
Instrument Run Loss
ELEMENT
Name/type of instrument
Instrument manufacturer and model number
Serial number
Date received and date placed in service
Instrument ID assigned by the laboratory (if used)
Service contract information, including service representative details
Description of each maintenance or repair activity performed
Date and time when of each maintenance or repair activity
Initials of maintenance or repair technicians
COMMENT
CHEMICAL STANDARDS
Chemical/Standard Receipt Loss
ELEMENT
Laboratory control number
Date of receipt
Initials or signature of person receiving chemical
Chemical name and catalog number
Vendor name and log number
Concentration or purity of standard
Expiration date
COMMENT
Standards/Reagent Preparation Los
ELEMENT
Date of preparation
Initials of analyst preparing the standard solution or reagent
Concentration or purity of standard or reagent
Volume or weight of the stock solution or neat materials
Final volume of the solution being prepared
Laboratory ID/control number assigned to the new solution
Name of standard reagent
Standardization of reagents, titrants, etc. (if applicable)
Expiration date
COMMENT
References
Roserance, A. and L. Kibler. 1994. "Generating Defensible Data," Environmental Testing and Analysis. May/June.
Roserance, A. and L. Kibler. 1996. "Documentation and Record Keeping Guidelines." In Proceedings of the 12th
Annual Waste Testing and Quality Assurance Symposium. July.
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AC.2 QAPP REVIEW CHECKLIST
ELEMENT
Al. Title and Approval Sheet
Title
Organization's name
Dated signature of project manager
Dated signature of quality assurance officer
Other signatures, as needed
A2. Table of Contents
A3. Distribution List
A4. Project/Task Organization
Identifies key individuals, with their responsibilities (data users, decision-
makers, project QA manager, subcontractors, etc.)
Organization chart shows lines of authority and reporting responsibilities
A5. Problem Definition/Background
Clearly states problem or decision to be resolved
Provides historical and background information
A6. Project/Task Description
Lists measurements to be made
Cites applicable technical, regulatory, or program-specific quality standards,
criteria, or objectives
Notes special personnel or equipment requirements
Provides work schedule
Notes required project and QA records/reports
A7. Quality Objectives and Criteria for Measurement Data
States project objectives and limits, both qualitatively and quantitatively
States and characterizes measurement quality objectives as to applicable
action levels or criteria
A8. Special Training Requirements/Certification Listed
States how provided, documented, and assured
A9. Documentation and Records
Lists information and records to be included in data report (e.g., raw data,
field logs, results of QC checks, problems encountered)
States requested lab turnaround time
Gives retention time and location for records and reports
Bl. Sampling Process Design (Experimental Design)
States the following:
Type and number of samples required
Sampling design and rationale
Sampling locations and frequency
Sample matrices
COMMENTS
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AC.2 QAPP REVIEW CHECKLIST (CONTINUED)
ELEMENT
Classification of each measurement parameter as either critical or needed for
information only
Appropriate validation study information, for nonstandard situations
B2. Sampling Methods Requirements
Identifies sample collection procedures and methods
Lists equipment needs
Identifies support facilities
Identifies individuals responsible for corrective action
Describes process for preparation and decontamination of sampling
equipment
Describes selection and preparation of sample containers and sample volumes
Describes preservation methods and maximum holding times
B3. Sample Handling and Custody Requirements
Notes sample handling requirements
Notes chain-of -custody procedures, if required
B4. Analytical Methods Requirements
Identifies analytical methods to be followed (with all options) and required
equipment
Provides validation information for nonstandard methods
Identifies individuals responsible for corrective action
Specifies needed laboratory turnaround time
B5. Quality Control Requirements
Identifies QC procedures and frequency for each sampling, analysis, or
measurement technique, as well as associated acceptance criteria and
corrective action
References procedures used to calculate QC statistics including precision and
bias/accuracy
B6. Instrument/Equipment Testing, Inspection, and Maintenance Requirements
Identifies acceptance testing of sampling and measurement systems
Describes equipment preventive and corrective maintenance
Notes availability and location of spare parts
B7. Instrument Calibration and Frequency
Identifies equipment needing calibration and frequency for such calibration
Notes required calibration standards and/or equipment
Cites calibration records and manner traceable to equipment
B8. Inspection/Acceptance Requirements for Supplies and Consumables
States acceptance criteria for supplies and consumables
Notes responsible individuals
B9. Data Acquisition Requirements for Nondirect Measurements
COMMENTS
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AC.2 QAPP REVIEW CHECKLIST (CONTINUED)
ELEMENT
Identifies type of data needed from nonmeasurement sources (e.g., computer
databases and literature files), along with acceptance criteria for their use
Describes any limitations of such data
Documents rationale for original collection of data and its relevance to this
project
BIO. Data Management
Describes standard record-keeping and data storage and retrieval requirements
Checklists or standard forms attached to QAPP
Describes data handling equipment and procedures used to process, compile,
and analyze data (e.g., required computer hardware and software)
Describes process for assuring that applicable Office of Information Resource
Management requirements are satisfied
Cl. Assessments and Response Actions
Lists required number, frequency and type of assessments, with approximate
dates and names of responsible personnel (assessments include but are not
limited to peer reviews, management systems reviews, technical systems
audits, performance evaluations, and audits of data quality)
Identifies individuals responsible for corrective actions
C2. Reports to Management
Identifies frequency and distribution of reports for:
Project status
Results of performance evaluations and audits
Results of periodic data quality assessments
Any significant QA problems
Preparers and recipients of reports
Dl. Data Review, Validation, and Verification
States criteria for accepting, rejecting, or qualifying data
Includes project-specific calculations or algorithms
D2. Validation and Verification Methods
Describes process for data validation and verification
Identifies issue resolution procedure and responsible individuals
Identifies method for conveying these results to data users
D3. Reconciliation with User Requirements
Describes process for reconciling project results with DQOs and reporting
limitations on use of data
COMMENTS
References
Personal Communication, Margo Hunt, EPA Region II, February, 1996.
Personal Communication, Robert Dona, EPA Region VII, November, 1997.
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AC.3 CHAIN-OF-CUSTODY CHECKLIST
Item
1 . Is a sample custodian designated?
If yes, name of sample custodian.
2. Are the sample custodian's procedures and responsibilities
documented?
If yes, where are these documented?
3. Are written Standard Operating Procedures (SOPs) developed
for receipt of samples?
If yes, where are the SOPs documented (laboratory manual,
written instructions, etc.)?
4. Is the receipt of chain-of-custody record(s) with samples being
documented?
If yes, where is this documented?
5. Is the nonreceipt of chain-of-custody record(s) with samples
being documented?
If yes, where is this documented?
6. Is the integrity of the shipping container(s) being documented
(custody seal(s) intact, container locked, or sealed properly,
etc.)?
If yes, where is security documented?
7. Is the lack of integrity of the shipping container(s) being
documented (i.e., evidence of tampering, custody seals broken
or damaged, locks unlocked or missing, etc.)?
If yes, where is nonsecurity documented?
8. Is agreement between chain-of-custody records and sample
tags being verified and documented?
If yes, state source of verification and location of
documentation.
9. Are sample tag numbers recorded by the sample custodian?
If yes, where are they recorded?
10. Are written SOPs developed for sample storage?
If yes, where are the SOPs documented (laboratory manual,
written instructions, etc.)?
1 1 . Are samples stored in a secure area?
If yes, where and how are they stored?
12. Is sample identification maintained?
If yes, how?
13. Is sample extract (or inorganics concentrate) identification
maintained?
If yes, how?
14. Are samples that require preservation stored in such a way as
to maintain their preservation?
If yes, how are the samples stored?
Y
N
Comment
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AC.3 CHAIN-OF-CUSTODY CHECKLIST (CONTINUED)
Item
15. Based upon sample records examined to determine holding
times, are sample holding time limitations being satisfied?
Sample records used to determine holding times:
16. Are written SOPs developed for sampling handling and
tracking?
If yes, where are the SOPs documented (laboratory manual,
written instructions, etc.)?
17. Do laboratory records indicate personnel receiving and
transferring samples in the laboratory?
If yes, what laboratory records document this?
18. Does each instrument used for sample analysis (GC, GC/MS,
AA, etc.) have an instrument log?
If no, which instruments do not?
19. Are analytical methods documented and available to the
analysts?
If yes, where are these documented?
20. Are QA procedures documented and available to the analysts?
If yes, where are these documented?
21. Are written SOPs developed for compiling and maintaining
sample document files?
If yes, where are the SOPs documented (laboratory manual,
written instructions, etc.)?
22. Are sample documents filed by case number?
If no, how are documents filed?
23. Are sample document files inventoried?
24. Are documents in the case files consecutively numbered
according to the file inventories?
25. Are documents in the case files stored in a secure area?
If yes, where and how are they stored?
26. Has the laboratory received any confidential documents?
27. Are confidential documents segregated from other laboratory
documents?
If no, how are they filed?
28. Are confidential documents stored in a secure manner?
If yes, where and how are they stored?
29. Was a debriefing held with laboratory personnel after the audit
was completed?
30. Were any recommendations made to laboratory personnel
during the debriefing?
Y
N
Comment
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APPENDIX D
DATA QUALITY INDICATORS
INTRODUCTION
Data Quality Indicators (DQIs) are qualitative and quantitative descriptors used in interpreting
the degree of acceptability or utility of data. The principal DQIs are precision, bias, representativeness,
comparability, and completeness. Secondary DQIs include sensitivity, recovery, memory effects, limit of
quantitation, repeatability, and reproducibility. Establishing acceptance criteria for the DQIs sets
quantitative goals for the quality of data generated in the analytical measurement process. DQIs may be
expressed for entire measurement systems, but it is customary to allow DQIs to be applied only to
laboratory measurement processes. The issues of design and sampling errors, the most influential
components of variability, are discussed separately in EPA QA/G-5S, Guidance on Sampling Designs to
Support QAPPs.
Of the five principal DQIs, precision and bias are the quantitative measures, representativeness
and comparability are qualitative, and completeness is a combination of both quantitative and qualitative
measures.
The five principal DQIs are also referred to by the acronym PARCC, with the "A" in PARCC
referring to accuracy instead of bias. This inconsistency results because some analysts believe accuracy
and bias are synonymous, and PARCC is a more convenient acronym than PBRCC. Accuracy comprises
both random error (precision) and systematic error (bias), and these indicators are discussed separately in
this appendix. DQIs are discussed at length in EPA QA/G-5I, Guidance on Data Quality Indicators.
ADI. PRINCIPAL DQIs: PARCC
AD1.1 PARCC: Precision
Precision is a measure of agreement among replicate measurements of the same property, under
prescribed similar conditions. This agreement is calculated as either the range (R) or as the standard
deviation (s). It may also be expressed as a percentage of the mean of the measurements, such as relative
range (RR) (for duplicates) or relative standard deviation (RSD).
For analytical procedures, precision may be specified as either intralaboratory (within a
laboratory) or interlaboratory (between laboratories) precision. Intralaboratory precision estimates
represent the agreement expected when a single laboratory uses the same method to make repeated
measurements of the same sample. Interlaboratory precision refers to the agreement expected when two
or more laboratories analyze the same or identical samples with the same method. Intralaboratory
precision is more commonly reported; however, where available, both intralaboratory and interlaboratory
precision are listed in the data compilation.
When possible, a sample subdivided in the field and preserved separately is used to assess the
variability of sample handling, preservation, and storage along with the variability of the analysis process.
When collocated samples are collected, processed, and analyzed by the same organization,
intralaboratory precision information on sample acquisition, handling, shipping, storage, preparation, and
analysis is obtained. Both samples can be carried through the steps in the measurement process together
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to provide an estimate of short-term precision. Likewise, the two samples, if separated and processed at
different times or by different people and/or analyzed using different instruments, provide an estimate of
long-term precision.
AD1.2 PARCC: Bias
Bias is the systematic or persistent distortion of a measurement process that causes errors in one
direction. Bias assessments for environmental measurements are made using personnel, equipment, and
spiking materials or reference materials as independent as possible from those used in the calibration of
the measurement system. When possible, bias assessments should be based on analysis of spiked
samples rather than reference materials so that the effect of the matrix on recovery is incorporated into
the assessment. A documented spiking protocol and consistency in following that protocol are important
to obtaining meaningful data quality estimates. Spikes should be added at different concentration levels
to cover the range of expected sample concentrations. For some measurement systems (e.g., continuous
analyzers used to measure pollutants in ambient air), spiking samples may not be practical, so
assessments should be made using appropriate blind reference materials.
For certain multianalyte methods, bias assessments may be complicated by interferences among
multiple analytes, which prevents all of the analytes from being spiked into a single sample. For such
methods, lower spiking frequencies can be employed for analytes that are seldom or never found. The
use of spiked surrogate compounds for multianalyte gas chromatography/ mass spectrometry (GC/MS)
procedures, while not ideal, may be the best available procedure for assessment of bias.
AD1.3 PARCC: Accuracy
Accuracy is a measure of the closeness of an individual measurement or the average of a number
of measurements to the true value. Accuracy includes a combination of random error (precision) and
systematic error (bias) components that result from sampling and analytical operations.
Accuracy is determined by analyzing a reference material of known pollutant concentration or by
reanalyzing a sample to which a material of known concentration or amount of pollutant has been added.
Accuracy is usually expressed either as a percent recovery (P) or as a percent bias (P - 100).
Determination of accuracy always includes the effects of variability (precision); therefore, accuracy is
used as a combination of bias and precision. The combination is known statistically as mean square
error.
Mean square error (MSB) is the quantitative term for overall quality of individual measurements
or estimators. To be accurate, data must be both precise and unbiased. Using the analogy of archery, to
be accurate, one must have one's arrows land close together and, on average, at the spot where they are
aimed. That is, the arrows must all land near the bull's-eye (see Figure AD. 1).
Mean square error is the sum of the variance plus the square of the bias. (The bias is squared to
eliminate concern over whether the bias is positive or negative.) Frequently, it is impossible to quantify
all of the components of the mean square error—especially the biases—but it is important to attempt to
quantify the magnitude of such potential biases, often by comparison with auxiliary data.
AD1.4 PARCC: Representativeness
Representativeness is a measure of the degree to which data accurately and precisely represent a
characteristic of a population parameter at a sampling point or for a process condition or environmental
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(a) High bias + low precision = low accuracy
(b) Low bias + low precision = low accuracy
(c) High bias + high precision = low accuracy
(d) Low bias + high precision = high accuracy
Figure ADI. Measurement Bias and Random Measurement Uncertainties:
Shots at a Target
condition. Representativeness is a qualitative term that should be evaluated to determine whether in situ
and other measurements are made and physical samples collected in such a manner that the resulting data
appropriately reflect the media and phenomenon measured or studied.
AD1.5 PARCC: Comparability
Comparability is the qualitative term that expresses the confidence that two data sets can
contribute to a common analysis and interpolation. Comparability must be carefully evaluated to
establish whether two data sets can be considered equivalent in regard to the measurement of a specific
variable or groups of variables. In a laboratory analysis, the term comparability focuses on method type
comparison, holding times, stability issues, and aspects of overall analytical quantitation.
There are a number of issues that can make two data sets comparable, and the presence of each
of the following items enhances their comparability:
• two data sets should contain the same set of variables of interest;
• units in which these variables were measured should be convertible to a common metric;
• similar analytic procedures and quality assurance should be used to collect data for both
data sets;
• time of measurements of certain characteristics (variables) should be similar for both
data sets;
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• measuring devices used for both data sets should have approximately similar detection
levels;
• rules for excluding certain types of observations from both samples should be similar;
• samples within data sets should be selected in a similar manner;
• sampling frames from which the samples were selected should be similar; and
• number of observations in both data sets should be of the same order or magnitude.
These characteristics vary in importance depending on the final use of the data. The closer two
data sets are with regard to these characteristics, the more appropriate it will be to compare them. Large
differences between characteristics may be of only minor importance, depending on the decision that is
to be made from the data.
Comparability is very important when conducting meta-analysis, which combines the results of
numerous studies to identify commonalities that are then hypothesized to hold over a range of
experimental conditions. Meta-analysis can be very misleading if the studies being evaluated are not
truly comparable. Without proper consideration of comparability, the findings of the meta-analysis may
be due to an artifact of methodological differences among the studies rather than due to differences in
experimentally controlled conditions. The use of expert opinion to classify the importance of differences
in characteristics among data sets is invaluable.
AD1.6 PARCC: Completeness
Completeness is a measure of the amount of valid data obtained from a measurement system,
expressed as a percentage of the number of valid measurements that should have been collected (i.e.,
measurements that were planned to be collected).
Completeness is not intended to be a measure of representativeness; that is, it does not describe
how closely the measured results reflect the actual concentration or distribution of the pollutant in the
media sampled. A project could produce 100% data completeness (i.e., all samples planned were
actually collected and found to be valid), but the results may not be representative of the pollutant
concentration actually present.
Alternatively, there could be only 70% data completeness (30% lost or found invalid), but, due to
the nature of the sample design, the results could still be representative of the target population and yield
valid estimates. Lack of completeness is a vital concern with stratified sampling. Substantial incomplete
sampling of one or more strata can seriously compromise the validity of conclusions from the study. In
other situations (for example, simple random sampling of a relatively homogeneous medium), lack of
completeness results only in a loss of statistical power. The degree to which lack of completeness affects
the outcome of the study is a function of many variables ranging from deficiencies in the number of field
samples acquired to failure to analyze as many replications as deemed necessary by the QAPP and
DQOs. The intensity of effect due to incompleteness of data is sometimes best expressed as a qualitative
measure and not just as a quantitative percentage.
Completeness can have an effect on the DQO parameters. Lack of completeness may require
reconsideration of the limits for the false negative and positive error rates because insufficient
completeness will decrease the power of the statistical test.
The following four situations demonstrate the importance of considering the planned use of the
data when determining the completeness of a study. The purpose of the study is to determine whether the
average concentration of dioxin in surface soil is no more than 1.0 ppb. The DQOs specified that the
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sample average should estimate the true average concentration to within ±0.30 ppb with 95 %
confidence. The resulting sampling design called for 30 samples to be drawn according to a simple
random sampling scheme. The results were as follows:
Study result Completeness Outcome
1. 1.5 ppb ± 0.28 ppb 97% satisfies DQOs and study purpose
2. 500 ppb ± 0.28 ppb 87% satisfies DQOs and study purpose
3. 1.5 ppb ±0.60 ppb 93% doesn't satisfy either
4. 500 ppb ±0.60 ppb 67% fails DQOs but meets study purpose
For all but the third situation, the data that were collected completely achieved their purpose,
meeting data quality requirements originally set out, or providing a conclusive answer to the study
question. The degree of incompleteness did not affect some situations (situations 2 and 4) but may have
been a prime cause for situation 3 to fail the DQO requirements. Expert opinion would then be required
to ascertain if further samples for situation 3 would be necessary in order to meet the established DQOs.
Several factors may result in lack of completeness: (1) the DQOs may have been based on poor
assumptions, (2) the survey design may have been poorly implemented, or (3) the design may have
proven impossible to carry out given resource limitations. Lack of completeness should always be
investigated, and the lessons learned from conducting the study should be incorporated into the planning
of future studies.
AD2. OTHER DATA QUALITY INDICATORS
AD2.1 Sensitivity
Sensitivity is the capability of a method or instrument to discriminate between measurement
responses representing different levels of a variable of interest. Sensitivity is determined from the value
of the standard deviation at the concentration level of interest. It represents the minimum difference in
concentration that can be distinguished between two samples with a high degree of confidence.
AD2.2 Recovery
Recovery is an indicator of bias in a measurement. This is best evaluated by the measurement of
reference materials or other samples of known composition. In the absence of reference materials, spikes
or surrogates may be added to the sample matrix. The recovery is often stated as the percentage
measured with respect to what was added. Complete recovery (100%) is the ultimate goal. At a
minimum, recoveries should be constant and should not differ significantly from an acceptable value.
This means that control charts or some other means should be used for verification. Significantly low
recoveries should be pointed out, and any corrections made for recovery should be stated explicitly.
AD2.3 Memory Effects
A memory effect occurs when a relatively high-concentration sample influences the measurement
of a lower concentration sample of the same analyte when the higher concentration sample precedes the
lower concentration sample in the same analytical instrument. This represents a fault in an analytical
measurement system that reduces accuracy.
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AD2.4 Limit of Quantitation
The limit of quantitation is the minimum concentration of an analyte or category of analytes in a
specific matrix that can be identified and quantified above the method detection limit and within
specified limits of precision and bias during routine analytical operating conditions.
AD2.5 Repeatability
Repeatability is the degree of agreement between independent test results produced by the same
analyst using the same test method and equipment on random aliquots of the same sample within a short
time period.
AD2.6 Reproducibility
Reproducibility is the precision that measures the variability among the results of measurements
of the same sample at different laboratories. It is usually expressed as a variance and low values of
variance indicate a high degree of reproducibility.
AD2.7 DQIs and the QAPP
At a minimum, the following DQIs should be addressed in the QAPP: accuracy and/or bias,
precision, completeness, comparability, and representativeness. Accuracy (or bias), precision,
completeness, and comparability should be addressed in Section A7.3, Specifying Measurement
Performance Criteria. Refer to that section of the G-5 text for a discussion of the information to present
and a suggested format. Representativeness should be discussed in Sections B4.2 (Subsampling) and Bl
(Sampling Design).
Table ADI. Principal Types of Error
Types of Error
Sources of Error
Random Error
(precision; "P" in PARCC)
Natural variability in the population from which the sample is
taken.
Measurement system variability, introduced at each step of
sample handling and measurement processes.
Systematic Error
(accuracy/bias; "A" in PARCC)
Interferences that are present in sample matrix.
Loss (or addition) of contaminants during sample collection and
handling.
Loss (or addition) of contaminants during sample preparation
and analysis.
Calibration error or drift in the response function estimated by
the calibration curve.
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Lack of representativeness
("R" in PARCC)
Sample is not representative of the population, which often
occurs in judgmental sampling because not all the units of the
population have equal or known selection probabilities.
Sample collection method does not extract the material from its
natural setting in a way that accurately captures the desired
qualities to be measured.
Subsample (taken from a sample for chemical analysis) is not
representative of the sample, which occurs because the sample
is not homogeneous and the subsample is taken from the most
readily available portion of the sample. Consequently, other
parts of the sample had less chance of being selected for analysis.
Lack of comparability
("C" in PARCC)
Failure to use similar data collection methods, analytical
procedures, and QA protocols.
Failure to measure the same parameters over different data sets.
Lack of completeness
("C" in PARCC)
Lack of completeness sometimes caused by loss of a sample,
loss of data, or inability to collect the planned number of
samples.
Incompleteness also occurs when data are discarded because
they are of unknown or unacceptable quality.
AD2.8 References
American Society for Quality Control. 1996. Definitions of Environmental Quality Assurance Terms.
Milwaukee, WI: ASQC Press.
Gilbert, R.O. 1987. Statistical Methods for Environmental Pollution Monitoring. New York: Van
Nostrand.
Ott, W.R. 1985. Environmental Statistics and Data Analysis. Boca Raton, FL: Lewis Publishers Inc.
Taylor, J.K. and T.W. Stanley, eds. 1985. Quality Assurance for Environmental Measurements.
Philadelphia, PA: American Society for Testing and Materials.
Taylor, J.K. 1987. Quality Assurance of Chemical Measurements. Chelsea, MI: Lewis Publishers Inc.
U.S. Environmental Protection Agency. 1984. Chapter 5. Calculation of Precision, Bias, and Method
Detection Limit for Chemical and Physical Measurements.
U.S. Environmental Protection Agency. 1994. AEERL Quality Assurance Procedures Manual for
Contractors and Financial Assistance Recipients.
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U.S. Environmental Protection Agency. 1994. EPA Requirements for Quality Management Plans. EPA
QA/R-2, Draft Interim Final. August.
Youden, W.J. 1967. Journal of the Association of Official Analytical Chemists. Vol. 50. p. 1007.
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APPENDIX E
QUALITY CONTROL TERMS
AE1. QUALITY CONTROL OPERATIONS
Quality control (QC) plays an increasingly important role in environmental studies, especially
when those studies are conducted to decide how to address an environmental problem. To minimize the
chance of making an incorrect decision, data of adequate quality must be collected. The purpose of QC
is to ensure that measurement and other data-producing systems operate within defined performance
limits as specified in planning. QC programs can both lower the chances of making an incorrect decision
and help the data user understand the level of uncertainty that surrounds the decision. QC operations
help identify where error is occurring, what the magnitude of that error is, and how that error might
impact the decision-making process. This appendix provides a brief overview of this complex topic. It
surveys the different types of QC samples that can be applied to environmental studies and evaluates
how they are currently deployed as specified by EPA methods and regulations.
AE1.1 General Objectives
The most important QC questions a project manager should consider are:
• What are the QC requirements for the methods to be used in the project?
• What types of problems in environmental measurement systems do these requirements
enable the Agency to detect?
Addressing these questions should provide the manager with the background needed for defining
a uniform, minimum set of QC requirements for any environmental data collection activity.
Understanding existing QC requirements for environmental data generation activities provides a
framework for considering what set of QC requirements should be considered "core" requirements
irrespective of the end use of the data.
While it is difficult to define a standard of data quality regardless of its intended use, core QC
requirements can be established that will enable one to provide data of known quality in accordance with
the Agency's QA program. This program requires that all environmental data collection efforts gather
information on bias, variability, and sample contamination. These error types are incurred throughout
the data generation process, including all sampling and analytical activities (i.e., sample collection,
handling, transport, and preparation; sample analysis; and subsampling). The principal issue centers on
what level of detail in the error structure should QC operations be capable of revealing, given that it is
impractical to explore every known potential source of error.
AE1.2 Background
Many of the essential elements of a Quality Assurance Project Plan (QAPP) apply directly to
sampling and analytical activities and include: Quality assurance (QA) objectives for measurement data
specified in terms of Data Quality Indicators (precision, accuracy, bias, representativeness and
comparability); sampling procedures; sample custody; calibration procedures and frequency; analytical
procedures; internal QC checks and frequency; performance and system audits and frequency; and
specific routine procedures that should be used to assess both data precision and the completeness of the
specific measurement parameters involved.
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AE1.3 Definitions and Terminology
In order to ensure that managers have a uniform perspective of QC requirements, it is necessary
to discuss some basic terminology and definitions. QC and QA, total study error and its components,
types of QC operations, and Good Laboratory Practices (GLPs) will be discussed here. Specific
definitions of these terms and others are provided in Appendix B, Glossary of Quality Assurance and
Monitoring Terms, while Table E. 1 summarizes the results of a study on how these terms are defined and
used in EPA and non-EPA literature. Five commonly available sources are discussed in Table E.I:
Appendix B in EPA QA/G-5; American Society for Quality Control (1996); van Ee, Blume, and Starks
(1989); Taylor (1987); and Keith (1988).
AE1.1.3 Quality Control vs. Quality Assurance
All of the cited literature provides somewhat similar definitions for both QA and QC. QC
activities are designed to control the quality of a product so that it meets the user's needs. QA includes
QC as one of the activities needed to ensure that the product meets defined standards of quality.
These two terms have been defined in slightly different ways by other authors, but all are in
agreement that QC is a component of QA. Many authors define QC as "those laboratory operations
whose objective is to ensure that the data generated by the laboratory are of known accuracy to some
stated, quantitative degree of probability" (Dux 1986). The objective of QC is not to eliminate or
minimize errors but to measure or estimate what they are in the system as it exists. The same authors
then define QA as the ability to prove that the quality of the data is as reported. QA relies heavily on
documentation, including documentation of implemented QC procedures, accountability, traceability,
and precautions to protect raw data.
AE1.3.2 PC Samples
Table E.I offers a broad survey of commonly used QC terms, including the definitions of QC
sample types that span the measurement process. The authors cited in Table E.I define different sample
types in varied ways; however, the definitions are not contradictory.
AE1.3.3 Good Laboratory Practices
The Food and Drug Administration (FDA) promulgated the first version of the Good Laboratory
Practices (GLPs) in 1978. The EPA enacted similar guidance requirements in 1983 for Resource
Conservation Recovery Act (RCRA) and Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA)
compliance. The FIFRA GLPs were revised in 1988. Though much of the content relates to laboratory
animal science, many requirements are relevant to the analytical chemist. The GLP standards for FIFRA
(40 Code of Federal Regulations [CFR] Part 160) and the Toxic Substances Control Act (TSCA) (40
CFR 792) are similar (Dux 1986). Selected topics of FIFRA subparts A through K appear below.
Subpart A General Provisions.
Subpart B Organization and Personnel. Includes QA unit.
Subpart C Facilities. Includes: facilities for handling test, control, and reference
substances; laboratory operations areas; and specimen and data storage
facilities.
Subpart D Equipment. Includes: maintenance and calibration of equipment.
Subpart E Testing Facilities Operation. Includes: standard operation procedures
(SOPs); reagents and solutions.
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Subpart F Test, Control, and Reference Substances. Includes: characterization and
handling; mixtures of substances with carriers.
Subpart G Protocol for and Conduct of a Study.
Subpart H Reserved.
Subpart I Reserved.
Subpart J Records and Reports. Includes: reporting of study results; storage and
retrieval of records and data; and retention of records.
GLPs are defined similarly by the Agency and by Taylor (1987) as an acceptable way to perform
some basic laboratory operation or activity that is known or believed to influence the quality of its
outputs.
AE2. QUALITY CONTROL REQUIREMENTS IN EXISTING PROGRAMS
To identify QC requirements for this section, standard EPA method references, such as SW-846,
and the CFR were reviewed together with information on non-EPA methods identified through a
computerized literature search. Within the EPA literature, some of the major programs were reviewed,
including the Drinking Water, Air and the Contract Laboratory Program (CLP). Different types of
methods, such as gas chromatography (GC), atomic absorption (AA), and inductively coupled plasma
(ICP), and different media were included in this process, but it was not intended to be exhaustive.
AE2.1 Summary of QC Requirements by Program and Method
Table AE.2 presents the frequency of QC requirements for different selected programs and Table
AE.3 presents information for methods. In cases where different programs use dissimilar terms for
similar QC samples, the table uses the term from the program or method.
AE2.2 Comparing Various QC Requirements
AE2.2.1 QC Requirements for Program Offices
Table AE.2 shows that QC requirements vary considerably and are established by the Program
Office responsible for the data collection activity. Ambient air monitoring methods (Office of Air
Quality Planning and Standards [OAQPS]) require periodic analysis of standards for assessment of
accuracy (combination of imprecision and bias) for manual methods, and analysis of collocated samples
for the assessment of imprecision. Prevention of Significant Deterioration (PSD) and State and Local
Air Monitoring Stations (SLAMS) make a unique distinction in defining two terms: precision checks and
accuracy checks. These checks entail essentially the same QC requirements, but they are performed by
different parties; the accuracy check is essentially an external audit, while the precision check is an
internal QC operation. It should be noted that some water methods require additional QC operations for
GC/MS than for other methods (e.g., tuning, isotopic dilution).
In general, the wet chemistry analytical methods (the toxicity characteristic leaching procedure
[TCLP] being a preparation method) require periodic analysis of blanks and calibration standards. Most
require analysis of matrix spikes and replicate samples, the exceptions being the 200 Series (no spikes or
replicates) and the 600 series (GC/MS require no replicates).
While the QC operations for the PSD and SLAMS methods appear minimal, these monitoring
programs require active QA programs that include procedures for zero/span checks. (The zero check
may be considered a blank sample, while the span check may be considered a calibration check sample.)
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The Program Office Quality Assurance Officer (QAO) or representative should have details on
specific QC requirements.
AE2.2.2 Organized by Type of Potential Problem
Table AE.3 lists the QC requirements of various EPA measurement methods and presents the
required frequencies for different kinds of QC operations. The table is divided into four sections, one for
each general type of QC problem:
• Contamination: This occurs when the analyte of interest or an interferant is introduced
through any of a number of sources, including contaminated sample equipment,
containers, and reagents. The contaminant can be the analyte of interest or another
chemical that interferes with the measurement of the analyte or causes loss or generation
of the analyte.
• Calibration Drift: This is a nonrandom change in the measurement system over time,
such as a (systematic) change in instrument response over time. It is often detectable by
periodic remeasurement of a standard.
• Bias: This can be regarded as a systematic error caused by contamination and calibration
drift and also by numerous other causes, such as extraction efficiency by the solvent,
matrix effect, and losses during shipping and handling.
• Imprecision: This is a random error, observed as different results from repeated
measurements of the same or identical samples.
For internal consistency, the names of QC operations used in Table AE.3 are those given in the specific
reference methods.
AE2.3 Using QC Data
The relationships between monitoring design specifications and the final use of the data
described above incorporate two significant assumptions: (1) laboratory measurements, through the use
of internal standards or other adjustments that are integral to the analytical protocol, are unbiased; and
(2) the variance structure of these measurements does not change over time. Bias enters as a
consequence of under-recovery of the contaminant of interest during the sample preparation stage of the
analytical protocol and as undetected drift in calibration parameters. The variance of measurements also
may change over time due to unintentional changes in the way samples are prepared and/or to
degradation of the electromechanical instrumentation used to analyze the samples. QC samples are
intended to detect bias and variability changes and should be specified in the QAPP.
QC samples that address bias are calibration check standards (CCSs) and spiked samples
(performance check samples [PCSs]). CCSs typically consist of reagent water samples spiked with the
concentrations used to develop the calibration curve. Measurements obtained by analyzing these
samples, which reflect the existing calibration relationship, are compared to the actual concentrations
that were added to the samples. If the difference exceeds a prespecified calibration test limit, the
measurement system is considered "out of control" and the calibration function is re-estimated.
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Detecting a change in calibration parameters is a statistical decision problem in detecting a
material change in the calibration function. In many QC programs, CCSs typically are analyzed at the
beginning and end of each shift and after any other QC sample has detected a failure. By definition,
significant change in the calibration parameters leads to biased measurements of field samples. This can
be detected through use of statistical tests.
A spiked sample typically has the same matrix characteristics found in field samples, but it has
been spiked (as soon after the sample is taken as is practical) with a known concentration of the target
contaminant. Because spiked samples are intended to detect recovery changes, they are processed
through the same preparation steps as field samples, and the spiked sample measurement is used to form
an estimate of recovery. Significant changes lead to the conclusion that measurements of field samples
are biased.
The second of the two monitoring program assumptions identified at the beginning of this
section is a constant variance structure for monitoring data over time. Measurements from split (or
duplicate) field samples provide a check on this variance assumption. Changes in measurement
variability, for example a uniform increase in the standard deviation or changes in the way variability
depends on concentration, have a direct impact on subsequent investigations.
AE2.4 Classifying QC Samples: Control versus Assessment
QC programs are designed foremost to detect a measurement process entering an "out of control"
state so that corrective measures can be initiated. QC samples used in this way are performing a control
function. Each of the three types of QC samples previously discussed, CCSs, spiked samples, and split
(or duplicate) samples, may be used for control. In addition, spiked samples and split samples also may
be used to estimate measurement bias and variability. QC samples that also can be used to estimate
measurement parameters are sometimes referred to as quality assessment samples. These should not be
confused with the much larger Data Quality Assessment Process; see also EPA QA/G-9, Guidance for
Data Quality Assessment.
QC samples that are used for control must be analyzed and reported soon after they are obtained
if their intervention potential is to be realized. Among the three types of QC samples discussed above,
CCSs are the most likely to be effective for control purposes. Spiked samples and split samples
generally are not effective for control purposes, in part because they are analyzed "blind" and therefore
the results cannot be reviewed immediately. Spiked samples and split samples, however, may be used
for control if consecutive batches of similar field samples are being analyzed.
Spiked samples and split samples can be effective quality assessment samples. For example,
spiked samples may be used to indicate the presence of bias. The estimate is applied as a bias correcting
adjustment to individual measurements or to batches of measurements before the measurements are used
in compliance tests. The adjustment improves the test by eliminating bias. However, the variance of the
adjusted estimate used in the test is greater than the variance of the unadjusted estimate.
Split (or duplicate) samples also can be used as quality assessment samples, but their application
in the monitoring program is not as constructive as the application of spiked samples. Split samples lead
to an estimate of the measurement replication component of variability. (The variance of a measurement
has, at a minimum, a sampling component and a measurement replication component, which is
sometimes referred to as measurement error. If the sampling design involves stratification, the variance
will include additional components.) If the estimate based on split samples suggests a measurement
replication standard deviation larger than the value assumed in establishing the original sampling design,
a loss in efficiency will result.
EPA QA/G-5 E-5 QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Blank sample
A clean sample or a sample of matrix
processed so as to measure artifacts in the
measurement (sampling and analysis)
process.
Blanks provide a measure of various
cross-contamination sources, background
levels in reagents, decontamination
efficiency, and other potential error that
can be introduced from sources other
than the sample. A rinsate blank
(decontamination sample) measures any
chemical that may have been on the
sampling and sample preparation tools
after the decontamination process is
completed.
The measured value obtained when a
specified component of a sample is not
present during measurement. Measured
value/signal for the component is
believed to be due to artifacts; it should
be deducted from a measured value to
give a net value due to the component
contained in a sample. The blank
measurement must be made to make the
correction process valid.
Samples expected to have negligible or
unmeasurable amounts of the substance
of interest. They are necessary for
determining some of the uncertainty due
to random errors. Three kinds required
for proper quality assurance: equipment
blanks, field blanks, and sampling
blanks.
Blind sample
A subsample submitted for analysis with
a composition and identity known to the
submitter but unknown to the analyst.
Used to test analyst or laboratory
proficiency in execution of the
measurement process.
Single-Blind Samples: Field Rinsate
Blanks, Preparation Rinsate Blank, Trip
Blank
A sample submitted for analysis whose
composition is known to the submitter
but unknown to the analyst. One way to
test the proficiency of a measurement
process.
Calibration
standard
A substance or reference material used to
calibrate an instrument, (calibration
check standard, reference standard,
quality control check sample)
In physical calibration, an artifact
measured periodically, the results of
which typically are plotted on a control
chart to evaluate the measurement
process.
Or quality control calibration standard
(CCS). In most laboratory procedures, a
solution containing the analyte of
interest at a low but measurable
concentration. Standard deviation of the
CCSs is a measure of instrument
precision unless the CCS is analyzed as
a sample, in which case it is a measure
of method precision.
Checks sample
Example: ICP Interference Check Sample -
Part A contains potential interfering
analytes. Part B contains both the
analytes of interest and the target
analytes. Part A and B are analyzed
separately to determine the potential for
interferences.
Check standard
A substance or reference material
obtained from a source independent from
the source of the calibration standard;
used to prepare check samples, (control
standard")
Laboratory control standards are
certified standards, generally supplied by
an outside source. They are used to
ensure that the accuracy of the analysis
is in control.
EPA QA/G-5
E-6
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Double blind
samples
Samples that can not be distinguished
from routine samples by analytical
laboratory. Examples: Field Evaluation
Samples, Low Level Field Evaluation
Samples, External Laboratory Evaluation
Samples, Low Level External Laboratory
Evaluation Samples, Field Matrix Spike,
Field Duplicate, Field Split
A sample known by the submitter but
submitted to an analyst so that neither its
composition nor its identification as a
check sample are known to the analyst.
Duplicate
measurement
A second measurement made on the same
(or identical) sample of material to assist
in the evaluation of measurement
Duplicate
sample
Two samples taken from and
representative of the same population and
carried through all steps of the sampling
and analytical procedures in an identical
manner. Used to assess variance of the
total method including sampling and
analysis.
Field duplicate - an additional sample
taken near the routine field sample to
determine total within-batch
measurement variability.
Analytical laboratory duplicate - a
subsample of a routine sample analyzed
by the same method. Used to determine
method precision. It is non-blind so it
can only be used by the analyst in
internal control, not an unbiased estimate
of analytical precision.
A second sample randomly selected from
a population of interest to assist in the
evaluation of sample variance.
Error
The difference between a computed,
observed, or measured value or condition
and the true, specified, or theoretical
value or condition.
Difference between the true or expected
value and the measured value of a
quantity or parameter.
Field blank
Used to estimate incidental or accidental
contamination of a sample during the
collection procedure. One should be
allowed per sampling team per day per
collection apparatus. Examples include
matched-matrix blank, sampling media
or trip blank, equipment blank.
EPA QA/G-5
E-7
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Good
Laboratory
Practices
(GLPs)
Either general guidelines or formal
regulations for performing basic
laboratory operations or activities that are
known or believed to influence the
quality and integrity of the results.
An acceptable way to perform some basic
operation or activity in a laboratory that
is known or believed to influence the
quality of its outputs. GLPs ordinarily
are essentially independent of the
measurement techniques used.
Instrument
blank
Also called system blank. Used to
establish baseline response of an
analytical system in the absence of a
sample. Not a simulated sample but a
measure of instrument or system
background response.
Method blank
One of the most important in any
process. DDI water processed through
analytical procedure as a normal sample.
After use to determine the lower limit of
detection, a reagent blank is analyzed for
each 20 samples and whenever a new
batch of reagents is used.
Non-blind
sample
QC samples with a concentration and
origin known to the analytical laboratory.
Examples: Laboratory Control Sample,
Pre-digest Spike, Post-digest Spike,
Analytical Laboratory Duplicate, Initial
Calibration Verification and Continuing
Calibration Verification Solutions, Initial
Calibration Blank and Continuing
Calibration Blank Solution, CRDL
Standard for ICP and AA, Linear Range
Verification Check Standard, ICP
Interference Check Sample.
Performance
Evaluation
(PE)
A type of audit in which the quantitative
data generated in a measurement system
are obtained independently and compared
with routinely obtained data to evaluate
the proficiency of an analyst or
laboratory.
rPefmed in EPA OA/G-5. App. B1
EPA QA/G-5
E-8
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Quality
assessment
Assessment is the evaluation of
environmental data to determine if they
meet the quality criteria required for a
specific application.
The overall system of activities that
provides an objective measure of the
quality of data produced.
The overall system of activities whose
purpose is to provide assurance that the
quality control activities are done
effectively. It involves a continuing
evaluation of performance of the
production system and the quality of the
products produced.
Quality
assessment
sample (QAS)
Those samples that allow statements to
be made concerning the quality of the
measurement system. Allow assessment
and control of data quality to assure that
it meets original objectives. Three
categories: double-blind, single-blind,
and non-blind.
Quality
assurance
(QA)
An integrated system of activities
involving planning, quality control,
quality assessment, reporting and quality
improvement to ensure that a product or
service meets defined standards of
quality with a stated level of confidence.
A system of activities whose purpose is
to provide to the producer or user of a
product or service the assurance that it
meets defined standards of quality. It
consists of two separate, but related
activities, quality control and quality
assessment.
Same as van Ee.
Quality
control (QC)
The overall system of technical activities
whose purpose is to measure and control
the quality of a product or service so that
it meets the needs of users. The aim is to
provide quality that is satisfactory,
adequate, dependable, and economical.
The overall system of activities whose
purpose is to control the quality of the
measurement data so that they meet the
needs of the user.
The overall system of activities whose
purpose is to control the quality of a
product or service so that it meets the
needs of users. The aim is to provide
quality that is satisfactory, adequate
dependable, and economic.
Quality
control sample
An uncontaminated sample matrix spiked
with known amounts of analytes from a
source independent from the calibration
standards. Generally used to establish
intralaboratory or analyst specific
precision and bias or to assess
performance of all or part of the
measurement system. (Laboratory control
sample)
rPefmed in EPA OA/G-5. App. B1
A sample of well-characterized soil,
whose analyte concentrations are known
to the laboratory. Used for internal
laboratory control. Also called QC audit
sample.
A material of known composition that is
analyzed concurrently with test samples
to evaluate a measurement process.
Used in quality control procedures to
determine whether or not the analytical
procedures are in control.
EPA QA/G-5
E-9
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Reagent blank
A sample consisting of reagent(s),
without the target analyte or sample
matrix, introduced into analytical
procedure at the appropriate point and
carried through all subsequent steps to
determine the contribution of the reagents
in the absence of matrix and the involved
analytical steps to error in the observed
value (analytical blank, laboratory blank).
(Defined in EPA QA/G-5, App. B)
Also called method blank. Used to
detect and quantitate contamination
introduced during sample preparation
and analysis. Contains all reagents used
in sample preparation and analysis and is
carried through the complete analytical
procedure.
Reference
material
A material or substance, one or more
properties of which are sufficiently well
established to be used for the calibration
of an apparatus, the assessment of a
measurement method, or for the
assignment of values to materials.
Sample
preparation
blank
Required when methods like stirring,
mixing, blending, or subsampling are
used to prepare a sample prior to
analysis. One should be prepared per 20
samples processed.
Sampling
equipment
blank
Used to determine types of contaminants
introduced through contact with
sampling equipment; also to verify the
effectiveness of cleaning procedures.
Prepared by collecting water or solvents
used to rinse sampling equipment.
Solvent blank
Used to detect and quantitate solvent
impurities; the calibration standard
corresponds to zero analyte
concentration. Consists only of solvent
used to dilute the sample.
EPA QA/G-5
E-10
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Spiked sample
A sample prepared by adding a known
mass of target analyte to a specified
amount of matrix sample for which an
independent estimate of target analyte
concentration is available. Spiked
samples are used, for example, to
determine the effect of the matrix on a
method's recovery efficiency (matrix
spike).
A sample prepared by adding a known
amount of reference chemical to one of a
pair of split samples. Comparing the
results of the analysis of a spiked
member to that of the non-spiked
member of the split measures spike
recovery and provides a measure of the
analytical bias.
Field matrix spike - a routine sample
spiked with the contaminant of interest in
the field.
Matrix control or field spike -for sample
matrices where a complex mixture (e.g.
sediments, sludges) may interfere with
analysis, a field spike may be required to
estimate the magnitude of those
interferences. Losses from transport,
storage treatment, and analysis can be
assessed by adding a known amount of
the analyte of interest to the sample in
the field.
Split sample
Two or more representative portions
taken from a sample or subsample and
analyzed by different analysts or
laboratories. Split samples are used to
replicate the measurement of the
variable(s) of interest.
Samples can provide: a measure of
within-sample variability; spiking
materials to test recovery; and a measure
of analytical and extraction errors.
Where the sample is split determines the
components of variance that are
measured. Field split - a sample is
homogenized and spilt into two samples
of theoretically equal concentration at the
sampling site. Indicate within-batch
measurement error. Also called
replicates.
A replicate portion or subsample of a
total sample obtained in such a manner
that is not believed to differ significantly
from other portions of the same sample.
Total
measurement
error
The sum of all the errors that occur from
the taking of the sample through the
reporting of results; the difference
between the reported result and the true
value of the population that was to have
been sampled.
Transport
blank
Used to estimate sample contamination
from the container and preservative
during transport and storage of the
sample. One should be allowed per day
per type of sample.
EPA QA/G-5
E-ll
QA98
-------
Table AE1. Comparison of QC Terms
Terms
ASQC, Definitions of Environmental
Quality Assurance Terms
or
EPA QA/G-5 Appendix B
van Ee, Blume, and Starks
A Rationale for the Assessment of Errors
in the Sampling of Soils
John Keenan Taylor
Quality Assurance of Chemical
Measurements
Lawrence H. Keith, ed.
Principles of Environmental Sampling
Trip blank
A clean sample of matrix that is carried
to the sampling site and transported to
the laboratory for analysis without having
been exposed to sampling procedures.
(Defined in EPA QA/G-5, App. B)
Used when volatile organics are sampled.
Consists of actual sample containers
filled with ASTM Type II water, kept
with routine samples throughout
sampling event, packaged for shipment
with routine samples and sent with each
shipping container to the laboratory.
Used to determine the presence or
absence of contamination during
shipment.
A type of field blank also called
sampling media blank. To detect
contamination associated with the
sampling media such as filters, traps, and
sample bottles. Consists of sampling
media used for sample collection.
EPA QA/G-5
E-12
QA98
-------
Table AE2. QC Requirements for Programs
Potential
Problems:
QC
Samples to
Identify
Potential
Problems:
CLP
Organics:
1991
Statement of
Work,
Exhibit E
Contamination
Blanks
Volatiles
Semi-
volatiles
Pesticides/
Aroclor
A method
blank once
every 12
hours.
A method
blank with
every batch.
Instrument
blank at start
of analyses
and every 12
hours.
Method
blank with
each case,
14 days, or
batch.
Sulfur
blanks are
sometimes
required.
Calibration
Drift
Calibration
Check
Samples
Continuing
calibration
standard every
12 hours. BFB
analysis once
every 12 hours.
DFTPP analysis
once every 12
hours.
Continuing
calibration
standard every
12 hours.
Performance
evaluation
mixture to
bracket 12-hour
periods.
Bias
Spike
Matrix spike
with every
case, batch, 20
samples, or 14
days.
Matrix spike
with every
case, batch, 20
samples, or 14
days.
Matrix spike
with every 20
samples.
Standard
3 system monitoring
compounds added to
every sample.
8 surrogates spiked into
each sample.
2 surrogates added to
each sample.
Imprecision
Replicate
Matrix spike
duplicate with
every case,
batch, 20
samples, or 14
days.
Matrix spike
duplicate with
every case,
batch, 20
samples, or 14
days.
Matrix spike
duplicate with
every 20
samples.
Collocated
Other
EPA QA/G-5
E-13
QA98
-------
Table AE2. QC Requirements for Programs
Potential
Problems:
QC
Samples to
Identify
Potential
Problems:
CLP
Inorganics:
1991
Statement
of Work,
Exhibit E
PSD
40CFR
Part 58
Appendix B
Contamination
Blanks
Initial calibration blank; then
continuing calibration blank
10% or every 2 hours.
Preparation blank with every
batch.
Calibration
Drift
Calibration
Check
Samples
Initial
calibration
verification
standard; then
continuing
calibration
verification
10% or every 2
hours.
Bias
Spike
1 spike for
every batch.
Method of
standard
additions for
AA if spikes
indicate
problem.
Standard
Interference check
sample for ICP
2 x /8 hours.
Laboratory control
sample with each
batch.
For SO2, NO2, O3, and
CO, response check I/
sampling quarter. For
TSP and lead, sample
flow check
I/sampling quarter.
For lead, check with
audit strips I/quarter.
Imprecision
Replicate
1 duplicate/
batch. For AA,
duplicate
injections.
Collocated
For TSP and
lead, collocated
sample I/week
or every 3rd
day for
continuous
sampling.
Other
For S02, N02,
O3, and CO,
precision
check once
every 2
weeks.
EPA QA/G-5
E-14
-------
Table AE2. QC Requirements for Programs
Potential
Problems:
QC
Samples to
Identify
Potential
Problems:
SLAMS
40CFR
Part 58
Appendix A
A Rationale
for the
Assessment
of Errors in
the
Sampling of
Soils, by
van Ee,
Blume, and
Starks
Contamination
Blanks
Preparation rinsate blanks and
field rinsate blanks discussed,
but no frequency given.
Calibration
Drift
Calibration
Check
Samples
Bias
Spike
Standard
For automated SO2,
N02, 03, and CO
response check for at
least 1 analyzer (25%
of all) each quarter.
For manual SO2 and
NO2, analyze audit
standard solution each
day samples are
analyzed (at least
2x/quarter). For TSP,
PM10, and lead,
sample flow rate
check at least 1
analyzer/quarter (25%
of all analyzers). For
lead, check with audit
strips I/quarter.
At least 2 1 pairs of
field evaluation
samples. At least 20
pairs of external
laboratory evaluation
samples if estimating
components of
variance is important.
Imprecision
Replicate
At least 20
pairs or 10
triples of field
duplicates. At
least 20 pairs of
preparation
splits if
estimating
variance is
important.
Collocated
For manual
methods,
including lead,
collocated
sample I/week.
Other
For automated
S02, N02, 03,
and CO,
precision
check once
every 2
weeks.
EPA QA/G-5
E-15
QA98
-------
Table AE3. QC Requirements for Methods
Potential
Problems:
QC Samples to
Identify Potential
Problems:
SW-846 Method
7000 (Proposed
Update I)
Atomic Absorption
SW-846 Method
8000 (Proposed
Update I) Gas
Chromatography
503.1 Volatile
Aromatic and
Unsaturated Organic
Compounds in
Water by Purge and
Trap GC (from
PB89-220461)
200 Atomic
Absorption Methods
(from EPA-600-4-
79-020)
624-Purgeables
40 CFR Part 136,
Appendix A
Contamination
Blanks
Reagent blank as
part of daily
calibration.
Reagent blank
before sample
analysis and for
each batch of up
to 20 samples.
Laboratory
reagent blank with
each batch. Field
reagent blank with
each set of field
samples.
Reagent blank at
least daily.
Reagent water
blank daily.
Calibration Drift
Calibration
Check Samples
Mid-range standard
analyzed every 10
samples.
A daily calibration
sample analyzed.
Calibration verified
daily with 1 or more
calibration standards.
Daily checks at least
with reagent blank and
1 standard.
Verification with an
additional standard
every 20 samples.
Analyze BFB every
day analyses are
performed.
Bias
Spike
1 spiked matrix
sample analyzed
every 20 samples or
analytical batch.
Method of standard
additions required
for difficult matrices.
1 matrix spike for
each batch of up to
20 samples.
Laboratory-fortified
blank with each
batch or 20 samples.
Spike a minimum of
5% of samples.
Standard
QC check sample
required, but
frequency not
specified.
QC sample
analyzed at least
quarterly.
Analysis of an
unknown
performance
sample at least
once per year.
Surrogate
standards used
with all samples.
Analyze QC
check samples as
5% of analyses.
Imprecision
Replicate
1 replicate sample
every 20 samples or
analytical batch; 1
spiked replicate
sample for each
matrix type.
1 replicate or matrix
spike replicate for
each analytical batch
of up to 20 samples.
Samples collected in
duplicate.
Laboratory- fortified
blanks analyzed in
duplicate at least
quarterly.
Collocated
Other
EPA QA/G-5
E-16
QA98
-------
Table AE3. QC Requirements for Methods
Potential
Problems:
QC Samples to
Identify Potential
Problems:
1624-Volatile
Organic Compounds
by Isotope Dilution
GC/MS
40 CFR Part 136,
Appendix A
TCLP-Fed. Reg.,
Vol 55, No. 126
Friday, June 29,
1990
SW-846 Method
6010 (Proposed
Update I)
Inductively Coupled
Plasma Atomic
Emission
Spectroscopy
Contamination
Blanks
Blanks analyzed
initially and with
each sample lot.
1 blank for every
20 extractions.
At least 1 reagent
blank with every
sample batch.
Calibration Drift
Calibration
Check Samples
Aqueous standard with
BFB, internal
standards, and
pollutants is analyzed
daily. A standard used
to compare syringe
injection with purge
and trap.
Verify calibration
every 10 samples and
at the end of the
analytical run with a
blank and standard.
Bias
Spike
All samples spiked
with labeled
compounds.
1 matrix spike for
each waste type and
for each batch.
Spiked replicate
samples analyzed at
a frequency of 20%.
Standard
An interference
check sample
analyzed at the
beginning and end
of each run or 8-
hour shift.
Imprecision
Replicate
8 aliquots of the
aqueous
performance
standard analyzed
initially.
1 replicate with
every batch or 20
samples. Also spiked
replicates analyzed,
as discussed under
"Spikes."
Collocated
Other
EPA QA/G-5
E-17
QA98
-------
AE2.4 References
American Society for Quality Control. Environmental Restoration Committee. Terms and Definitions Task Group.
1996. Definitions of Environmental Quality Assurance Terms. Milwaukee, WI: Quality Press.
American Society for Quality Control. Chemical Process Industries Division. 1987. Quality Assurance for the
Chemical Process Industries, a Manual of Good Practices. Washington, DC.
Dux, James P. 1986. Handbook of Quality Assurance for the Analytical Chemistry Laboratory.
Federal Insecticide, Fungicide andRodenticide Act (FIFRA). 1989. Good Laboratory Practices Standards. Final
Rule. Federal Register, vol. 54, no. 158, August.
Good Laboratory Practices: An Agrochemical Perspective. 1987. Division of Agrochemicals, 194th Meeting of the
American Chemical Society.
Grant, E.L. and R.S. Leavenworth. 1988. Statistical Quality Control, 6th Edition. New York: McGraw-Hill.
Griffith, Gary K. 1996. Statistical Process Control Methods for Long and Short Runs, 2nd Edition. Milwaukee,
WI: ASCQ Quality Press.
Hayes, Glenn E. and Harry G. Romig. 1988. Modern Quality Control. Revised Edition. Encino, CA.
Juran, J.M. and Frank M. Gryna. 1993. Quality Planning and Analysis, 3rd Edition. New York: McGraw-Hill.
Keith, Lawrence H., ed. 1988. Principles of Environmental Sampling. Washington, DC: American Chemical
Society Press.
Taylor, John Keenan. 1987. Quality Assurance of Chemical Measurements. Chelsea, MI: Lewis Publishers, Inc.
van Ee, J. Jeffrey, Louis J. Blume, and Thomas H. Starks. 1989. A Rationale for the Assessment of Errors in
Sampling of Soils. EPA/600/X-89/203.
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APPENDIX F
SOFTWARE FOR THE DEVELOPMENT AND PREPARATION OF A QUALITY
ASSURANCE PROJECT PLAN
This appendix contains three sections:
AF1. an overview of the potential need for software in QAPP preparation,
AF2. information on existing software, and
AF3. information on software availability and sources.
The information presented in this appendix on various types of software that may be useful in
constructing a QAPP is only a subset of what is available to the QA Manager. Mention of certain
products or software does not constitute endorsement, only that some potentially useful material can be
obtained from those products.
AF1. OVERVIEW OF POTENTIAL NEED FOR SOFTWARE IN QAPP PREPARATION
The general structure of a QAPP can be adapted easily for an organization's needs by automating
some of the components of the QAPP. Several commercial and governmental organizations have
produced software to facilitate this automation. The software needs are categorized under the four
classes of QAPP elements. Within each category is an explanation of the general functions of the
software that could prove useful in preparing, reviewing, or implementing a QAPP. In addition, the
QAPP elements to which the software applies are listed.
AF1.1 Class A: Project Management
This type of software can be used to produce planning documentation and preparation of the
QAPP document. In addition, this type of software can be used to produce other project documentation
such as Standard Operating Procedures (SOPs), Quality Management Plans (QMPs), and Data Quality
Objectives (DQOs) reports.
GENERAL SOFTWARE FUNCTIONS
Provides the user guidance on what to address in each QAPP element and
serves as a template for the production of the QAPP document.
Generates flowcharts to assist in preparing project organization charts and in
illustrating processes that occur in the project, such as sample collection and
analysis or data management.
Identifies training or certification required for personnel in given program
areas.
Provides applicable regulatory standards (e.g., action or clean-up levels) for the
various program areas (e.g., air, water, and solid waste).
Provides guidance on implementing the DQO Process.
QAPP
ELEMENTS
All elements
A4, BIO
A8
A6
A5, A6, A7
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AF1.2 Class B: Measurement and Data Acquisition
This type of software can be used to assist in the design of a sampling plan. In addition, this
software can provide information on analytical methods and sample collection and handling.
GENERAL SOFTWARE FUNCTIONS
Assists in the development of sampling designs that will meet specified
DQOs. The software should handle a variety of general design types with
and without compositing, such as simple random sampling, grid sampling,
and stratified sampling.
Provides information on analytical procedures and sampling methods for
various contaminants and media. This software provides QC data for the
analytical method (method detection limit [MDL], precision, and bias),
references to standard methods, and SOPs (where calibration and
maintenance information could be found).
Assists in tracking samples and assisting with documenting sample handling
and custody.
Integrates QC design and sampling design to meet DQOs and facilitate Data
Quality Assessment (DQA).
QAPP ELEMENTS
Bl
B2, B4, B5, B6, B7
B3
Bl, B5, BIO
AF1.3 Class C: Assessment and Oversight
This software can assist in assessment and oversight activities.
GENERAL SOFTWARE FUNCTIONS
Produces checklists, checklist templates, or logic diagrams (such as problem
diagnostics) for Technical Systems Audits (TSAs), Management Systems
Reviews (MSRs), and Audits of Data Quality (ADQs).
Perform DQA and facilitates corrective actions during the implementation
phase as preliminary or field screening data become available.
QAPP ELEMENTS
Cl
Cl, C2
AF1.4 Class D: Data Validation and Usability
This software assists in validating data and assessing its usability.
GENERAL SOFTWARE FUNCTIONS
Assists in performing data validation and usability.
Assists in performing data quality assessment.
QAPP ELEMENTS
D2
D3
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AF2. EXISTING SOFTWARE
This information is summarized as a list of identified software; a more detailed description of
each item is found in Section AF3. A variety of commercial software packages are available to assist in
statistical analysis, laboratory QC, and related activities, but this appendix focuses on software used
specifically by those preparing, implementing, and reviewing QAPPs. See Table AF.l for a summary of
the software described below.
AF2.1 Template Software
Several applications have been implemented in word-processing software that provide guidance
on how to complete each QAPP element and a template for the discussion portion. Four examples of
these applications are:
• Quality Integrated Work Plan Template (QIWP) (Section AF3, No. 2)
• QAPP Template (Section AF3, No. 3)
• Region 5 QAPP Template (Section AF3, No. 4)
A more sophisticated application, Quality Assurance Sampling Plan for Environmental Response
(QASPER), was identified that combines a template with links to a variety of lists that provide the user
response options (Section AF3, No. 1).
AF2.2 Flowcharting Software
Various flowcharting software is commercially available. One example found in QA/QC
literature is allCLEAR III (Section AF3, No. 5). Other more sophisticated packages link the flowchart
diagrams to active databases or simulation modeling capabilities.
AF2.3 Regulatory Standards Software
This software provides regulatory limits under the various statutes for a wide variety of
contaminants:
• Environmental Monitoring Methods Index (EMMI) (Section AF3, No. 6)
• Clean-Up Criteria for Contaminated Soil and Groundwater (an example of a commercially
available product) (Section AF3, No. 8)
AF2.4 Sampling Design Software
A variety of software has been developed to assist in the creation of sampling designs:
• Decision Error Feasibility Trials (DEFT) (Section AF3, No. 9)
• GeoEAS (Section AF3, No. 10)
• ELIPGRID-PC (Section AF3, No. 11)
• DQOPro (Section AF3, No. 12)
In addition, there are many statistical packages that support sampling design.
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AF2.5 Analytical Methods Software
This software provides information on method detection limits (MDLs) and method summaries
for a wide variety of analytical methods:
• EMMI (Section AF3, No. 6)
• EPA's Sampling and Analysis Methods Database (Section AF3, No. 7)
AF2.6 Data Validation Software
The Research Data Management and Quality Control System (RDMQ) (Section AF3, No. 13) is
a data management system that allows for the verification, flagging, and interpretation of data.
AF2.7 Data Quality Assessment Software
Several software packages have been developed to perform data quality assessment tasks.
Examples of this software include:
• DataQUEST (Section AF3, No. 14)
• ASSESS (Section AF3, No. 15)
• RRELSTAT (Section AF3, No. 16)
Note that most commercially available statistical packages (not listed above) perform a variety of
DQA tasks.
AF2.8 QAPP Review
QATRACK (Section AF3, No. 17) is used to track QAPPs undergoing the review process.
laoie AVI. aonware Avaiiaoie to ivieet i^vrr ueveio
SOFTWARE NEED
PROJECT MANAGEMENT
Template guidance
Flowcharting
Regulatory standards
MEASUREMENT AND DATA
ACQUISITION
Sample design
Analytical and sampling procedures
Integrating QC design and sampling design
to meet DQOs and facilitate DQA.
QAPP
ELEMENTS
All elements
A4, BIO
A6
Bl
B2, B4, B5, B6,
B7
B1,B5, BIO
)ineiu i>eeas
EXISTING SOFTWARE
QASPER, QWIP, QAPP Template
allCLEAR III
EMMI, Clean-Up Criteria for Contaminated
Soil and Groundwater
DEFT, GeoEAS, ELIPGRID-PC, DRUMs,
DQOPro, miscellaneous statistical packages
EMMI, EPA's Sampling and Analysis Database
DQOPro
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SOFTWARE NEED
ASSESSMENT AND OVERSIGHT
Data Quality Assessment
DATA VALIDATION AND USABILITY
Data validation
Data Quality Assessment
QAPP
ELEMENTS
Cl, C2
D2
D3
EXISTING SOFTWARE
DataQUEST, ASSESS, RRELSTAT
RDMQ
DataQUEST, ASSESS, RRELSTAT,
miscellaneous statistical packages
AF3. SOFTWARE AVAILABILITY AND SOURCES
The wide variety of existing software has potential to meet the needs identified for preparing
QAPPs. As illustrated in Table AF.l, at least one example of a software tool was identified that could
potentially be applied to aspects of QAPP preparation or implementation for all but three of the need
areas. The capabilities of the existing software should match the QAPP needs, as most of the software
was developed for use with a QAPP or for environmental data collection or analysis. Software not
designed for these uses could be modified or used to form the basis of an application that is more tailored
to QAPP preparation or implementation.
AF3.1 Quality Assurance Sampling Plan for Environmental Response (QASPER), Version 4.0
QASPER allows the creation and editing of a Quality Assurance sampling plan for
environmental response. The plan template consists of 11 sections: (1) title page, (2) site background,
(3) data use objectives, (4) sampling design, (5) sampling and analysis, (6) SOPs, (7) QA requirements,
(8) data validation, (9) deliverables, (10) project organization and responsibilities, and (11) attachments.
While preparing the plan, the user may enter the required information or select from the options provided
in a variety of "picklists." The picklists cover topics such as holding times, methods, preservatives, and
sampling approaches. The user may add or delete options from the picklists. QASPER also provides
various utility functions such as backing up, restoring, exporting, and importing a plan. Output may be
directed to a file or a printer. Contact: EPA, (732) 906-6921, Quality Assurance Sampling Plan for
Environmental Response (QASPER Version 4.0 User's Guide; latest version is QASPER Version 4.1,
January 1995.
AF3.2 Quality Integrated Work Plan (QIWP) Template for R&D and Monitoring Projects
The QIWP template is a tool designed to assist with planning, managing, and implementing a
specific monitoring or R&D project. The QIWP template is formatted with comment boxes that provide
guidance on the information to provide in each section. When activated, the text in the comment boxes
will appear on screen; however, they will not appear in a printout. An asterisk indicates where the user
should begin entering the discussion for each section. The QIWP document control format is already set
up in the template header. When a particular element is considered not applicable, the rationale for that
decision must be stated in response to that element. Once the user is satisfied with the information
entered under all elements of the template, the resulting printout is the combined project work plan and
QA plan. In addition, a printout of the QIWP template, prior to entering project related information, can
be used as a checklist for planning and review purposes. Other software packages available are the
QWIP Template for Model Development Projects and the QWIP Template for Model Application
Projects. Contact: EPA, (919) 541-3779 and North American Research Strategy for Tropospheric Ozone
(NARSTO) homepage.
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AF3.3 Region 2 QAPP Template
This package contains an annotated template containing instructions for completing each section
of the QAPP. The users are also instructed where to insert their discussions within the template. After
completing the QAPP, the italicized instructions are not printed, leaving only the preparer's discussion.
In addition, a table of contents is automatically generated. The template describes the information that
should be provided under the main topics of project management, measurement/data acquisition, data,
assessment/oversight, and references. The project management section covers the introduction, goals of
the project, organization of the project participants and of QA, and DQOs. The measurement/data
acquisition section discusses the topics to address to describe the statistical research design and
sampling. This section also covers the elements related to sample analysis: description of the instrument,
calibration, QC, consumables, and preventative maintenance. The data section provides for a discussion
of the data management procedures. The assessment/oversight section covers audits and QA reports.
The next section is a list of references. Finally, six tables are provided as examples for displaying
information on the following topics: (1) measurement quality criteria; (2) sample collection, handling,
and preservation; (3) instrument data and interferences; (4) instrument calibration, (5) QC checks; and
(6) preventive maintenance. Contact: EPA, (401) 782-3163, or (503) 754-4670.
AF3.4 Region 5 QAPP Template
This software consists of two model documents (one for Superfund sites and one for RCRA
sites) that describe the preparation of a QAPP in a series of elements. Each element contains two types
of information: (1) content requirements that are presented as smaller text and (2) structural guidance
that is presented as larger text and headed by the appropriate section number. This information is
intended to show to the QAPP preparer the requirements that must be described in each element and the
level of detail that is typically needed to gain Region 5 approval. Example text is provided that should be
deleted and replaced with the specific site information.
A TSCA Model Plan template is also available that attempts to be a comprehensive guide to all
the data gathering activities for Fiscal Year 94 Title IV grantees. In this template, headers are provided
in "background" format, and text that may apply to specific situations is in an italic font. Open spaces
indicate where the preparer's input is required. Contact: EPA, (312) 886-6234.
AF3.5 allCLEARIII
This software enables the creation of simple process diagrams, organizational charts, or decision
trees. It also creates diagrams from text outlines, spreadsheets, and database information. Contact:
American Society for Quality Control Quality Press, Publications Catalogue, (800) 248-1946.
AF3.6 Environmental Monitoring Methods Index (EMMI)
This software consists of an analytical methods database containing more than 4,200 analytes,
3,400 analytical and biological methods, and 47 regulatory and nonregulatory lists. EMMI cross-
references analytes, methods, and lists and has information about related laws, organizations, and other
chemical databases. This information does not include measurement method performance such as
precision and bias. Contact: DynCorp Environmental Technical Support, (703) 519-1222.
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AF3.7 EPA's Sampling and Analysis Methods Database, 2nd Edition
This software has a menu-driven program allowing the user to search a database of 178 EPA-
approved analytical methods with more than 1,300 method and analyte summaries. The database covers
industrial chemicals, pesticides, herbicides, dioxins, and PCBs and focuses on water, soil matrices, and
quality parameters. The software generates reports that are stand-alone documents that can be browsed,
printed, or copied to files. Each report contains information for initial method selection such as
applicable matrices, analytical interferences and elimination recommendations, sampling and
preservation requirements, MDLs, and precision, accuracy, and applicable concentration ranges.
Contact: Radian Corporation, (512) 454-4797.
AF3.8 Clean-Up Criteria for Contaminated Soil and Groundwater, 2nd edition
This software consists of a one-volume document and diskette summarizing cleanup criteria
developed by EPA, all 50 State regulatory agencies, and select countries outside the United States.
Contact: ASTM Publications Catalogue, (610) 832-9585, http://www.astm.org.
AF3.9 Decision Error Feasibility Trials (DEFT)
This package allows quick generation of cost information about several simple sampling designs
based on the DQO constraints. The DQO constraints can be evaluated to determine their appropriateness
and feasibility before the sampling and analysis design is finalized.
This software supports the Guidance for the Data Quality Objectives Process, EPA QA/G-4, that
provides general guidance to organizations on developing data quality criteria and performance
specifications for decision-making. The Data Quality Objectives Decision Error Feasibility Trials
(DEFT) User's Guide, contains detailed instructions on how to use DEFT software and provides
background information on the sampling designs that the software uses. Contact: EPA, (202) 564-6830.
AF3.10 GeoEAS
Geostatistical Environmental Assessment Software (GeoEAS) is a collection of interactive
software tools for performing two-dimensional geostatistical analyses of spatially distributed data.
Programs are provided for data file management, data transformations, univariate statistics, variogram
analysis, cross-validation, kriging, contour mapping, post plots, and line/scatter plots. Users may alter
parameters and re-calculate results or reproduce graphs, providing a "what if analysis capability.
This software and a user's guide can be downloaded through the Office of Research and
Development (ORD) World Wide Web site at http://www.epa.gov/ORD or
http://www.epa.gov/ORD/nerl.htm. Contact: GEO-EAS 1.2.1 User's Guide, EPA/600/8-91/008, April,
1991, EPA, (702)798-2248.
AF3.11 ELIPGRID-PC
ELIPGRID-PC calculates the probabilities related to hitting a single hot spot. The user has the
following options: (1) calculating the probability of detecting a hot spot of given size and shape when
using a specified grid, (2) calculating the grid size required to find a hot spot of given size and shape with
specified confidence, (3) calculating the size of the smallest hot spot likely to be hit with a specified
sampling grid, (4) calculating a grid size based on fixed sampling cost, and (5) displaying a graph of the
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probability of hitting a hot spot versus sampling costs. Contact: ELIPGEJD-PC: UPGRADED
VERSION, Oak Ridge National Laboratory/TM-13103, (970) 248-6259.
AF3.12 DQOPro
This software consists of a series of three computer programs that calculate the number of
samples needed to meet specific DQOs. DQOPro provides answers for three objectives: (1) determining
the rate at which an event occurs, (2) determining an estimate of an average within a tolerable error, and
(3) determining the sampling grid necessary to detect "hot-spots." Contact: Radian International, (512)
454.4797.
AF3.13 Research Data Management and Quality Control System (RDMQ)
This software is a data management system that allows for the verification, flagging, and
interpretation of data. PvDMQ is a menu-driven application with facilities for loading data, applying QC
checks, viewing and changing data, producing tabular and graphical reports, and exporting data in ASCII
files. PvDMQ provides a shell environment that allows the user to perform these tasks in a structured
manner. Contact: Environment Canada, (416) 639-5722, or EPA, (919) 541-2408.
AF3.14 DataQUEST
This tool is designed to provide a quick and easy way for managers and analysts to perform
baseline Data Quality Assessment. The goal of the system is to allow those not familiar with standard
statistical packages to review data and verify assumptions that are important in implementing the DQA
Process. This software supports the Guidance for Data Quality Assessment, EPA QA/G-9, that
demonstrates the use of the DQA Process in evaluating environmental data sets. Contact: EPA, (202)
564-6830.
AF3.15 ASSESS I.Ola
This software tool was designed to calculate variances for quality assessment samples in a
measurement process. The software performs the following functions: (1) transforming the entire data
set, (2) producing scatter plots of the data, (3) displaying error bar graphs that demonstrate the variance,
and (4) generating reports of the results and header information. Contact: EPA, (702) 798-2367.
AF3.16 QATRACK
This Microsoft Access software provides a database that tracks QAPPs requiring approval. Data
are entered into QATRACK during the assistance agreement start-up stage, as soon as the QA manager
reviews and signs the agreement. Users can edit the data, query the database to perform data reviews,
and archive files once the QAPP is approved. Contact: EPA, (919) 541-2408.
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APPENDIX G
ISSUES IN DATA MANAGEMENT
AG1. INTRODUCTION
EPA QA/G-5 provides guidance on many different operations that involve generating, collecting,
manipulating, and interpreting environmental data. These activities include field sampling, sample
handling and storage, laboratory analysis, modeling, data storage and retrieval, and Data Quality
Assessment. All these activities generate data or require data to be manipulated in some way, usually
with the aid of a computerized data management tool such as a database, spreadsheet, computer model, or
statistical program.
This appendix expands the guidance currently provided in EPA QA/G-5, Section BIO, Data
Management. Guidance is provided on Quality Assurance (QA) considerations and planning for the
development, implementation, and testing of computer-based tools that perform the data management
aspects of the overall environmental project described in the Quality Assurance Project Plan (QAPP).
These data management aspects include data storage, data acquisition, data transformations, data
reduction, modeling, and other data management tasks associated with environmental data collection
projects. This guidance can be used for applications developed in-house or for those developed using
commercial software. It can be used for systems of different sizes, from individual spreadsheet
applications to large integrated systems. The amount of planning and documentation involved are
tailored according to the use of the data and the size and complexity of the application.
This appendix incorporates into EPA QA/G-5 the QA elements of guidance from the EPA Office
of Information Resources Management (OIRM) and applicable industry standards, such as those of the
Institute of Electronic and Electrical Engineers, relating to development of information and data
management systems. Because data and information system development projects differ widely in many
different respects, this appendix does not attempt to address the low-level details of planning,
implementation and assessment nor does it provide step-by-step procedures to follow when developing a
data management system. These details are left to other EPA guidance documents (See Section AG2.4),
national consensus standards, and the best judgement of the personnel on each project.
AG2. REGULATORY AND POLICY FRAMEWORK
This section provides a brief overview of the legislation, policies, standards and guidelines most
applicable to the development of EPA data management and information systems. Sections AG2.1 and
AG2.2 of this overview are intended to provide the QAPP preparer (specifically the preparer of the data
management section) with a general understanding of the relevant agency-level policies, Sections AG2.3
and AG2.4 provide a reference for the major guidance documents containing more specific and detailed
information on development of data management systems.
AG2.1 Legislation
The following is a summary of the major legislative policies that pertain to information
technology and the development of data management systems. The two most relevant pieces of legislation
are:
(1) the Paperwork Reduction Act (PRA) of 1980 (P.L. 96-511) as amended in 1986 (P.L. 99-500)
and 1995 (P.L. 104-13), and
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(2) the Clinger-Cohen Act of 1996 (P.L.-104-208). (Note that the Clinger-Cohen Act is the
amended title for the Information Technology Management Reform Act and the Federal
Acquisition Reform Act of 1996 (P.L. 104-106)).
The overall purpose of the PRA is to reduce paperwork and enhance the economy and efficiency
of the government and private sector by improving Federal information policy development and
implementation. The PRA establishes a broad mandate for executive agencies to perform their
information activities in an efficient, effective, and economical manner. The 1995 amendments
established several broad objectives for improving the management of Federal information resources.
These objectives include maximizing the utility of information, improving the quality and use of
information to strengthen decision making, and establishing uniform resource management policies.
The Clinger-Cohen Act (CCA) sets forth requirements for the Office of Management and Budget
(OMB) and the individual executive agencies. OMB responsibilities include promoting and improving
the acquisition, use, and disposal of information technology by the Federal Government to improve the
productivity, efficiency, and effectiveness of Federal programs. In addition, the CCA requires each
agency to design and implement a process for maximizing the value and assessing and managing the risks
of information technology acquisitions. The CCA also requires each agency to utilize the same
performance- and results-based management practices as encouraged by OMB.
AG2.2 Policy Circulars and Executive Orders
Circular A-130 implements OMB authority under the PRA and sets forth the policy that applies
to the information activities of all the executive agencies. The policies include requirements for
information management planning as well as information systems and information technology
management. Part of the information management policy is that agencies, when creating or collecting
data, need to plan from the outset how to perform the following data management functions: (1) data
processing and transmission, (2) data end use and integrity protection, (3) data access, (4) data
dissemination, (5) data storage and retrieval, and (6) data disposal. In addition, these planning activities
need to be documented. The information systems and information technology management policies
describe an information system life cycle that is defined as the phases through which an information
system passes. These phases are typically characterized as initiation, development, operation, and
termination. However, no specific number of phases is set, and the life cycle management techniques
that agencies use may vary depending on the complexity and risk inherent in the project. In addition, the
division between the phases of the system life cycle may not be distinct.
Current implementation of the CCA comes through Executive Order 13011, which outlines the
executive agencies. The agencies are to strengthen the quality of decisions about the use of information
resources to meet mission needs and establish mission-based performance measures for information
systems. In addition, to establish agency-wide and project-level management structures and processes
responsible and accountable for managing, selecting, controlling, and evaluating investments in
information systems.
G2.3 Federal Information Processing Standards
The National Institute of Standards and Technology (NIST) develops standards for Federal
computer systems. NIST issues these standards and guidelines as Federal Information Processing
Standards (FIPS) for government-wide use. NIST develops FIPS when there are compelling Federal
government requirements (such as for security and interoperability) and there are no acceptable industry
standards or solutions. FIPS publications include standards, guidelines, and program information
EPA QA/G-5 G-2 QA98
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documents in the following seven subject areas: (1) general publications, (2) hardware standards and
guidelines, (3) software standards and guidelines, (4) data standards and guidelines, (5) computer
security standards and guidelines, (6) operations standards and guidelines, and (7) telecommunications
standards. Additional information about FIPS, including ordering information and a list and description
of the individual documents, is available online using the World Wide Web (WWW) at the following
Uniform Resource Locator (URL) address: http//www.nist.gov/itl/div879/pubs/.
AG2.4 EPA Guidance
EPA's Office of Information Resources Management (OIRM), which has the primary functional
responsibility for Information Resources Management (IRM) policy development and overall
management of EPA's IRM program, has published several IRM guidance documents. The Information
Resources Management Policy Manual 2100 establishes a policy framework for managing information
resources in the Agency. The document is intended to provide a structure for the implementation of
legislation concerning the management of Federal information resources such as the PRA. Also, the
manual establishes the authorities and responsibilities under which the OIRM will function. The Policy
Manual consists of twenty chapters that cover subjects such as software management, information
security, system life cycle management, and information and data management. The Policy Manual can
be obtained online using the WWW at the following URL address: http://www.epa.gov/irmpoli8/.
The System Design and Development Guidance document provides a framework that Agency
managers can use to document a problem and justify the need for an information-system-based solution.
The document also provides guidance for identifying solutions to specified problems and for information
system development. The guidance consists of three volumes (A, B, and C). Volume A provides a
method for documenting the need for an information system and developing an initial system concept
that describes the inputs, outputs, and processes of the proposed system. Volume B provides guidance
for developing design options that satisfy the initial system concept developed in Volume A. Volume B
also gives guidance for selecting the most cost-effective solution. Volume C describes the system-design
and development process (and the required associated documentation) and outlines a software
management plan that is used to ensure the quality of EPA software design, development,
implementation, and maintenance efforts. This document can be obtained online using the WWW at the
following URL address: http://www.epa.gov/irmpoli8/.
Additional EPA guidance documents pertaining to information system development, operations,
and maintenance are listed in Section G4, References. Up-to-date OIRM documents can be obtained
online using the WWW at the following URL address: http://www.epa.gov/irmpoli8/.
Another source of guidance is EPA Quality Assurance Division's (QAD) Development
Management System Template. The template includes a description of the roles of management in
planning for the development of data management systems. The responsible project officer or
contracting officer representative outlines a management scheme based upon the planning and
documentation activities that satisfy OIRM policy or an organization's Quality Management Plan. The
project manager works with the quality assurance manager to identify the tasks, work products, and
management procedures for the project.
AG3. QA PLANNING FOR INFORMATION SYSTEMS
Data generated or managed by an information system must be defensible and appropriate to their
final use or the conclusions to be drawn from the data. To help ensure that data will be defensible,
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project teams should include adequate QA planning in the development of data management or other
information systems. There are three elements to QA planning for data management:
• Needs Analysis—identifying applicable qualitative and quantitative requirements and
establishing corresponding quality goals.
• Planning and Implementing—implementing an appropriate planning and management
framework for achieving these goals.
• Verification—testing and auditing to determine that the established goals are being met.
AG3.1 Quality Assurance Needs Analysis
The type and magnitude of the QA effort needed in developing a new information system
depends on the qualitative and quantitative criteria that the data must meet and on the complexity and
magnitude of the project. Other specific concerns such as security and system performance also help
define the QA program requirements. Only by establishing the ultimate needs and objectives for data
quality in the early planning stages can appropriate decisions be made to guide the system development
process to a successful conclusion.
AG3.1.1 Quantitative and Qualitative Criteria
Considerations similar to those in the Data Quality Objectives (DQO) framework can be used to
identify and define the general criteria that computer-processed data must meet. For example, very high
standards must be set for information systems that generate or manage data supporting Congressional
testimony, for developing new laws and regulations, for litigation, or for real-time health and safety
protection. More modest levels of defensibility and rigor are required for data used for technology
assessment or "proof of principle," where no litigation or regulatory actions are expected. Still lower
levels of defensibility apply to basic exploratory research requiring extremely fast turn-around, or high
flexibility and adaptability. In this case, the work may have to be replicated under tighter controls or the
results carefully reviewed prior to publication. By analyzing the end-use needs, appropriate criteria can
be established to guide the information system development process.
More detailed criteria can also be developed to address the specific ways in which computer-
generated or computer-processed results can be in error. The following are some specific questions to be
asked when quantitative or qualitative objectives are being defined:
• What is the required level of accuracy/uncertainty for numerical approximations?
• Are the correct data elements being used in calculations (e.g., the correct "cell" in a
spreadsheet)?
• Have the appropriate statistical models, mathematical formulas, etc. been chosen?
• What "chain-of-custody" requirements pertain to the data and results?
AG3.1.2 Project Scope. Magnitude, and Complexity Criteria
Software and systems development projects vary widely in scope and magnitude. Application of
effective management controls (including the QA program) are critical for successful performance on
large projects. Risks associated with large, complex projects commonly include overruns and schedule
delays. The integrity of results can also be compromised by rushing to complete an overdue project.
Table Gl summarizes risks as a function of project size or scope.
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Table AG1. Project Scope and Risks
PROJECT SCOPE
Large Project (information system development
is a major component)
Medium Size Project (including projects in
which an information system is not the major
component)
Small Projects (including projects in which
computer-related development is a minor
component)
Projects with ad hoc software development and
data management practices (no QA program)
POTENTIAL RISKS
• Major budget overruns
• Schedule slippage
• Unusable system or data
• Public relations problems
• Budget overrun
• Schedule slippage
• Uncertain data quality
• Lack of confidence in data
• Lack of data traceability
• Schedule slippage
• Lack of confidence in data
• Inefficient use of time and resources
EPA OIRM's Chapter 17, System Life Cycle Management, in Information Resources
Management Policy Manual, provides a similar rationale for categorizing information systems. Four
system types are defined based on the significance of the risk assessment for the Information System.
Major factors included in this risk assessment are the importance of the data, the cost of the system, and
the organizational scope of the system. For the purposes of a management review, OIRM defines
Information Systems using the following classes:
• A Major Agency System is a system that is mission critical for multiple AAships or
Regions or Agency Core Financial System or has a life cycle cost greater than $25
million or $5 million annually.
• A Major AAship or Regional System is a system that is mission critical for one AAship
or Regional Office or has a life cycle cost greater than $10 million or $1 million
annually.
• A Significant Program Office System is a system that is mission critical in one Program
Office or has a life cycle cost greater than $2 million or $100,000 annually.
• A Local Office or Individual Use System is a system for local office or individual user or
costs less than $100,000 annually for one project.
AG3.1.3 Other Quality Issues
While the issues discussed in the preceding two sections are of key importance in determining
the necessary level of the QA effort, there are many individual quality issues that should not be
overlooked in defining the requirements for a particular project. These issues should be addressed in
project planning, implementation, and testing. Some commonly encountered issues are discussed in the
following text.
AG3.1.3.1 Security Issues. There are many different types of threats to data security and
communications. Common concerns include viruses, hackers, and interception of e-mail. If these
concerns apply for a particular system, the following issues should be addressed during system planning.
Tests and audits may be planned to assess system vulnerability. Some of the management and QA
techniques that can be employed in this assessment include:
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• reviewing the project requirements documentation to ensure that security issues are
included among project requirements;
• reviewing the testing documents to ensure that security features are adequately and
thoroughly tested; and
• planning audits to be conducted by security personnel outside the immediate project
team.
AG3.1.3.2 Communication Issues. Most business computers are extensively interconnected through the
Internet, agency networks, or local networks. Computer communications is a rapidly changing area of
technology. Consequently, communications software and hardware are frequently the source of
problems to developers and users. Some communication issues that might be addressed in system
planning, design, and testing include the following:
• adequately defining the communication interfaces;
• thoroughly testing the communications hardware and software, including "stress testing"
under high load and adverse conditions; and
• conducting a beta test that encompasses users with a variety of different hardware and
communications connections.
AG3.1.3.3 Software Installation Issues. Many software packages are being developed and distributed by
the Agency to run on the individual user's personal computer. Many of these use auto-installation
routines that copy files into various directories and modify system initialization and registry files.
Planning the necessary systems requirements should address the following considerations:
• testing on as many different platforms as possible including various combinations of
processors, memory sizes, video controllers, and printers (Beta Testing can be extremely
helpful for this);
• including an "uninstall" program that not only deletes files, but also properly removes
entries in the initialization and registry files; and
• ensuring that both the "setup" and "uninstall" routines are thoroughly tested and
debugged before release.
AG3.1.3.4 Response Time Issues. A frequently overlooked aspect of computerized systems is the
impact of of system load and the resulting effect on response time. Response time is important not only
for real-time data acquisition and control systems, but also for interactive user interfaces. It is a good
idea to establish quantitative objectives for response time performance for all interactive and real-time
systems. These goals must be explicit and testable. A typical specification might be that the user should
not wait longer than x seconds for a response after submitting a request to the program.
AG3.1.3.5 Compliance with EPA and other Federal Policies and Regulations. Since individual
managers and scientists may not track information systems regulations and policy, requirements should
be determined at project inception. Some of the more important policies have been summarized in
Section AG2 of this appendix. Many of the policies and guidances are aimed at ensuring individual
project success, while others are intended to foster Agency-wide goals, including consistency of
hardware and software platforms, purchasing economies, and security. For example, EPA's Acquisition
Regulation requires Agency contractors to collect and review OIRM's most recent policies by
downloading the most current documents available online at OIRM's WWW Site.
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AG3.2 System Development Planning
Proper planning, execution, and QA protocols are vital to the success of projects involving
information systems development, software development, or computer data processing. The project
management team should work closely with the responsible QA staff to implement a program that best
suits the needs of the individual project. A few of the issues to be addressed include the level of
documentation required, schedule, personnel assignments, and change control. The following section
describes a commonly used planning framework and associated documentation that is based on the
widely recognized software- or system-development life cycle.
AG3.2.1 System Development Life Cycle
Software and information system development projects tend to evolve in distinct phases.
Recognition of this fact can be helpful in planning and managing a new project. Table G2 outlines eight
commonly recognized stages in the system development life cycle, along with typical activities and
documentation for each stage. This approach can be modified to meet the needs of individual projects.
Table AG2. Software Development Life Cycle
LIFE CYCLE
STAGE
TYPICAL ACTIVITIES
DOCUMENTATION
Needs Assessment
and High- Level
Requirements
Definition
Assessment of needs and requirements
through literature search, interviews with
users and other experts.
• Needs Assessment
Documentation (e.g., QA
Project Plan)
• Requirements Document
Detailed
Requirements
Analysis
Listing of all inputs, outputs, actions,
computations, etc. that the system is to
perform.
Listing of ancillary needs such as
security, user interface requirements.
Design team meetings.
Detailed Requirements
Document, including
Performance, Security, User
Interface Requirements etc.
System Development
Standards
System Design
Translation of requirements into a design
to be implemented.
Design Document(s)
including Technical Design
(algorithms, etc.),
Software/Systems Design
Implementation
Controls
Coding and configuration control.
Design/implementation team meetings.
In-line comments
Change control
documentation
Testing, Verification,
and Validation
Verification that the system meets
requirements.
Verification that the design has been
correctly implemented.
Beta Testing (users outside team).
Acceptance Testing (for final acceptance
of a contracted product).
Implement necessary corrective actions.
• Test Plan
• Test Result Documentation
• Corrective Action
Documentation
• Beta Test Comments
• Acceptance Test Results
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LIFE CYCLE
STAGE
Installation and
Training
Operations,
Maintenance, and
User Support
System Retirement
and Archival
TYPICAL ACTIVITIES
Installing data management system and
training users.
Use of the system or data requires usage
instructions and maintenance resources.
Information on how data or software can
be retrieved if needed.
DOCUMENTATION
• Installation Documentation
• User's Guide
• User's Guide
• Maintenance Manual or
Programmer's Manual
• Project files
• Final Report
AG3.2.2 Planning Documentation
Individual project and QA managers should tailor documentation to meet the specific needs of
their project. References in Section AG4 such as EPA System Design and Development Guidance and
Chapter 17, System Life Cycle Management, in Information Resources Management Policy Manual
describe in more detail the various types of documentation related to the system life cycle planning
phases. The following list describes in more detail some of the planning documentation listed in Table
AG2:
• Requirements Documentation—The high-level requirements document gives an
overview of the functions an information system must perform. Detailed requirements
documents define all critical functions that the completed information system must
support. Performance goals derived from analysis of the project's DQOs should be
included among the requirements. In addition, frequently overlooked issues such as
those described in Section AG3.1.3 should be addressed. Requirements documentation
should be reviewed by the end-user, if possible, to ensure that critical functions and other
requirements have not been overlooked.
• Design Documentation—Design documents are used to plan and describe the structure of
the computer program. These are particularly important in multi-programmer projects in
which modules written by different individuals must interact. Even in small or single-
programmer projects, a formal design document can be useful for communication and
for later reference.
• Coding Standards or SOPs—These may apply to a single project, an entire organizational
Branch, or other functional group. Uniform standards for code formats, subroutine
calling conventions, and in-line documentation can significantly improve the
maintainability of software.
• Testing Plans—Testing, which is discussed in Section AG3.3, must be planned in
advance and must address all original requirements and performance goals. Specific
procedures for the corrective action and retesting process should be described in QA
planning documents and implemented in the Testing Plan.
• Data Dictionary—A data dictionary can be useful to developers, users, and maintenance
programmers who may need to modify the system later. The data dictionary is often
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developed before code is written as part of the design process. The dictionary should be
updated as necessary when new elements are added to the data structure. A data
dictionary need not be a separately written document. For example, the record definition
files required for many database systems can serve this purpose, provided that they are
available in a form that is readily accessible to the user or maintenance programmer.
• User's Manual—The user's manual can often borrow heavily from the requirements
document because all of the software's functions should be specified there. The scope of
the user's manual should take into account such issues as the level and sophistication of
the intended user and the complexity of the interface. Online help can also be used to
serve this function.
• Maintenance Manual—The maintenance manual's purpose is to explain a program's logic
and organization for the maintenance programmer. This manual should also contain
crucial references documenting algorithms, numerical methods, and assumptions.
Instructions on how to rebuild the system from source code must be included. The
maintenance manual will often borrow heavily from the design manual.
• Source Code—It is usually not necessary to print the source code in hard copy form
unless needed for a specific purpose. However, it is very important to archive computer-
readable copies of source code according to the policies of each Office, Region, National
Center, or Laboratory.
AG3.3 Audits and Testing
As with any project involving generation or handling of environmental data, audits can be used
to verify that goals and objectives are being met. Audits of the Information System development
process, audits of security, and data verification audits may be particularly helpful when conducted by
personnel outside the immediate project team. Security audits by someone with expertise in this field
can be valuable when data confidentiality and prevention of tampering are important issues. Data
verification audits can be conducted using a known data set. Such a data set might be developed by an
end-user or an outside expert to verify that the information system produces the expected results.
Testing procedures and criteria need not be specified in detail by the QA Project Plan (or
equivalent document); however, the general extent and approach to testing should be described. QA
planning documents for developing a new information system should generally provide the following
project elements:
• a list of planned test documentation to be written;
• a description of the types of testing that will be conducted;
• a schedule for testing and audits; and
• a section on corrective actions.
The purpose of testing is not simply to detect errors but also to verify that the completed software
meets user requirements. In designing any test, the "correct" or "acceptable" outputs should be known in
advance, if possible. Testing should be planned in an orderly, structured way and documented. A phased
approach to testing, which is often employed in larger scale information system development projects,
might employ a sequence of testing procedures such as those presented in Sections AG3.3.1 through
AG3.3.5.
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AG3.3.1 Individual Module Tests
Individual module tests are applied to individual functions. For sequential programming
languages, such as FORTRAN, BASIC, or C, individual modules might include functions and
subroutines. For other types of software (e.g., spreadsheets), defining a functional module is more
problematic, because the software may not be designed in a modular way. However, well-planned design
strategies, such as compartmentalized design, can ease the testing effort.
AG3.3.2 Integration Tests
Integration tests are done to check the interfaces between modules and to detect unanticipated
interactions between them. Integration testing should be done in a hierarchical way, increasing the
number of modules tested and the subsystem complexity as testing proceeds. Each level of subsystem
integration should ideally correspond to a unified subset of system functions such as the "user interface."
Because all the elements may not be present, it may be necessary to develop test data sets or
hardware/software test beds to conduct the tests effectively.
When problems are encountered at any level of integration or system testing, it is necessary to
track the errors back to their origin, which may be any phase of the project. When the original reason for
the problem is identified, all affected modules and subsystems should be corrected and retested as
described in the next section.
AG3.3.3 Regression Testing
After a system module has been modified, all testing performed on the original version of the
module should be repeated, including all integration tests that include the module. This reduces the
chance that any new "bugs" introduced by the changes will go undetected while modifying the code to
correct an existing problem. Spreadsheets may be particularly difficult to test thoroughly after changes
have been made because their data dependencies are often difficult to trace. In such cases, it may be
useful to have a suite of tests that can be run whenever a change is made to verify that other functions are
not affected.
AG3.3.4 System Testing
Testing the full system is the ultimate level of integration testing and should be done in a realistic
simulation of the end-user's operational environment. If a detailed requirements document was written,
each requirement should be tested systematically. It is often helpful for a representative end-user to
participate in the system test to verify that all requirements have been implemented as intended.
Elements of the special tests described in Section AG3.3.5 can be incorporated into the system test.
For some projects, the in-house system test may be the final stage of testing. For larger or more
critical projects, formal acceptance tests or beta testing would follow. The system test should exercise all
functions possible, and the data sets used to demonstrate the software should be as realistic as possible.
AG3.3.5 Other Special Testing
AG3.3.5.1 Stress Testing should be included in the system-level testing whenever a system might be
load-sensitive (e.g., real-time data acquisition and control systems). The stress test should attempt to
simulate the maximum input, output, and computational load expected during peak usage. The specific
rates of input, output, and processing for which the system is designed are important criteria in the
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original requirements specification. The maximum load is a key quality indicator and should have been
specified early in planning. The load can be defined quantitatively using criteria such as the frequency of
inputs and outputs or the number of computations or disk accesses per unit of time. Developing an
artificial test bed to supply the necessary inputs may be necessary. The test bed can consist of hardware,
software, or a combination of the two that presents the system with realistic inputs to be processed. The
project team can write programs to carry out this testing, or automated tools may be available
commercially. Test data sets may be necessary if the software needs external inputs to run.
AG3.3.5.2 Acceptance Testing refers to contractually required testing that must be done before
acceptance by the customer and final payment. Specific procedures and the criteria for passing the
acceptance test should be listed before the test is done. A stress test is a recommended part of the
acceptance test, along with thorough evaluation of the user interface.
AG3.3.5.3 Beta Testing refers to a system-level verification in which copies of the software are
distributed outside the project group. In beta testing, the users typically do not have a supplied testing
protocol to follow; instead, they use the software as they would normally and record any anomalies
encountered. Users report these observations to the developers, who address the problems before release
of the final version.
AG3.3.5.4 Spreadsheet testing is particularly difficult because of an inherent lack of readability and
structure. One of the best ways to test spreadsheets is to challenge them with known data, although this
can be very time-consuming. Another approach is to independently recede some or all of the spreadsheet
and compare results. Software packages for spreadsheet analysis exist, but their usefulness for testing
must be evaluated on a case-by-case basis.
AG3.4 Examples
The following examples present three different data management projects: a computer model, a
spreadsheet, and a local-area-network-distributed database. Some of the QA, management, and testing
issues peculiar to each type of project are discussed.
AG3.4.1 Model Development
Mathematical models are widely used in the environmental sciences. Modeling is necessary
when the complexity of a particular situation makes a simple solution impossible, as when many different
processes are closely coupled and occur simultaneously. Some models are used to generate data that may
be used for planning and regulatory purposes.
A high level of mathematical and scientific expertise is required to develop and test the
algorithms used to represent the different physical processes. This expertise is often a scarce and
valuable resource. Consequently, a team approach may be used under which the senior scientific staff
concentrates on developing, testing, and documenting the "core" algorithms, while support staff take care
of other duties on the development project, including developing the user interface, communications,
coding, and documentation. Quality Assurance planning for developing a new model should include the
following:
• The staffing section of the QAPP should state the relevant qualifications for the key
scientific personnel. The need for peer review of novel algorithms should be addressed
if new research developments are to be incorporated in the model. Guidance documents
on conducting peer review of models are referenced in Section AG4.5. The topics
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addressed include verification testing, model code documentation, and review of the
conceptual and mathematical performance of a model.
• The end use of the data produced will dictate how exhaustively the models must be
tested and the types of demonstrations that should be done before release. A regulatory
model should be compared with existing regulatory models using identical or similar
data sets. Environmental models such as air dispersion models can be compared with
actual field data. However, care should be taken in evaluating discrepancies between
model results and the field data, because differences between monitoring data and model
results can arise from a variety of sources.
• Capabilities and needs of the end-users will dictate how much effort is spent developing
and testing the user interface and on providing user documentation and online help
functions. User interface issues should be addressed in the requirements definition, and
these functions should be tested exhaustively. Beta testing results should be reviewed
carefully to identify problems with the user interface.
• It may be possible to develop specific objectives for parameters such as bias and
precision by modeling cases that have known and accurate results. This is usually
possible only in relatively simple cases, since new models are usually developed to
expand beyond the capabilities of currently available models.
AG3.4.2 Spreadsheet for Data Processing in an Ongoing Project
Spreadsheets have replaced hand calculators for many simple applications but can sometimes
grow to approach the complexity of a database management system. Spreadsheets developed on an ad
hoc basis are usually not tested in any systematic way and may not be archived with project data.
Consequently, there can be little accountability for the correctness of calculations, even when those
results are used for sensitive applications such as regulatory reporting. This lack of testing and
verification can present significant risks. The following QA guidelines are suggested for spreadsheets
developed or used in support of projects involving environmental data:
• QA or other project planning documents should indicate all data processing tasks to be
done using spreadsheets. The origin of any spreadsheets obtained from outside the
project group should be documented.
• Spreadsheets should be developed by personnel with the appropriate education and
training. Personnel who maintain or use the spreadsheet should also have appropriate
qualifications and training.
• Documentation should be provided for correct use and maintenance of the spreadsheet.
• Data quality audits for projects processing environmental data should examine all
spreadsheets used to produce reportable data for the project. Questions such as the
following should be asked during the audit:
Have all critical calculations performed by the spreadsheet been verified (i.e.,
has the spreadsheet been tested)? Is there a record of validation including the
date and the specific inputs and outputs?
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Have significant changes been made to the spreadsheet since the last time its
output was validated?
Are users properly trained in the use of the spreadsheet? Do they have sufficient
reference material? An interview with users other than the spreadsheet
developer may be helpful in determining this.
What provisions are there for quality control of manual data input? As with any
other type of manual data entry situation, duplicate key entry or similar means of
quality control should be used when entering large data sets.
Does the spreadsheet incorporate complex table lookup functions or macros?
These features significantly complicate spreadsheets and can make their detailed
operation virtually impossible to understand fully. In such cases, the auditor
should review the reasonableness of outputs produced by the spreadsheet using a
known data set.
• Provisions should be made for archiving the spreadsheet in a format that is usable in case
data have to be reprocessed. The time window in which data may have to be reprocessed
should be considered. Some spreadsheets (as well as other types of computer software)
can sometimes remain in use long after the original project has ended, and
documentation must be provided so that the spreadsheet's functions can be understood at
a later time.
AG3.4.3 A Local-Area-Network-Distributed Database Application
Communication software is complex and is evolving rapidly. This leads to fundamental concerns
in the areas of security, privacy, and accountability. The following example, based on a real system now
in use for reporting and distributing environmental data, will illustrate some of the QA considerations
relevant to a relatively simple distributed application:
The data base application resides on a centralized server with PC- or workstation-based clients
accessing the data over a local area network (LAN). Users can also communicate with the server
using dial-up access or via the Internet. By relying on a commercially available communications
product, the system has been developed by existing project personnel, none of whom have formal
training in computer science. The database programming was done using a popular,
commercially available data base development product. Individual project team members and
some outside users can log on remotely and are able to add and modify data, query the data base,
and generate reports.
Management and QA planning for this project should address the normal concerns of ensuring
that the system is acquired and installed within budget and on-schedule, and that calculations and reports
are correct. QA concerns specific to this system include the following:
AG3.3.4.1 Security. There are many potential security vulnerabilities, and planners should identify as
many of these as possible and state explicitly how they will be prevented. Specific tests should be
conducted that address the security features of the system. Some specific methods for addressing
security vulnerabilities include the following:
• Using separate passwords for user log on, for remote dial-in, and for access to sensitive
portions of the database.
• Restricting downloads of files that could contain viruses and performing regular virus
checks on all machines on the network. Viruses are easily transmitted over the Internet,
and can spread rapidly over LANs. Viruses represent both an operational and a security
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risk. Recent viruses have infected wordprocessor macros. Because word processing
files are frequently interchanged via the WWW and e-mail, even such "nonexecutable"
files can pose a danger.
AG3.4.3.2 Privacy. Since this system may contain records of proprietary business data and voluntarily
submitted emissions information, the records must be kept private. The means for ensuring that privacy
is protected was a fundamental requirement for the system. Regular QA reviews are done to verify that
established privacy-related procedures are being followed. These include encrypting the identifying
records and restricting use only to personnel with special password-protected access.
AG3.4.3.3 Personnel Qualifications and Training. Although it is common for technical or clerical
personnel to develop small information systems using currently available "User-friendly" software and
systems environments, this practice can represent a significant risk to a larger project. On the project
described in the example, the qualifications of project staff had been carefully evaluated with respect to
experience with similar information-systems development projects of comparable magnitude. A key
person with this experience was identified and was made the lead programmer/developer. The QA
Officer also had significant computer background and was able to provide additional support during
project implementation. A number of books, utility programs, and other aids were purchased for the
project.
AG3.4.3.4 Data Defensibility and Traceability. With many different users having read/write access to a
common data set, assurance of data integrity was a concern. If absolute traceability of each data item had
been required, an Audit Trail, which records each transaction and includes the date, time, and person
responsible for the change, would be a fundamental part of the requirement. However, an audit trail was
not deemed necessary for this particular project. Backup copies of the data base are being maintained on
a weekly basis and are archived. This serves the dual purpose of providing a backup as well as to trace
any data tampering that might occur. Backups have proved valuable in this relatively open environment
when a user inadvertently overwrites or deletes files. Occasional internal audits are performed to detect
any unexplained changes in the data set overtime.
AG4. REFERENCES
This section provides references that were used in developing this appendix along with
documents that provide more detailed coverage of the topics listed below.
AG4.1 General
U.S. Environmental Protection Agency. 1995. Air Pollution Prevention and Control Division Quality
Assurance Procedures Manual, Appendix G Quality Assurance Planning for Software and Data
Management Projects. Revision 1. Research Triangle Park, NC.
U.S. Environmental Protection Agency. 1996. EPA Guidance for Quality Assurance Project Plans, EPA
QA/G-5. Washington, DC.
AG4.2 Legislation
Clinger-Cohen Act of 1996 (P.L.-104-208). (Note that the Clinger-Cohen Act is the amended title for
the Information Technology Management Reform Act and the Federal Acquisition Reform Act
of 1996 (P.L. 104-106).
Information Technology Management Reform Act of 1996.
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Paperwork Reduction Act of 1980 (P.L. 96-511) as amended in 1986 (P.L. 99-500) and 1995
(P.L. 104-13).
AG4.3 Executive Orders and Policy Directives
Executive Order, Federal Information Technology, July 17, 1996.
Office of Management and Budget Circular Number A-130, Management of Federal Information
Resources, February, 1996.
AG4.4 System Development, Operations and Maintenance Guidance Documents
Institute of Electrical and Electronics Engineers. 1994. Software Engineering. Piscataway, NJ.
U.S. Environmental Protection Agency. 1987. Data Standards for the Electronic Transmission of
Laboratory Measurement Results. EPA Directive Number 2180.2. Washington, DC.
U.S. Environmental Protection Agency. 1993. EPA Information Security Manual. EPA Directive
Number 2195. Washington, DC.
U.S. Environmental Protection Agency. 1993. EPA System Design and Development Guidance. EPA
Directive Number 2182. Washington, DC.
U.S. Environmental Protection Agency. 1993. Hardware and Software Standards. Washington, DC.
U.S. Environmental Protection Agency. 1993. Operations and Maintenance Manual. EPA Directive
Number 2181. Washington, DC.
U.S. Environmental Protection Agency. 1994. EPA Information Paper, Distributed System Management,
Draft for Comments. EPA 722/003. Washington, DC.
U.S. Environmental Protection Agency. 1995. Information Resources Management Policy Manual. EPA
Directive Number 2100. Washington, DC.
U.S. Environmental Protection Agency. 1995. Information Technology Architecture Road Map. EPA
612/002A. Washington, DC.
AG4.5 Modeling
U.S. Environmental Protection Agency. 1994. Guidance for Conducting External Peer Review of
Environmental Regulatory Models. EPA 100-B-94-001.
U.S. Environmental Protection Agency. 1994. Report of the Agency Task Force on Environmental
Regulatory Modeling. EPA 500-R-94-001.
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FEDERAL EMERGENCY MANAGEMENT AGENCY FEMA 265/JULY 1995
MANAGING FLOODPLAIN DEVELOPMENT
IN
APPROXIMA TE ZONE A AREAS
A GUIDE FOR OBTAINING AND DEVELOPING
BASE (100-YEAR) FLOOD ELEVATIONS
APRIL 1995
-------
FOREWORD
This guide was developed for use by community officials, property
owners, developers, surveyors, and engineers who may need to
determine Base (100-year) Flood Elevations (BFEs) in special flood
hazard areas designated as approximate Zone A on the Federal
Emergency Management Agency's Flood Insurance Rate Maps published
as part of the National Flood Insurance Program. One of the
primary goals of this document is to provide a means of determining
BFEs at a minimal cost.
The guidance provided herein is primarily intended for use in
riverine and lake areas where flow conditions are fairly uniform,
and do not involve unusual flow regimes (rapidly varying flow, two-
dimensional flow, supercritical flow, hydraulic jumps, etc.).
This guide is not to be used for areas that experience alluvial fan
flooding or areas that contain characteristics of alluvial fan
flooding. In addition, this guide is not to be used in Zone V
(velocity) areas or coastal Zone A areas that are subject to
flooding due to storm surge from hurricanes and other coastal
storms. Furthermore, guidance on determining regulatory floodways
is not provided in this guide.
Notes on the .PDF version of the Zone A Manual
Appendices 8 and 9 (hand calculations) were not included in the
.PDF (Internet) version of this Manual. The information was not in
a text format. The scanned images of Appendices 8 & 9 would have
made the file size of this document much larger. To keep the file
size down they were omitted.
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TABLE OF CONTENTS
Page
I. INTRODUCTION 1-1
II. NATIONAL FLOOD INSURANCE PROGRAM BACKGROUND II-1
III. APPLICABLE NATIONAL FLOOD INSURANCE PROGRAM FLOODPLAIN
MANAGEMENT REQUIREMENTS IN APPROXIMATE ZONE A AREAS ..111-1
Requirements for Obtaining Base (100-year)
Flood Elevation Data III-l
Requirements for Developing Base (100-year)
Flood Elevation Data Ill-2
Use of Draft or Preliminary Flood Insurance
Study Data Ill-7
Advantages of Developing Base (100-year)
Flood Elevation Data Ill-8
IV. OBTAINING EXISTING BASE (100-YEAR) FLOOD ELEVATIONS ...IV-1
Federal Emergency Management Agency IV-1
Other Federal Agencies IV-3
Other State and Local Agencies IV-4
V. DEVELOPING BASE (100-YEAR) FLOOD ELEVATIONS V-l
Simplified Methods V-l
Contour Interpolation V-2
Data Extrapolation V-7
Detailed Methods V-ll
Topography V-ll
Existing Topographic Maps V-ll
Datum Requirements for Field Surveys V-12
Number of Cross Sections Required V-13
Proper Location of Cross Sections V-13
Hydrology V-l5
Discharge-Drainage Area Relationships V-16
Regression Equations V-19
TR-55 V-20
Rational Formula V-20
Other Hydrograph Methods V-21
Hydraulics V-22
Normal Depth V-23
Critical Depth V-26
Step-Backwater Analysis V-28
Hydraulic Structures V-28
VI. OBTAINING LETTERS OF MAP CHANGE VI -1
11
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TABLE OF CONTENTS (continued)
FIGURES
Figure 1 - Flood Hazard Boundary Map II-2
Figure 2 - Flood Insurance Rate Map II-3
Figure 3 - Proposed 76-Lot Subdivision Ill-2
Figure 4 - Proposed 6.7-Acre Subdivision Ill-3
Figure 5 - Proposed 76-Lot Subdivision III-4
Figure 6 - Proposed 5.6-Acre Subdivision III-4
Figure 7 - Proposed 6.7-Acre Subdivision Ill-5
Figure 8 - Contour Interpolation Method -
Riverine Flooding Example 1 V-4
Figure 9 - Contour Interpolation Method -
Riverine Flooding Example 2 V-5
Figure 10 - Contour Interpolation Method -
Lacustrine Flooding Example 3 V-6
Figure 11 - Data Extrapolation Method - Profile V-8
Figure 12 - Data Extrapolation Method - Plan View V-8
Figure 13 - Data Extrapolation Method - Profile V-9
Figure 14 - Data Extrapolation Method - Plan View V-9
Figure 15 - Data Extrapolation Method - Profile V-10
Figure 16 - Cross Section Orientation V-14
Figure 17 - Locate Cross Sections at Points of Flood
Discharge Changes V-14
Figure 18 - Cross Section Locations at Structures V-15
Figure 19 - Wendy Run Drainage Basin V-18
Figure 20 - Discharge-Drainage Area Plot V-18
Figure 21 - 100-Year Discharge Estimates for Site A
and Site B V-19
Figure 22 - Channel Bank Stations V-25
Figure 23 - Weir Flow - Embankment Profile is Not
Horizontal V-30
Figure 24 - Weir Flow Over Road V-31
111
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TABLE OF CONTENTS (continued)
Page
APPENDICES
Appendix 1 - Glossary of Floodplain Analysis Terms Al-1
Appendix 2 - Flood Insurance Study Data Request Form A2-1
Appendix 3 - Federal Emergency Management Agency Offices
and Other Federal and State Agencies A3-1
Appendix 4 - State Hydrology Reports A4-1
Appendix 5 - Manning1s "n" Values A5-1
Appendix 6 - QUICK-2 Computer Program Manual A6-1
Appendix 7 - Hydraulic Computer Manuals A7-1
Appendix 8 - Normal Depth Hand Calculations A8-1
Appendix 9 - Weir Flow Hand Calculations A9-1
Appendix 10 - Worksheet A10-1
IV
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Guide for Approximate Zone A Areas Introduction
I. INTRODUCTION
This guide is primarily intended to assist local community
officials in administering and enforcing the floodplain
management requirements of the National Flood Insurance
Program (NFIP). This document provides guidance for
determining Base (100-year) Flood Elevations (BFEs) in special
flood hazard areas that have been identified and designated as
approximate Zone A on a community's NFIP maps. Zone A
identifies an approximately studied special flood hazard area
for which no BFEs have been provided. Although BFEs are not
provided, the community is still responsible for ensuring that
new development within approximate Zone A areas is constructed
using methods that will minimize flood damages. This often
requires obtaining or calculating BFEs at a development site.
Developers, property owners, engineers, surveyors, and others
at the local level who may be required to develop BFEs for use
in approximate Zone A areas should also find this guide
useful. Included in this guide are methodologies that can be
used to develop BFEs, which can be used to determine the
elevation or floodproofing requirements for buildings. The
detailed methodologies described in this guide can also be
used to develop the BFE information necessary to obtain a
Letter of Map Amendment or a Letter of Map Revision Based on
Fill from the Federal Emergency Management Agency (FEMA) to
remove a legally defined property or structure from a special
flood hazard area. In addition, Letter of Map Revision
requestors may use the detailed methods in this document to
develop the BFE information that must be submitted to FEMA to
demonstrate that an area will not be flooded during the 100-
year flood.
1-1
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Guide For Approximate Zone A Areas NFIP Background
II. NATIONAL FLOOD INSURANCE PROGRAM BACKGROUND
In 1968, the United States Congress passed the National Flood
Insurance Act, which created the NFIP. Congress recognized
that the success of this program required that community
participation be widespread, that studies be conducted to
accurately assess the flood risk within each participating
flood-prone community, and that insurance premium rates be
established based on the risks involved and accepted actuarial
principles. To meet these objectives, the 1968 Act called
for: 1) the identification and publication of information
within five years for all floodplain areas that have special
flood hazards; and 2) the establishment of flood-risk zones in
all such areas to be completed over a 15-year period following
the passage of the act.
Within the first year of NFIP operation, it became evident
that the time required to complete the detailed flood
insurance studies would markedly delay implementation in many
flood-prone communities. As a result, an interim means for
more rapid community participation in the NFIP had to be
provided. The Housing and Urban Development Act of 1969
expanded participation by authorizing an Emergency Program
under which insurance coverage could be provided at non-
actuarial, federally-subsidized rates in limited amounts
during the period prior to completion of a community's flood
insurance study.
Until engineering studies could be conducted for these
communities, Flood Hazard Boundary Maps, such as the one shown
in Figure 1, "Flood Hazard Boundary Map," which delineated the
boundaries of the community's special flood hazard areas, were
prepared using available data or approximate methods. The
Flood Hazard Boundary Maps identified, on an approximate
basis, the areas within a community subject to inundation by
the 100-year flood (i.e., Zone A). The 100-year flood has a
one-percent chance of being equalled or exceeded in any given
year. The Flood Hazard Boundary Map was intended to assist
communities in managing floodplain development, and insurance
agents and property owners in identifying those areas where
the purchase of flood insurance was advisable.
The Flood Disaster Protection Act of 1973, which also amended
the 1968 Act, required that flood-prone communities be
notified of their flood hazards to encourage program
participation. This notification was accomplished through the
publication of Flood Hazard Boundary Maps for all communities
that were identified as containing flood hazard areas. In
addition, the 1973 Act required the purchase of flood
insurance by property owners who were being assisted by
II-l
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Guide For Approximate Zone A Areas
NFIP Background
Figure 1 - Flood Hazard Boundary Map
Federal programs, or by Federally supervised, regulated, or
insured agencies or institutions, in the acquisition or
improvement of land or facilities located, or to be located,
in special flood hazard areas. This act also severely limited
Federal financial assistance in the flood hazard areas of
communities which did not join the NFIP.
The initial Flood Hazard Boundary Maps for communities
identified as having flood hazards were prepared using
available floodplain data contained in reports developed by a
variety of Federal, State, and local sources. For those
communities that had no available flood information,
approximate hydrologic and hydraulic methods or historical
flood data were used to determine the extent of the special
flood hazard areas.
Flood Insurance Studies that used detailed hydrologic and
hydraulic analyses to develop BFEs and designate floodways and
risk zones were subsequently developed for most NFIP
communities. The results of a Flood Insurance Study were
II-2
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Guide For Approximate Zone A Areas
NFIP Background
issued to the community in the form of a Flood Insurance Rate
Map (FIRM), such as the one shown in Figure 2, "Flood
Insurance Rate Map," and, in most cases, a Flood Boundary and
Floodway Map and a Flood Insurance Study report. Once more
detailed risk data were provided, the community could enter
the Regular Program whereby more comprehensive floodplain
management requirements were imposed and higher amounts of
insurance could be purchased by owners of structures.
LIMIT OF
DETAILED STUDY
\ "CORPORATE
Figure 2 - Flood Insurance Rate Map
As early as 1976, FEMA recognized that some communities did
not require a detailed Flood Insurance Study because there
were few existing buildings in the floodplain and minimal
development pressure. Shortly thereafter, FEMA began
utilizing a special conversion process whereby communities
11-3
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Guide For Approximate Zone A Areas NFIP Background
were converted to the Regular Program without a Flood
Insurance Study. Consequently, these communities were
converted using FIRMs in which all of the special flood hazard
areas were designated as approximate Zone A, without BFEs.
Although over 10,000 communities have now been provided
detailed Flood Insurance Studies and issued FIRMs that include
BFEs, many floodplains are still designated as approximate
Zone A without BFEs. Due to the costs of developing detailed
risk data, areas not subject to development pressure are
studied using approximate methodologies and continue to be
shown on the FIRM as approximate Zone A areas. FEMA only
provides BFEs for the floodplains of those flooding sources
that are currently subject to development pressure or are
projected at the initiation of a Flood Insurance Study or
Flood Insurance Study restudy to be subject to development
pressure during the immediate future. Generally, a planning
period of approximately five years is used. Even in these
cases, BFEs are provided on a priority basis due to funding
constraints. The community plays a major part in the
determination of the level of detail required in the study of
selected streams. As a result, most communities will have
FIRMs that include special flood hazard areas for flooding
sources that have been studied in detail with BFEs and special
flood hazard areas for flooding sources that have been studied
using approximate methods, and have been designated as
approximate Zone A.
II-4
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Guide For Approximate Zone A Areas Floodplain Management
III. APPLICABLE NATIONAL FLOOD INSURANCE PROGRAM
FLOODPLAIN MANAGEMENT REQUIREMENTS IN APPROXIMATE
ZONE A AREAS
The primary requirement for community participation in the
NFIP is the adoption and enforcement of floodplain management
regulations that meet the minimum standards of the NFIP
regulations in Title 44 of the Code of Federal Regulations
(CFR) Section 60.3. These minimum standards vary depending on
the type of flood risk data provided to the community by FEMA.
The intent of floodplain management regulations is to minimize
the potential for flood damages to new construction and to
avoid aggravating existing flood hazard conditions that could
increase potential flood damages to existing structures. To
protect structures in riverine and lacustrine areas, the NFIP
regulations require that the lowest floor (including basement)
of all new construction and substantial improvements of
residential structures be elevated to or above the BFE. New
or substantially improved non-residential structures in
riverine areas must either be elevated or floodproofed (made
watertight) to or above the BFE.
Requirements for Obtaining BFE Data
In areas designated as approximate Zone A, where BFEs have not
been provided by FEMA, communities must apply the provisions
of Paragraph 60.3(b) of the NFIP regulations. Subparagraph
60.3(b)(4) requires that communities:
Obtain, review and reasonably utilize any
base flood elevation and floodway data
available from a Federal, State, or other
source... [ 44 CFR 60.3 (b)(4) ]
Section IV describes the sources from which BFE data may be
obtained. These data are to be used as criteria for requiring
that new construction, substantial improvements, and other
development within all approximate Zone A areas meet the
applicable requirements in Paragraphs 60.3 (c) and (d) of the
NFIP regulations, including the requirement that structures
have their lowest floors elevated to or above the BFE (or
floodproofed to or above the BFE for non-residential
structures) . These data should be used as long as they
reasonably reflect flooding conditions expected during the
base (100-year) flood, are not known to be scientifically or
technically incorrect, and represent the best data available.
Communities should consider formally adopting these data by
reference as part of their floodplain management regulations.
III-l
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Guide For Approximate Zone A Areas
Floodplain Management
Requirements for Developing BFE Data
Under Subparagraph 60.3(b)(3) of the NFIP regulations,
communities must also:
Require that all new subdivision proposals
and other proposed development (including
proposals for manufactured home parks and
subdivisions) greater than 50 lots or 5
acres, whichever is the lesser, include
within such proposals base flood elevation
data; [ 44 CFR 60.3 (b)(3) ]
This means that any subdivision which meets this threshold must
be evaluated to determine if the subdivision proposal is
affected by an approximate Zone A area and whether BFE data are
required. BFE data are required for the affected lots in the
subdivisions shown in Figure 3, "Proposed 76-Lot Subdivision,"
and Figure 4, "Proposed 6.7-Acre Subdivision." Figure 3 clearly
shows a 76-lot subdivision with several lots affected by an
approximate Zone A area. The subdivision depicted in Figure 4
is only 12 lots, but because the subdivision is greater than 5
acres and clearly shows buildable sites affected by an
approximate Zone A area, BFE data are required.
ZONE A
Figure 3 - Proposed 76-Lot Subdivision
III-2
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Guide For Approximate Zone A Areas
Floodplain Management
ZONE A
Figure 4 - Proposed 6.7-Acre Subdivision
Communities are encouraged to address the flood hazards at the
earliest stages of subdivision planning rather than at the
actual placement of individual structures. If a community can
work with the developer and others when land is being
subdivided, many long-term floodplain management benefits can be
achieved, particularly if the floodplain is avoided altogether.
In Figure 5, "Proposed 76-Lot Subdivision," the entire
approximate Zone A area is to be dedicated as open space. If
the planned subdivision shows the floodplain is contained
entirely within an open space lot, it may not be necessary to
conduct a detailed engineering analysis to develop BFE data.
Also, it may not be necessary to develop detailed BFE data in
large-lot subdivisions or single-lot subdivisions that are
within the thresholds under Subparagraph 60.3 (b) (3) of the NFIP
regulations when the actual building sites are clearly outside
of the Zone A area. In Figure 6, "Proposed 5.6-Acre
Subdivision," it is evident from the topographic features of
this 5.6-acre subdivision that the building sites would be
clearly out of the floodplain since the proposal indicates a
steep grade between the approximate Zone A area and the building
sites which are located on natural high ground.
III-3
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Guide For Approximate Zone A Areas
Floodplain Management
ZONE A
Open Space Lot
Figure 5 - Proposed 76-Lot Subdivision
Building j
Site
ZONE A
Figure 6 - Proposed 5.6-Acre Subdivision
III-4
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Guide For Approximate Zone A Areas
Floodplain Management
If the grade between the actual building sites and the
approximate Zone A area of the proposed subdivision is
relatively gradual, asshown in Figure 7, "Proposed 6.7-Acre
Subdivision," the floodplain could extend beyond what is shown
on the Flood Insurance Rate Map. It is very likely that flooding
could affect the building sites.In this case, an analysis should
be conducted to determine the location of the 100-year
floodplain and the BFE.
280
ZONE A
275
Figure 7 - Proposed 6.7 Acre Subdivision
For developments that exceed the thresholds identified in NFIP
regulations Subparagraph 60.3 (b) (3), BFEs must be either
obtained from other sources or developed using detailed
methodologies comparable to those contained in a Flood Insurance
Study. Section V describes some of the detailed methodologies
available that can be used to develop BFE data when none are
available from the sources listed in Section IV.
If the size of the new subdivision or other proposed development
falls below the thresholds specified in NFIP regulations
Subparagraph 60.3 (b) (3) and no BFE data are available from the
sources listed in Section IV, the community must still apply, at
a minimum, the requirements of Subparagraph 60.3(a)(3) to
proposed structures or Subparagraph 60.3 (a) (4) to subdivisions
and other developments within approximate Zone A areas. These
paragraphs require that permit officials:
III-5
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Guide For Approximate Zone A Areas Floodplain Management
Review all permit applications to determine whether
proposed building sites will be reasonably safe from
flooding. If a proposed building site is in a flood-
prone area, all new construction and substantial
improvements shall (i) be designed (or modified) and
adequately anchored to prevent floatation, collapse, or
lateral movement..., (ii) be constructed with materials
resistant to flood damage, (iii) be constructed by
methods and practices that minimize flood damages, and
(iv) be constructed with electrical, heating,
ventilation, plumbing, and other service facilities that
are designed and/or located so as to prevent water from
entering or accumulating within the components during
conditions of flooding. [44 CFR 60.3(a)(3)]
Review subdivision proposals ... including manufactured
home parks or subdivisions ... to assure that (i) all
such proposals are consistent with the need to minimize
flood damage within the flood-prone area, (ii) all
public utilities and facilities ... are located and
constructed to minimize or eliminate flood damage, and
(iii) adequate drainage is provided to reduce exposure
to flood hazards; [44 CFR 60.3(a)(4)]
One way that communities can ensure that building sites will be
reasonably safe from flooding for proposed developments that
fall below the thresholds in Subparagraph 60.3 (b) (3) is to use
the simplified methods outlined in Section V for estimating a
BFE. Another approach to ensure that a building site is
reasonably safe from flooding is to require the structure to be
elevated above the highest adjacent grade by a specified number
of feet based on the local official's knowledge of flood
conditions in the area. In the absense of available BFE data
from other sources, the community may require the permit
applicant to elevate the structure two or more feet above the
highest adjacant grade which qualifies the structure for reduced
flood insurance rates. Elevation of the structure to four feet
above the highest adjacant grade will enable the structure to
qualify for substantially reduced flood insurance rates.
However, some states and communities require that BFE data be
developed for all subdivisions and/or floodplain development
within approximate Zone A areas, not just those subdivisions
III-6
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Guide For Approximate Zone A Areas Floodplain Management
which meet the 50-lot or 5-acre threshold. A community may, at
its discretion, require the use of detailed methods for such
development. While this requirement is more restrictive than
NFIP minimum requirements, the NFIP regulations specifically
recognize and encourage states and communities to adopt and
enforce more restrictive floodplain management regulations in
those instances where the state or community believes that it is
in the best interest of its citizens.
No matter what the size of the subdivision or other development
proposal, requests to revise or amend effective Flood Insurance
Study information through the procedures outlined in Part 65 and
Part 70 of the NFIP regulations must be based on detailed
methodologies presented in Section V or other methodologies
comparable to those found in a Flood Insurance Study. The
analysis used to develop the BFEs must be certified by a
registered professional engineer or licensed land surveyor, as
appropriate, if the BFEs are to be used to revise or amend an
NFIP map.
Use of Draft or Preliminary Flood Insurance Study Data
The data from a draft or preliminary flood insurance study
constitutes "available data" under Subparagraph 60.3(b)(4).
Communities must reasonably utilize the draft or preliminary
flood insurance study data under the section of their ordinance
that requires the use of other base flood data when detailed BFE
data has not been published in a flood insurance study.
Communities are given discretion in using draft or preliminary
flood insurance study data only to the extent that the technical
or scientific validity of the proposed flood elevation data is
questioned. If a community decides not to use the draft or
preliminary flood insurance data in a FIS because it is
questioning the data through a valid appeal, the community must
still assure that buildings are constructed using methods and
practices that minimize flood damages in accordance with the
requirements under Subparagraphs 60.3(a)(3) and (4).
When all appeals have been resolved and a notice of final flood
elevations has been provided by FEMA, communities are required
to use the data from the flood insurance study for regulating
floodplain development in accordance with Subparagraph
60.3(b)(4) since the data represents the best data available.
Communities must regulate floodplain development using the flood
insurance study data under Subparagraph 60.3(b)(4) until such
time as the community has adopted the effective FIRM and flood
insurance study.
III-7
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Guide For Approximate Zone A Areas Floodplain Management
Advantages of Developing BFE Data
While the NFIP regulations do not require that communities
develop BFE data in approximate Zone A areas when proposed
development is below the thresholds in NFIP regulations
Subparagraph 60.3 (b) (3), there are significant advantages and
financial benefits for communities and individual property
owners that develop BFE data. These advantages and benefits
include:
• protecting structures up to the BFE will minimize and
reduce future flood losses, resulting in long-term
savings to the individual, the community, and the
National Flood Insurance Fund;
• flood insurance policies in approximate Zone A areas
that are rated using a BFE will often qualify for
significantly lower insurance rates than policies that
are rated without a BFE. The difference in flood
insurance premiums could be substantial;
• less burden will be placed on the permit official
because he or she can require protection to a specified
elevation. Without a BFE, the permit official must make
judgements as to what constitutes "reasonably safe from
flooding" and "constructed with materials and practices
that minimize flood damages";
• the NFIP's Community Rating System establishes flood
insurance premium discounts of up to 45 percent for
policy holders within communities that have a
floodplain management program that exceeds NFIP minimum
requirements. Sizable Community Rating System credits
are available for Community Rating System communities
that develop BFEs for areas designated as approximate
Zone A on their Flood Hazard Boundary Map or FIRM, or
that require site-specific engineering analyses for
development proposals; and
• by specifying a BFE in an approximate Zone A area, a
building or property can, in some circumstances, be
removed from the floodplain by issuance of a Letter of
Map Amendment or Letter of Map Revision in accordance
with Part 65 and Part 70 of the NFIP regulations. While
these procedures eliminate the requirement that flood
insurance be purchased as a condition of obtaining a
loan from a Federally insured or regulated lender, a
lending institution may, at its discretion, require the
purchase of flood insurance.
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Guide For Approximate Zone A Areas Obtaining BFEs
IV. OBTAINING EXISTING BASE (100-YEAR) FLOOD ELEVATIONS
The NFIP Regulations at 44 CFR 60.3 require that structures be
elevated or floodproofed (non-residential structures only) to
provide protection from flood damage. A BFE must be established
before such flood protection measures can be used. There are a
variety of computational methods that can be employed to
determine BFEs. However, these methods can be costly. Before
computational methods are used, every attempt should be made to
obtain information, in the form of floodplain studies or
computations, from Federal, State, or local agencies. Data
obtained from these agencies may be adequate to determine BFEs
with little or no additional research, computation, or cost.
Local officials who obtain BFE data should maintain the
information for future reference. Local officials should also
consider making a search for BFE data for the entire community.
By doing so, the local officials may not have to conduct a
search each time a floodplain development permit is requested.
If the data reasonably reflect flooding conditions, a community
should consider adopting the information into its floodplain
management ordinance.
Provided below are a list of agencies that can be contacted to
determine if any BFE data have already been developed. When
obtained, these data should be evaluated to ensure that they
reasonably reflect flooding conditions expected at the site
during the 100-year flood, are scientifically or technically
correct, and represent the best data available.
Three major sources of existing data are highlighted in this
section: FEMA, other Federal agencies, and State and local
agencies.
FEMA
FEMA's technical evaluation contractors maintain libraries that
contain technical and administrative data developed during the
preparation of Flood Insurance Studies, as well as the resulting
Flood Insurance Study reports and NFIP maps. FEMA can be
contacted to determine whether or not sufficient information
exists in the back-up data to calculate BFEs. For some flooding
sources that are designated as approximate Zone A, FEMA may have
detailed flooding information that has not yet been incorporated
into the community's Flood Insurance Study. FEMA can be
contacted to obtain this information where it exists.
IV-1
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Guide For Approximate Zone A Areas Obtaining BFEs
FEMA regularly conducts restudies of flood hazards in an effort
to keep Flood Insurance Studies accurate and up-to-date. As
part of these restudies, detailed BFE data for approximate Zone
A areas may be developed. During the time that elapses between
FEMA obtaining restudy data and the incorporation of BFE data
areas into a revised Flood Insurance Study, a community may
reasonably use the BFE data from the restudy in approximate Zone
A areas in accordance with Subparagraph 60.3(b)(4).
In addition, flooding sources restudied by FEMA may often impact
several communities. FEMA may be unable to immediately update
the Flood Insurance Study for every community impacted due to
funding constraints. Therefore, BFEs may have been developed
for streams within a community that have not yet been
incorporated into the Flood Insurance Study.
It is also possible that a previous request to revise flood
hazards along a stream or lake may be on file with FEMA, and
that BFEs, which may be applicable to other areas of the same
stream or lake, may have been computed for that request.
FEMA data should be sought when trying to obtain or determine
BFEs for an approximate Zone A area, so that if BFEs have
already been determined for an approximate Zone A area, then
other BFE determinations in the same area can be based on the
same methodology. However, if it is determined that a more
scientifically or technically accurate determination than that
which is available in FEMA's back-up data is warranted, then a
more detailed methodology, such as those described in Section V,
should be utilized.
Data requests should be directed to the appropriate FEMA
technical evaluation contractor at the address listed on the
following page:
IV-2
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Guide For Approximate Zone A Areas Obtaining BFEs
FEMA Regions I-V
(States East of the Mississippi
River and Minnesota)
Flood Insurance Information Specialist
c/o Dewberry & Davis
2953 Prosperity Avenue
Fairfax, Virginia 22031
FAX: (703) 876-0073
Phone: (703) 876-0148
FEMA Regions VI-X
(States West of the
Mississippi River)
FEMA Project Library
c/o Michael Baker, Jr., Inc.
3601 Eisenhower Avenue
Suite 600
Alexandria, Virginia 22304
FAX: (703) 960-9125
Phone: (703) 960-8800
An instruction sheet entitled Flood Insurance Study (FIS) Data
Requests is provided in Appendix 2. This sheet contains
pertinent information and instructions for requesting Flood
Insurance Study data.
A fee is charged for locating, retrieving, copying, and mailing
Flood Insurance Study back-up data based on the cost of
materials and a standard hourly rate for time spent to fill the
request. FEMA will inform the requestor if the requested data
are available and of the required fee. The requestor should
allow two to four weeks for the request to be processed.
Other Federal Agencies
Information regarding BFEs may be obtained from other Federal
agencies involved in floodplain management. A fee may be
required to obtain some of the products or services available
through these agencies. The following is a list of some of the
Federal Agencies involved in floodplain management and the
information, which may be useful in obtaining and determining
BFEs, that they produce.
IV-3
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Guide For Approximate Zone A Areas
Obtaining BFEs
AGENCY
U.S. Army Corps
of Engineers
U.S. Department of the
Interior, Geological
Survey
U.S. Department of
Agriculture, Natural
Resources Conservation
Service (NRCS)
U.S. Department of
Transportation, Federal
Highway Administration
U.S. Department of Commerce
National Technical
Information, Service
Tennessee Valley Authority
Other State and Local Agencies
PRODUCT
Floodplain Information
Reports
Technical Manuals
Computer Programs
Computational Assistance
Topographic Maps
Water Resource
Investigations
Technical Bulletins
Water Supply Papers
Computer Programs
Watershed Studies
Technical Releases
Computer Programs
Floodplain Studies
Design Manuals
Computer Programs
Design Manuals
Computer Programs
Floodplain Studies
If back-up data from Federal agencies are unavailable or are not
useful, information regarding BFEs may be obtained from State or
local agencies involved in floodplain management. On the
following page is a list of State and local agencies involved in
floodplain management that may be contacted to obtain BFE
information. Again, fees may be applicable for this
information.
For example, some state agencies, such as a Department of
Natural Resources or a Geological Survey, may conduct floodplain
studies using state funds. In some states, these agencies may
maintain a repository for flood data. The NFIP state
coordinating agency can also be contacted. A list of the State
coordinating agencies is provided in Appendix 3. Other state
agencies, such as a Department of Transportation, do engineering
for specific types of projects, such as road and bridge
construction, in which BFE data may have been developed for
these projects. In general, when calling these agencies, the
IV-4
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Guide For Approximate Zone A Areas Obtaining BFEs
caller should ask for a copy of any back-up data (reports,
computations, computer models, maps) associated with a
FLOODPLAIN STUDY or DRAINAGE STUDY, for the area of the
particular stream of interest. In addition, some state
agencies, such as a Department of Natural Resources, may
maintain historic lake level data.
The local public works department or the local transportation
department may have developed flood data in designing sewer and
storm drainage systems and local roads. For example, plans for
a sanitary sewer line which runs parallel to the stream and area
of interest may have 100-year flood elevations on the profile of
the sanitary sewer. Also, if there are culverts or bridges
which cross the same stream within 1,000 feet of the area of
interest, there may be hydrologic and hydraulic information
pertaining to the 100-year flood discharge and elevation which
may be pertinent to the site. Finally, if there are any nearby
residential or commercial developments along the same stream,
the development (site) plans for these projects may also include
information about the 100-year flood.
Other possible sources of data include regional organizations,
such as Flood Control Districts, Levee Improvement Districts,
Watershed Districts, or Soil and Water Conservation Districts.
These organizations may have developed flood profiles for
smaller streams or for reaches of streams impacted by flood
control or drainage projects.
State Agencies;
Departments of Environmental Conservation
Departments of Environmental Protection
Departments of Floodplain Management
Departments of Natural Resources
Departments of Transportation
Departments of Water Resources
Geological Survey
Local or Regional Agencies;
Flood Control Districts
Levee Improvement Districts
Local Planning Commissions
Local Public Works Departments
Municipal Utility Districts
River Basin Commissions
Water Control Boards
A partial list of Federal and State agencies is provided in
Appendix 3.
IV-5
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Guide For Approximate Zone A Areas Developing BFEs
V. DEVELOPING BASE (100-YEAR) FLOOD ELEVATIONS
If sufficient BFE information cannot be obtained from the
sources described in Section IV, then the community should
consider conducting, or requiring the applicant to conduct, a
site specific engineering analysis to determine a BFE. This
section describes several simplified and detailed methods for
estimating or developing BFE data, and provides guidance for
using them.
As noted in Section III, a detailed method is required under
Subparagraph 60.3(b)(3) of the NFIP regulations for proposed
development greater than 50 lots or 5 acres, whichever is the
lesser. If the BFEs developed will be used to revise or amend
NFIP maps, they must be developed using the detailed
methodologies described in this section or other methods
comparable to those in a Flood Insurance Study.
If no BFE data are available and the proposed development is
below the thresholds specified in Subparagraph 60.3(b)(3) of the
NFIP regulations, the simplified methods for estimating BFEs
described in the following section may be used. Simplified
methods are less expensive and less time consuming than the
detailed methods described later in this section. However,
communities have the discretion to determine which method should
be used when a proposed development is below the aforementioned
thresholds.
Simplified Methods
There are situations in which a simplified approach for
estimating the BFE may yield an acceptable level of accuracy.
For simplified methods to be used, very specific conditions must
be met as discussed below.
Simplified methods are appropriate for floodplain management
purposes only. These methods may be used for the purpose of
meeting the requirements of NFIP regulations Subparagraphs
60.3(a)(3) and 60.3(a)(4) for developments, such as isolated
small subdivisions in rural areas which are below the threshold
in Subparagraph 60.3(b)(3), or single lots. Subparagraphs
60.3(a)(3) and 60.3(a)(4) require the community to determine
whether proposed building sites are reasonably safe from
flooding and ensuring that subdivision proposals are consistent
with the need to minimize flood damage within flood-prone areas.
V-l
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Guide For Approximate Zone A Areas Developing BFEs
Simplified methods may not be used by the community to complete
an Elevation Certificate used for flood insurance rating.
Communities must use the detailed methodologies described in
this section or other methods comparable to those in a Flood
Insurance Study for completing the Elevation Certificate. A
flood insurance policy for a structure for which a simplified
method is used may be rated without an elevation certificate.
However, the flood insurance rate may be higher than if the
structure is rated using an Elevation Certificate.
Contour Interpolation
Contour interpolation involves superimposing approximate Zone A
boundaries onto a topographic map in order to estimate a BFE.
BFEs obtained by this method can only be assumed to be as
accurate as one-half of the contour interval of the topographic
map that is used. Therefore, the smaller the contour interval
of the topographic map, the higher the accuracy of the BFE
determined from the map. The procedures for using this method
are outlined below. Steps 1 through 5 are the same for both
riverine and lacustrine (lake) flooding sources.
Step 1 - Obtain a topographic map showing the site being
analyzed
Step 2 - Reduce or enlarge the FIRM or topographic map as
necessary so that the two are at the same scale
Step 3 - Superimpose the approximate Zone A (100-year)
floodplain boundary from the FIRM onto the topographic
map
Step 4 - Determine if this method is within the acceptable
accuracy limits. The floodplain boundary must
generally conform with the contour lines along the
flooding source in question. The difference between
the water-surface elevations determined on the right
overbank and the left overbank must be within one-half
of the map contour interval. For lacustrine flooding
sources, the difference between the highest and lowest
determined water-surface elevations around the
flooding source must be within one-half of the map
contour interval. Otherwise, this method is not
acceptable.
Step 5 - If the method is acceptable, then determine the BFE.
Detailed guidance for determining the BFE is provided
below.
V-2
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Guide For Approximate Zone A Areas Developing BFEs
Determining BFEs for Riverine flooding:
On each side of the stream in the vicinity of the site,
determine the ground elevation at which the superimposed Zone A
boundary lies by interpolating between two contour lines. Add
one-half of the map contour interval to the lower of the two
interpolated elevations. This is the approximate BFE for the
site (be sure to perform this method at each structure
location).
By adding one-half of the contour interval to the lowest
interpolated water-surface elevation, two things are achieved:
1) the final BFE is within one-half of the contour interval of
both interpolated water-surface elevations and, therefore, is
still within the acceptable tolerance of the topographic map
(generally regarded as ± one-half of the map contour interval);
2) it is a conservative estimate of the BFE. If the BFE
determined under this procedure seems too high, then a detailed
analysis may be performed to justify lowering it.
Example 1
Using a county topographic map with a contour interval of 5
feet, the approximate Zone A boundary crosses contour
elevations on the left and right bank at 323 and 325 feet,
respectively, as shown in Figure 8, "Contour Interpolation
Method - Riverine Flooding Example 1." The difference
between these two water-surface elevations is 2 feet, which
is less than one-half of the contour interval or 2.5 feet.
Therefore, this method is acceptable for use on this
portion of the stream. Add 323 feet (lowest interpolated
water-surface elevation) plus 2.5 feet (one-half of the
contour interval), which yields a BFE of 325.5.
V-3
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Guide For Approximate Zone A Areas
Developing BFEs
Proposed Structur
s m / i j-- ->.
Figure 8 - Contour Interpolation Method
Riverine Flooding Example 1
Example 2
Using a U.S. Geological Survey quadrangle map with a
contour interval of 10 feet, the approximate Zone A
boundary crosses contour elevations on the left and right
bank of 422 and 430 feet, respectively, as shown in Figure
9, "Contour Interpolation Method - Riverine Flooding
Example 2." The difference between these two water-surface
elevations is 8 feet, which is greater than one-half of the
contour interval or 5 feet. Therefore, this method is not
acceptable for use on this portion of the stream, and
another method must be used to determine the BFE.
V-4
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Guide For Approximate Zone A Areas
Developing BFEs
Proposed
Structure
Figure 9 - Contour Interpolation Method -
Riverine Flooding Example 2
Determining BFEs for Lacustrine (Lake) flooding:
Determine the contour elevations that the approximate Zone A
boundary crosses (i.e. the BFE) around the perimeter of the lake
or ponding area. Assuming that the highest and lowest
determined water-surface elevations are within one-half of the
map contour interval of each other, add one-half of the map
contour interval to the lowest water-surface elevation to
determine the BFE for the site.
Example 3
Using a U.S. Geological Survey quadrangle map with a
contour interval of 10 feet, the approximate Zone A
boundary crosses low and high determined water-surface
elevations along the perimeter of the ponding area of 280
and 283 feet, respectively, as shown in Figure 10, "Contour
Interpolation Method - Lacustrine Flooding Example 3." The
difference between these two water-surface elevations is 3
feet, which is less than one-half of the contour interval
or 5 feet. Therefore, this method is acceptable for use on
this ponding area. Add 280 feet (lowest water-surface
elevation) and 5 feet (one-half of the contour interval),
which yields a BFE of 285 feet.
V-5
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Guide For Approximate Zone A Areas
Developing BFEs
. ,,^. \
roposed Structure
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r
Figure 10 - Contour Interpolation Method -
Lacustrine Flooding Example 3
V-6
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Guide For Approximate Zone A Areas Developing BFEs
Data Extrapolation
If a site is within 500 feet upstream of a stream reach for
which a 100-year flood profile has been computed by detailed
methods, and the floodplain and channel bottom slope
characteristics are relatively similar to the downstream
reaches, data extrapolation may be used to determine the BFE.
The stream in the vicinity of the site, however, must be free
of backwater effects from downstream hydraulic structures.
The procedure for using this method is outlined below.
Step 1 - Determine the location of the site on the flood
profile for the detailed study stream
Step 2 - Extrapolate the last segment of the 100-year flood
profile that has a constant water-surface slope to
the location of the site. The BFE at the site can
then be obtained directly from the profile
Figures 11-12 on the following pages depict situations (i.e.,
properties "Y" and "Z"), in which the data extrapolation
method may and may not be used. Figures 13-14 depict a
situation in which the data extrapolation method may not be
used because the highway may have an effect on the 100-year
water-surface elevations. If the 100-year flood profile
changes just prior to the limit of detailed study, as shown in
Figure 15, the data extrapolation method should not be used.
V-7
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Guide For Approximate Zone A Areas
Developing BFEs
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500-YEAR FLOOD
100-YEAR FLOOD
2KSWSSS STREAM BED
40
35
30
25
1400 1600 1800 2000 2200
STREAM DISTANCE IN FEET ABOVE THE CORPORATE LIMITS
Figure 11 - Data Extrapolation Method - Profile
1"=400'
Figure 12 - Data Extrapolation
Method - Plan View
-Property Y is approximately 370' upstream of the limit of
detailed study (as measured along the streamline). Using
the profile below, we can extrapolate the 100-year flood
profile to determine that the BFE for property Y is equal to
33'.
-Property Z is approximately 700' upstream of the limit of
detailed study (as measured along the streamline), and is
therefore beyond the limit of data extrapolation.
V-8
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Guide For Approximate Zone A Areas
Developing BFEs
100
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500-YEAR FLOOD
100-YEAR FLOOD
STREAM BED
1500
100
95
90
85
2000 2500 3000 3500 4000 4500
STREAM DISTANCE IN FEET ABOVE THE CORPORATE LIMITS
5000
1"=1000'
Figure 13 - Data Extrapolation Method - Profile
ZONE A
Figure 14 -
Data Extrapolation
Method - Plan View
-State Route 27 may have an effect on the 100-year
water-surface elevations. Therefore, data
extrapolation should not be used to obtain a BFE
for property R.
V-9
-------
Guide For Approximate Zone A Areas
Developing BFEs
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500-YEAR FLOOD
100-YEAR FLOOD
STREAM BED
215
210
205
200
46 48 50 52 54 56 58
STREAM DISTANCE IN HUNDREDS OF FEET ABOVE THE CORPORATE LIMITS
Figure 15 - Data Extrapolation Method - Profile
60
V-10
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Guide For Approximate Zone A Areas Developing BFEs
Detailed Methods
Three essential factors must be determined either by hand
calculations or by computer model to determine a BFE by detailed
methods. These factors are: 1) floodplain geometry
(topography); 2) flood discharge and/or volume (hydrology); and
3) flood height (hydraulics).
Topography involves the measurement of the geometry of a cross
section(s) of the floodplain, which includes horizontal and
vertical coordinates. The vertical coordinate, or elevation, is
related to a vertical datum, such as the National Geodetic
Vertical Datum of 1929 or North American Vertical Datum of 1988.
The horizontal coordinate, or station, is measured from a
reference point along the cross section to establish actual
ground points.
Hydrology for the particular location along a stream involves
the determination of the peak rate of stream flow [usually
measured in cubic feet per second (cfs)] that will occur during
a flood (for purposes of determining the BFE, the 100-year
flood). When determining lake or pond elevations, a 100-year
flood hydrograph is required to determine the BFE.
Hydraulics involves the determination of the water-surface
elevation that will occur during a flood (for purposes of
determining the BFE, the 100-year flood), the selection of a
method to relate the flood discharge to a flood depth, and the
selection of Manning's roughness coefficients or "n" values.
These "n" values vary depending on the type of materials; degree
of irregularity; variation of shape, obstructions, and
vegetation; and degree of meandering related to the channel and
the floodplain of a stream.
The following sections discuss various methods for determining
the topography, hydrology, and hydraulics for a particular
location in order to determine a BFE.
Topography
Existing Topographic Maps
Before initiating field surveys, determine if there is existing
detailed topographic mapping that can be used to generate cross-
section data. To adequately describe a floodplain and for use
with a hydraulic method to calculate a BFE, topographic map
scales and contour intervals must be the same as, or more
detailed than, those used to prepare the
V-ll
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Guide For Approximate Zone A Areas Developing BFEs
community's Flood Insurance Study. The greater the level of
detail on the topographic map, the more accurate the BFE
determination. If the community does not have a Flood Insurance
Study, an existing topographic survey should, at a minimum, be
as detailed as the U.S. Geological Survey quadrangle map for the
area. Regardless of the level of detail of the existing
topographic map used, it is suggested that the geometry of the
actual stream channel be obtained by a site visit if the cross
sections are to be used for hydraulic analyses.
Datum Requirements for Field Surveys
If a greater level of detail is desired than is available from
existing topographic mapping, then a field survey should be
performed. If it is necessary to establish a BFE for insurance
purposes or to meet the requirements of 60.3 of the NFIP
Regulations, the survey must be referenced to the same datum
that is used to produce the FIRM, which is usually the National
Geodetic Vertical Datum of 1929 or the North American Vertical
Datum of 1988. Reference marks giving elevations to this datum
are given in the published Flood Insurance Studies. If the
reference marks cannot be located in the field, or are simply
too far away, additional reference mark information may be
obtained from the State's U.S. Geological Survey or
Transportation office. Local surveyors are generally familiar
with nearby reference marks. In approximate Zone A areas, if it
is not economically feasible to reference survey information to
a known reference mark, an assumed datum may be used, provided
that the BFE, structure, and lot elevations are referenced to
the same assumed datum; however, data developed using such an
assumed datum may not be sufficient to revise a FIRM. All
surveys must be certified by a registered professional engineer
or land surveyor.
If the sole purpose of determining relative flood heights is to
meet the requirements set forth in Section 60.3 (a) of the NFIP
regulations, any assumed datum may be used. In this instance, a
depth of flooding can be established at a particular location
without having to reference it to a datum (i.e., National
Geodetic Vertical Datum). However, in order for an insurable
structure to be eligible for a lower insurance rate based on the
BFE, the survey may need to be referenced to the same datum that
was used for the FIRM (i.e., National Geodetic Vertical Datum or
North American Vertical Datum).
V-12
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Guide For Approximate Zone A Areas Developing BFEs
Number of Cross Sections Required
If the determination of the BFE is for only one lot, one cross
section is required across the 100-year floodplain through the
property in question. For large parcels and multi-lot
subdivisions, at least one cross section is required at each end
of the parcel or subdivision. Additional cross sections must be
added if the difference in the computed 100-year water-surface
elevations at the two cross sections is more than one foot and
the distance between the cross sections is greater than 500
feet.
Proper Location of Cross Sections
The following guidelines should be used to determine the proper
location for cross sections:
Flow Path: Cross sections must be oriented perpendicular
to the anticipated flow path of the 100-year flood, as
shown in Figure 16, "Cross Section Orientation."
Channel Characteristics: Cross sections should be located
where changes in channel characteristics, such as slope,
shape, and roughness, occur.
Discharge: Cross sections should be located at points
along a stream where changes in flood discharge occur, such
as upstream of tributaries, as shown in Figure 17, "Locate
Cross Sections at Points of Flood Discharge Changes."
Structures: A minimum of two cross sections are required
to compute a BFE at or near a structure, such as a bridge
or dam. If the floodplain configurations upstream and
downstream of the structure are similar, two cross sections
may be used. One cross section should represent the
structure profile including the profile of the road or
embankment. When obtaining the structure profile in the
field, measurements of the structure opening, if there is
one, and any piers should also be obtained. The other
cross section should represent the natural valley cross
section downstream of the structure and should not include
any part of the structure or embankment. The natural
valley cross section should be located at a distance equal
to the width of the structure opening, W, measured from the
downstream foot of the embankment or wing walls, as shown
in Figure 18, "Cross Section Locations at Structures."
V-13
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Guide For Approximate Zone A Areas
Developing BFEs
Figure 16 - Cross Section Orientation
Drainage
area
boundary
Contours
Figure 17 - Locate Cross Sections at Points of Flood Discharge
Changes
V-14
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Guide For Approximate Zone A Areas
Developing BFEs
Roadway
Embankment
Roadway
Embankment
Figure 18 - Cross Section Locations at Structures
If the floodplain configurations upstream and downstream of
the structure are different and the structure is a bridge,
an additional cross section should be used upstream of the
structure. The cross section should be located at a
distance equal to the width of the structure opening
upstream of the structure as measured from the foot of the
embankment or wing walls.
The stations and elevations for cross section ground points
outside of the stream channel may be obtained from a
topographic map. The size of the structure opening, piers,
and channel geometry, however, should be obtained by field
survey.
Hydrology
There are a number of methodologies that may be used to develop flood
discharges for approximate Zone A areas. The methods discussed below
were selected because they are fairly simple to use, require
information that is easily obtainable, and provide reasonable
discharge estimates for streams where more detailed hydrologic
analyses have not been performed. These methods, which have been
ordered based on ease of use and expected level of accuracy, include
discharge-drainage area relationships, regression equations, the
NRCS TR-55 graphical peak discharge and tabular hydrograph methods,
V-15
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Guide For Approximate Zone A Areas Developing BFEs
and the rational formula. Other hydrograph methods will also be
noted but not described in detail due to their complexity.
Discharge-Drainage Area Relationships
This method is suggested for approximate Zone A areas because it
is straightforward and the only data needed are drainage areas
and corresponding 100-year flood discharges. These data can be
obtained from the Summary of Discharges table in a Flood
Insurance Study report.
The relationship between drainage area and discharge is non-
linear in most cases; therefore, the drainage areas and
corresponding 100-year flood discharges from the Flood Insurance
Study should be plotted on log-log paper as shown in Figure 20
from the example which begins on the following page. The
streams plotted may have varying drainage areas; however, other
watershed characteristics should be similar. A straight line
should be drawn through the plotted points as shown in Figure
21. The 100-year flood discharge for a particular location can
then be estimated based on the drainage area at the location as
shown in Figure 21 from the example.
Limitations - If the relationship of plotted points cannot be
approximated by a straight line, then this method should not be
used. In addition, this method is not appropriate when the
stream along which the site is located is regulated by dams,
detention ponds, canals, or other flow control structures or
diversions.
V-16
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Guide For Approximate Zone A Areas
Developing BFEs
EXAMPLE: DISCHARGE-DRAINAGE AREA RELATIONSHIPS
The following is a Summary of Discharges table from a Flood
Insurance Study report.
TABLE 1 - SUMMARY OF DISCHARGES
FLOODING SOURCE
AND LOCATION
DRAINAGE AREA
(sa. miles)
PINE CREEK
At confluence with
Saddle River 20.39
At Calvin Street 16.3
At Caitlin Avenue 14 . 9
ROCK RUN
Downstream of confluence
of Ramsey Brook 12.6
Upstream of confluence
of Ramsey Brook 10.1
GOOSE CREEK
Downstream of confluence
of Valentine Brook 9 .1
Upstream of confluence
of Valentine Brook 6 . 2
COON CREEK
Downstream of confluence
of Allendale Brook 14.3
Upstream of confluence
of Allendale Brook 12.9
PEAK DISCHARGES (cfs)
10-YEAR 50-YEAR 100-YEAR 500-YEAR
2,220
1, 907
1,860
4, 165
3,617
3,285
5,310
4,612
4,090
9,010
7,300
6,570
1,640
1,390
1,285
965
1, 805
1,670
2,895
2,455
2,270
1,700
3,185
2, 950
3,605
3,055
2,825
2,120
3, 965
3,670
5,795
4, 910
4,540
3,405
6,370
5, 900
Assume that Wendy Run is a stream within the same community as
the streams listed in the table, and that the Wendy Run drainage
basin, shown in Figure 19, has similar characteristics to the
stream basins from the table. First, plot the drainage areas
and corresponding 100-year discharges on log-log paper as shown
in Figure 20 on the following page. Then draw a straight line
through the plotted points as shown in Figure 21.
At Property A, the drainage area for Wendy Run is 8.5 square
miles. Using the drainage area curve created from the Flood
Insurance Study Summary of Discharges table, the 100-year
discharge at Property A is estimated to be 2,750 cfs, as shown
on Figure 21. At Property B, with a drainage area of 12.0
square miles, an estimated 100-year discharge of 3,600 is
obtained, as shown on Figure 21.
V-17
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Guide For Approximate Zone A Areas
Developing BFEs
Drainage
area
boundary
Contours
Figure 19 - Wendy Run Drainage Basin
10000
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Figure 20 - Discharge-Drainage Area Plot
V-18
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Guide For Approximate Zone A Areas
Developing BFEs
Discharge-Drainage Area Curve
T3
O
U
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Guide For Approximate Zone A Areas Developing BFEs
The general form of these regression equations is:
Q = K * Ax * By * Cz
where: Q = discharge (cfs)
K = regression equation constant
A,B,
and C = watershed variables
X,Y,
and Z = exponents
Watershed variables may include parameters such as drainage area
(in square miles), stream slope (in feet/mile), and impervious
area (in percent).
Limitations - Care must be taken when using these publications
because restrictions generally apply when the watershed is
heavily urbanized (i.e., high percentage of impervious land), or
where the runoff is regulated by the use of dams, detention
ponds, canals and other flow diversions. Other restrictions
based on the physical parameters of the watershed, such as
drainage area or stream slope, may also apply. Limitations of
these equations are normally stated in each report and should be
examined closely.
TR-55
The NRCS TR-55 "Urban Hydrology for Small Watersheds" contains
two methods for computing flood discharges: the Graphical Peak
Discharge method and the Tabular Hydrograph method. TR-55 is
straightforward in its approach and method of computation. TR-
55 takes into account the effects of urbanization, rainfall
distribution, soil types and conditions, ground cover types, and
other watershed characteristics. A method for estimating the
effects of storage on peak flood discharges is also included in
TR-55.
Limitations - In general, TR-55 should not be used in areas
where flow is divided between closed storm sewer systems and
overland conveyance areas, or where drainage areas exceed 2,000
acres. More specific limitations for using TR-55 are contained
in Chapters 2 through 6 of the NRCS TR-55 manual.
Rational Formula
This method estimates peak discharge rates for small watershed
areas not covered by regression equations and for areas where
the NRCS TR-55 method is not applicable. The Rational Formula
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Guide For Approximate Zone A Areas Developing BFEs
is based on the drainage area, rainfall intensity, watershed
time of concentration, and a runoff coefficient. The
generalized equation is:
Q = C * I * A
where: Q = discharge (cfs)
C = runoff coefficient
I = rainfall intensity (inches/hour)
A = drainage area (acres)
The runoff coefficient, C, varies with soil type, land use, and
terrain slope and can be obtained from text books on hydrology.
The intensity of rainfall, I, is determined based on the total
rainfall for a selected exceedence probability and a duration
equal to the time of concentration for the watershed. The time
of concentration for the watershed can be computed using the
method described in the NRCS TR-55 manual or methods described
in hydrology text books. For approximate Zone A areas, the
exceedence probability is equal to 1 percent (100-year storm
frequency). The 1 percent exceedence probability total rainfall
(100-year rainfall) for the computed duration can be obtained
from Technical Paper No. 40, Hydro 35, and precipitation-
frequency atlases published by the National Weather Service.
Dividing the total rainfall by the computed duration will yield
the intensity of rainfall.
Limitations - This method must not be used where the runoff is
regulated by the use of dams, detention ponds, canals and other
flow diversions. Also, this method is not recommended for
drainage areas greater than 200 acres, but can be used with
caution for drainage areas up to 640 acres (one square mile).
Other Hydrograph Methods
There are numerous other methods that can be used to determine
flood discharges based on rainfall-runoff relationships. The
following hydrograph methods are described in detail within
their respective technical reports and, therefore, will not be
described in detail within this guide. These methodologies in
general are good for any size watershed, and most of the methods
include computations that take into consideration areas where
the runoff is regulated by the use of dams, detention ponds,
canals and other flow diversions. These methods are recommended
for determining BFEs for ponds or lakes that are designated as
approximate Zone A. Besides TR-55, two of the more widely used
hydrograph methods are the NRCS' TR-20 and the U.S. Army Corps
of Engineers' HEC-1 computer programs.
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Guide For Approximate Zone A Areas Developing BFEs
TR-20 and HEC-1 provide a very detailed calculation of discharge
through the generation, addition, and routing of runoff
hydrographs. The effect on peak flood discharges due to dams,
road crossings, and large floodplain storage areas is more
accurately assessed with these programs. These models require
experience on the part of the user if they are to produce
realistic determinations of peak discharge.
Limitations - The limitations of these methods are thoroughly
described in their manuals. Because these methods involve many
variables and assumptions, the potential for error is great.
The users of these models must be thoroughly versed in the
limitations and assumptions of the computational methods
contained in these models. As with any synthetic model
depicting rainfall-runoff relationships, extreme care needs to
be taken to ensure that the results of the model are reasonable.
It is highly recommended that the discharges produced by these
hydrograph methods be compared to discharges produced by another
hydrologic method of equal accuracy or by calibrating the model
to an actual storm event.
Hydraulics
There are various hydraulic methods that may be used to
determine BFEs along riverine flooding sources. The appropriate
method to use depends on flow conditions and the size of the
area that is being analyzed. For developments of equal to or
less than 50 lots or 5 acres, the normal depth method, which is
described in greater detail below, is usually adequate for
determining BFEs. After normal depth has been computed, flow
conditions should be analyzed. If flow is classified as
subcritical (i.e., normal depth is greater than critical depth),
normal depth is used as the BFE. If flow is classified as
supercritical (i.e., normal depth is less than critical depth),
then critical depth is used as the BFE for natural channels.
For engineered channels, supercritical (normal) depth may be
used for the BFE, provided that the backwater from the normal
depth of the downstream cross section is considered properly.
If more than one cross section is required, step-backwater
computations should be used to determine BFEs along riverine
flooding sources.
The procedures for computing normal depth, critical depth, and
step-backwater by hand are outlined below. As an alternative to
hand calculations, the QUICK-2 computer program may be used.
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Guide For Approximate Zone A Areas Developing BFEs
QUICK-2 is a user-friendly computer program developed by FEMA
that may be used for normal depth, critical depth, or step-
backwater computations for regular or irregular shaped cross
sections. To aid the users of this guide in computing BFEs, the
QUICK-2 computer program and user's manual are located in
Appendix 6. The user's manual contains a tutorial section which
leads a new user through the calculation process using "real
life" examples. For those not using the QUICK-2 program, the
following sections on Normal Depth and Critical Depth illustrate
how to compute these depths by hand (see Appendix 8 for an
example of a Normal Depth hand calculation).
Normal Depth
Normal depth is the depth expected for a stream when the flow is
uniform, steady, one-dimensional, and is not affected by
downstream obstructions or flow changes. For uniform flow, the
channel bottom slope, water-surface slope, and energy slope are
parallel and are, therefore, equal. For normal depth
computations, the flow is considered steady because the
discharge is assumed to be constant; therefore, the depth of
flow does not change during the time interval under
consideration.
Normal depth calculations (also called the "slope/area method")
compute BFEs at a cross section. The standard formula for
determining normal depth at a cross section is Manning's
formula. The standard Manning's equation is:
Q = 1.486 x A x (R6") x S5 / n
where: Q = discharge (cfs)
A = cross section area (ft2)
R = hydraulic radius (ft) = A/WP
WP = wetted perimeter (ft)
S = energy slope (ft/ft)
n = Manning's roughness coefficient
The cross section area refers to the area below the water-
surface elevation, and the wetted perimeter refers to the length
of the ground surface along the cross section below the water-
surface elevation. The channel bottom slope is used in lieu of
the energy slope.
As noted earlier, Manning's "n" values vary depending on the
physical features of the stream channel and the channel
overbanks. The results of normal depth calculations can differ
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Guide For Approximate Zone A Areas Developing BFEs
significantly depending on the Manning's "n" values used;
therefore, care should be taken to ensure that the Manning's "n"
values selected accurately reflect conditions at the site being
analyzed. Manning's "n" values should be selected based on
field inspection, field photographs, and topographic mapping. A
list of accepted Manning's "n" values has been included in
Appendix 5. Various methods for computing normal depth are
described below.
Computer Programs for Computing Normal Depth
In addition to QUICK-2, the following Federal Government
computer programs have the capability to perform normal depth
computations:
Computer Program Agency
HEC-2 U.S. Army Corps of Engineers
HEC-RAS U.S. Army Corps of Engineers
WSPRO U.S. Geological Survey
WSP2 NRCS
SFD FEMA
PSUPRO FEMA
Please note that HEC-RAS is still being tested and had not yet
been released to the general public when this guide was
published. Furthermore, FEMA has not yet approved the model for
requests to revise NFIP maps. Please contact our Headquarters
office to determine the current status of HEC-RAS.
In addition to the above-referenced programs, there are other
engineering computer programs and models, which perform normal
depth calculations, that are available through various
commercial vendors. References for the hydraulic computer
programs listed above are in Appendix 7.
Normal Depth Hand Calculations
If a computer is not available, it is possible to perform hand
computations to calculate normal depth for the 100-year flood at
a cross section by following steps 1-11 listed below.
Step 1 - Obtain a topographic map or conduct a field survey to
obtain a cross section at the site where normal depth
should be determined. If a topographic map is used,
the channel geometry should be obtained from
measurements taken in the field. The cross section
should be oriented perpendicular to the expected 100-
year floodplain.
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Guide For Approximate Zone A Areas Developing BFEs
Step 2 - Compute the 100-year discharge by applying one of the
methods described in the hydrology section of this
guide.
Step 3 - Plot the cross section on graph paper with the
stations and the corresponding elevations. (The
stations and elevations are obtained from the
topographic map and/or from field survey).
Step 4 - Select the left and right channel bank stations. The
channel bank stations are those stations where the
ground slope becomes flatter moving away from the
channel bottom as shown in Figure 22, "Channel Bank
Stations." Photographs taken in the field and the
contours on the topographic maps are also helpful when
defining the channel bank stations. Do not place the
channel bank stations at the bottom of the channel.
Figure 22 - Channel Bank Stations
Step 5 - Select appropriate Manning's roughness coefficients
for the left overbank, channel, and right overbank
from the "n" values given in Appendix 5. These values
should be determined by reviewing the field
photographs and visiting the site.
Step 6 - Compute the cross section area, wetted perimeter,
hydraulic radius, and conveyance for each segment
(i.e., left overbank, channel, and right overbank) for
at least three elevations. The conveyance, K, of a
segment is given as:
K = (1.486/n) x A x R
where: A = cross section area (ft2
R = hydraulic radius (ft)
WP = wetted perimeter (ft)
R = A/WP
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Guide For Approximate Zone A Areas Developing BFEs
Step 7 - Compute the channel bottom slope, S, from the
topographic map or from field survey.
Step 8 - Compute the discharge, Q, for each segment of the
cross section at each elevation by multiplying K by
S°'5.
Step 9 - Add the discharges from each segment at the same
elevation to obtain the total discharge.
Step 10 - Plot the total discharges and the corresponding
elevations on graph paper.
Step 11 - The BFE can be determined from this graph for the 100-
year flood discharge computed in Step 2.
An example of a normal depth hand calculation is included in
Appendix 8.
Critical Depth
After computing normal depth, the type of flow should be
checked. If the velocity head from the normal depth computation
is equal to or more than half the hydraulic depth, the flow is
supercritical and the critical depth should be used to establish
the BFE. The velocity head, HV, for an irregular cross section
is computed using the following equation:
HV = aV2/2g
where: a = velocity coefficient
V = mean velocity = QT / A,, (fps)
QT = total discharge (cfs)
A,, = total flow area (ft2)
g = acceleration due to gravity = 32.2 ft/sec2
The velocity coefficient, a, is determined using the following
equation:
F (K/A) + (K3/A2) +
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Guide For Approximate Zone A Areas Developing BFEs
where: K^, Kc, K^, K^ = conveyance for left overbank,
channel, right overbank, and
total conveyance, respectively
A^, Ac, \, A,, = flow area for left overbank,
channel, right overbank, and
total flow area, respectively
Hydraulic depth, h, is computed by using the following
relationship:
h = A, / T
where: T = top water-surface width at the normal depth
A,, = Total Flow Area
If the velocity head is greater than or equal to one-half the
hydraulic depth, the flow is supercritical.
For prismatic channels, the following equation can be used to
determine the critical depth:
Q! = A! or Q = VgA3 / T
g T
For a series (3 or more) of water-surface elevations, compute
the corresponding total area, A, water-surface topwidth, T, and
the critical discharge, Q, using Q = VgA3 / T. Compute the
value of right hand side of the above equation. Plot the water-
surface elevations and the corresponding discharge values on
graph paper. The critical water-surface elevation and,
therefore, critical depth, can be determined from this graph for
a range of discharge values.
For rectangular channels, critical depth can be computed
directly from the above equation and is expressed in the
following relationship:
Dc = { Q / (5.67 T) }
The energy is minimum at the critical depth. For irregular
cross sections, critical depth is determined from the
relationship between the water-surface elevation and the energy.
The energy is computed by adding the water-surface elevation and
the corresponding velocity head (or energy grade elevation).
For irregular cross sections, the velocity coefficient, a (a),
must be considered when computing velocity head (HV) . Several
water-surface elevations should be assumed and corresponding
energy grade elevations computed. These values are then plotted
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Guide For Approximate Zone A Areas Developing BFEs
on a graph of water-surface elevation versus energy grade
elevation. The critical water-surface elevation and, therefore,
critical depth, can be determined from this graph where the
energy (i.e., energy grade elevation) is minimum.
Step-Backwater Analysis
Step-backwater computations are based on the principle of
conservation of energy, which states that the energy at the
upstream cross section is equal to the energy at the downstream
cross section plus the losses between the two cross sections.
The losses considered in the step-backwater analysis are the
friction loss and the transition loss.
The equations and the procedure used in the step-backwater
analysis are explained in the QUICK-2 user's manual in Appendix
6. Although hand computations can be done to perform the step-
backwater analysis, it is advisable to use the QUICK-2 program
or other Federally approved programs for ease of computation.
The QUICK-2 program currently does not model the effects of
bridges or culverts or supercritical flow.
The QUICK-2 program uses the default friction slope method,
which is the average conveyance method, from the HEC-2 program
to compute friction losses. For transition losses, a
contraction coefficient of 0.1 and an expansion coefficient of
0.3 should be used in the computations.
The reach lengths between the two cross sections for the left
overbank, channel, and right overbank are required for step-
backwater computations. The distance for the left overbank
should be measured between the center of the floodplains of the
left overbank at each cross section. The same is true for the
right overbank. The channel distance should be measured along
the streambed, and therefore will account for the meandering of
the stream channel.
In general, starting water-surface elevations are obtained from
normal depth computations (slope/area method) at the first cross
section. If there is a structure downstream of the study area,
the backwater effects of the structure must be considered in
determining the starting water-surface elevation. If there is a
known 100-year water-surface elevation at the downstream end of
the study area, that water-surface elevation should be used as
the starting water-surface elevation.
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Guide For Approximate Zone A Areas Developing BFEs
Hydraulic Structures
As stated earlier, normal depth is the depth expected for a
stream when the flow is uniform, steady, one-dimensional, and is
not affected by downstream obstructions or flow changes.
However, there are situations in which a physical structure
located downstream of a particular site will cause an
obstruction or alteration of the flow, resulting in a flood
depth at the site higher than the normal depth. The discussion
below describes the appropriate methods for determining BFEs for
reaches that include hydraulic structures.
Hydraulic structures that are common in approximate Zone A areas
include road and railroad crossings, including embankments,
dams, bridges and culverts, and canal crossings. The flow over
the road, railroad, embankment, dam or canal can be described as
weir flow. Weir flow can be calculated by hand or by computer
program in order to determine the BFE. When flow passes through
a bridge or culvert, the BFE can be determined through the use
of nomographs or computer programs. The BFE at a structure
where flow travels through a bridge or culvert and over the
crossing can be determined by nomographs, but is more easily
determined with a computer program.
Weir Flow
Determination of the water-surface elevation for weir flow
requires at least two cross sections. The first cross section
represents the natural valley section downstream of the
structure, and the second cross section represents the road
profile and the opening of the structure (refer to Figure 18,
"Cross Section Locations at Structures." If the approach
velocity head is to be considered, then a third cross section is
required that represents the natural valley section upstream of
the structure. In most situations, however, the velocity head
can be assumed to be negligible, and a third cross section is
not necessary.
The water-surface elevation downstream of the structure should
be determined by using normal depth computations at the first
cross section, provided there are no structures further
downstream that can create backwater effects (refer to the
methods for determining normal depth described previously).
The second cross section, which represents the profile along the
top of the structure including the road or the embankment,
should be used to determine the weir length for use in the
equation for weir flow, as shown on the following page.
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Guide For Approximate Zone A Areas
Developing BFEs
Q =
x C x L x H
where: Q = discharge (cfs)
@ = submergence factor
C = weir coefficient, varies from 2.6 to 3.0 and can
be obtained from hydraulic text books
L = weir length (ft)
H = available head (ft), measured from top of weir to
the selected energy grade elevation
Several values for H should be selected and the corresponding
discharge computed until the total weir flow is larger than the
100-year flood discharge. Plot the discharges and the
corresponding energy grade elevations on graph paper. The 100-
year flood energy grade elevation can be determined from this
graph. For an approximate analysis, the computed energy
gradient elevation can be considered the BFE.
If the structure profile is not horizontal, as shown in Figure
23, "Weir Flow - Embankment Profile is Not Horizontal," several
structure segments should be used and an average energy depth,
H, for that segment should be determined for use in the above
equation for selected energy grade elevations. The sum of the
weir flow from each segment will then be equal to the total weir
flow for the selected energy grade elevation.
ROAD OR EMBANKMENT PROFILE
ENERGY GRADE LINE
L(3) - I - L(4)
Q = (@ CL(1) HAVG(1)3/2) + (@ CL(2) HAVG(2)3/2) + (@ CL(3) HAVG(3)3/2
(@ CL(4) HAVG(4)3/2)
Figure 23 - Weir Flow - Embankment Profile is Not Horizontal
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Guide For Approximate Zone A Areas
Developing BFEs
If the downstream water-surface elevation is higher than the
minimum road elevation, a submergence factor may be considered
in the weir flow computation. The submergence factor is
dependent upon the D/H ratio, where D is the downstream depth of
water above the road and H is the upstream energy grade depth
above the road, as shown in Figure 24, "Weir Flow Over Road."
The submergence factor must be considered when the D/H ratio is
more than 0.79. For a non-horizontal road profile, the D/H
ratio must be computed for each road segment. The submergence
factor, @, can be determined from the curve in "Hydraulics of
Bridge Waterways" (Reference 1, Figure 24) and some typical
values are given in the table below.
Figure 24 - Weir Flow Over Road
D/H
0.998
0.992
0.986
0.976
0.962
@
0.30
0.40
0.50
0.60
0.70
D/H
0.944
0.932
0.915
0.889
0.700
@
0.80
0.85
0.90
0.95
1.00
Other procedures used in Federal agency backwater computer programs
can also be used to determine the submergence factor.
A third cross section may be used to determine a more accurate water-
surface elevation upstream of the structure. This may be done by
assuming water-surface elevations and calculating the corresponding
velocity heads (HV) until an assumed water-surface elevation plus its
velocity head at that elevation equal the same energy gradient
elevation obtained from the weir flow equation. The velocity head,
HV, can be calculated using the following equation:
HV = a (Q/A)2/2g
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Guide For Approximate Zone A Areas Developing BFEs
where: a = velocity coefficient
Q = 100-year flood discharge (cfs)
A = cross section area (ft2) at the assumed
water-surface elevation
g = Acceleration due to gravity = 32.2 ft/sec2
An example of a weir flow computation is included in Appendix 9.
Flow Through Structures
Culverts
At least two cross sections are required to determine the water-
surface elevation upstream of a culvert. The first cross section
should represent the natural valley cross section downstream of the
culvert, and the second cross section should represent the top of the
embankment profile and the opening of the structure (refer to Figure
13, "Cross Section Locations at Structures"). The size, type,
length, and upstream and downstream invert elevations of the culvert
should be obtained by field survey. The wing wall angle and the
entrance opening configuration, such as sharp edge or rounded edge,
should also be determined from a field survey. The Federal Highway
Administration publication "Hydraulic Design of Highway Culverts"
(Reference 2) should be referenced before going to the field so that
all the necessary information for culvert flow computations can be
collected during one field survey. Water-surface elevations upstream
of the culvert can then be computed using the nomographs contained in
the above-mentioned publication and the procedures outlined below.
The first cross section should be used to determine the normal depth
downstream of the culvert, which will be considered as the tailwater
(refer to section on normal depth computations).
Two computations are required to determine the headwater when using
Federal Highway Administration nomographs. One computation is for
inlet control, and the other computation is for outlet control. The
headwater elevations from the two computations are then compared.
The higher of the two should be selected as the upstream headwater
elevation. If this headwater elevation is higher than the top of
embankment profile, weir flow will occur. Perform at least three
weir flow computations for headwater elevations between the headwater
that assumes that all the flow is culvert flow (the first
computation) and the minimum top of embankment elevation. For each
selected headwater elevation, compute the culvert flow using Federal
Highway Administration nomographs. Combine the weir flow and culvert
flow for each selected headwater elevation and plot on graph paper.
The BFE for the 100-year flood discharge can then be obtained from
this graph.
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Guide For Approximate Zone A Areas Developing BFEs
If the site in question is not located immediately upstream of a
structure, a normal depth should be computed at the site. The 100-
year water-surf ace elevation at the site should be the higher of the
two elevations from the culvert computation and the normal depth
computation.
Federal Highway Administration nomographs predict only the energy
grade elevation upstream of the culverts. In most applications, the
velocity head is assumed to be negligible and, therefore, the energy
grade elevation approximates the actual water-surface elevation. If
a more accurate water-surface elevation is desired, a hydraulic
computer model, such as HEC-2, should be used to determine the BFE.
The procedure outlined in the weir flow section to compute a water-
surface elevation that corresponds to a certain energy grade
elevation may also be used to determine a BFE upstream of a culvert.
Bridges
Although hand computations can be performed by following the
procedures for bridge routines in Federal agency computer models, it
is recommended that the water-surface elevation upstream of bridges
be determined using a computer model. The number of cross sections
required at the structure depends upon the type of bridge routine
used. Four cross sections are required if the special bridge routine
in the HEC-2 program is used, and six cross sections are required if
the normal bridge routine in the HEC-2 program is used. Three cross
sections are required if the bridge routines in the WSPRO program and
the WSP2 program are used. A step-backwater analysis is also
required to compute the water-surface elevations with these bridge
routines. The following programs are recommended to compute the
water-surface elevation upstream of a bridge:
Computer Program Agency
HEC-2 U.S. Army Corps of Engineers
*HEC-RAS U.S. Army Corps of Engineers
WSPRO U.S. Geological Survey
WSP2 NRCS
*Not available for general use when this guide was published;
please contact our Headquarters office for current status.
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Guide For Approximate Zone A Areas Developing BFEs
REFERENCE
I. U.S. Department of Transportation, Federal Highway
Administration, Hydraulics of Bridge Waterways, Washington,
B.C., March 1978.
2. U.S. Department of Transportation, Federal Highway
Administration, Hydraulic Design of Highway Culverts,
Washington, B.C., September 1985.
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Guide For Approximate Zone A Areas Letters of Map Change
VI. OBTAINING LETTERS OF MAP CHANGE
Once detailed methods have been applied to develop BFE data, these
data may be suitable for revising an NFIP map via a Letter of Map
Correction. On October 1, 1992, FEMA implemented the use of
detailed application and certification forms for requesting
revisions to NFIP maps. Therefore, if a map revision is requested,
the appropriate forms should be submitted.
FEMA has implemented a procedure to recover costs associated with
reviewing and processing requests for modifications to published
flood information and maps. Specific information about these fees
is presented in the application and certification forms.
These forms, along with other useful documents pertaining to the
NFIP, may be obtained from our technical evaluation contractors at
the addresses listed below:
FEMA Regions I-V
Dewberry & Davis
Management Engineering and
Technical Services Division
8401 Arlington Boulevard
Fairfax, Virginia 22031
FAX: (703) 876-0073
FEMA Regions VI-X
Michael Baker, Jr., Inc.
3601 Eisenhower Avenue
Suite 600
Alexandria, Virginia 22304
FAX: (703) 960-9125
This information is also available through the FEMA Regional
Offices listed in Appendix 3.
To provide additional assistance to those who develop BFE data, a
worksheet that synopsizes the procedures detailed in this guide is
found in Appendix 10.
VI-1
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Appendix 1
Glossary of Floodplain Analysis Terms
1-Percent Annual Chance Flood: the flood that has a one-percent chance of being
equaled or exceeded on the average in any given year; equivalent to the 100-year
flood.
100-Year Flood: the flood that is equaled or exceeded once in 100 years on the
average; equivalent to the one percent annual chance flood.
Alluvial Stream: a stream that has formed its channel by the process of
aggradation. The sediment in the stream is similar to the material in the bed
and banks.
Base Flood: the flood having a one percent chance of being equalled or exceeded
in any given year (the 100-year flood).
Base Flood Elevation (BFE): the water-surface elevation associated with the base
flood.
Conveyance: a measure of the carrying capacity of the channel section. Flow
(Discharge (Q) ) is directly proportional to conveyance (K) . The proportional
factor is the square root of the energy slope; expressed as Q = K * SK.
Cross Section: a vertical profile of the ground surface taken perpendicular to
the direction of flood flow. The profile is defined by coordinates of ground
elevation and horizontal distance (station).
Discharge: a measure of flow volume per unit of time. In hydrology, units of
flow are usually cubic feet per second (cfs).
Exceedence Frequency: the frequency that a flood of a certain discharge will be
equaled or exceeded in any given year; equal to the inverse of the recurrence
interval.
Flood: (a) a general and temporary condition of partial or complete inundation of
normally dry land areas from: (1) the overflow of inland or tidal waters; (2) the
unusual and rapid accumulation or runoff of surface waters from any source;
(3) mudslides (i.e., mudflows), which are proximately caused by flooding as
defined in (a)(2) above and are akin to a river of liquid and flowing mud on the
surfaces of normally dry land areas, as when earth is carried by a current of
water and deposited along the path of the current. (b) The collapse or
subsidence of land along the shore of a lake or other body of water as a result
of erosion or undermining caused by waves or currents of water exceeding
anticipated cyclical levels or suddenly caused by an unusually high water level
in a natural body of water, accompanied by a severe storm, or by an unanticipated
force of nature, such as flash flood or abnormal tidal surge, or by some
similarly unusual and unforeseeable event, which results in flooding as defined
in (a) (1) above.
Flood Crest: the maximum height of a flood, usually measured as an elevation or
depth.
Flood Hazard: the potential for inundation that involves the risk to life,
health, property, and natural floodplain values.
Al-1
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Appendix 1 - continued
Glossary of Floodplain Analysis Terms
Floodplain: any land area, such as the lowland and relatively flat areas
adjoining inland and coastal waters, susceptible to being inundated by water from
any source.
Floodway: the channel of a river or other watercourse and the adjacent land areas
that must be reserved in order to discharge the base flood without cumulatively
increasing the water-surface elevation more than a designated height. The base
flood is defined as the one-percent chance flood and the designated height is
usually one foot above the base flood elevation; however, this height may vary
(but is not more than one foot) depending on what the State has adopted.
Floodway Fringe: the area between the floodway boundary and the 100-year
floodplain boundary.
Flow: equivalent to discharge.
Flow Area: the cross section (see discharge) area of the floodplain below a given
water-surface elevation.
Hazardous Flow: conditions that exist when the product of the depth of flow and
its corresponding velocity are greater than ten (10) . For example a flow depth
of 3 feet and a flow velocity of 4 feet per second (3x4 = 12) would be
considered hazardous flow.
Hydraulic Depth: an average depth computed as the Flow Area divided by the top
width of the floodplain for a given water-surface elevation.
Lacustrine Flooding: Flooding produced by a lake or pond.
Peak Discharge: the maximum instantaneous discharge of a flood at a given
location.
Recurrence Interval: the average interval of time required for a flood of a
specific discharge to occur or be exceeded; equal to the inverse of the
exceedence frequency.
Riverine Flooding: Flooding produced by a river or stream.
Shallow Flooding: a designated AO, AH, or VO zone on a community's Flood
Insurance Rate Map with a one percent or greater annual chance of flooding to an
average depth of one to three feet where a clearly defined channel does not
exist, the path of flooding is unpredictable, and velocity flow may be evident.
Such flooding is characterized by ponding or sheet flow.
Slope (Energy): the rate of energy loss of a watercourse.
Slope (Ground): the change in vertical ground elevation over a horizontal
distance, usually based on the change in the vertical elevation of the stream
bottom.
Steady Flow: state of flow where the depth of flow does not change with time.
Subcritical Flow: state of flow where the gravitational forces are more
pronounced than the inertial forces. The flow tends to have a low velocity. In
general, in this flow regime, the hydraulic depth is more than twice the velocity
head.
Supercritical Flow: state of flow where the inertial forces become dominant. The
flow tends to have a high velocity. In general, in this flow regime, the
velocity head is equal to or more than half the hydraulic depth.
Al-2
-------
Appendix 1 - continued
Glossary of Floodplain Analysis Terms
Unsteady Flow: state of flow where the depth of flow changes with time.
Uniform Flow: depth is constant over channel length, and the channel shape, slope
and boundary roughness are constant over the channel length.
Varied Flow: depth of flow changes along the channel length.
Gradually Varied Flow: depth of flow changes gradually over the channel length.
Rapidly Varied Flow: depth changes abruptly over a short channel length.
Velocity: a rate of movement (i.e., distance divided by time). For water, the
rate is expressed in feet per second. Because water in a channel does not all
move at the same velocity at every point, an average value is used to described
flow velocity. This average velocity equals the discharge divided by the flow
area (Q/A).
Velocity Head: the kinetic energy term (a V2 / 2g) , in the total energy of flow.
The velocity coefficient (a) is used to adjust for the distribution of velocity
in a cross section of differing roughness.
Al-3
-------
Appendix 2
Flood Insurance Study Data Request Form
FLOOD INSURANCE STUDY (FIS) DATA REQUESTS
Requests for FIS data should be made in writing to:
Regions I-V Regions VI-X
Flood Insurance Information Specialist FEMA Project Library
c/o Dewberry & Davis c/o Michael Baker, Jr., Inc.
2953 Prosperity Avenue 3601 Eisenhower Avenue
Fairfax, Virginia 22031 Suite 600
FAX: (703) 876-0073 Alexandria, Virginia 22304
FAX: (703) 960-9125
The following information should be included in your written request:
• Complete community name (including county)
• Community Identification Number
• Name(s) of flooding source(s) and specific location(s) for which
data are needed
• Specific data needed:
HEC-2 input and output files
Topographic data
etc.
• Effective date of FIRM/FBFM for which data are requested (enclose an
annotated copy of FIRM/FBFM if available identifying area of interest)
• Agreement to pay costs associated with processing the request
• Fee limit after which authorization is needed
• Contact person's name, address, and phone number
The average request takes approximately 2 to 4 weeks to fill and may cost between
$100 to $200.
You will be contacted after we have determined if the data are available and the
cost to fill the request has been determined.
Do not include payment with your request letter.
Checks or money orders should be made payable to the National Flood Insurance
Program and sent to:
Federal Emergency Management Agency
Fee Collection System
P.O. Box 398
Merrifield, Virginia 22116
Data will be released upon receipt of payment.
A2-1
-------
Appendix 3
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Federal Emergency Management Agency Offices
HEADQUARTERS
500 C Street, SW
Washington, D.C. 20472
(202) 646-3680
FAX: (202) 646-4596
REGION I
(Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island & Vermont)
J.W. McCormack Post Office & Courthouse Building, Room 462
Boston, MA 02109
(617) 223-9561
FAX: (617) 223-9574
REGION II
(New Jersey, New York, Puerto Rico & Virgin Islands)
26 Federal Plaza, Room 1349
New York, NY 10278
(212) 225-7200
FAX: (212) 225-7262
REGION III
(Delaware, District of Columbia, Maryland, Pennsylvania, Virginia & West
Virginia)
Liberty Square Building, Second Floor
105 South Seventh Street
Philadelphia, PA 19106
(215) 931-5512
FAX: (215) 931-5501
REGION IV
(Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina
& Tennessee)
1371 Peachtree Street, N.E., Suite 700
Atlanta, GA 30309
(404) 853-4400
FAX: (404) 853-4440
REGION V
(Illinois, Indiana, Michigan, Minnesota, Ohio & Wisconsin)
175 West Jackson Boulevard
Fourth Floor
Chicago, IL 60604
(312) 408-5552
FAX: (312) 408-5551
A3-1
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Federal Emergency Management Agency Offices (continued)
REGION VI
(Arkansas, Louisiana, New Mexico, Oklahoma & Texas)
Federal Regional Center
800 North Loop 288
Denton, TX 76201-3698
(817) 898-5165
FAX: (817) 898-5195
REGION VII
(Iowa, Kansas, Missouri & Nebraska)
Federal Office Building, Room 300
911 Walnut Street
Kansas City, MO 64106
(816) 283-7002
FAX: (816) 283-7018
REGION VIII
(Colorado, Montana, North Dakota, South Dakota, Utah & Wyoming)
Denver Federal Center, Bldg. 710
P.O. Box 25267
Denver, CO 80225-0267
(303) 235-4830
FAX: (303) 235-4849
REGION IX
(Arizona, California, Hawaii & Nevada)
Presidio of San Francisco
Building 105
San Francisco, CA 94129
(415) 923-7100
FAX: (415) 923-7147
REGION X
(Alaska, Idaho, Oregon & Washington)
Federal Regional Center
130 - 228th Street, SW
Bothell, WA 98021-9796
(206) 487-4678
FAX: (206) 487-4613
A3-2
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
ALABAMA
Alabama Department of Economics
and Community Affairs
State Planning Division
401 Adams Avenue
Montgomery, AL 36103
(205) 242-5442
U.S. Geological Survey
District Chief
Water Resources Division
520 19th Avenue
Tuscaloosa, AL 35401
(205) 752-8104
U.S. Department of Agriculture
Natural Resources Conservation
Service
665 Opelika Rd.
P.O. Box 311
Auburn, AL 36830
(205) 887-4506
NFIP State Coordinator
Mr. Gene Anderson
Director, Alabama Department
of Economic and Community Affairs
P.O. Box 5690
401 Adams Avenue
Montgomery, AL 36103-5690
(205) 242-5499
ALASKA
Alaska Department of
Community and Regional Affairs
Municipal and Regional
Assistance Division
333 West 4th Avenue, Suite 220
Anchorage, AK 99501
(907) 269-4500
U.S. Geological Survey
District Chief
Water Resources Division
4230 University Drive, Suite 201
Anchorage, AK 99508-4138
(907) 786-7100
U.S. Department of Agriculture
Natural Resources Conservation
Service
949 East 36th Avenue
Suite 400
Anchorage, AK 99504
(907) 271-2424
NFIP State Coordinator
Mr. Bob Walsh
Municipal and Regional
Assistance Division
333 West 4th Avenue, Suite 220
Anchorage, AK 99501
(907) 269-4500
ARIZONA
Arizona Department of Water Resources
15 South 15th Avenue
Phoenix, AZ 85004
(602) 242-1553
U.S. Geological Survey
District Chief
Water Resources Division
375 South Euclid
Tucson, AZ 85719
(602) 670-6671
U.S. Department of Agriculture
Natural Resources Conservation
Service
3008 Federal Building
230 N. 1st Avenue
Phoenix, AZ 85025
(602) 261-6711
NFIP State Coordinator
Ms. Elizabeth A. Rieke
Director, Arizona Department
of Water Resources
15 South 15th Avenue
Phoenix, AZ 85007
(602) 542-1540
ARKANSAS
Arkansas Soil and Water
Conservation Commission
1 Capitol Mall, Suite 2D
Little Rock, AR 72201
(501) 371-1611
A3-3
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Geological Survey
Water Resources Division
401 Hardin Road
Little Rock, AR 72211
(501) 228-3600
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Office Bldg.
700 West Capitol
P.O. Box 2323
Little Rock, AR 72203
(501) 324-6335
NFIP State Coordinator
Mr. Randy Young
Director
Arkansas Soil & Water
Conservation Commission
101 East Capitol
Little Rock, AR 72201
(501) 682-1611
CALIFORNIA
California Department of
Water Resources
P.O. Box 942836
Sacramento, CA 94236-0001
(916) 653-5791
U.S. Geological Survey
District Chief
Water Resources Division
Federal Building, Room W-2233
2800 Cottage Way
Sacramento, CA 95825
(916) 978-4633
U.S. Department of Agriculture
Natural Resources Conservation
Service
2121 C 2nd Street
Davis, CA 95616
(916) 757-8200
NFIP State Coordinator
Mr. David Kennedy, Director
California Department of
Water Resources
P.O. Box 942836
Sacramento, CA 94236-0001
(916) 653-7007
COLORADO
Urban Drainage and Flood Control
District
2480 West 26th Avenue
Suite 156B
Denver, CO 80211
Colorado Water Conservation Board
State Centennial Building, Room 721
1313 Sherman Street
Denver, CO 80203
(303) 866-3441
U.S. Geological Survey
District Chief
Water Resources Division
Denver Federal Center, Building 53
Box 25046 (Mail Stop 415)
Lakewood, CO 80225-0046
(303) 236-4882
U.S. Department of Agriculture
Natural Resources Conservation
Service
655 Parfait Street
Room E200C
Lakewood, CO 80215
(303) 236-2886
NFIP State Coordinator
Mr. Daries C. Lile, P.E.
Director, Colorado Water
Conservation Board
State Centennial Building
1313 Sherman Street
Denver, CO 80203
(303) 866-3441
CONNECTICUT
State Department of
Environmental Protection
79 Elm Street, 3rd Floor
Hartford, CT 06106
(203) 424-3706
U.S. Geological Survey
Hydrologist-in-Charge
Connecticut Office
Water Resources Division
Abraham A. Ribicoff Federal
Building, Room 525
450 Main Street
Hartford, CT 06103
(203) 240-3060
A3-4
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Department of Agriculture
Natural Resources Conservation
Service
16 Professional Park Road
Storrs, CT 06268
(203) 429-9361
NFIP State Coordinator
Mr. Timothy Keeney, Commissioner
State Department of Environmental
Protection
165 Capitol Avenue
State Office Building
Hartford, CT 06106
(203) 566-2110
DELAWARE
Department of Natural Resources and
Environmental Control
Division of Soil and Water
Conservation
P.O. Box 1401
89 Kings Highway
Dover, DE 19903
(302) 739-4403
U.S. Geological Survey
Hydrologist-in-Charge
Delaware Office
Water Resources Division
Federal Building, Room 1201
300 South New Street
Dover, DE 19904
(302) 734-2506
U.S. Department of Agriculture
Natural Resources Conservation
Service
3500 South DuPont Highway
Dover, DE 19901
(302) 697-6176
NFIP State Coordinator
Mr. John A. Hughes, Director
Delaware Department of Natural &
Environmental Control
Richardson and Robbins Building
P.O. Box 1401
Dover, DE 19903
(301) 736-4411
DISTRICT OF COLUMBIA
Department of Consumer
Regulatory Affairs
614 H Street Northwest
Washington, DC 20001
(202) 727-7170
U.S. Geological Survey
District Chief
Water Resources Division
208 Carroll Building
8600 La Salle Road
Towson, MD 21286
(410) 828-1535
NFIP State Coordinator
Mr. Donald G. Murray, Director
Department of Consumer Regulatory
Affairs
Office of the Director
614 H Street, NW., Suite 1120
Washington, D.C. 20001
(202) 727-7170
FLORIDA
Department of Community Affairs
East Howard Building
2740 Centerview Drive
Tallahassee, FL 32399-2100
(904) 488-8466
U.S. Geological Survey
District Chief
Water Resources Division
227 North Bronough Street,
Suite 3015
Tallahassee, FL 32301
(904) 942-9500
U.S. Department of Agriculture
Natural Resources Conservation
Service
P.O. Box 141510
Gainesville, FL 32614
(904) 338-9500
NFIP State Coordinator
Ms. Linda Lomis Shelley, Secretary
Florida Department of Community
Affairs
2740 Centerview Drive
Tallahassee, FL 32399-2100
(904) 488-8466
A3-5
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
GEORGIA
Department of Natural Resources
Environmental Protection Division
Floyd Towers East, Suite 1252
205 Butler Street Southeast
Atlanta, GA 30334
(404) 656-4713
U.S. Geological Survey
District Chief
Water Resources Division
3039 Amwiler Road, Suite 130
Atlanta, GA 30360
(404) 447-9803
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building
355 East Hancock Avenue
P.O. Box 832
Athens, GA 30613
(404) 546-2272
NFIP State Coordinator
Mr. Joe D. Tanner, Commissioner
Georgia Department of Natural
Resources
205 Butler Street, S.E.
Floyd Towers East, Suite 1252
Atlanta, GA 30334
(404) 656-3500
HAWAII
Hawaii Board of Land and
Natural Resources
1151 Punchbowl Road, Room 220
Honolulu, HI 96813
(808) 587-0446
U.S. Geological Survey
District Chief
Water Resources Division
677 Ala Moana Boulevard, Suite 415
Honolulu, HI 96813
(808) 522-8290
U.S. Department of Agriculture
Natural Resources Conservation
Service
300 Ala Moana Boulevard
P.O. Box 50004
Honolulu, HI 96850
(808) 546-3165
NFIP State Coordinator
Mr. William W. Paty, Chairperson
Commission on Water Resource
Management and Board of Land and
Natural Resources
State of Hawaii
P.O. Box 621
Honolulu, HI 96809
(808) 587-0401
IDAHO
Department of Water Resources
State House
1301 North Orchard Street
Boise, ID 83706
(208) 327-7900
U.S. Geological Survey
District Chief
Water Resources Division
230 Collins Road
Boise, ID 83702
(208) 387-1300
U.S. Department of Agriculture
Natural Resources Conservation
Service
3244 Elder Street
Room 124
Boise, ID 83705
(208) 334-1601
NFIP State Coordinator
Mr. R. Keith Higginson, Director
Idaho Department of Water Resources
1301 N. Orchard
Boise, ID 83706
(208) 327-7900
ILLINOIS
Illinois Department of Transportation
Local Flood Plains Programs
310 South Michigan, Room 1606
Chicago, IL 60604
(312) 793-3123
U.S. Geological Survey
District Chief
Water Resources Division
Champaign County Bank Plaza
102 East Main Street
Fourth Floor
Urbana, IL 61801
(217) 398-5353
A3-6
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Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building
2110 West Park Court
Suite C
Champaign, IL 61821
(217)398-5212
NFIP State Coordinator
Mr. Michael Lene, Secretary
Illinois Department of Transportation
2300 S. Dirksen Parkway
Springfield, IL 62764
(217) 728-5597
INDIANA
Department of Natural Resources
608 State Office Building W-256
402 West Washington Street
Indianapolis, IN 46204-2748
(317) 232-4020
U.S. Geological Survey
District Chief
Water Resources Division
5957 Lakeside Boulevard
Indianapolis, IN 46278
(317) 290-3333
U.S. Department of Agriculture
Natural Resources Conservation
Service
6013 Lakeside Boulevard
Indianapolis, IN 46275
(317) 290-3030
NFIP State Coordinator
Mr. James B. Ridenour, Director
Indiana Department of Natural
Resources
608 State Office Building
Indianapolis, IN 46204
(317) 232-4020
IOWA
Iowa Department of Natural Resources
Wallace State Office Building
Des Moines, IA 50319-0034
(515) 281-5385
U.S. Geological Survey
District Chief
Water Resources Division
P.O. Box 1230
Iowa City, IA 52244-1230
(Street Address:
Federal Building, Room 269
400 South Clinton Street)
(319) 337-4191
U.S. Department of Agriculture
Natural Resources Conservation
Service
Wallace Building
Des Moines, IA 50319
(515) 284-5851
NFIP State Coordinator
Mr. Larry Wilson, Director
Iowa Department of Natural Resources
Wallace State Office Building
Des Moines, IA 50319
(515) 281-5385
KANSAS
Division of Water Resources
Kansas State Board of Agriculture
901 South Kansas Avenue, 2nd Floor
Topeka, KS 66612-1283
(913) 296-3717
U.S. Geological Survey
District Chief
Water Resources Division
4821 Quail Crest Place
Lawrence, KS 66049
(913) 842-9901
U.S. Department of Agriculture
Natural Resources Conservation
Service
P.O. Box 600
760 South Broadway
Salina, KS 67401
(913) 823-4500
NFIP State Coordinator
Mr. David L. Pope, P.E.
Chief Engineer & Director
Kansas State Board of Agriculture
Division of Water Resources
901 S. Kansas, 2nd Floor
Topeka, KS 66612-1283
(913) 296-3717
A3-7
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
KENTUCKY
Kentucky Department
of Natural Resources
Division of Water
Fort Boone Plaza
14 Reilly Road
Frankfort, KY 40601
(502) 564-3410
U.S. Geological Survey
District Chief
Water Resources Division
2301 Bradley Avenue
Louisville, KY 40217
(502) 582-5241
U.S. Department of Agriculture
Natural Resources Conservation
Service
771 Corporate Drive, Suite 110
Lexington, KY 40503
(606) 224-7350
FTS 355-2749
NFIP State Coordinator
Mr. Jack Wilson, Director
Kentucky Division of Water
18 Reilly Road
Fort Boone Plaza
Frankfort, KY 40601
(502) 564-3410
LOUISIANA
Louisiana Department of
Urban and Community Affairs
P.O. Box 94455, Capitol Station
Baton Rouge, LA 70804
(504) 342-9794
U.S. Geological Survey
District Chief
Water Resources Division
P.O. Box 66492
Baton Rouge, LA 70896
(Street Address:
6554 Florida Boulevard
Baton Rouge, LA 70806)
(504) 389-0281
U.S. Department of Agriculture
Natural Resources Conservation
Service
3636 Government Street
Alexandria, LA 71301
(318) 487-8094
NFIP State Coordinator
General Jude W. P. Patlin, Secretary
Louisiana Department of
Transportation & Development
P.O. Box 94245
Baton Rouge, LA 70804-9245
(504) 379-1200
MAINE
Maine State Planning Office
State House Station 38
184 State Street
Augusta, ME 04333
(207) 287-3261
U.S. Geological Survey
Hydrologist-in-Charge
Maine Office
Water Resources Division
26 Ganneston Drive
Augusta, ME 04330
(207) 622-8208
U.S. Department of Agriculture
Natural Resources Conservation
Service
USDA Building
University of Maine
5 Godfrey Drive
Orono, ME 04473
(207) 866-7241
NFIP State Coordinator
Mr. Michael W. Aube, Commissioner
Department of Economic and
Community Development
State House Station 59
State Street
Augusta, ME 04333
(207) 287-2656
MARYLAND
Maryland State Resources
Administration
Tawes State Office Building, D-2
501 Taylor Avenue
Annapolis, MD 21401
(410) 974-3041
U.S. Geological Survey
District Chief
Water Resources Division
208 Carroll Building
8600 La Salle Road
Towson, MD 21286
(410) 828-1535
A3-S
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Department of Agriculture
Natural Resources Conservation
Service
339 Busch's Frontage Road
Suite 301
Annapolis, MD 21401-5534
(410) 757-0861
NFIP State Coordinator
Ms. Catherine Pieper Stevenson
Director, Maryland Water Resources
Administration
Tawes State Office Building D-2
Annapolis, MD 21401
(301) 974-3896
MASSACHUSETTS
Massachusetts Water
Resources Commission
State Office Building
100 Cambridge Street
Boston, MA 02202
(617) 727-3267
U.S. Geological Survey
District Chief
Water Resources Division
28 Lord Road
Marlborough, MA 01752
(508) 485-6360
U.S. Department of Agriculture
Natural Resources Conservation
Service
451 West Street
Amherst, MA 01002
(413) 253-4350
NFIP State Coordinator
Mr. Peter C. Webber, Commissioner
Massachusetts Department of
Environmental Management
State Office Building
100 Cambridge Street
Boston, MA 02202
(617) 727-3180 x600
MICHIGAN
Engineering Water
Management Commission
Michigan Department of
Natural Resources
P.O. Box 30028
Lansing, MI 48909
(517) 373-3930
U.S. Geological Survey
District Chief
Water Resources Division
6520 Mercantile Way, Suite 5
Lansing, MI 48910
(517) 887-8903
U.S. Department of Agriculture
Natural Resources Conservation
Service
Room 101
1405 S. Harrison Road
East Lansing, MI 48823
(517) 337-6701
NFIP State Coordinator
Mr. Roland Harms, Director
Michigan Department of Natural
Resources
Land and Water Management Division
P.O. Box 30028
Lansing, MI 48909
(517) 373-3930
MINNESOTA
Flood Plains/Shoreline
Management Section
Division of Waters
Department of Natural Resources
500 Lafayette Road, Box 30
St. Paul, MN 55515-4032
(612) 297-2405
U.S. Geological Survey
District Chief
Water Resources Division
2280 Woodale Road
Moundsville, MN 55112
(612) 783-3100
U.S. Department of Agriculture
Natural Resources Conservation
Service
600 Farm Credit Building
375 Jackson Street
St. Paul, MN 55101
(612) 290-3675
NFIP State Coordinator
Mr. Ronald Nargang, Director
Minnesota Department of Natural
Resources
Division of Water
500 LaFayette Road, Box 32
St. Paul, MN 55515-0432
(612) 296-4800
A3-9
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
MISSISSIPPI
Mississippi Research and
Development Center
3825 Ridgewood Road
Jackson, MI 39211
(601) 982-6376
U.S. Geological Survey
District Chief
Water Resources Division
Federal Office Building, Suite 710
100 West Capitol Street
Jackson, MS 39269
(601) 965-4600
U.S. Department of Agriculture
Natural Resources Conservation
Service
100 W. Capitol
Suite 1321
Federal Building
Jackson, MS 39269
(601) 969-5205
NFIP State Coordinator
Mr. J. E. Maher, Director
Mississippi Emergency Management
Agency
1410 Riverside Drive
P.O. Box 4501
Jackson, MS 39216
(601) 352-9100
MISSOURI
Department of Natural Resources
P.O. Box 176
205 Jefferson Street
Jefferson City, MO 65102
(314) 751-4422
U.S. Geological Survey
District Chief
Water Resources Division
1400 Independence Road,
(Mail Stop) 200
Rolla, MO 65401
(314) 341-0824
U.S. Department of Agriculture
Natural Resources Conservation
Service
601 Business Loop
70 West Parkdale Center, Suite 250
Columbia, MO 65202
(314) 876-0903
NFIP State Coordinator
Director
Missouri Department of Natural
Resources
101 N. Jefferson Street
P.O. Box 176
Jefferson City, MO 65102
(314) 751-4422
MONTANA
Montana Department of Natural
Resources and Conservation
1520 East Sixth Avenue
Helena, MT 59620
(406) 444-6646
U.S. Geological Survey
Federal Building, Room 428
Drawer 10076
301 South Park Avenue
Helena, MT 59626-0076
(406) 449-5302
U.S. Department of Agriculture
Natural Resources Conservation
Service
10 E. Babcock
Room 443
Bozeman, MT 59715
(406) 587-6811
NFIP State Coordinator
Mr. Mark Simonich, Director
Montana Department of Natural
Resources and Conservation
1520 East 6th Ave.
Helena, MT 59620
(406) 444-6699
NEBRASKA
Nebraska Natural Resources
Commission
P.O. Box 94876
Lincoln, NE 68509-4876
(402) 471-2081
U.S. Geological Survey
District Chief
Water Resources Division
Federal Building, Room 406
100 Centennial Mall North
Lincoln, NE 68508
(402) 437-5082
A3-10
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Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building, Rm. 345
U.S. Courthouse
100 Centennial Mall, North
P.O. Box 82502
Lincoln, NE 68508-3866
(402) 437-5300
NFIP State Coordinator
Mr. Dayle Williamson, Director
Nebraska Natural Resources
Commission
P.O. Box 94876
Lincoln, NE 68509
(402) 471-2081
NEVADA
Division of Emergency Management
State of Nevada
Capitol Complex
Carson City, NV 89710
(702) 885-4240
U.S. Geological Survey
Hydrologist-in-Charge
Nevada Office
Water Resources Division
Federal Building, Room 224
705 North Plaza Street
Carson City, NV 89701
(702) 882-1388
U.S. Department of Agriculture
Natural Resources Conservation
Service
5301 Longway Lane
Building F, Suite 201
Reno, NV 89511
(702) 784-5863
NFIP State Coordinator
Mr. David McNinch
Nevada Division of Emergency
Management
2525 S. Carson
Capitol Complex
Carson City, NV 89710
(702) 885-4240
NEW HAMPSHIRE
Office of Emergency Management
State Office Park South
107 Pleasant Street
Concord, NH 03301
(603) 271-2231
U.S. Geological Survey
Hydrologist-in-Charge
New Hampshire Office
Water Resources Division
525 Clinton Street, RFD 2
Bow, NH 03304
(603) 225-4681
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building
2 Madbury Road
Durham, NH 03824
(603) 868-7581
NFIP State Coordinator
Col. George L. Iverson, Director
Governor's Office of
Emergency Management
State Office Park South
107 Pleasant Street
Concord, NH 03301
(603) 271-2231
NEW JERSEY
New Jersey Department of
Environmental Protection and Energy
Flood Plain Management Section
CN 419
Trenton, NJ 08625-0419
(609) 292-2296
New Jersey Department of
Environmental Protection and Energy
Division of Natural and Historic
Resources Engineering and
Construction
Element
Floodplain Management Section
Station Plaza 5
501 East State Street, 1st Floor
Trenton, New Jersey 08625-0419
(609) 292-2296
A3-11
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Geological Survey
District Chief
Water Resources Division
Mountain View Office Park,
Suite 206
810 Bear Tavern Road
West Trenton, NJ 08628
(609) 771-0065
U.S. Department of Agriculture
Natural Resources Conservation
Service
1370 Hamilton Street
Somerset, NJ 08873
(908) 725-3848
NFIP State Coordinator
Mr. Scott A. Weiner, Commissioner
New Jersey Department of
Environmental Protection and Energy
CN 402
Trenton, NJ 08625
(609) 292-2885
NEW MEXICO
New Mexico State Engineer's Office
Bataan Memorial Building
P.O. Box 25102
Santa Fe, NM 87504-5102
(505) 827-6091
U.S. Geological Survey
District Chief
Water Resources Division
4501 Indian School Road, NE
Suite 200
Albuquerque, NM 87110
(505) 262-5300
U.S. Department of Agriculture
Natural Resources Conservation
Service
P.O. Box 2007
517 Gold Avenue, SW., Rm. 301
Albuquerque, NM 87102
(505) 766-3277
NFIP State Coordinator
Mr. Keith Lough
Office of Emergency Planning
and Coordination
Department of Public Safety
P.O. Box 1628
Santa Fe, NM 87503
(505) 827-6091
NEW YORK
Flood Protection Bureau
New York Department of
Environmental Conservation
50 Wolf Road
Albany, NY 12233-3507
(518) 457-3157
U.S. Geological Survey
District Chief
Water Resources Division
445 Broadway, Room 343
Albany, NY 12201
(518) 472-3107
U.S. Department of Agriculture
Natural Resources Conservation
Service
441 South Salina Street
5th floor, Suite 354
Syracuse, NY 13202
(315) 477-6508
FTS 950-5521
NFIP State Coordinator
Mr. James F. Kelly, Director
Flood Protection Bureau
New York State Department of
Environmental Conservation
50 Wolf Road, Room 330
Albany, NY 12233-3507
(518) 457-3157
NORTH CAROLINA
North Carolina Department of
Crime Control and Public Safety
Division of Emergency Management
116 West Jones Street
Raleigh, NC 27603
(919) 733-3867
U.S. Geological Survey
District Chief
Water Resources Division
P.O. Box 30728
3916 Sunset Ridge Road
Raleigh, NC 27622
(919) 856-4510
U.S. Department of Agriculture
Natural Resources Conservation
Service
4405 Bland Avenue
Suite 205
Raleigh, NC 27609
(919) 790-2888
A3-12
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
NFIP State Coordinator
Mr. Joseph F. Myers, Director
North Carolina Division of
Emergency Management
116 West Jones Street
Raleigh, NC 27603
(919) 733-3867
NORTH DAKOTA
State Water Commission
900 East Boulevard
Bismarck, ND 58505
(701) 224-2750
U.S. Geological Survey
District Chief
Water Resources Division
821 East Interstate Avenue
Bismarck, ND 58501
(701) 250-4601
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building, Rm. 270
Rosser Ave. & Third St.
P.O. Box 1458
Bismarck, ND 58502
(701) 250-4435
NFIP State Coordinator
Mr. David A. Sprycnzynatyk
State Engineer
North Dakota State Water Commission
900 E. Boulevard
Bismark, ND 58505
(701) 224-4940
OHIO
Ohio Department of Natural Resources
Flood Plain Planning Unit
Division of Water
1939 Fountain Square
Columbus, OH 43224
(614) 265-6750
U.S. Geological Survey
District Chief
Water Resources Division
975 West Third Avenue
Columbus, OH 43212
(614) 469-5553
U.S. Department of Agriculture
Natural Resources Conservation
Service
Room 522
Federal Building
200 North High Street
Columbus, OH 43215
(614) 469-6962
NFIP State Coordinator
Mrs. Frances S. Buchholzer, Director
Ohio Department of Natural
Resources
Fountain Square
Columbus, OH 43224
(614) 264-6875
OKLAHOMA
Oklahoma Water Resources Board
600 North Harvey Avenue
P.O. Box 150
Oklahoma City, OK 73101-0150
(405) 231-2500
U.S. Geological Survey
District Chief
Water Resources Division
202 NW Sixty Sixth, Building 7
Oklahoma City, OK 73116
(405) 843-7570
U.S. Department of Agriculture
Natural Resources Conservation
Service
100 USDA
Suite 203
Stillwater, OK 74074
(405) 742-1200
NFIP State Coordinator
Mrs. Patricia P. Eaton
Executive Director
Oklahoma Water Resources Board
600 N. Harvey
Oklahoma City, OK 73101
(405) 231-2500
OREGON
Department of Land Conservation
and Development
1175 Court Street Northeast
Salem, OR 97310
(503) 373-0050
A3-13
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Geological Survey
Hydrologist-in-Charge
Oregon Office
Water Resources Division
847 Northeast 19th Avenue,
Suite 300
Portland, OR 97323
(503) 251-3200
U.S. Department of Agriculture
Natural Resources Conservation
Service
2115 SE Morrison
Portland, OR 97214
(503) 231-2270
NFIP State Coordinator
Mr. Richard Benner
Oregon Department of Land
Conservation and Development
1175 Court Street, N.E.
Salem, OR 97310
(503) 378-4928
PENNSYLVANIA
Department of Community Affairs
317 Forum Building
Harrisburg, PA 17120
(717) 787-7160
U.S. Geological Survey
District Chief
Water Resources Division
840 Market Street
Harrisburg, PA 17043-1586
(717) 730-6900
Ms. Karen A. Miller, Secretary
Pennsylvania Department of
Community Affairs
P.O. Box 155
317 Forum Building
Harrisburg, PA 17120
(717) 787-7160
U.S. Department of Agriculture
Natural Resources Conservation
Service
One Credit Union Place
Suite 340
Harrisburg, PA 17110-2993
NFIP State Coordinator
Federal Building
U.S. Courthouse
805 985
Federal Square Station
Harrisburg, PA 17108
(717) 782-2202
FTS 590-2202
PUERTO RICO
Puerto Rico Planning Board
1492 Ponce De Leon Avenue, Suite 417
Santurce, Puerto Rico 00907
(809) 729-6920
U.S. Geological Survey
District Chief
Water Resources Division
GPO Box 4424
San Juan, PR 00936
(Street Address:
GSA Center, Building 652
Highway 28, Pueblo Viejo)
(809) 783-4660
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building, Rm. 639
Chardon Avenue
GPO Box 4868
San Juan, PR 00936
(809) 753-4206
NFIP State Coordinator
Ms. Norma N. Burgos, President
Puerto Rico Planning Board
P.O. Box 41119
San Juan, PR 00940-9985
(809) 727-4444
RHODE ISLAND
Statewide Planning Program
Rhode Island Office of State Planning
1 Capitol Hill
Providence, RI 02908
(401) 277-2656
U.S. Geological Survey
Hydrologist-in-Charge
Rhode Island Office
Water Resources Division
275 Promanade Street, Suite 150
Providence, RI 02908
(401) 331-9050
A3-14
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
U.S. Department of Agriculture
Natural Resources Conservation
Service
40 Quaker Lane, Suite 46
West Warwick, RI 02886
(401) 828-1300
NFIP State Coordinator
Mr. Daniel W. Varin
Associate Director
Department of Transportation
Office of Systems Planning
1 Capitol Hill
Providence, RI 02908-5872
(401) 277-6578
SOUTH CAROLINA
South Carolina Water and Natural
Resources Commission
1201 Main Street, Suite 1100
Columbia, SC 29201
(803) 737-0800
U.S. Geological Survey
District Chief
Water Resources Division
Stevenson Center, Suite 129
720 Gracern Road
Columbia, SC 29210-7651
(803) 750-6100
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Bldg., Rm. 950
1835 Assembly St.
Columbia, SC 29201
(803) 765-5681
NFIP State Coordinator
Mr. Danny Johnson, Director
Surface Water Division
South Carolina Water Resources
Commission
1201 Main Street, Suite 1100
Columbia, SC 29201
(803) 737-0800
SOUTH DAKOTA
Disaster Assistance Programs
Emergency and Management Services
500 East Capitol
Pierre, SD 57501
(605) 773-3231
U.S. Geological Survey
District Chief
Water Resources Division
Federal Building, Room 317
200 Fourth Street Southwest
Huron, SD 57350-2469
(605) 353-7176
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building, Rm. 203
200 4th Street, SW
Huron, SD 57350
(605) 353-1092
NFIP State Coordinator
Mr. Gary N. Whitney, Director
South Dakota Department of
Military and Veteran Affairs
Division of Emergency and
Disaster Services
500 E. Capitol
Pierre, SD 57501
(605) 773-3231
TENNESSEE
Tennessee Department of Economic
and Community Development
Division of Community Development
320 Sixth Avenue North, Sixth Floor
Nashville, TN 37243-0405
(615) 741-1888
U.S. Geological Survey
District Chief
Water Resources Division
810 Broadway, Suite 500
Nashville, TN 37203
(615) 736-5424
U.S. Department of Agriculture
Natural Resources Conservation
Service
U.S. Courthouse, Rm. 675
801 Broadway Street
Nashville, TN 37203
(615) 736-5471
FTS 852-5471
A3-15
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
NFIP State Coordinator
Mr. Michael McGuire
Assistant Commissioner
Tennessee Department of Economic and
Community Development
320 Sixth Avenue
North Nashville, TN 37219-5408
(615) 741-2211
TEXAS
Texas Natural Resource
Conservation Commission
P.O. Box 13087
Capitol Station
Austin, TX 78711-3087
(512) 239-1000
U.S. Geological Survey
District Chief
Water Resources Division
8011 Cameron Road
Austin, TX 78754
(512) 873-3000
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Bldg.
101 S. Main Street
Temple, TX 76501
(817) 774-1214
NFIP State Coordinator
Mr. Jesus Galza
Executive Director
Texas Water Commission
P.O. Box 13087
Capitol Station
Austin, TX 78711-3087
(512) 463-7791
UTAH
Office of Comprehensive
Emergency Management
State Office Building, Room 1110
Salt Lake City, UT 84114
(801) 538-3400
U.S. Geological Survey
District Chief
Water Resources Division
2363 Foothill Drive
Salt Lake City, UT 84109
(801) 467-7970
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Building
125 S. State Street
P.O. Box 11350
Salt Lake City, UT 84147
(801) 524-5068
NFIP State Coordinator
Director
Ms. Lorayne Frank,
Department of Public Safety
Division of Comprehensive Emergency
Management
State Office Building, Room 1110
450 North Main
Salt Lake City, UT 84114
(801) 538-3400
VERMONT
Agency of Natural Resources
Department of Environmental
Conservation
Water Quality Division
103 South Main Street - ION
Waterbury, VT 05671-0408
(802) 241-3777
U.S. Geological Survey
District Chief
Water Resources Division
P.O. Box 628
Montpelier, VT 05602
(802) 828-4479
U.S. Department of Agriculture
Natural Resources Conservation
Service
69 Union Street
Winooski, VT 05404
(802) 951-6795
NFIP State Coordinator
Mr. Chuck Clarde, Secretary
Agency of Natural Resources
Center Building
103 South Main Street
Waterbury, VT 05671-0301
(802) 244-7347
A3-16
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
VIRGIN ISLANDS
Virgin Islands of the U.S.
Virgin Islands Planning Department
and Natural Resources
Charlotte Amalie
Nisky Center, Suite 231
St. Thomas, VI 00802
(809) 774-3320
U.S. Geological Survey
District Chief
Water Resources Division
GPO Box 4424
San Juan, PR 00936
(Street Address:
GSA Center, Building 652
Highway 28, Pueblo Viejo)
(809) 783-4660
NFIP State Coordinator
Mr. Roy E. Adams, Commissioner
Virgin Islands Department of
Planning and Natural Resources
Suite 231, Nisky Center
Charlotte Amalie
St. Thomas, VI 00802
(809) 774-3320
VIRGINIA
Virginia State Department of
Environmental Quality
4900 Cot Road
Glen Allen, VA 23060
(804) 527-5000
U.S. Geological Survey
Hydrologist-in-Charge
Virginia Office
Water Resources Division
3600 West Broad Street
Room 606
Richmond, VA 23230
(804) 771-2427
U.S. Department of Agriculture
Natural Resources Conservation
Service
1606 Santa Rosa Road
Suite 209
Richmond, VA 23229
(804) 287-1689
NFIP State Coordinator
Mr. Roland B. Geddes, Director
Department of Conservation and
Historic Resources
203 Governor Street, Suite 206
Richmond, VA 23219
(804) 786-4356
WASHINGTON
Department of Ecology
P.O. Box 47600
Olympia, WA 98504-7600
(206) 407-6000
U.S. Geological Survey
District Chief
Water Resources Division
1201 Pacific Avenue, Suite 600
Tacoma, WA 98402
(206) 593-6510
U.S. Department of Agriculture
Natural Resources Conservation
Service
316 Boone Avenue
Suite 456
Spokane, WA 99201
(509) 353-2336
NFIP State Coordinator
Mr. Chuck Clark
Washington Department of Ecology
P.O. Box 47600
Olympia, WA 98504-7600
(206) 459-6168
WEST VIRGINIA
West Virginia Office of
Emergency Services
Room EB-80, Capitol Building
Charleston, WV 25305
(304) 348-5380
U.S. Geological Survey
District Chief
Water Resources Division
11 Dunbar Street
Charleston, WV 25301
(304) 347-5130
U.S. Department of Agriculture
Natural Resources Conservation
Service
75 High Street, Rm. 301
Morgantown, WV 26505
(304) 291-4151
A3-17
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
Other Federal and State Agencies
NFIP State Coordinator
Mr. Carl Bradford, Director
West Virginia Office of
Emergency Services
Room EBI-80
Capitol Building
Charleston, WV 25305
(304) 348-5380
WISCONSIN
Department of Natural Resources
Dam Safety/Floodplain
Management Section
P.O. Box 7921
Madison, WI 53707
(608) 266-2621
U.S. Geological Survey
District Chief
Water Resources Center
University of Wisconsin/Madison
1975 Willard Drive
Madison, WI 53706-4042
(608) 262-3577
U.S. Department of Agriculture
Natural Resources Conservation
Service
6515 Watts Road
Suite 200
Madison, WI 53719
(608) 264-5341
NFIP State Coordinator
Mr. Carroll D. Besandy, Secretary
Wisconsin Department of Natural
Resources
P.O. Box 7921
Madison, WI 53707
(608) 266-2121
WYOMING
Wyoming Emergency Management Agency
P.O. Box 1709
Cheyenne, WY 82003-1709
(307) 777-4900
U.S. Geological Survey
District Chief
Water Resources Division
P.O. Box 1125
Cheyenne, WY 82003
(Street Address:
2617 East Lincoln Way
Cheyenne, WY 82001
(307) 772-2153
U.S. Department of Agriculture
Natural Resources Conservation
Service
Federal Office Building
100 East "B" Street
Casper, WY 82601
(307) 261-5231
NFIP State Coordinator
Mr. Joe Daly, Coordinator
Wyoming Emergency Management
Agency
P.O. Box 1709
Cheyenne, WY 82003
(307) 777-7566
A3-18
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
U.S. Army Corps of Engineers
U.S. Army Corps of Engineers
Headquarters
20 Massachusetts Ave., NW
Washington, D.C. 20314-1000
Attn: CECW-PF
202/272-0169
U.S. Army Corps of Engineers
Lower Miss. Valley Division
P.O. Box 80
Vicksburg, MS 39181-0080
Attn: CELMV-PD-CM
601/634-5827
U.S. Army Corps of Engineers
Memphis District
167 North Main Street, B-202
Memphis, TN 38103-1894
Attn: CELMM-PD-M
901/544-3968
U.S. Army Corps of Engineers
New Orleans District
P.O. Box 60267
New Orleans, LA 70160-0267
Attn: CELMN-PD-FG
504/865-1121
U.S. Army Corps of Engineers
St. Louis District
1222 Spruce Street
St. Louis, MO
63103-2833
Attn: CELMS-PD-M
314/331-8483
U.S. Army Corps of Engineers
Vicksburg District
2101 North Frontage Road
Vicksburg, MS 39180-0060
Attn: CELMK-PD-FS
601/631-5416
U.S. Army Corps of Engineers
Missouri River Division
12565 West Center Road
Omaha, NE 68104-3869
Attn: CEMRD-PD-F
402/221-7273
U.S. Army Corps of Engineers
Kansas City District
700 Federal Building Kansas
City, MO 64106-2896
Attn: CEMRK-PD-P
816/426-3674
U.S. Army Corps of Engineers
Omaha District
215 North 17th Street
Omaha, NE 68102-4978
Attn: CEMRO-PD-F
402/221-4596
U.S. Army Corps of Engineers
North Atlantic Division
90 Church Street
New York, NY 10007-2979
Attn: CENAD-PL-FP
212/264-7482
U.S. Army Corps ot Engineers
Baltimore District
Supervisor of Baltimore Harbor
P.O. Box 1715
Baltimore, MD 21201-1715
Attn: CENAB-PL-B
410/962-7608
U.S. Army Corps of Engineers
New York District, Planning Division,
Floodplain Management Section
26 Federal Plaza
New York, NY 10278
Attn: CENAN-PL-FP
212/264-8870
U.S. Army Corps of Engineers
Norfolk District
Supervisor of Norfolk Harbor
803 Front Street
Norfolk, VA 23510-1096
Attn: CENAO-PL-FP
804/441-7779
U.S. Army Corps of Engineers
Philadelphia District
U.S. Customs House
2nd & Chestnut Streets
Philadelphia, PA 19106-2991
Attn: CENAP-PL-F
215/656-6516
U.S. Army Corps of Engineers
North Central Division
111 North Canal Street, 14th Floor
Chicago, IL 60606
Attn: CENCD-PD-FP
312/353-1279
A3-19
-------
Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
U.S. Army Corps of Engineers
U.S. Army Corps of Engineers
Buffalo District, Planning Division,
Floodplain Management Section
1776 Niagara Street
Buffalo, NY 14207-3199
Attn: CENCB-PD-FP
716/879-4104
U.S. Army Corps of Engineers
Chicago District
111 North Canal Street
14th Floor
Chicago, IL 60606
Attn: CENCC-PD-R
312/353-6400
U.S. Army Corps of Engineers
Detroit District
477 Michigan Avenue
Detroit, MI 48226
Attn: CENCE-PD-PF
313/226-6773
U.S. Army Corps of Engineers
Rock Island District
P.O. Box 2004
Clock Tower Building
Rock Island, IL 61204-2004
Attn: CENCR-PD-F
309/788-4750
U.S. Army Corps of Engineers
St. Paul District
190 Phipps Street East
St. Paul, MN 55101-1638
Attn: CENCS-PD-FS
612/290-5200
U.S. Army Corps of Engineers
New England Division
424 Trapelo Road
Waltham, MA 02254-9149
Attn: CENED-PL-B
617/647-8111
U.S. Army Corps of Engineers
North Pacific Division
333 Southwest 1st Avenue
Portland, OR 97204
Attn: CENPD-PL-FS
503/326-6021
U.S. Army Corps of Engineers
Alaska District
P.O. Box 898
Anchorage, AK 99506-0898
Attn: CENPA-EN-PL-FP
907/753-2504
U.S. Army Corps of Engineers
Portland District
P.O. Box 2946
Portland, OR 97208-2946
Attn: CENPP-PL-CF
503/326-6411
U.S. Army Corps of Engineers
Seattle District
P.O. Box 3755
Seattle, WA 98124-2255
Attn: CENPS-EN-HH
206/764-3660
U.S. Army Corps of Engineers
Walla Walla District
Bldg. 602 City-County Airport
Walla Walla, WA 99362-9265
Attn: CENPW-PL-FP
509/522-6589
U.S. Army Corps of Engineers
Ohio River Division
P.O. Box 59
Louisville, KY 40201-0059
Attn: CEORD-PD-J
502/582-5782
U.S. Army Corps of Engineers
Huntington District
502 8th Street
Huntington,WV 25701-2070
Attn: CEORH-PD-S
304/529-5644
U.S. Army Corps of Engineers
Louisville District
P.O. Box 59
Louisville, KY 40201-0059
Attn: CEORL-PD-S
502/582-5742
U.S. Army Corps of Engineers
Nashville District
P.O. Box 1070
Nashville, TN 37202-1070
Attn: CEORN-ED-P
615/736-5055
U.S. Army Corps of Engineers
Pittsburgh District
William S. Moorehead Fed. Bldg.
1000 Liberty Avenue
Pittsburgh, PA 15222-4186
Attn: CEORP-PD-J
412/644-6924
A3-20
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Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
U.S. Army Corps of Engineers
U.S. Army Corps of Engineers
Pacific Ocean Division
Ft. Shatter, HI 96858-5440
Attn: CEPOD-ED-PH
808/438-7009
U.S. Army Corps of Engineers
Charleston District, P.O. Box 919
Charleston, SC 29402-0919
Attn: CESAC-EN-PH
803/727-4263
U.S. Army Corps of Engineers
South Atlantic Division
611 South Cobb Drive
Marietta, GA 30060
Attn: CESAD-PD-A
404/421-5296
U.S. Army Corps of Engineers
Jacksonville District
P.O. Box 4970
Jacksonville, FL 32232-0019
Attn: CESAJ-PD-FP
904/232-2234
U.S. Army Corps of Engineers
Mobile District
P.O. Box 2288
Mobile, AL 36628-0001
Attn: CESAM-PD-P
205/694-3879
U.S. Army Corps of Engineers
Savannah District
P.O. Box 889
Savannah, GA 31402-0889
Attn: CESAS-PD-F
912/652-5822
U.S. Army Corps of Engineers
Wilmington District
P.O. Box 1890
Wilmington, NC 28402-1890
Attn: CESAW-PD-F
910/251-4822
U.S. Army Corps of Engineers
South Pacific Division, Room 720
630 Sansome Street
San Francisco, CA 94111-2206
Attn: CESPD-PD-P
415/705-2427
U.S. Army Corps of Engineers
Los Angeles District
P.O. Box 2711
Los Angeles, CA 90053-2325
Attn: CESPL-PD-WF
213/894-5450
U.S. Army Corps of Engineers
Sacramento District
1325 G Street
Sacramento, CA 95814-4794
Attn: CESPK-PD-F
916/557-6700
U.S. Army Corps of Engineers
San Francisco District
211 Main Street
San Francisco, CA 9410S-1905
Attn: CESPN-PE-W
415/744-3029
U.S. Army Corps of Engineers
Southwestern Division
1114 Commerce Street
Dallas, TX 75242-0216
Attn: CESWD-PL-M
214/767-2310
U.S. Army Corps of Engineers
Albuquerque District
P.O. Box 1580
Albuquerque, NM 87103-1580
Attn: CESWA-ED-PH
505/766-2635
U.S. Army Corps of Engineers
Fort Worth District
P.O. Box 17300
Fort Worth, TX 76102-0300
Attn: CESWF-PL-F
817/334-3207
U.S. Army Corps of Engineers
Galveston District
P.O. Box 1229
Galveston, TX 77553-1229
Attn: CESWG-PL-P
409/766-3023
U.S. Army Corps of Engineers
Little Rock District
P.O. Box 867
Little Rock, AR 72203-0867
Attn: CESWL-PL-F
501/378-5611
U.S. Army Corps of Engineers
Tulsa District
P.O. Box 61
Tulsa, OK 74121 0061
Attn: CESWT-PL-GF
918/581-7896
A3-21
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Appendix 3 - continued
Federal Emergency Management Agency Offices
and Other Federal and State Agencies
River Basin Commissions
Delaware River Basin Commission
25 State Police Drive
Box 7360
West Trenton, NJ 08628
609/883-9500
Susquehanna River Basin Commission
1721 North Front Street
Harrisburg, PA
717/238-0422
A3-22
-------
Appendix 4
State Hydrology Reports
ALABAMA
Olin, D.A. and Bingham, R.H., 1982, Synthesized flood frequency of urban streams
in Alabama: U.S. Geological Survey Water-Resources Investigations 82-683.
Olin, D.A., 1984, Magnitude and frequency of floods in Alabama: U.S. Geological
Survey Water-Resources Investigations 84-4191.
ALASKA
Lamke, R.D., 1978, Flood characteristics of Alaskan streams: U.S. Geological
Survey Water-Resources Investigations 78-129.
ARIZONA
Eychaner, J.H., 1984, Estimation of magnitude and frequency of floods in Pima
County, Arizona, with comparisons of alternative methods: U.S. Geological Survey
Water-Resources Investigations 84-4142.
ARKANSAS
Neely, B.L., Jr., 1986, Magnitude and frequency of floods in Arkansas: U.S.
Geological Survey Water-Resources Investigations 86-4335.
CALIFORNIA
Waananen, A.O., and Crippen, J.R., 1977, Magnitude and frequency of floods in
California: U.S. Geological Survey Water-Resources Investigations 77-21 (PB-272
510/AS).
COLORADO
Hedman, E.R., Moore, D.O. and Livingston, R.K., 1972, Selected streamflow
characteristics as related to channel geometry of perennial streams in Colorado:
U.S. Geological Survey Open-File Report.
Kircher, J.E., Choquette, A.F., and Richter, B.D., 1985, Estimation of natural
streamflow characteristics in Western Colorado: U.S. Geological Survey Water-
Resources Investigations 85-4086.
Livingston, R.K., 1980, Rainfall-runoff modeling and preliminary regional flood
characteristics of small rural watersheds in the Arkansas River Basin in
Colorado: U.S. Geological Survey Water-Resources Investigations 80-112 (NTIS).
Livingston, R.K., and Minges, D.R., 1987, Techniques for estimating regional
flood characteristics of small rural watersheds in the plains regions of eastern
Colorado: U.S. Geological Survey Water-Resources Investigations 87-4094.
McCain, J.R., and Jarrett, R.D., 1976, Manual for estimating flood
characteristics of natural flow streams in Colorado: Colorado Water Conservation
Board, Technical Manual No. 1.
A4-1
-------
Appendix 4 - continued
State Hydrology Reports
CONNECTICUT
Weiss, L.A., 1975, Flood flow formula for urbanized and non-urbanized areas of
Connecticut: Watershed Management Symposium of ASCE Irrigation and Drainage
Division, August 11-13, 1975, pp. 658-675.
DELAWARE
Simmons, R.H., and Carpenter, D.H., 1978, Technique for estimating the magnitude
and frequency of floods in Delaware: U.S. Geological Survey Water-Resources
Investigations Open-File Report 78-93.
DISTRICT OF COLUMBIA
None listed
FLORIDA
Bridges, W.C., 1982, Technique for estimating the magnitude and frequency of
floods on natural-flow streams in Florida: U.S. Geological Survey Water-Resources
Investigations Open-File Report 82-4012.
Franklin, M.A., 1984, Magnitude and frequency of floods from urban streams in
Leon County, Florida: U.S. Geological Survey Water-Resources Investigations
84-4004.
Lopez, M.A., and Woodham, W.M., 1982, Magnitude and frequency of floods on small
urban watersheds in the Tampa Bay area, west-central Florida: U.S. Geological
Survey Water-Resources Investigations 82-42.
GEORGIA
Inman, E.J., 1983, Flood-frequency relations for urban streams in metropolitan
Atlanta, Georgia: U.S. Geological Survey Water-Resources Investigations
83-4203.
Price, M., 1979, Floods in Georgia, magnitude and frequency: U.S. Geological
Survey Water-Resources Investigations 78-137 (PB-80 146 244).
HAWAII
Matsuoka, I., 1978, Flow characteristics of streams in Tutuila, American Somoa:
U.S. Geological Survey Open-File Report 78-103.
Nakahara, R.H., 1980, An analysis of the magnitude and frequency of floods on
Oahu, Hawaii: U.S. Geological Survey Water-Resources Investigations 80-45
(PB-81 109 902) .
A4-2
-------
Appendix 4 - continued
State Hydrology Reports
IDAHO
Harenberg, W.A., 1980, Using channel geometry to estimate flood flows at ungaged
sites in Idaho: U.S. Geological Survey Water-Resources Investigations 80-32
(PB-81 153 736) .
Kjelstrom, L.C., and Moffatt, R.L., 1981, Method of estimating flood-frequency
parameters for streams in Idaho: U.S. Geological Survey Open-File Report 81-909.
Thomas, C.A., Harenburg, W.A., and Anderson, J.M., 1973, Magnitude and frequency
of floods in small drainage basins in Idaho: U.S. Geological Survey Water-
Resources Investigations 7-73 (PB-222 409) .
ILLINOIS
Allen, H.E., Jr., and Bejcek, R.M., 1979, Effects of urbanization on the
magnitude and frequency of floods in northeastern Illinois: U.S. Geological
Survey Water-Resources Investigations 79-36 (PB-299 065/AS).
Curtis, G.W., 1987, Technique for estimating flood-peak discharges and
frequencies on rural streams in Illinois: U.S. Geological Survey Water-Resources
Investigations 87-4207.
INDIANA
Glatfelter, D.R., 1984, Technique for estimating the magnitude and frequency of
floods in Indiana: U.S. Geological Survey Water-Resources Investigations 84-4134.
IOWA
Lara, 0., 1978, Effects of urban development on the flood flow characteristics of
Walnut Creek basin, Des Moines metropolitan area, Iowa: U.S. Geological Survey
Water-Resources Investigations 78-11 (PB-284 093/AS).
KANSAS
Clement, R.W., 1987, Floods in Kansas and techniques for estimating their
magnitude and frequency: U.S. Geological Survey Water-Resources Investigations
87-4008.
Hedman, E.R., Kastner, W.M., and Hejl, H.R., 1974, Selected streamflow
characteristics as related to active-channel geometry of streams in Kansas:
Kansas Water Resources Board Technical Report No. 10.
Peek, C.O., and Jordan, P.R., 1978, Determination of peak discharge from rainfall
relations for urbanized basins, Wichita, Kansas: U.S. Geological Survey Open-File
Report 78-974.
KENTUCKY
Choquette, A.F., 1987, Regionalization of peak discharges for streams in
Kentucky: U.S. Geological Survey Water-Resources Investigations 87-4029.
A4-3
-------
Appendix 4 - continued
State Hydrology Reports
LOUISIANA
Lee, F.N., 1985, Floods in Louisiana, Magnitude and Frequency, Fourth Edition:
Department of Transportation and Development, Water Resources Technical Report
No. 36.
Lowe, A.S., 1979, Magnitude and frequency of floods in small watersheds in
Louisiana: Louisiana Department of Transportation and Development, Office of
Highways, Research Study No. 65-2H.
MAINE
Morrill, R.A., 1975, A technique for estimating the magnitude and frequency of
floods in Maine: U.S. Geological Survey Open-File Report.
MARYLAND
Carpenter, D.H., 1980, Technique for estimating magnitude and frequency of floods
in Maryland: U.S. Geological Survey Water-Resources Investigations Open-File
Report 80-1016.
MASSACHUSETTS
Wandle, S.W., 1983, Estimating peak discharges and frequencies of small rural
streams in Massachusetts: U.S. Geological Survey Water-Supply Paper 2214.
MICHIGAN
Holtschlag, D.J., and Croskey, H.M., 1984, Statistical models for estimating flow
characteristics of Michigan streams: U.S. Geological Survey Water-Resources
Investigations 84-4270.
MINNESOTA
Jacques, J.E., and Lorenz, D.L., 1987, Techniques for estimating the magnitude
and frequency of floods in Minnesota: U.S. Geological Survey Water-Resources
Investigations 87-4170.
MISSISSIPPI
Colson, B.E., and Hudson, J.W., 1976, Flood frequency of Mississippi streams:
Mississippi State Highway Department.
MISSOURI
Becker, L.D., 1986, Techniques for estimating flood-peak discharges for urban
streams in Missouri: U.S. Geological Survey Water-Resources Investigations Report
86-4322.
Hauth, L.D., 1974, A technique for estimating the magnitude and frequency of
Missouri floods: U.S. Geological Survey Open-File Report.
A4-4
-------
Appendix 4 - continued
State Hydrology Reports
MISSOURI continued
Spencer, D.W., and Alexander, T.W., 1978, Techniques for estimating the magnitude
and frequency of floods in St. Louis County, Missouri: U.S. Geological Survey
Water-Resources Investigations 78-139 (PB-298 245/AS).
MONTANA
Omang, R.J., 1983, Mean annual runoff and peak flow estimates based on channel
geometry of streams in southeastern Montana: U.S. Geological Survey Water-
Resources Investigations Report 82-4092.
Omang, R.J., Parrett, C., and Hull, J.A., 1986, Methods of estimating magnitude
and frequency of floods in Montana based on data through 1983: U.S. Geological
Survey Water-Resources Investigations Report 86-4027.
Parrett, C., 1983, Mean annual runoff and peak flow estimates based on channel
geometry of streams in northeastern and western Montana: U.S. Geological Survey
Water-Resources Investigations Report 83-4046.
Parrett, C., Hull, J.A., and Omang, R.J., 1987, Revised techniques for estimating
peak discharges from channel width in Montana: U.S. Geological Survey Water-
Resources Investigations 87-4121.
NEBRASKA
Beckman, E.W., 1976, Magnitude and frequency of floods in Nebraska: U.S.
Geological Survey Water-Resources Investigations 76-109 (PB-260 842/AS).
NEVADA
Moore, D.O., 1974, Estimating flood discharges in Nevada using channel-geometry
measurements: Nevada State Highway Department Hydrologic Report No. 1.
Moore, D.O., 1976, Estimating peak discharges from small drainages in Nevada
according to basin areas within elevation zones: Nevada State Highway Department
Hydrologic Report No. 3.
NEW HAMPSHIRE
LeBlanc, D.R., 1978, Progress report on hydrologic investigations of small
drainage areas in New Hampshire--Preliminary relations for estimating peak
discharges on rural, unregulated streams: U.S. Geological Survey Water-Resources
Investigations 78-47 (PB-284 127/AS).
NEW JERSEY
Stankowski, S.J., 1974, Magnitude and frequency of floods in New Jersey with
effects of urbanization: New Jersey Department of Environmental Protection
Special Report 38.
Velnick, Anthony J. and Laskowski, Stanley L. , 1979, Technique for estimating
depth of 100-year flood in New Jersey: Open-File Report 79-419.
A4-5
-------
Appendix 4 - continued
State Hydrology Reports
NEW MEXICO
Hejl, H.R., Jr., 1984, Use of selected basin characteristics to estimate mean
annual runoff and peak discharges for ungaged streams in drainage basins
containing strippable coal resources, northwestern New Mexico: U.S. Geological
Survey Water-Resources Investigations 84-4264.
Scott, A.G., and Kunkler, J.L., 1976, Flood discharges of streams in New Mexico
as related to channel geometry: U.S. Geological Survey Open-File Report.
Waltmeyer, S.D., 1986, Techniques for estimating flood-flow frequency for
unregulated streams in New Mexico: U.S. Geological Survey Water-Resources
Investigations 86-4104.
NEW YORK
Lomia, Richard, 1991, Regionalization of flood discharges for rural, unregulated
streams in New York, excluding Long Island: U.S. Geological Survey Water-
Resources Investigations Report 90-4197.
NORTH CAROLINA
Gunter, H.C., Mason, R.R., and Stamey, T.C., 1987, Magnitude and frequency of
floods in rural and urban basins of North Carolina: U.S. Geological Survey Water-
Resources Investigations 87-4096.
Martens, L.S., 1968, Flood inundation and effects of urbanization in metropolitan
Charlotte, North Carolina: U.S. Geological Survey Water-Supply Paper 1591-C.
Putnam, A.L., 1972, Effect of urban development on floods in the Piedmont
province of North Carolina: U.S. Geological Survey Open-File Report.
NORTH DAKOTA
Crosby, O.A., 1975, Magnitude and frequency of floods in small drainage basins of
North Dakota: U.S. Geological Survey Water-Resources Investigations 19-75
(PB-248 480/AS).
OHIO
Roth, D.K., 1985, Estimation of flood peaks from channel characteristics in Ohio:
U.S. Geological Survey Water-Resources Investigations Report 85-4175.
Sherwood, J.M., 1986, Estimating peak discharges, flood volumes, and hydrograph
stages of small urban streams in Ohio: U.S. Geological Survey Water-Resources
Investigations Report 86-4197.
Webber, E.E., and Bartlett, W.P., Jr., 1977, Floods in Ohio magnitude and
frequency: State of Ohio, Department of Natural Resources, Division of Water,
Bulletin 45.
Webber, E.E., and Roberts, J.W., 1981, Floodflow characteristics related to
channel geometry in Ohio: U.S. Geological Survey Open-File Report 81-1105.
A4-6
-------
Appendix 4 - continued
State Hydrology Reports
OKLAHOMA
Sauer, V.B., 1974, An approach to estimating flood frequency for urban areas in
Oklahoma: U.S. Geological Survey Water-Resources Investigations 23-74
(PB-235 307/AS).
Tortorelli, R.L., and Bergman, D.L., 1984, Techniques for estimating flood peak
discharge for unregulated streams and streams regulated by small floodwater
retarding structures in Oklahoma: U.S. Geological Survey Water-Resources
Investigations 84-4358.
OREGON
Harris, D.D., and Hubbard, L.E., 1982, Magnitude and frequency of floods in
eastern Oregon: U.S. Geological Survey Water-Resources Investigations 82-4078.
Harris, D.D., Hubbard, L.E., and Hubbard, L.L., 1979, Magnitude and frequency of
floods in western Oregon: U.S. Geological Survey Open-File Report 79-553.
Laenen, Antonius, 1980, Storm runoff as related to urbanization in the Portland,
Oregon-Vancouver, Washington, area: U.S. Geological Survey Water-Resources
Investigations Open-File Report 80-689.
PENNSYLVANIA
Bailey, J.F., Thomas, W.O., Jr., Wetzel, K.L., and Ross, T.J., 1987, Estimation
of flood-frequency characteristics and the effects of urbanization for streams in
the Philadelphia, Pennsylvania, area: U.S. Geological Survey Water-Resources
Investigations 87-4194.
Flippo, H.N., Jr., 1977, Floods in Pennsylvania: A manual for estimation of their
magnitude and frequency: Pennsylvania Department of Environmental Resources
Bulletin No. 13.
PUERTO RICO
Lopez, M.A., Colon-Dieppa, E., and Cobb, E.D., 1978, Floods in Puerto Rico:
magnitude and frequency: U.S. Geological Survey Water-Resources Investigations
78-141 (PB-300 855/AS).
RHODE ISLAND
Johnson, C.G., and Laraway, G.A., 1976, Flood magnitude and frequency of small
Rhode Island streams--Preliminary estimating relations: U.S. Geological Survey
Open-File Report.
SOUTH CAROLINA
Whetstone, B.H., 1982, Floods in South Carolina--Techniques for estimating
magnitude and frequency of floods with compilation of flood data: U.S. Geological
Survey Water-Resources Investigations 82-1.
A4-7
-------
Appendix 4 - continued
State Hydrology Reports
SOUTH DAKOTA
Becker, L.D., 1974, A method for estimating the magnitude and frequency of floods
in South Dakota: U.S. Geological Survey Water-Resources Investigations 35-74
(PB-239 831/AS).
Becker, L.D., 1980, Techniques for estimating flood peaks, volumes, and
hydrographs on small streams in South Dakota: U.S. Geological Survey Water-
Resources Investigations 80-80 (PB-81 136 145).
TENNESSEE
Neely, B.L., Jr., 1984, Flood frequency and storm runoff of urban areas of
Memphis and Shelby County, Tennessee: U.S. Geological Survey Water-Resources
Investigations 84-4110.
Randolph, W.J., and Gamble, C.R., 1976, A technique for estimating magnitude and
frequency of floods in Tennessee: Tennessee Department of Transportation.
Robbins, C.H., 1984, Synthesized flood frequency of small urban streams in
Tennessee: U.S. Geological Survey Water-Resources Investigations 84-4182.
Wibben, H.C., 1976, Effects of urbanization on flood characteristics in
Nashville-Davidson County, Tennessee: U.S. Geological Survey Water-Resources
Investigations 76-121 (PB-266 654/AS).
TEXAS
Land, L.F., Schroeder, E.E., and Hampton, B.B., 1982, Techniques for estimating
the magnitude and frequency of floods in the Dallas-Fort Worth Metropolitan Area,
Texas: U.S. Geological Survey Water-Resources Investigations 82-18.
Liscum, F., and Massey, B.C., 1980, Techniques for estimating the magnitude and
frequency of floods in the Houston, Texas metropolitan area: U.S. Geological
Survey Water-Resources Investigations 80-17 (ADA-089 495).
Schroeder, E.E., and Massey, B.C., 1977, Techniques for estimating the magnitude
and frequency of floods in Texas: U.S. Geological Survey Water-Resources
Investigations Open-File Report 77-110.
Veenhuis, J.E., and Garrett, D.G., 1986, The effects of urbanization on floods in
the Austin metropolitan area, Texas: U.S. Geological Survey Water-Resources
Investigations 86-4069.
UTAH
Fields, F.K., 1974, Estimating streamflow characteristics for streams in Utah
using selected channel-geometry parameters: U.S. Geological Survey Water-
Resources Investigations 34-74 (PB-241 541/AS).
Thomas, B.E., and Lindskov, K.L., 1983, Methods for estimating peak discharges
and flood boundaries of streams in Utah: U.S. Geological Survey Water-Resources
Investigations 83-4129.
A4-S
-------
Appendix 4 - continued
State Hydrology Reports
VERMONT
Johnson, C.G., and Tasker, G.D., 1974, Flood magnitude and frequency of Vermont
Streams: U.S. Geological Survey Open-File Report 74-130.
VIRGIN ISLANDS
None listed
VIRGINIA
Anderson, D.G., 1970, Effects of urban development on floods in Northern
Virginia: U.S. Geological Survey Water-Supply Paper 2001-C.
Miller, E.M., 1978, Technique for estimating the magnitude and frequency of
floods in Virginia: U.S. Geological Survey Water-Resources Investigations Open-
File Report 78-5.
WASHINGTON
Cummans, J.E., Collins, M.R., and Nassar, E.G., 1974, Magnitude and frequency of
floods in Washington: U.S. Geological Survey Open-File Report 74-336.
Haushild, W.L., 1978, Estimation of floods of various frequencies for the small
ephemeral streams in eastern Washington: U.S. Geological Survey Water-Resources
Investigations 79-81.
WEST VIRGINIA
Runner, G.S., 1980, Technique for estimating the magnitude and frequency of
floods in West Virginia: U.S. Geological Survey Open-File Report 80-1218.
WISCONSIN
Conger, D.H., 1980, Techniques for estimating magnitude and frequency of floods
for Wisconsin streams: U.S. Geological Survey Water-Resources Investigations
Open-File Report 80-1214.
Conger, D.H., 1986, Estimating magnitude and frequency of floods for ungaged
urban streams in Wisconsin: U.S. Geological Survey Water-Resources Investigations
Report 86-4005.
WYOMING
Craig, G.S., Jr., and Rankl, J.G., 1977, Analysis of runoff from small drainage
basins in Wyoming: U.S. Geological Survey Water-Supply Paper 2056.
Lowham, H.W., 1976, Techniques for estimating flow characteristics of Wyoming
streams: U.S. Geological Survey Water-Resources Investigations 76-112
(PB-264 224/AS).
A4-9
-------
Appendix 5
Manning's "n" Values
The value of "n" may be computed by
n = (no + ni + n2 + n3 + n^mj
where: n0 =
n4
m5
basic "n" value for a straight, uniform, smooth channel
value added to correct for the effect of surface irregularities
value added for variation in the shape and size of the channel
cross section
value added for obstructions
value added for vegetation and flow conditions
correction factor for meandering of the channel
Proper values of n0 to n4 and m5 may be selected from the following table according to the
given conditions:
Channel Conditions
Material
involved
Degree of
irregularity
Variations of
channel cross
section
Relative
effect of
obstructions
Vegetation
Degree of
meandering
Earth
Rock cut
Fine gravel
Coarse gravel
Smooth
Minor
Moderate
Severe
Gradual
Alternating occasionally
Alternating frequently
Negligible
Minor
Appreciable
Severe
Low
Medium
High
Very High
Minor
Appreciable
Severe
Values
n0
ni
n2
n3
n4
ms
0.020
0.025
0.024
0.028
0.000
0.005
0.010
0.020
0.000
0.005
0.010-0.015
0.000
0.010-0.015
0.020-0.030
0.040-0.060
0.005-0.010
0.010-0.025
0.025-0.050
0.050-0.100
1.000
1.150
1.300
1.
REFERENCE
Chow, Ven Te, Ph.D.: "Open-Channel Hydraulics," McGraw-Hill Book Company,
New York, 1959, pp. 106-114.
The computed "n" values should be compared with the typical "n" values from the following
pages, or with those in the U.S. Geological Survey Report (Reference 2) or the Federal
Highway Administration Report (Reference 3).
A5-1
-------
Appendix 5 - continued
Manning's "n" Values
Type of channel and description
A. Closed Conduits Flowing Partly Full
A-l. Metal
a. Brass, smooth
b. Steel
1 . Lockbar and welded
2. Riveted and spiral
c. Cast iron
1. Coated
2. Uncoated
d. Wrought iron
1. Black
2. Galvanized
e. Corrugated metal
1 . Subdrain
2. Storm drain
A-2. Nonmetal
a. Lucite
b. Glass
c. Cement
1. Neat, surface
2. Mortar
d. Concrete
1 . Culvert, straight and free of
debris
2. Culvert with bends, connections,
and some debris
3. Finished
4. Sewer with manholes, inlet, etc.,
straight
5. Unfinished, steel form
6. Unfinished, smooth wood form
7. Unfinished, rough wood form
e. Wood
1. Stave
2. Laminated, treated
f. Clay
1 . Common drainage tile
2. Vitrified sewer
3 . Vitrified sewer with manholes,
inlet, etc.
4. Vitrified subdrain with open joint
g. Brickwork
1. Glazed
2. Lined with cement mortar
h. Sanitary sewers coated with sewage
slimes, with bends and connections
i. Paved invert, sewer, smooth bottom
j. Rubble masonry, cemented
Minimum
0.009
0.010
0.013
0.010
0.011
0.012
0.013
0.017
0.021
0.008
0.009
0.010
0.011
0.010
0.011
0.011
0.013
0.012
0.012
0.015
0.010
0.015
0.011
0.011
0.013
0.014
0.011
0.012
0.012
0.016
0.018
Normal
0.010
0.012
0.016
0.013
0.014
0.014
0.016
0.019
0.024
0.009
0.010
0.011
0.013
0.011
0.013
0.012
0.015
0.013
0.014
0.017
0.012
0.017
0.013
0.014
0.015
0.016
0.013
0.015
0.013
0.019
0.025
Maximum
0.013
0.014
0.017
0.014
0.016
0.015
0.017
0.021
0.030
0.010
0.013
0.013
0.015
0.013
0.014
0.014
0.017
0.014
0.016
0.020
0.014
0.020
0.017
0.017
0.017
0.018
0.015
0.017
0.016
0.020
0.030
A5-2
-------
Appendix 5 - continued
Manning's "n" Values
Type of channel and description
B. Lined or Built-up Channels
B-l. Metal
a. Smooth steel surface
1. Unpainted
2. Painted
b. Corrugated
B-2. Nonmetal
a. Cement
1. Neat, surface
2. Mortar
b. Wood
1 . Planed, untreated
2. Planed, creosoted
3. Unplaned
4. Plank with battens
5 . Lined with roofing paper
c. Concrete
1 . Trowel finish
2. Float finish
3 . Finished, with gravel on bottom
4. Unfinished
5. Gunite, good section
6. Gunite, wavy section
7. On good excavated rock
8. On irregular excavated rock
d. Concrete bottom float finished with
sides of
1 . Dressed stone in mortar
2. Random stone in mortar
3 . Cement rubble masonry, plastered
4. Cement rubble masonry
5. Dry rubble or riprap
e. Gravel bottom with sides of
1 . Formed concrete
2. Random stone in mortar
3 . Dry rubble or riprap
f. Brick
1. Glazed
2. In cement mortar
g. Masonry
1 . Cemented rubble
2. Dry rubble
h. Dressed ashlar
i. Asphalt
1. Smooth
2. Rough
j. Vegetal lining
Minimum
0.011
0.012
0.021
0.010
0.011
0.010
0.011
0.011
0.012
0.010
0.011
0.013
0.015
0.014
0.016
0.018
0.017
0.022
0.015
0.017
0.016
0.020
0.020
0.017
0.020
0.023
0.011
0.012
0.017
0.023
0.013
0.013
0.016
0.030
Normal
0.012
0.013
0.025
0.011
0.013
0.012
0.012
0.013
0.015
0.014
0.013
0.015
0.017
0.017
0.019
0.022
0.020
0.027
0.017
0.020
0.020
0.025
0.030
0.020
0.023
0.033
0.013
0.015
0.025
0.032
0.015
0.013
0.016
Maximum
0.014
0.017
0.030
0.013
0.015
0.014
0.015
0.015
0.018
0.017
0.015
0.016
0.020
0.020
0.023
0.025
0.020
0.024
0.024
0.030
0.035
0.025
0.026
0.036
0.015
0.018
0.030
0.035
0.017
0.500
A5-3
-------
Appendix 5 - continued
Manning's "n" Values
Type of channel and description
C. Excavated or Dredged
a. Earth, straight and uniform
1 . Clean, recently completed
2. Clean, after weathering
3 . Gravel, uniform section, clean
4. With short grass, few weeds
b. Earth, winding and sluggish
1 . No vegetation
2. Grass, some weeds
3 . Dense weeds or aquatic plants in
deep channels
4. Earth bottom and rubble sides
5. Stony bottom and weedy banks
6. Cobble bottom and clean sides
c. Dragline-excavated or dredged
1 . No vegetation
2. Light brush on banks
d. Rock cuts
1 . Smooth and uniform
2. Jagged and irregular
e. Channels not maintained, weeds and
brush uncut
1 . Dense weeds, high as flow depth
2. Clean bottom, brush on sides
3 . Same, highest stage of flow
4. Dense brush, high stage
D. Natural Streams
D-l. Minor streams (top width at flood stage
<100 ft)
a. Streams on plain
1 . Clean, straight, full stage, no rifts
or deep pods
2. Same as above, but more stones
and weeds
3 . Clean, winding, some pools and
shoals
4. Same as above, but some weeds
and stones
5. Same as above, lower stages,
more ineffective slopes and
sections
6. Same as 4, but more stones
7. Sluggish reaches, weedy, deep
pools
8. Very weedy reaches, deep pools,
or floodways with heavy stand of
timber and underbrush
Minimum
0.016
0.018
0.022
0.022
0.023
0.025
0.030
0.028
0.025
0.030
0.025
0.035
0.025
0.035
0.050
0.040
0.045
0.080
0.025
0.030
0.033
0.035
0.040
0.045
0.050
0.075
Normal
0.018
0.022
0.025
0.027
0.025
0.030
0.035
0.030
0.035
0.040
0.028
0.050
0.035
0.040
0.080
0.050
0.070
0.100
0.030
0.035
0.040
0.045
0.048
0.050
0.070
0.100
Maximum
0.020
0.025
0.030
0.033
0.030
0.033
0.040
0.035
0.040
0.050
0.033
0.060
0.040
0.050
0.120
0.080
0.110
0.140
0.033
0.040
0.045
0.050
0.055
0.060
0.080
0.150
A5-4
-------
Appendix 5 - continued
Manning's "n" Values
Type of channel and description
b. Mountain streams, no vegetation in
channel, banks usually steep, trees
and brush along banks submerged at
high stages
1. Bottom: gravels, cobbles, and
few boulders
2. Bottom: cobbles with large
boulders
D-2. Floodplains
a. Pasture, no brush
1 . Short grass
2. High grass
b. Cultivated areas
1 . No crop
2. Mature row crops
3 . Mature field crops
c. Brush
1 . Scattered brush, heavy weeds
2. Light brush and trees, in winter
3 . Light brush and trees, in summer
4. Medium to dense brush, in winter
5. Medium to dense brush, in
summer
d. Trees
1 . Dense willows, summer, straight
2. Cleared land with tree stumps, no
sprouts
3 . Same as above, but with heavy
growth of sprouts
4. Heavy stand of timber, a few
down trees, little undergrowth,
flood stage below branches
5. Same as above, but with flood
stage below branches
D-3. Major streams (top width at flood stage
>100 ft). The n value is less than that
for minor streams of similar description,
because banks offer less effective
resistance.
a. Regular section with no boulders or
brush
b. Irregular and rough section
Minimum
0.030
0.040
0.025
0.030
0.020
0.025
0.030
0.035
0.035
0.040
0.045
0.070
0.110
0.030
0.050
0.080
0.100
0.025
0.035
Normal
0.040
0.050
0.030
0.035
0.030
0.035
0.040
0.050
0.050
0.060
0.070
0.100
0.150
0.040
0.060
0.100
0.120
Maximum
0.050
0.070
0.035
0.050
0.040
0.045
0.050
0.070
0.060
0.080
0.110
0.160
0.200
0.050
0.080
0.120
0.160
0.060
0.100
REFERENCES
1. Chow, Ven Te, Ph.D.: "Open-Channel Hydraulics," McGraw-Hill Book Company, New York, 1959, pp.
106-114.
2. Geological Survey, Roughness Characteristics of Natural Channels. Water-Supply Paper 1849,
Washington, D.C., 1967.
3. Department of Transportation, Federal Highway Administration, Guide for Selecting Manning's
Roughness Coefficients for Natural Channels and Floodplains. Report No. FHWA-TS-84-204, McLean,
Virginia, April 1984.
A5-5
-------
Appendix 6
QUICK-2 Computer Program Manual
A6-1
-------
QUICK-2
Computer Program
COMPUTATION OF WATER SURFACE
ELEVATIONS IN OPEN
CHANNELS
VERSION 1.0
JANUARY 1995
-------
QUICK-2
Computation of Water Surface Elevations in Open Channels
User's Guide
Federal Emergency Management Agency
1995
-------
TABLE OF CONTENTS
Chapter 1: INTRODUCTION 1-1
Chapter 2: OVERVIEW 2-1
Chapter 3: GETTING STARTED 3-1
Chapter 4: TUTORIALS 4-1
Normal Depth 4-2
Changing Variables 4-6
Step-Backwater 4-9
Running HEC-2 with QUICK-2 Files 4-18
Rerunning Using Saved Cross-Section Files 4-19
Channel Capacity 4-21
Rating Curve Plot 4-22
PLOT-2 4-23
Profile Plot 4-23
Cross-Section Plot 4-24
Chapter 5: FORMULAS 5-1
Critical Depth 5-2
Channel Capacity 5-4
Normal Depth 5-5
Step-Backwater 5-6
Appendix 1: DEFINITION OF VARIABLES A- 1
-------
QUICK-2 User's GuideIntroduction
Chapter 1: Introduction
QUICK-2 is a user friendly program that assists in the computation of flood
Water Surface Elevations (WSEs) in open channels of all types. It is much
easier to use than the United States Army Corps of Engineers (USAGE) HEC-2
program. However, a QUICK-2 step-backwater file can also be used, as is,
with the HEC-2 program, which is also included in the QUICK-2 package of
programs. Therefore a HEC-2 output file can be generated with a QUICK-2 input
data file, without ever leaving the QUICK-2 environment; and, without having
to know how to set-up and run the HEC-2 program. This version of QUICK-2
(Version 1.0) however, does not perform hydraulic calculations through
bridges or culverts.
QUICK-2 was primarily developed to accompany the FEMA technical guidance
manual titled, "MANAGING FLOODPLAIN DEVELOPMENT IN ZONE A AREAS - A GUIDE FOR
OBTAINING AND DEVELOPING BASE FLOOD ELEVATIONS." That manual is intended to
assist local community officials who are responsible for administering and
enforcing the floodplain management requirements of the National Flood
Insurance Program (NFIP). The purpose of that manual is to provide guidance
for obtaining and developing base flood (100-year) elevations (BFEs) where
Special Flood Hazard Areas (SFHAs) on a community's Flood Hazard Boundary Map
(FHBM) or Flood Insurance Rate Map (FIRM) have been identified and designated
as Zone A.
QUICK-2 will also be useful to community engineers, architect/engineer firms,
developers, builders and others at the local level who may be required to
develop BFEs for use in Special Flood Hazard Areas.
This manual includes four other chapters: Overview, Getting Started,
Tutorials and Formulas. The Formulas section describes the "complex"
equations and methodologies used in the development of the program. An
Appendix is also included that contains a list of Definitions of the
variables shown on the screen and on the printouts.
To get started as quickly as possible in using QUICK-2 we
recommend that the user read the Overview and Getting
Started chapters; and then work through the Tutorials.
MINIMUM SYSTEM REQUIREMENTS
Random Access Memory (RAM) - 512K
Hard disk storage - 800K
Monitor - Color or Monotone
Printer (prints to LPT1) - Dot-matrix to LaserJet
Disk Operating System (DOS) - Version 3.0 or higher
1-1
-------
QUICK-2 User's Guide Overview
Chapter 2: Overview
FOUR OPTIONS
This user friendly program computes:
• Critical Depth,
• Cross Section Capacity (Rating Curves),
• .Normal Depth, and
• Step-Backwater Analysis (similar to the USAGE HEC-2 program)
CRITICAL DEPTH: This option should be used to determine a Base Flood
Elevation (BFE) if a previous calculation using the Normal Depth option
computed a depth that was determined to be SUPERCRITICAL. Super Critical
depths are generally not accepted for use as BFEs.
CHANNEL CAPACITY: This option is used to determine a rating curve for a cross
section. The program computes a discharge based on the entered depth.
Repeating with other depths produces a rating curve. A BFE may be determined
by interpolation with the correct discharge.
NORMAL DEPTH: This is the usual option to use in determining BFEs. The user
should watch the "Flow Type" message to make sure that the calculation is
CRITICAL or SUBCRITICAL. Use Option 1 if SUPERCRITICAL.
STEP-BACKWATER: This option should be used to calculate BFEs if more than one
cross-section is warranted to cover the extent of the property. Generally if
the property parallels more than 500 feet of a flooding source this option
should be used.
HANDLES "REGULAR" AND "IRREGULAR" SHAPED CROSS SECTIONS
The REGULAR shape cross-sections include:
• V-shape,
• Trapezoidal,
• Rectangular, and
• Circular
2-1
-------
QUICK-2 User's Guide Overview
For IRREGULAR cross-sections:
- up to 40 points can be input to describe the ground points
- Ground points are easily modified using the Insert or Delete Keys
- Encroachments or other changes in the floodplain are easily modeled
- An unlimited number of cross sections may be modeled
In addition, ground points and other input variables for the irregular shape
cross-sections can be saved to a file, for later use.
SINGLE SCREEN DATA INPUT, COMPUTATION AND OUTPUT
One of the most user-friendly aspects of this program that sets it apart from
many other computational programs is that all of the data input, the
computation, and the printing or plotting, is performed from the same screen.
You will not get lost in a maze of menus.
GRAPHICS
• Cross-Section Plots,
• Water Surface Elevation Profiles, and
• Rating Curve Plots
Cross section plots and water surface elevation profiles from QUICK-2's step-
backwater analysis can be viewed on the screen using the USAGE PLOT-2 program
that comes with the QUICK-2 package of programs. The channel capacity option
of QUICK-2 can be used to generate rating curve plots of individual cross
sections that can be viewed on screen and printed.
AUTOMATIC ERROR CHECKING
This software is designed to virtually eliminate the need for user's manuals.
The program incorporates error-checking routines and warning messages to
alert the user to incorrect input data or potentially incorrect output data.
The program prompts the user for the required input data so that there is no
need to worry about which columns to put data in; whether or not it needs to
be left-justified, or right- justified, etc.
2-2
-------
QUICK-2 User's Guide Overview
SPECIAL FEATURES OF QUICK-2
»» Critical Depth, Channel Capacity, and Normal Depth Options ««
EASY VIEW: All of the input data is viewed on the same screen (and changes
can be made) before starting the computations
EASY CHANGE: After an initial calculation, the following parameters can be
changed, and the above options can be re-calculated in seconds:
Discharge Channel Slope Manning's N
Base width or Diameter Channel Side Slope Ground Points
Channel Stations
AUTO-SAVE: For irregular channels the program automatically stores all the
input variables to a file designated as "TEMP.XSC", which is stored in the
C:\QUICK2\DATA Directory.
RATING CURVES: A special feature of the Channel Capacity Option for irregular
channels is the Rating Curve Print Option. A rating curve plot can be
automatically generated with 20 computations of water surface elevation
versus discharge. The maximum elevation of the rating curve will be just
lower than the channel depth specified by the user. The rating curve can be
viewed on the screen and/or printed.
»» Step-Backwater Option ««
EASY VIEW: All of the input data is viewed on the same screen (and changes
can be made) before starting the computations
PRECISE: Balances the energy equation to within .01 foot.
COMPUTES CRITICAL DEPTH AUTOMATICALLY: After up to 40 energy balance trials
(without a balance) the program automatically computes critical depth.
OUTPUT OPTIONS: Detailed and Summary printouts are available
AUTO-SAVE: The program automatically saves the first cross-section into a
file designated as TO.XSC, and subsequent cross-sections are saved adding the
Channel distance (XLCH) to the previous cross-section's file name.
Therefore, if we run 3 cross-sections that are 200 feet apart their
filenames will be: TO.XSC, T200.XSC, and T400.XSC. These files are
automatically stored in a directory named C:\QUICK2\DATA.
HEC-2 RUNS WITH QUICK-2 FILES: The backwater option also automatically saves
all of the cross-sections into a HEC-2 compatible file called HEC2.DAT, which
is stored in the C:\QUICK2 Directory. The QUICK-2 program is linked with the
USACE HEC-2 program such that any backwater computation that is run using
QUICK-2 can also be run using the HEC-2 program within the QUICK-2
environment. The user does not need to have any previous experience in
running the HEC-2 model.
2-3
-------
QUICK-2 User's Guide Overview
AUTOMATIC ERROR CHECKS AND WARNING MESSAGES
ERROR CHECKS
Error checks prevent the user from continuing by re-prompting the user for
correct input data. The following are error checks performed automatically by
the program:
- Ground Point (GR) stations should be increasing
- Stations of the left and right bank should match a GR point
WARNING MESSAGES
Warning messages instruct the user that the program has had to modify the
input data in order to complete a calculation, or that the completed
calculation may not be valid. The following are warning messages performed by
the program:
Extended Cross Section
The computed water surface elevation is higher than one or both ends of the
cross-section, and the program automatically extended the end(s) of the
cross-section vertically to complete the computation.
Divided Flow
There is a ground point(s) within the cross-section which is higher than the
computed water surface elevation which is dividing the flow within the cross-
section.
No Energy Balance ... Computing Critical Depth
The program attempted up to 40 trial computations and could not arrive at an
energy balance; and therefore, critical depth is assumed to occur at this
cross-section.
Computing Critical Depth ... Critical Depth Assumed
Either the initial Starting Water Surface Elevation or an energy balance
between two cross sections occurred at an elevation for which the froude
number or the index froude number was equal to or greater than 1. Thus, the
computed water surface elevation is suspected of being below the critical
depth. Therefore the critical depth is computed and compared to the previous
calculated water surface elevation. In this case the critical depth elevation
was higher, and thus Critical Depth is Assumed.
Computing Critical Depth ... Critical Depth Not Assumed
Same as above except, the critical depth is computed and compared to the
previous calculated water surface elevation; and, in this case the critical
depth elevation was lower, and thus Critical Depth is Not Assumed.
2-4
-------
QUICK-2 User's Guide
Getting Started
Chapter 3: Getting Started
This section provides you with
convenient installation and run
procedures that will enable you
to run the program from the hard
disk drive or the floppy disk drive.
HARD DISK INSTALLATION AND RUN PROCEDURE
To install and run QUICK-2 simply place the floppy disk in either your "A"
disk drive or your "B" disk drive.
For "A" Drive users: Type A:\AQ2 and Press
For "B" Drive users: Type B:\BQ2 and Press
Follow the screen message to start the program. That's it!
The program resides in a C:\QUICK2 directory. To run the program in the
future, just change to that directory and type Q2 and press .
FLOPPY DISK INSTALLATION AND RUN PROCEDURE
To install and run QUICK-2 from the floppy disk drive simply place the floppy
disk in either your "A" disk drive or your "B" disk drive.
For "A" Drive users: Type A:\FAQ2 and Press
For "B" Drive users: Type B:\FBQ2 and Press
Follow the screen message to start the program. That's it!
To run the program in the future, just place the disk in your floppy drive,
change to that directory and type Q2 and press . Although the
program will run from the floppy disk drive it will run much faster if
installed and run on the hard disk drive.
REMINDER:
Entering and editing data, as well as moving around within the input screens
is performed using the Function keys, the Backspace Key and the Enter Key. DO
NOT USE THE CURSOR CONTROL KEYS (ARROW KEYS) FOR ENTERING, DELETING, OR
EDITING DATA.
3-1
-------
Chapter 4: TUTORIALS
Normal Depth
Step-Backwater
Channel Capacity
PLOT - 2
{TIME REQUIRED TO COMPLETE ALL THE TUTORIALS is ABOUT ONE HOUR}
4-1
-------
QUICK-2 User's Guide
Normal Depth Tutorial
NORMAL DEPTH {Tutorial Time: 5 to 10 minutes}
After pressing Q2 and to start the program you will come to
Main Menu screen of QUICK-2 as shown below.
the
QUICK - 2
MAIN MENU
Critical Depth
Channel Capacity
Normal Depth
Step -Backwater
QUIT
11 .
Press
1
2
3
4
1
MI
| - Help ||
1. Press 3 and then press to start the Normal Depth Option.
Next you will see the Shape of Cross Section screen:
SHAPE OF CROSS SECTION
V - Ditch
Rectangular Channel
Trapezoidal Channel
Circular Channel
Irregular Channel
| - Main Menu ||
Let's try the Trapezoidal Channel option.
2. Press T and then press to perform a Normal Depth calculation
for a trapezoidal channel.
4-2
-------
QUICK-2 User's Guide
Normal Depth Tutorial
The next screen you will see is the Input / Output screen
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
L Side Slope (H:V)
Bottom Width (ft)
Discharge (cfs)
Slope (ft/ft)
R Side Slope (H:V)
Manning's n
Depth (ft)
OUTPUT VARIABLES
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
Wet Perimeter (ft)
Hyd Radius
Froude #
Flow Type
Enter Left Side Slope and Press :1
<- Back Tab Main Menu
The program is currently prompting you to enter the Left Side Slope (in
terms of the Number of Horizontal feet (H) to every 1 foot Vertical (H :
1). Let's say our left side slope is 3 to 1 (3:1).
3. Enter 3 and then Press .
The next screen you will see is the Input / Output screen with a new
prompt:
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
L Side Slope (H:V) 3.0:1 R Side Slope (H:V)
Bottom Width (ft)
Discharge (cfs)
Slope (ft/ft)
Manning's n
Depth (ft)
OUTPUT VARIABLES
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
Wet Perimeter (ft)
Hyd Radius
Froude #
Flow Type
Enter Right Side Slope and Press :1
<- Back Tab Main Menu
Notice that the
3 has been entered
to the right of
"L Side Slope (H:V)"
The program is currently prompting you to enter the Right Side Slope (in
terms of the Number of Horizontal feet (H) to every 1 foot Vertical (H :
1). Let's say our right side slope is 2 to 1 (2:1).
4. Enter 2 and then Press .
4-3
-------
QUICK-2 User's Guide
Normal Depth Tutorial
1 NORMAL DEPTH
1 TRAPEZOIDAL CHANNEL
jj INPUT
1 L Side Slope (H:V) 3.0:1
1 Bottom Width (ft)
1 Discharge (cfs)
1 Slope (ft/ft)
||
1 OUTPUT
1 Area (sq ft)
1 Velocity (ft/s)
1 Top Width (ft)
jj
I <- Back Tab
VARIABLES
R Side Slope (H:V) 2.0:1
Manning ' s n
Depth (ft)
VARIABLES
Wet Perimeter (ft)
Hyd Radius
Froude #
Flow Type
Main Menu
The program will continue to prompt you for input data.
Let's say our channel is 10 feet wide, with a Manning's n value of 0.035,
the discharge is 300 cfs, and the channel slope is .005 ft/ft.
SCREEN PROMPT - "Enter Bottom Width and Press "
5. Enter 10 and then Press .
SCREEN PROMPT - "Enter Manning's n and Press "
6. Enter .035 and then Press .
SCREEN PROMPT - "Enter Discharge and Press "
7. Enter 300 and then Press .
SCREEN PROMPT - "Enter Slope and Press "
8. Enter .005 and then Press .
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
II ^H
II
L Side Slope (H:V) 3.0:1
Bottom Width (ft) 10.0
Discharge (cfs) 300
Slope (ft/ft) 0.0050
R Side Slope (H:V) 2.0:1
Manning's n 0.0350
Depth (ft) 0.00
OUTPUT VARIABLES
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
Wet Perimeter (ft)
Hyd Radius
Froude #
Flow Type
Begin Calculations
<- Back Tab
Main Menu
After all the data
is input your screen
should look like this
4-4
-------
QUICK-2 User's Guide
Normal Depth Tutorial
To begin the calculation simply ...
9. Press .
After a split second the screen should look like this:
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
| L Side Slope (H:V) 3.0:1 R Side Slope
| Bottom Width (ft) 10.0 Manning's n
| Discharge (cfs) 300 Depth (ft)
| Slope (ft/ft) 0.0050
(H:V) 2.0:1 |
0.0350 |
3.27 |
1
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
OUTPUT VARIABLES
59.6 Wet Perimeter (ft) 27.7
5.0 Hyd Radius 2.2
26.4 Froude # 0.6
•
•
•
1
Flow Type SUBCRITICAL
Begin Calculations
Print
<- Back Tab
Main Menu
1
Notice that the Depth is no longer 0.00, but equals 3.27 feet, which is the
Normal Depth for this particular Trapezoidal cross-section. If 300 cfs
represents the 100-year discharge, then the 100-year flood depth would
equal 3.27 feet. All of the output variables have also been computed and
listed.
10. To print the output simply Press the Function key.
The printed output is shown below.
QUICK - 2
NORMAL DEPTH
Trapezoidal Channel
INPUT VARIABLES
n = 0.035
Depth
3.0 \
v
\
Base Width = 10.0
Slope = 0.0050
/ 2.0
4-5
OUTPUT VARIABLES
Depth (ft) 3.27
Discharge (cfs) 300.0
Velocity (ft/s) 5.04
Top Width (ft) 26.4
Froude No. 0.59
Flow Type: SUBCRITICAL
-------
QUICK-2 User's Guide
Normal Depth Tutorial
CHANGING THE VARIABLES
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
L Side Slope (H:V) 3.0:1
Bottom Width (ft) 10.0
Discharge (cfs) 300
Slope (ft/ft) 0.0050
R Side Slope
Manning's n
Depth (ft)
(H:V) 2.0:1
0.0350
3.27
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
OUTPUT VARIABLES
59.6 Wet Perimeter (ft) 27.7
5.0 Hyd Radius 2.2
26.4 Froude # 0.6
I
1
Flow Type SUBCRITICAL
Begin Calculations
Print
<- Back Tab
Main Menu
1
Let's say we want to run this calculation again but with a discharge of 500
cfs instead of 300 cfs.
1. Press the Function Key
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT
L Side Slope (H:V) 3.0:1
Bottom Width (ft) 10.0
Discharge (cfs) 300
Slope (ft/ft) 0.0050
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
Enter Slope and
<- Back Tab
OUTPUT
59.6
5.0
26.4
Press
VARIABLES
R Side Slope (H:V)
Manning ' s n
Depth (ft)
VARIABLES
2.0:1
0.0350
3.27
Wet Perimeter (ft) 27.7
Hyd Radius 2.2
Froude # 0.6
Flow Type SUBCRITICAL
Main Menu
The above screen is what you should be looking at. The key will move
the prompt backwards through all the variables. Note that since we want to
change the Discharge (from 300 to 500) , we will need to Press again to
come to the Enter Discharge prompt. Follow the steps as shown on the
following page to rerun this calculation with a new discharge.
4-6
-------
QUICK-2 User's Guide
Normal Depth Tutorial
SCREEN PROMPT - "Enter Slope and Press "
2. Press .
SCREEN PROMPT - "Enter Discharge and Press "
3. Enter 500 and then Press .
SCREEN PROMPT - "Enter Slope and Press "
4. Press .
After all of the data is input your screen should look like this
NORMAL DEPTH
TRAPEZOIDAL CHANNEL
INPUT VARIABLES
L Side Slope (H:V) 3.0:1
Bottom Width (ft) 10.0
Discharge (cfs) 500
Slope (ft/ft) 0.0050
R Side Slope (H:V) 2.0:1
Manning's n 0.0350
Depth (ft) 3.27
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
OUTPUT VARIABLES
59.6 Wet Perimeter (ft) 27.7
5.0 Hyd Radius 2.2
26.4 Froude # 0.6
Flow Type SUBCRITICAL
Begin Calculations
<- Back Tab
Main Menu
4-7
-------
QUICK-2 User's Guide
Normal Depth Tutorial
5. Press to begin the calculation.
After a split second the screen should look like this
NORMAL DEPTH ||B
| TRAPEZOIDAL CHANNEL ||B
1 «•
| INPUT VARIABLES ||B
•
L Side Slope (H:V)
Bottom Width (ft)
Discharge (cfs)
Slope (ft/ft)
3.0:1
10.0
500
0.0050
OUTPUT
Area (sq ft)
Velocity (ft/s)
Top Width (ft)
Begin Calculations
| Print
j <- Back Tab
86.7
5.8
31.1
R Side Slope (H:V) 2.0:1
Manning's n 0.0350
Depth (ft) 4.22
VARIABLES
Wet Perimeter (ft) 32.8
Hyd Radius 2 . 6
Froude # 0.6
Flow Type SUBCRITICAL
III
Main Menu
•
•
•
•
•
•
•
•
•
•
•
Let's return to the Main Menu.
Just Press the Function Key
QUICK - 2
MAIN MENU
Critical Depth
Channel Capacity
Normal Depth
Step-Backwater
-II
I
QUIT
^ If you want to continue and to perform the Step-Backwater
Tutorial, then turn to the next page.
If you want to exit out of the program for now, Press
4-8
-------
QUICK-2 User's Guide
Step-Backwater Tutorial
STEP-BACKWATER {Tutorial Time: 20 to 25 minutes}
Let's say that we have a piece of property located in an unnumbered Zone A,
and we need to determine if our property is really in or out of the
floodplain. We will be referring to Figure 1 on the next page which
represents a plan view of our proposed floodplain study (step-backwater
analysis) . We have field surveyed 3 cross-sections to use in the step-
backwater analysis. The next page lists all of the data from the field
surveyed cross-sections.
If you have continued from the previous Normal Depth Tutorial you should
see the screen below. If you are just starting the program, you will see
the screen below after pressing Q2 and .
QUICK - 2
MAIN MENU
Critical Depth
Channel Capacity
Normal Depth
Step-Backwater
QUIT
1. Press 4 and then press to start the Step-Backwater Option.
Next you will see the Starting Water Surface Elevation Method screen:
Starting Water Surface Elevation Method
Input
NORMAL DEPTH (Slope-Area)
Enter the Slope in Ft/Ft
(for ex. .0025)
KNOWN WATER SURFACE ELEVATION
Enter the known WS Elevation (for ex. 656.78)
Enter a Slope or an Elevation:
Let's say that we do not have any previous information about flood
elevations for our sample stream. Thus we need to start the step-backwater
analysis assuming that the flow in our first cross-section is at Normal
Depth. (This assumes that the channel slope downstream of our first cross-
section will approximate the slope of the energy grade at the first cross-
section of our study.) Let's assume that our calculated downstream channel
slope is .0029 ft/ft.
2. Type .0029 and then press .
4-9
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QUICK-2 User's Guide
Step-Backwater Tutorial
CROSS SECTION INFORMATION
Cross-Section 1
GROUND POINTS
Station Elevation
362 505.0
425 499.1
509 498.0
512 496.9
602 496.9
605 498.2
732 500.1
1020 504.7
CHANNEL BANK STATIONS
Left 509 Right 605
MANNING ' S N VALUES
Left .065
Channel .040
Right .060
CHANNEL REACH LENGTHS
Left
Channel
Right
LOSS COEFFICIENTS
Cont Expan
100 -YEAR DISCHARGE
3000
Cross-Section 2
Station Elevation
0 510.0
150 504.8
233 502.2
236 500.9
331 500.9
334 501.8
402 505.5
591 510.1
Left 233 Right 334
Left .055
Channel .040
Right .060
Left 450
Channel 450
Right 450
Cont 0 . 1 Expan 0 . 3
3000
Cross-Section 3
Station Elevation
0 515.0
433 510.1
600 506.3
614 504.9
701 504.8
725 506.5
866 511.1
1240 514.6
Left 600 Right 725
Left .065
Channel .040
Right .060
Left 490
Channel 490
Right 490
Cont 0 . 1 Expan 0 . 3
3000
FIGURE 1
X SEC. 450
X SEC. 940
4-10
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QUICK-2 User's Guide
Step-Backwater Tutorial
The next screen you will see is the Input / Output screen as shown:
XSEC ID:
STAT
CHANNEL
MANNING
CHANNEL
LOSS
WS ELEV
EG ELEV
0 » STEP -
ELEV STAT ELEV
BANK STATIONS: Left
S N VALUES: Left
REACH LENGTHS: Left
COEFFICIENTS: Contractn
Depth
Flow Regime
BACKWATER «
STAT ELEV
Channel
Channel
Expansn
Top Wid
ChanVel
GROUND POINTS
STAT ELEV
Right
Right
Right
:Dschrg
Kratio
Froudtt
||F2}<-Back Tab F5}List Files F6}Retrieve File F7}Main Menu F10}Ed/Ex GrPt| |
F3}lnsert GrPt F4}Delete GrPt Fl }HELP
Before we go on let's read about how data is to be input for this screen.
3. Press to access the Help screen.
4. When you are finished reading the Help screen just Press .
If you refer to the previous page, you will see a tabulation of the Ground
Points for the first field surveyed cross-section listed by Station and
Elevation. You will also see the Channel Bank Stations, Manning's N values,
and Discharge.
5. Following the method explained in the Help Screen, enter the Ground
Points one at a time, by their respective Station and Elevation. Be sure to
Press after you have typed in each correct number.
Once you have entered all of the Ground Points correctly ...
6. Press to Exit from entering Ground Point data
NOTE: The Key will EXIT you from the top of the screen, or it will
RETURN you to the top of the screen if you need to go back to EDIT the
Ground Points.
4-11
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QUICK-2 User's Guide
Step-Backwater Tutorial
Your screen should now look like this:
XSEC ID:
STAT
362
602
CHANNEL
MANNING '
CHANNEL
LOSS
WS ELEV
EG ELEV
0
» STEP - BACKWATER «
ELEV STAT ELEV STAT ELEV
505.0 425
496.9 605
BANK STATIONS
S N VALUES
REACH LENGTHS
COEFFICIENTS
499.1 509 498.0
498.2 732 500.1
: Left
: Left Channel
: Left Channel
: Contractn Expansn
Depth Top Wid
Flow Regime ChanVel
1 F2}<- Back Tab F5}PRINT F6}SAVE F7}Main Menu F8}New XSEC
1 Enter LEFT Channel
Bank Station and Press
GROUND POINTS
STAT
512
1020
Right
Right
Right
Dschrg
Kratio
Froudtt
F10}Ed/Ex
ELEV
496.9
504.7
II
GrPt 1
1
The program is currently prompting you to enter the Left Channel Bank
Station. Using the information contained on the previous page, we know that
our Left Channel Bank Station is 509. Therefore ...
7. Enter 509 and then Press . (Notice that the 509 has been
entered to the right of "CHANNEL BANK STATIONS: Left".)
Next you will see the Input / Output screen with a new prompt:
SCREEN PROMPT - "Enter RIGHT Channel Bank Station and Press "
Using the information for Cross-section 1, simply follow the screen prompts
to input the required data, as follows:
SCREEN PROMPT - "Enter RIGHT Channel Bank Station and Press "
8. Type 605 and then Press .
SCREEN PROMPT - "Enter LEFT Manning's n Value and Press "
9. Type .065 and then Press .
SCREEN PROMPT - "Enter CHANNEL Manning's n Value and Press "
10. Type .040 and then Press .
SCREEN PROMPT - "Enter RIGHT Manning's n Value and Press "
11. Type .060 and then Press .
SCREEN PROMPT - "Enter Discharge and Press "
12. Type 3000 and then Press .
4-12
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QUICK-2 User's Guide
Step-Backwater Tutorial
Your screen should now look like this:
XSEC ID: 0
STAT ELEV STAT
362 505.0 425
602 496.9 605
CHANNEL BANK STATIONS:
MANNING ' S N VALUES :
CHANNEL REACH LENGTHS:
» STEP - BACKWATER «
ELEV STAT ELEV
499.1 509 498.0
498.2 732 500.1
Left 509.0
Left 0.0650 Channel 0.0400
Left Channel
LOSS COEFFICIENTS: Contractn Expansn
WS ELEV Depth Top Wid
EG ELEV Flow Regime ChanVel
F2}<- Back Tab F5} PRINT
TO BEGIN CALCULATIONS
F6}SAVE F7}Main Menu F8}New XSEC
Press
GROUND POINTS
STAT ELEV
512 496.9
1020 504.7
Right 605.0 |
Right 0.0600 |
Right I
Dschrg 3000 |
Kratio ||
Froudtt ||
II
F10}Ed/Ex GrPt|
!!
The program is now ready to begin the calculations since all of the
required data has been entered for the 1st cross-section of our step-
backwater analysis. Note that even at this point, if any of the data on the
screen has been typed in incorrectly, the user can simply press the
key to toggle backwards through all of the input data, even back to the
Ground Points. Remember that you can instantly go back to the Ground Points
by pressing , also.
13. Press to Begin the Calculations.
Your screen should now look like this:
XSEC ID: 0 » STEP - BACKWATER « GROUND POINTS
STAT ELEV STAT ELEV STAT ELEV STAT ELEV
362 505.0 425 499.1 509 498.0 512 496.9
602 496.9 605 498.2 732 500.1 1020 504.7
CHANNEL BANK STATIONS: Left 509.0
MANNING'S N VALUES: Left 0.0650
CHANNEL REACH LENGTHS: Left
LOSS COEFFICIENTS: Contractn
WS ELEV 501.03 Depth 4.13
EG ELEV 501.32 Flow Regime M-l
F2}<- Back Tab F5}PRINT F6}SAVE F7}Main
1
Right 605.0
Channel 0.0400 Right 0.0600
Channel Right
Expansn :Dschrg 3000
Top Wid 385 Kratio 1.00
ChanVel 5.10 Froudtt 0.50
Menu F8}New XSEC F10}Ed/Ex GrPt|
1
As you can see from the screen, the (100-year) Water Surface Elevation (WS
ELEV) has been computed (501.03), with other variables.
4-13
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QUICK-2 User's Guide
Step-Backwater Tutorial
Before we move on to enter the data for the next cross-section let's obtain
a printout of this first calculation.
Press .
The screen prompt will be ...
PRINT: Summary or Detailed? Press S or D
and
Let's obtain a Detailed Printout .
Press D and then Press .
Therefore
Assuming your printer is turned on, the detailed printout will look like
this:
H Cross Section: 0
I XLOB: 0 XLCH: 0 XROB: 0 CC: 0 CE: 0
I NLOB: .065 STCHL: 509 NCHL: .04 STCHR: 605 NROB: .06
i STAT
1 362.00
1 602.00
1 CWSEL
1 Chan Vel
1 Depth
1 Discharge
1 501.03
I 5.10
1 4.13
1 3000
ELEV STAT ELEV
505.00 425
496.90 605
EG
HV
HL
OL
501.317
0.29
0.00
0.00
.00 499.10
.00 498.20
ELMIN
KRATIO
Top Width
Froude #
496.90
1.00
385
0.50
STAT
509.00
732.00
QLOB
ALOB
STAT-L
CH- Slope
493
228
404.4
0.0000
If any of the above variables are unfamiliar,
provided in Appendix 1 .
ELEV STAT ELEV
498.00 512
500.10 1020
QCH
ACH
ST-MIDCH
EG-Slope
2003
392
557.0
0.0029
a description
.00 496.90
.00 504.70
QROB
AROB
STAT-R
FlowRegim
505
265
789.9
of each is
If you want to save the cross-section data to a different name and/or
directory, before pressing , you can Press , (F6}SAVE) , to
perform this.
Now we need to enter the data for the 2nd cross-section. Since we are
entering a new cross-section (New XSEC), we need to ...
_ Press .
Before the Screen changes you will notice that at the bottom of the screen
a message will briefly appear ...
SAVING TEMPORARY FILE C:\QUICK2\DATA\TO.XSC
This alerts you that your cross-section data has been saved to a
called TO.XSC, which is located in your C:\QUICK2\DATA directory.
4-14
file
-------
QUICK-2 User's Guide
Step-Backwater Tutorial
Your screen should be blank again as shown below:
XSEC ID: 0 » STEP - BACKWATER « GROUND POINTS
STAT ELEV STAT ELEV STAT ELEV STAT ELEV
CHANNEL BANK STATIONS: Left
MANNING'S N VALUES: Left
CHANNEL REACH LENGTHS: Left
LOSS COEFFICIENTS: Contractn
WS ELEV Depth
EG ELEV Flow Regime
|F2}<-Back Tab F5}List Files F6}Retrieve
1 F3} Insert GrPt F4}Delete GrPt
Right
Channel Right
Channel Right
Expansn :Dschrg
Top Wid Kratio
ChanVel Froudtt
File F7}Main Menu F10}Ed/Ex GrPt |
Fl }HELP 1
_ Following the method used before, for the 1st cross-section, enter the
Ground Points one at a time, by their respective Station and Elevation for
the 2nd cross-section using the data provided. Be sure to Press
after you have typed in each correct number.
_ Once you have entered all of the Ground Points correctly, remember to
Press to Exit from entering Ground Point data .
_ Follow the on screen prompts to enter all of the other data.
Remember that if any of the data on the screen has been typed in
incorrectly, the user can simply press the key to toggle backwards
through all of the input data, even back to the Ground Points. (You can
also Press to go back to the Ground Points immediately for editing).
After entering all the data your screen should now look like this:
XSEC ID:
STAT
0
331
1 CHANNEL
1 MANNING
1 CHANNEL
1 LOSS
I WS ELEV
EG ELEV
450 » STEP -
ELEV STAT ELEV
510.0 150 504.8
500.9 334 501.8
BANK STATIONS: Left
S N VALUES: Left
REACH LENGTHS: Left
COEFFICIENTS: Contractn
Depth
Flow Regime
BACKWATER «
STAT ELEV
233 502.2
402 505.5
233.0
0.0550 Channel 0.0400
450 Channel 450
0 . 1 Expansn 0 . 3
Top Wid
ChanVel
GROUND
STAT
236
591
Right
Right 0
Right
:Dschrg
Kratio
Froudtt
POINTS
ELEV
500.9
510.1
334.0 1
.0600 1
450 1
3000 1
II F2}<- Back Tab F5}PRINT F6}SAVE F7}Main Menu F8}New XSEC F10}Ed/Ex GrPt| |
TO BEGIN CALCULATIONS Press
4-15
-------
QUICK-2 User's Guide Step-Backwater Tutorial
The program is now ready to begin the calculations since all of the
required data has been entered for the 2nd cross-section of our step-
backwater analysis.
_ Press to Begin the Calculations.
Once the calculation is finished you may ...
_ Press to obtain a printout
Press to save the data to another name and/or directory
Finally, to finish our analysis we need to enter in the data for the 3rd
cross-section.
_ Press
Before the Screen changes you will notice that at the bottom of the screen
a message will briefly appear ...
SAVING TEMPORARY FILE C:QUICK2\DATA\T450.XSC
This alerts you that your 2nd cross-section data has been saved to a file
called T450.XSC, which is located in your C:\QUICK2\DATA directory. Notice
that the 450, represents the channel distance between the 1st and 2nd
cross-sections.
_ Following the method used before for the other cross-sections, enter the
Ground Points one at a time, by their respective Station and Elevation for
the 3rd cross-section using the data provided. Be sure to Press
after you have typed in each correct number.
_ Once you have entered all of the Ground Points correctly, remember to
Press to Exit from entering Ground Point data .
_ Follow the on screen prompts to enter all of the other data.
After entering all the data for the 3rd cross-section ...
_ Press to Begin the Calculations.
Once the calculation is finished you may ...
_ Press to obtain a printout
_ Press to save the data to another name and/or directory
TO EXIT OUT OF THIS SCREEN NOW THAT OUR ANALYSIS IS COMPLETED ...
_ Press
~ 4-16
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QUICK-2 User's Guide Step-Backwater Tutorial
You will see a screen prompt at the bottom ...
SUMMARY PRINTOUT: Press , otherwise Press
To print a summary of the output for all 3 cross-sections then ...
_ Press , otherwise just Press
The on screen Summary or the printed summary will look like this:
SEGNO
0
450
940
Q
3000
3000
3000
.0
.0
.0
XLCH
0
450
490
CWSEL
501
503
508
.03
.96
.54
FR#
0.50
1.06
0.71
ELMIN
496
500
504
.90
.90
.80
AVG.VEL.
3.39
7.54
4.95
AREA
885
398
606
.0
.1
.5
TOPWID
385.5
196.9
286.2
If we carefully compare the Computed Water Surface Elevations
(CWSELs) at each cross-section, to the topographic contours on
Figure 1, we will see that the property is clearly higher than
the CWSEL at every cross-section. Therefore this analysis with
more detailed cross-section data has proven that the property
has been inadvertently included in an unnumbered Zone A Special
Flood Hazard Area.
Turn to the next page to continue
4-17
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QUICK-2 User's Guide Step-Backwater Tutorial
RUNNING HEC-2 USING QUICK-2 FILES {Tutorial Time: 5 minutes}
_ You will be prompted one more time to Press . The next prompt will
ask you a question concerning running the HEC-2 or PLOT-2 programs.
Press Y and to rerun w/HEC-2 or PLOT-2: If NO Press
_ For purposes of this tutorial let's answer "Y" , (and Press ) to
run the HEC-2 program. The next screen that will appear will include the
following:
To Run Type
QUICK-2 Q2
HEC-2 H2
AUTOHEC-2 AH2
PLOT-2 P2
VIEW/PRINT LIST
_ Type AH2 and Press .
Once the HEC-2 run is complete it will return you to the above-mentioned
screen.
NOTE: Typing AH2 runs the HEC-2 program automatically using the QUICK-2
generated HEC2.DAT, HEC-2 data file.
If you are using a HEC-2 data file other than HEC2.DAT, then Type H2 and
Press . Follow the directions on the screen for naming the Input,
Output and Tape95 files; pressing after each filename is typed in.
_ Type LIST and Press , and then enter your output filename,
(Default is HEC2.0UT), to view the results. Note that you move up, down and
across the screen using the ,, the cursor keys, etc.
_ To Print the data that appears on the screen simply Press P.
_ To Exit from the screen simply Press X or the Escape key.
If you would like to complete the next tutorial example, then
Type Q2 and Press ; and, turn to the next page.
4-18
-------
QUICK-2 User's Guide Step-Backwater Tutorial
RERUNNING USING SAVED CROSS-SECTION FILES
{Tutorial Time: 5 minutes}
Let's say that in the analysis that was performed in the previous tutorial,
we want to change the discharge from 3000 to 5000, and run the step-
backwater option again with the same cross-sections. This is quite easily
done. Just follow the steps as shown below.
1. At the Main Menu Screen Type 4 and Press
2. At the Starting Water Surface Elevation Method Screen
Type .0029 and Press
3. At the Input/Output Screen Press to retrieve a saved cross-
section file
Assuming your 1st cross-section file is stored as
C:\QUICK2\DATA\TO.XSC
Type C and Press when prompted for the directory
Type QUICK2\DATA & Press when prompted for the subdirectory
Type TO and Press when prompted for the filename
4. Press to toggle back to the "Enter Discharge" prompt
5. Type 5000 and Press to enter the new discharge
6. Press to Begin the Calculations
7. Press to input another cross-section
Press to retrieve a saved cross-section file
Assuming your 2nd cross-section file is stored as
C:\QUICK2\DATA\T450.XSC
Type C and Press when prompted for the directory
Type QUICK2\DATA & Press when prompted for the subdirectory
Type T450 and Press when prompted for the filename
8. Press to toggle back to the "Enter Discharge" prompt
9. Type 5000 and Press to enter the new discharge
10. Press to Begin the Calculations
4-19
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QUICK-2 User's Guide Step-Backwater Tutorial
11. Press to input another cross-section
Press to retrieve a saved cross-section file
Assuming your 3rd cross-section file is stored as
C:\QUICK2\DATA\T940.XSC
Type C and Press when prompted for the directory
Type QUICK2\DATA & Press when prompted for the subdirectory
Type T940 and Press when prompted for the filename
12 . Press to toggle back to the "Enter Discharge" prompt
13 . Type 5000 and Press to enter the new discharge
14 . Press to Begin the Calculations
_ Press to Exit out of the screen
_ Press to obtain a summary printout
_ Press twice to get back to the main menu
_ Press to leave the program
Q.E.D.
4-20
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QUICK-2 User's Guide Channel Capacity Tutorial
CHANNEL CAPACITY OPTION WITH THE RATING CURVE PLOT
{Tutorial Time: 5 to 10 minutes}
Let's say that we need to determine a Base Flood Elevation (BFE) for the
property shown in Figure 1. We do not want to exempt the entire property
from the flood plain, only a structure which is located in the middle of
the property. Therefore, we can use one cross-section (the 2nd cross-
section (T450.XSC) from our previous tutorial and shown on Figure 1), to
compute a BFE.
Let's assume that we know the discharge is between 3000 cfs and 4000 cfs
based on our best estimates.
Let's assume our structure does not have a basement; the lowest adjacent
grade (LAG) to the house is at elevation 510 NGVD; and the first floor
elevation (FFE) is 510.5 NGVD.
Let's determine the maximum carrying capacity of the floodplain using a
depth equal to the lowest adjacent grade (510.0) minus the minimum stream
elevation (500.9). For purposes of this example we'll use a depth of 9 feet
(510-501) .
To perform a channel capacity calculation we also need to know the
downstream slope, which in this case is easy to compute from the
information on page 15. Slope = 500.9 - 496.9 / 450 = .0089.
The graphic below sums up our situation so far:
l< FFE = 510.5
\ %—'< LAG = 510.0
\ X/ WSE = ? / t
\ / I
\ Q=3000 - 4000__ / 9.1'
\" / I
\ / i Stream Invert = 500.9
Slope = .0089
Follow the steps as shown on the next page to compute the rating curve
4-21
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QUICK-2 User's Guide Channel Capacity Tutorial
1. At the Main Menu Screen Type 2 and Press
2. At the Shape of Cross Section Screen
Type I and Press
3. At the Input/Output Screen Press
We are using the 2nd cross-section file stored as
C:\QUICK2\DATA\T450.XSC
Type C and Press when prompted for the directory
Type QUICK2\DATA & Press when prompted for the subdirectory
Type T450 and Press when prompted for the filename
4. Type .0089 and Press to enter the slope
5. Type 9 and Press to enter the depth
6. Press to Begin the Calculations
7. Press to Plot to screen .... Press to Print
Looking at the rating curve plot we can see that for a discharge range of
between 3000cfs - 4000cfs the BFE ranges from about 504.3 to 504.8. Since
our lowest adjacent grade and first floor elevation are at or above 510, it
is clear that this structure is above the BFE.
FFE = 510.5
\ %—'< LAG = 510.0
\ WSE = 504.3-504.8 / t
\ I ~/ |
\ P=3000 - 4000__ / 9.1'
\" / I
\ / i Stream Invert = 500.9
Slope = .0089
8. Press to continue
9. Press to go back to the Main Menu
10. Press to Exit the program
4-22
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QUICK-2 User's Guide PLOT-2 Tutorial
PLOT-2
In general PLOT-2 will only work on QUICK-2 files that have been converted into HEC-2
format using QUICK-2's Step-Backwater option.
Profile plots from PLOT-2 will work only if the QUICK-2 generated data file (HEC2.DAT) is
also run using the HEC-2 program (see Running HEC-2 Using QUICK-2 Files, page 4-18), since
a HEC2.T95 file needs to be generated by the HEC-2 program for use by PLOT-2.
PLOT-2 Cross-section plots can be generated using the QUICK-2 generated data file
(HEC2.DAT) even if it is not run with HEC-2. However, the Cross-section plot will not show
the computed water surface elevation (CWSEL) unless the QUICK-2 HEC2.DAT file is run with
HEC-2, since the CWSEL is found on the HEC2.T95 file.
Note that the user can compute a normal depth elevation for only one cross-section and
have that cross-section plotted by choosing the Step-Backwater option and the Normal Depth
starting water surface elevation method. Once the computation is finished, the user simply
exits (Presses ), and the QUICK-2 program automatically creates the HEC2.DAT file for
that one cross-section, which can be used by the PLOT-2 program.
Let's say that we want to view the water surface elevation profile and the
cross-section plots from our previous tutorial on the Step-Backwater
option.
_ From the QUICK-2 Title screen Press P2
_ You are now into the PLOT-2 program, Press to continue
PROFILE PLOT {Tutorial Time: 5 to 10 minutes}
1. Let's view the profile first. Press 1 from the PLOT-2 main menu
selection
2. Cursor to the HEC2 Tape95 file name entry and Type ?
This will list all of the data files in the QUICK-2 directory. T95 files
are designated with the 3 letter extension .T95 . Therefore cursor over to
highlight that file (HEC2.T95) and Press .
3. Move up to highlight the Plot profiles entry and Press .
4. Your profile is now plotted. Pressing moves you back to the
Profile plots main menu screen. You can explore the different Profile and
Plotting options and replot the profile if you wish.
5. When you are finished plotting, highlight the Return to main menu
message and Press
4-23
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QUICK-2 User's Guide PLOT-2 Tutorial
CROSS-SECTION PLOT {Tutorial Time: 5 to 10 minutes}
1. From the PLOT-2 main menu Press 2 from the menu selection.
2. Cursor down to the HEC2 input file name entry and Type ?
This will list all of the data files in the QUICK-2 directory. Input files
are designated with the 3 letter extension .DAT . Therefore cursor over to
highlight that file (HEC2.DAT) and Press . If we want to view a
different data file than that of the profile we previously viewed, we would
have to specify a different file here before proceeding.
3. Cursor down to the HEC2 Tape95 file name entry
Note that we do not have to re-enter this file since we have already
entered it previously. If we want to view a different Tape95 file than that
of the profile we previously viewed, we would have to Type ?, and then
specify a different file here before proceeding.
4. Move up to highlight the Plot cross sections entry and Press .
5. You now have the option of printing all or selected cross sections from
your data file. Press Y for plotting all, or N for plotting selected cross
sections.
Your first cross-section is now plotted. Pressing moves you back to
the Cross-section plots main menu screen or plots additional cross-sections
depending on how many cross-sect ion plots you have. You can explore the
different Cross-section and Plotting options if you wish.
6. Highlight the Return to main menu message and Press
Pressing 4 at the PLOT-2 main menu exits you from PLOT-2 and back to the
QUICK-2 title screen.
Note: To use PLOT-2 and to access data files that are in another directory
(i.e., they are not in the C:\QUICK2 directory), just change to that data
directory (i.e., CD\dirname) and access PLOT-2 by typing C:\QUICK2\PLOT2
(or A:\PLOT2 if using the program from the floppy drive) from that data
directory.
4-24
-------
Chapter 5: FORMULAS
Critical Depth
Channel Capacity
Normal Depth
Step-Backwater
5-1
-------
QUICK-2 User's Guide Critical Depth Formulas
1. CRITICAL DEPTH
In every cross-section for a given discharge there exists a critical depth, where the
energy grade (depth of water plus velocity head - V2/2G) is at a minimum. Increasing the
discharge above this given discharge will force the flow into the super-critical regime.
Discharges below the given discharge will remain in the sub-critical regime.
Super-critical depths will be lower than the critical depth, and sub-critical depths will
be above the critical depth. Super-critical flow is characterized by small water depths
with large velocity heads; while, sub-critical flow is characterized by large water depths
with small velocity heads. A rule of thumb used to determine critical depth is that when
the Velocity Head equals 1/2 the hydraulic depth (Area/Topwidth) critical flow is
probable.
A formula which can be used to approximate critical depth (Dc) is
given below.
Qc2 / g = A3 / T
Where Qc is the discharge (in cfs) based on critical depth, g is the gravitational
constant (32.15 feet/second squared), A is the cross-section area (in square feet), and T
is the top width of the water surface (in feet). Note: for rectangular channels the above
equation can be reduced so that
Dc = (Qc/5.67 T) -6".
The more exact way to compute critical depth (minimum specific energy) is to find a
specific depth of water within a cross-section for a given discharge which produces the
lowest energy grade. The following represents the process that the Critical Depth option
of QUICK-2 goes through to calculate critical depth.
After the cross-section information (ground points, channel stations, etc.) has been input
the program starts computing the water surface elevation (WSE) and corresponding energy
grade elevation (EG) at a depth of 0.1 foot above the lowest elevation in the
cross-section. It continues to calculate WSE and EG at intervals of 0.5 foot. As the depth
of water in the cross-section increases the EG will decrease. At one point the EG will
begin to increase. This means that between the last 0.5 foot interval there exists a
minimum energy grade. Once this has occurred the program decreases the WSE in intervals of
.02 foot. As the depth of water decreases in the cross-section the EG will also decrease
as it approaches the minimum specific energy. At one point the EG will begin to increase
again.
This means that between the last .02 foot interval critical depth exists. At this point
the screen will display the actual critical water surface elevation (along with other
variables) by assuming that the next to the last iteration was the critical depth.
The calculations performed by the program for a given cross-section are listed on the next
page. The calculations include the iterations that the program goes through to arrive at
critical depth.
5-2
-------
QUICK-2 User's Guide
Critical Depth Formulas
ELMIN =92.5
WSE =92.6
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
Qc= 6.026845E-02 Q= 260
Qc =
Qc =
Qc =
Qc =
Oc =
5.314738
24.18726
61.71717
121.451
204.4488
Oc= 311.15
Q=
Q=
Q=
Q=
Q=
Q=
260
260
260
260
260
260
EG=466080.8
EG=452.6406
EG=125.4259
EG=101.21
EG=96.99928
EG=96.14474
EG=96.14897
EG Decreasing
+ EG Increasing
Therefore Minimum Specific Energy is between WSE's of 95.1 and 95.6. Note also that the
Discharge (Q = 260) is also within the computed Critical Discharge (Qc) range of 204 -
311.
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
WSE =
95
95
95
95
95
95
95
95
95
95
95
95
95
95
.58
.56
.54
.52
.50
.48
.46
.44
.42
.40
.38
.36
.34
.32
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
Qc=
QC =
306.5006
301.8821
297.2962
292.7447
288.2256
283.737
279.2825
274.8583
270.4678
266.1075
261.7806
257.4854
253.22
248.986
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
260
260
260
260
260
260
260
260
260
260
260
260
260
260
EG=96.14179
EG=96.13499
EG=96.12863
EG=96.12271
EG=96.11724
EG=96.11225
EG=96.10776
EG=96.1038
EG=96.10038
EG=96.09752
EG=96.09525
EG=96.09361
EG=96.09262
EG=96.09199
- EG Decreasing
minimum
WSE= 95.30
Qc= 244.76
Q= 260
EG=96.09271 + EG Increasing
We assume that
Critical Depth = 95.32', Minimum Specific Energy = 96.09199'
The Froude number would be, Q / Qc, or 260 / 248.986 = 1.04.
It is not unusual for the Froude number to not equal exactly 1.0, since the calculation of
critical discharge using the formula Qc2 / g = A3 / T, does not always yield a WSE that
is exactly at the True minimum specific energy.
You should notice from the above tabulation, that as you approach critical depth (minimum
specific energy), for very small changes in EG there are large jumps in the water surface
elevation. The EG is only changing by .001' to .003' while the WSE changes by .02'. A
0.01' difference in EG can cause a 0.10' change in WSE.
5-3
-------
QUICK-2 User's Guide Channel Capacity Formulas
2. CHANNEL CAPACITY
In this option, a Normal Depth elevation (see 3. NORMAL DEPTH) is input and the program
computes the corresponding discharge. (In the Normal Depth option, the discharge is input
and the program computes a normal depth elevation) . The Manning's equation is used as the
formula for determining the (normal) discharge.
Q = 1.486 A (R-667) S5 / N
Where Q is the discharge (in cfs) , A is the cross-section area (in square feet) , R is the
hydraulic radius (in feet), S is the energy slope (in feet/feet), and N is the Manning's
roughness value.
After the cross-section information (ground points, channel stations, streambed slope,
normal depth elevation(s), etc.) has been input, the program simply solves for the area
(A) and hydraulic radius (R) below the normal depth elevation (specified by the user) and
computes the (normal) discharge directly using the Manning's equation. This is not an
iterative process. The screen will display the (normal) discharge (which represents the
channel capacity) along with other variables.
5-4
-------
QUICK-2 User's Guide
Normal Depth Formulas
3. NORMAL DEPTH
The standard formula for determining normal depth in a cross-section is the Manning's
formula. Water is flowing at normal depth when the energy grade and the hydraulic grade
(water surface) slopes are the same as the stream bed slope. Normal depth profiles occur,
in general, when the flow is uniform, steady, one-dimensional, and is not affected by
downstream obstructions or flow changes. The standard Manning's equation is:
Q = 1.486 A (R-667) S5 /N
Where Q is the discharge (in cfs) , A is the cross-section area (in square feet) , R is the
hydraulic radius (in feet), S is the energy slope (in feet/feet), and N is the Manning's
roughness value.
The exact method for computing normal depth for a given discharge at a particular
cross-section, is to assume that S is equal to the downstream streambed slope and to solve
iteratively for the depth (this obviously assumes N is known). The following represents
the process that the Normal Depth option of QUICK-2 goes through to calculate normal
depth.
After the cross-section information (ground points, channel stations, discharge, streambed
slope, etc.) has been input, the program starts computing discharge using the Manning's
equation at an initial depth of 0.1 foot above the lowest point in the cross-section, and
from that point in 0.5 foot intervals. At some point, the computed discharge will exceed
the given target discharge. The program then uses a converging technique to compute a
discharge (with a corresponding normal depth) that is within 1% of the given discharge. At
this point the screen will display the actual normal depth water surface elevation (along
with other variables).
The calculations performed by the program for a given cross-section are listed below. The
calculations include the iterations that the program goes through to arrive at normal
depth.
ELMIN= 92.5
WSE =92.6
WSE= 93.1
WSE= 93.6
WSE= 94.1
WSE= 94.6
WSE= 95.1
WSE= 95.6
WSE =
WSE =
96.1
95.65697
Q= 260
Q=
Q=
Q=
Q=
Q=
Q=
Q=
Q=
260
260
260
260
260
260
260
260
Computed Q= .023579 below target Q
Computed Q=
Computed Q=
Computed Q=
Computed Q=
Computed Q=
Computed Q=
2.803083
14.11313
38.33264
80.01045
146.9773
245.9516
below
below
below
below
below
below
Computed Q=
Computed Q=
369.2461
258.7531
above target Q
within 1%
We assume that
Normal depth = 95.66 for a Discharge (Q) of 260 cfs
5-5
-------
QUICK-2 User's Guide Step-Backwater Formulas
4. STEP-BACKWATER
The Energy Equation which represents one-dimensional, uniform, and steady flow in open
channels is shown below.
(1) WSEa + HVa = WSEU + HVU + HL + OL
Where WSEa is the water surface elevation at the downstream cross-section, HVa is the
velocity head at the downstream cross-section, WSEu is the water surface elevation at the
upstream cross-section, HVu is the velocity head at the upstream cross-section, HL is the
friction loss between the two cross-sections, and OL is the eddy (contraction or
expansion) loss between the two cross-sections.
Velocity Head, HV, is calculated as follows:
HV = (a) V2 / 2g
Where (a) is alpha the velocity coefficient, V is velocity (Q/A), and g is the
gravitational constant. Alpha (a) is calculated as follows:
(A2) |K13 Kc3 Kr3
(a) = I--- + --- + ---
(K3) JA12 Ac2 Ar2
Where A and K are the total area and conveyance below the water surface, respectively; and
Kl, Kc, Kr and Al, Ac, Ar, are the conveyance and area in the left overbank, channel, and
right overbank, respectively.
Friction Loss, HL, is calculated as follows:
HL = Lw ( Qa + Qu )2 / ( Ka + Ku )2
Where Lw is the discharge weighted reach length between cross- sections, Qa is the
discharge at the downstream cross-section, Qu is the discharge at the upstream cross
section, Ka is the conveyance at the downstream cross-section, and K^ is the conveyance
at the upstream cross-section. This is derived from the Average Conveyance Friction slope
equation.
The Discharge Weighted Reach Length, Lw, is calculated as follows:
Lw = {(LI * Ql) + (Lc * Qc) + (Lr * Qr)} / Qa
Where Qa is the average total discharge between cross-sections; and, LI, Lc, Lr, and Ql,
Qc, Qr, represent the reach length and average discharge between cross-sections for the
left overbank, channel, and right overbank, respectively.
Eddy Loss, OL, is calculated as follows:
OL = (Ce or Cc) * ABS HVa - HVU
Where Ce is the expansion coefficient, Cc is the contraction coefficient, HVa is the
velocity head at the downstream cross-section, and HVu is the velocity head at the
upstream cross-section. When HVu is greater than HVa Ce is utilized. When HVu is greater
than or equal to HVa Cc is utilized.
5-6
-------
QUICK-2 User's Guide
Step-Backwater Formulas
After the cross-section information for the first cross-section has been input, either a
known water surface elevation is input to start the calculations or the water surface
elevation could have been determined by the Normal or Critical Depth options or by another
source or method. The program then computes all pertinent variables for the first
cross-section that will be needed for an energy balance with the next upstream
cross-section. After this the user must put in the appropriate information for the next
cross-section (i.e., ground points, channel stations, reach lengths, contraction and
expansion coefficients, etc.) . Once this is done the program performs a series of trial
iterations to make sure that the Energy Equation (1) listed previously will balance to
within .014 foot. The sequence of trial elevations is listed below.
1ST TRIAL:
Uses the depth of water (DP) of the previous cross-section added to the lowest elevation
(ELMIN) within the current cross-section. If DP + ELMIN is less than the previous WSE
(i.e., adverse slope condition) then the program uses the previous WSE for the 1st trial
at the current cross-section.
2ND TRIAL:
Uses the average of the computed WSE and the WSE assumed in Trial number 1.
3RD TRIAL AND ON ...:
Uses a formula designed to help converge quickly to balance the energy equation as shown
below:
Trial WSE = WSE- (WSE+HV-DG-HL-OL) / (1- ( (Q/QC)2) + ( (1. 5*HL) / (A/W) ) )
Where WSE, HV, HL, OL, QC, A, and W are the latest computations of water surface
elevation, velocity head, friction loss, eddy loss, critical discharge, total area, and
total wetted perimeter, respectively; and, DG is the computed energy grade elevation from
the previous cross-section; and, Q is the discharge at the current cross-section.
For most energy balances between cross-sections that are not at or near critical flow, the
program will balance the energy equation within 5 trials.
The calculations performed by the program for an energy balance between two cross-sections
are listed below. The calculations include the iterations that the program goes through to
arrive at the energy balance.
WSE
Assumed
98.75
98.53744
98.32476
WSE
Calculated
98.32489
98.32472
98.32513
Difference
+.4251099
+.2127228
-.00037
Trial
1
2
3
We assume that the correct WSE = 98.32
Note: Energy balance in this case was accurate to .00037 foot.
5-7
-------
QUICK-2 User's Guide Definition of Variables
Appendix 1: Definition of Variables
ACH - Area within the specified channel below the water surface elevation
ALOB - Area within the specified left overbank below the water surface elevation
AROB - Area within the specified right overbank below the water surface elevation
ALPHA - Velocity head coefficient
AREA or Area - Total area within the cross-section below the water surface elevation
AVG.VEL or Velocity - Average Velocity within the entire cross-section
Base Width - Channel bottom width of a trapezoidal or rectangular cross-section
Bottom Width - Channel bottom width of a trapezoidal or rectangular cross-section
CC - Contraction Coefficient
CE - Expansion Coefficient
CH-SLOPE - Slope of the streambed, Channel Slope
CHAN-VEL or ChanVel - Velocity within the main channel of cross-section
Critical Slope - Slope of the Energy Grade line at Critical Flow
CWSEL - Computed water surface elevation within a cross-section
Depth - Max depth of water in the cross-sect as measured below the water surface elevation
Diameter - Width or Height of a circular pipe
Discharge - The rate of the flow of a volume of water within a cross-section, usually
expressed in cubic feet per second (cfs)
EG or EG ELEV - Energy grade elevation, expressed as, WSE + HV
EG-Slope - Energy grade slope
ELEV - Elev of a ground pt of a cross-sect, as ref to some datum (i.e., NGVD, NAVD, etc.)
ELMIN - Lowest elevation in a cross section
Flow Regime - Type of water surface profile (Supercritical regimes are not computed)
Ml: EG-Slope <= Ch-Slope and FR# < .8 M2: EG-Slope > Ch-Slope and FR# < .8
Cl: EG-Slope <= Ch-Slope and FR# >= .8 C3: EG-Slope > Ch-Slope and FR# >= .8
Flow Type - either, Supercritical, Critical or Subcritical
Froudett, Froude No., Froudtt or FR# - Froude number, used to determine the flow type
(i.e., sub- (FR# < 1), critical (FR# = 1) or super-critical (FR# > 1) flow)
HL - Friction loss between cross sections
HV - Velocity head
Hyd Radius or Hyd R - Hydraulic Radius: equal to (Area / Wet Perimeter)
A-l
-------
QUICK-2 User's Guide Definition of Variables
KRATIO - Ratio of upstream total conveyance to downstream total conveyance
L Side Slope - Ratio of the slope of the left side of a channel in terms of Horizontal
distance in feet to 1 foot Vertical.
Manning's n - Coefficient used to account for the friction caused by earthen, vegetative,
and/or man-made surfaces within a floodplain cross-section.
Max Discharge - The maximum flow possible within a circular pipe, (usually occurring at
.94 * Diameter).
NCHL, NLOB, NROB - Manning's "N" value for the specified channel, left overbank, and right
overbank, respectively.
OL - Expansion/contraction loss
Q - Total discharge in the cross-section
QC - Critical discharge within entire cross-section for a specific water surface elevation
QCH - Discharge within the specified channel of a cross-section
QIC - Critical discharge within the entire cross-section for a specific water surface
elevation, assuming that critical flow is limited to the channel, even if flow is
occurring in the overbanks
QLOB, QROB - Discharge within the specified left overbank, and right overbank,
respectively, of a cross-section
R Side Slope - Ratio of the slope of the right side of a channel in terms of Horizontal
distance in feet to 1 foot Vertical.
SEGNO - Cross section number or identifier
Slope or EG-Slope - Energy grade slope
STAT-L, STAT-R - Station, within a cross-section, of the left edge, and right edge,
respectively, of the water surface
STAT - Station of a ground point of a cross-section
STCHL, STCHR, ST-MIDCH - Station of the left bank, right bank, and mid-point,
respectively, of a cross-section
Top Width or Top Wid - Top width of the water surface within a cross-section
Velocity - Average Velocity within the entire cross-section
Wet Perimeter or Wet Per - actual width of ground within a cross-section below the water
surface elevation.
WS ELEV or CWSEL - Water surface elevation within a cross-section
XLCH, XLOB, XROB - Distance between cross-sections as measured along the channel, left
overbank, and right overbank, respectively.
A-2
-------
NOTES
-------
Appendix 7
Hydraulic Computer Manuals
HEC-2
U.S. Army Corps of Engineers, Hydrologic Engineering Center (HEC), "Water
Surface Profiles, HEC-2, User's Manual," Davis, California, 1991.
HEC-RAS
U.S. Army Corps of Engineers, Hydraulic Engineering Center (HEC), "HEC-RAS,
River Analysis System, User's Manual - Draft," BETA 2 Test Version, Davis,
California, February 1995.
PSUPRO
Federal Emergency Management Agency, "PSUPRO Encroachment Analysis User's
Manual", Washington, D.C., 1989.
SFD
Federal Emergency Management Agency, "Simplified Floodway Determination
Computer Program User's Manual", Washington, D.C., 1989.
WSPRO
U.S. Geological Survey, "Water Surface PROfiles, WSPRO, User's Manual, Reston,
Virginia, 1990.
WSP2
U.S. Department of Agriculture, Natural Resources Conservation Service, "WSP2
Computer Program User's Manual", Technical Release No. 61, Washington, D.C.,
1976.
A7-1
-------
Appendix 8
Normal Depth Hand Calculation
A8-1
-------
Appendix 8 - continued
Normal Depth Hand Calculation
A8-2
-------
Appendix 8 - continued
Normal Depth Hand Calculation
A8-3
-------
Appendix 9
Weir Flow Hand Calculations
A9-1
-------
Appendix 9 - continued
Weir Flow Hand Calculations
A9-2
-------
Appendix 9 - continued
Weir Flow Hand Calculations
A9-3
-------
Appendix 9 - continued
Weir Flow Hand Calculations
A9-4
-------
Appendix 10
Worksheet
Base Flood Elevations in Zone A Areas
Community Name:
Community ID# :
State:
Panel #:
FIRM Date:
Project Identifier
This request is for: Existing
Single Lot [ ]
Other
[ ] Proposed [ ] <5 acres [ ] >5 acres [ ]
Multi-Lot [ ] <50 lots [ ] >50 lots [ ]
-APPROACH USED TO DEVELOP THE BASE FLOOD ELEVATION (BFE)
EXISTING DATA
Available
FEMA
Federal
Other
Not Available
Did Not Check
SIMPLIFIED
Contour Interpolation [ ]
DETAILED
Hydraulics Normal Depth [ ] Weir Flow [ ]
Other
Data Extrapolation [ ]
Culvert Flow [ ]
Hydrology Regression Equations [ ]
Discharge-Drainage [ ]
Other
Rational Formula [ ]
TR-55 [ ]
Topography Topographic Map [ ]
Map Scale: 1" = '
Field Survey tied to Datum?
Datum: NGVD 1929 [ ] Other
# Cross-Sections [ ] Length of Stream
RESULTS
BFE or Depth of 100-year Flood
First Floor Elevation or Depth
Lowest Adjacent Grade to Structure
Lowest Grade on entire Property N/A
or Field Survey [ ]
Contour Interval:
YES
NO
N/A
ft.
A10-1
-------
A Unofficial Reproduction of the
WETLANDS RESEARCH PROGRAM'S
Technical Report Y-87-1
CORPS OF ENGINEERS WETLANDS
DELINEATION MANUAL
by
Environmental Laboratory
DEPARTMENT OF THE ARMY
Waterways Experiment Station, Corps of Engineers
PO Box 631, Vicksburg, Mississippi 39180-0631
January 1987
Final Report
Approved for Public Release; Distribution Unlimited
Prepared for DEPARTMENT OF THE ARMY
US Army Corps of Engineers
Washington, DC 20314-1000
Reproduced by Environmental Technical Services Company, Austin, Texas as a courtesy.
This reproduction will not be formatted as the original and errors will occur in this
reproduction. Some items are missing due to our inability to reproduce them adequately.
Date: February 25, 1997
-------
Wetland Delination Manual, 1987
ABSTRACT
This document presents approaches and methods for identifying and delineating wetlands for
purposes of Section 404 of the Clean Water Act. It is designed to assist users in making
wetland determinations using a multiparameter approach. Except where noted in the manual,
this approach requires positive evidence of hydrophytic vegetation, hydric soils, and wetland
hydrology for a determination that an area is a wetland. The multiparameter approach provides
a logical, easily defensible, and technical basis for wetland determinations. Technical
guidelines are presented for wetlands, deepwater aquatic habitats, and nonwetlands (uplands).
Hydrophytic vegetation, hydric soils, and wetland hydrology are also characterized, and
wetland indicators of each parameter are listed.
Methods for applying the multiparameter approach are described. Separate sections are
devoted to preliminary data gathering and analysis, method selection, routine determinations,
comprehensive determinations, atypical situations, and problem areas. Three levels of routine
determinations are described, thereby affording significant flexibility in method selection.
Four appendices provide supporting information. Appendix A is a glossary of technical terms
used in the manual. Appendix B contains data forms for use with the various methods.
Appendix C, developed by a Federal interagency panel, contains a list of all plant species
known to occur in wetlands of the-region. Each species has been assigned an indicator status
that describes its estimated probability of occurring in wetlands. A second list contains plant
species that commonly occur in wetlands of the region. Morphological, physiological, and
reproductive adaptations that enable a plant species to occur in wetlands are also described,
along with a listing of some species having such adaptations. Appendix D describes the
procedure for examining the soil for indicators of hydric soil conditions, and includes a
national list of hydric soils developed by the National Technical Committee for Hydric Soils.
PREFACE
This manual is a product of the Wetlands Research Program (WRP) of the US Army Engineer
Waterways Experiment Station (WES), Vicksburg, Miss. The work was sponsored by the
Office, Chief of Engineers (OCE), US Army. OCE Technical Monitors for the WRP were Drs.
John R. Hall and Robert J. Pierce, and Mr. Phillip C. Pierce.
The manual has been reviewed and concurred in by the Office of the Chief of Engineers and
the Office of the Assistant Secretary of the Army (Civil Works) as a method approved for
voluntary use in the field for a trial period of 1 year.
This manual is not intended to change appreciably the jurisdiction of the Clean Water Act
(CWA) as it is currently implemented. Should any District find that use of this method
appreciably contracts or expands jurisdiction in their District as the District currently interprets
25 February 1997 Environmental Techncal Services Co. 834 Castle Ridge Rd Austin, Texas 78746
-------
Wetland Delination Manual, 1987
CWA authority, the District should immediately discontinue use of this method and furnish a
full report of the circumstances to the Office of the Chief of Engineers.
This manual describes technical guidelines and methods using a multiparameter approach to
identify and delineate wetlands for purposes of Section 404 of the Clean Water Act.
Appendices of supporting technical information are also provided.
The manual is presented in four parts. Part II was prepared by Dr. Robert T. Huffman,
formerly of the Environmental Laboratory (EL), WES, and Dr. Dana R. Sanders, Sr., of the
Wetland and Terrestrial Habitat Group (WTHG), Environmental Resources Division (ERD),
EL. Dr. Huffman prepared the original version of Part II in 1980, entitled "Multiple Parameter
Approach to the Field Identification and Delineation of Wetlands." The original version was
distributed to all Corps field elements, as well as other Federal resource and environmental
regulatory agencies, for review and comments. Dr. Sanders revised the original version in
1982, incorporating review comments. Parts I, III, and IV were prepared by Dr. Sanders, Mr.
William B. Parker (formerly detailed to WES by the US Department of Agriculture (USDA),
Soil Conservation Service (SCS)) and Mr. Stephen W. Forsythe (formerly detailed to WES by
the US Department of the Interior, Fish and Wildlife Service (FWS)). Dr. Sanders also served
as overall technical editor of the manual. The manual was edited by Ms. Jamie W. Leach of the
WES Information Products Division.
The authors acknowledge technical assistance provided by: Mr. Russell F. Theriot, Mr. Ellis J.
Clairain, Jr., and Mr. Charles J. Newling, all of WTHG, ERD; Mr. Phillip Jones, former SCS
detail to WES; Mr. Porter B. Reed, FWS, National Wetland Inventory, St. Petersburg, Fla.; Dr.
Dan K. Evans, Marshall University, Huntington, W. Va.; and the USDA-SCS. The authors also
express gratitude to Corps personnel who assisted in developing the regional lists of species
that commonly occur in wetlands, including Mr. Richard Macomber, Bureau of Rivers and
Harbors; Ms. Kathy Mulder, Kansas City District; Mr. Michael Gilbert, Omaha District; Ms.
Vicki Goodnight, Southwestern Division; Dr. Fred Weinmann, Seattle District; and Mr.
Michael Lee, Pacific Ocean Division. Special thanks are offered to the CE personnel who
reviewed and commented on the draft manual, and to those who participated in a workshop
that consolidated the field comments.
The work was monitored at WES under the direct supervision of Dr. Hanley K. Smith, Chief,
WTHG, and under the general supervision of Dr. Conrad J. Kirby, Jr., Chief, ERD. Dr. Smith,
Dr. Sanders, and Mr. Theriot were Managers of the WRP. Dr. John Harrison was Chief, EL.
Director of WES during the preparation of this report was COL Allen F. Grum, USA. During
publication, COL Dwayne G. Lee, CE, was Commander and Director. Technical Director was
Dr. Robert W. Whalin.
This report should be cited as follows:
Environmental Laboratory. 1987. "Corps of Engineers Wetlands
Delineation Manual," Technical Report Y-87-1, US Army Engineer
Waterways Experiment Station, Vicksburg, Miss.
25 February 1997 Environmental Techncal Services Co. 834 Castle Ridge Rd Austin, Texas 78746
-------
Wetland Delination Manual, 1987
CONTENTS
Page
ABSTRACT 2
PREFACE 2
PART I: INTRODUCTION 5
Background 5
Purpose and Objectives 5
Scope 5
Organization 7
Use 8
PART II: TECHNICAL GUIDELINES 12
Wetlands 12
Deepwater Aquatic Habitats 13
Nonwetlands 13
PART III: CHARACTERISTICS AND INDICATORS OF HYDROPHYTIC
VEGETATION, HYDRIC SOILS, AND WETLAND HYDROLOGY 14
Hydrophytic Vegetation 14
Hydric Soils 19
Wetland Hydrology 24
PART IV: METHODS 29
Section A. Introduction 29
Section B. Preliminary Data Gathering and Synthesis 29
Section C. Selection of Method 38
Section D. Routine Determinations 38
Section E. Comprehensive Determinations 51
Section F. Atypical Situations 60
Section G. Problem Areas 68
REFERENCES 72
BIBLIOGRAPHY 74
APPENDIX A: GLOSSARY 77
APPENDIX C: VEGETATION 91
APPENDIX D: HYDRIC SOILS 98
Modifications and Clarifications to the 1987 Wetland Delineation Manual 101
25 February 1997 Environmental Techncal Services Co. 834 Castle Ridge Rd Austin, Texas 78746
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Wetland Delination Manual, 1987
CORPS OF ENGINEERS WETLANDS DELINEATION MANUAL
PART I: INTRODUCTION
Background
1. Recognizing the potential for continued or accelerated degradation of the Nation's waters,
the US Congress enacted the Clean Water Act (hereafter referred to as the Act), formerly
known as the Federal Water Pollution Control Act (33 U.S.C. 1344). The objective of the Act
is to maintain and restore the chemical, physical, and biological integrity of the waters of the
United States. Section 404 of the Act authorizes the Secretary of the Army, acting through the
Chief of Engineers, to issue permits for the discharge of dredged or fill material into the waters
of the United States, including wetlands.
Purpose and Objectives
Purpose
2. The purpose of this manual is to provide users with guidelines and methods to determine
whether an area is a wetland for purposes of Section 404 of the Act.
Objectives
3. Specific objectives of the manual are to:
a. Present technical guidelines for identifying wetlands and distinguishing them from aquatic
habitats and other nonwetlands. Definitions of terms used in this manual are presented in the
Glossary Appendix A.
b. Provide methods for applying the technical guidelines.
c. Provide supporting information useful in applying the technical guidelines.
4. This manual is limited in scope to wetlands that are a subset of "waters of the United
States" and thus subject to Section 404. The term "waters of the United States" has broad
meaning and incorporates both deep-water aquatic habitats and special aquatic sites, including
wetlands (Federal Register 1982), as follows:
a. The territorial seas with respect to the discharge of fill material.
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b, Coastal and inland waters, lakes, rivers, and streams that are navigable waters of the United
States, including their adjacent wetlands.
c. Tributaries to navigable waters of the United States, including adjacent wetlands.
d. Interstate waters and their tributaries, including adjacent wetlands.
e. All others waters of the United States not identified above, such as isolated wetlands and
lakes, intermittent streams, prairie potholes, and other waters that are not a part of a tributary
system to interstate waters or navigable waters of the United States, the degradation or
destruction of which could affect interstate commerce.
Determination that a water body or wetland is subject to interstate commerce and therefore is a
"water of the United States" shall be made independently of procedures described in this
manual.
Special aquatic sites
5. The Environmental Protection Agency (EPA) identifies six categories of special aquatic
sites in their Section 404 b.(l) guidelines (Federal Register 1980), including:
a. Sanctuaries and refuges.
b. Wetlands.
c. Mudflats.
d. Vegetated shallows.
e. Coral reefs.
f. Riffle and pool complexes.
Although all of these special aquatic sites are subject to provisions of the Clean Water Act, this
manual considers only wetlands. By definition (see paragraph 26a), wetlands are vegetated.
Thus, unvegetated special aquatic sites (e.g. mudflats lacking macrophytic vegetation) are not
covered in this manual.
Relationship to wetland classification systems
6. The technical guideline for wetlands does not constitute a classification system. It only
provides a basis for determining whether a given area is a wetland for purposes of Section 404,
without attempting to classify it by wetland type.
7. Consideration should be given to the relationship between the technical guideline for
wetlands and the classification system developed for the Fish and Wildlife Service (FWS), US
Department of the Interior, by Cowardin et al. (1979). The FWS classification system was
developed as a basis for identifying, classifying, and mapping wetlands, other special aquatic
sites, and deepwater aquatic habitats. Using this classification system, the National Wetland
Inventory (NWI) is mapping the wetlands, other special aquatic sites, and deepwater aquatic
habitats of the United States, and is also developing both a list of plant species that occur in
wetlands and an associated plant database. These products should contribute significantly to
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application of the technical guideline for wetlands. The technical guideline for wetlands as
presented in the manual includes most, but not all, wetlands identified in the FWS system. The
difference is due to two principal factors:
a. The FWS system includes all categories of special aquatic sites identified in the EPA
Section 404 b.(I) guidelines. All other special aquatic sites are clearly within the purview of
Section 404; thus, special methods for their delineation are unnecessary.
b. The FWS system requires that a positive indicator of wetlands be present for any one of the
three parameters, while the technical guideline for wetlands requires that a positive wetland
indicator be present for each parameter (vegetation, soils, and hydrology), except in limited
instances identified in the manual.
Organization
8. This manual consists of four parts and four appendices. PART I presents the background,
purpose and objectives, scope, organization, and use of the manual.
9. PART II focuses on the technical guideline for wetlands, and stresses the need for
considering all three parameters (vegetation, soils, and hydrology) when making wetland
determinations. Since wetlands occur in an intermediate position along the hydrologic
gradient, comparative technical guidelines are also presented for deepwater aquatic sites and
nonwetlands.
10. PART III contains general information on hydrophytic vegetation, hydric soils, and
wetland hydrology. Positive wetland indicators of each parameter are included.
11. PART IV, which presents methods for applying the technical guideline for wetlands, is
arranged in a format that leads to a logical determination of whether a given area is a wetlands
Section A contains general information related to application of methods. Section B outlines
preliminary data-gathering efforts. Section C discusses two approaches (routine and
comprehensive) for making wetland determinations and presents criteria for deciding the
correct approach to use. Sections D and E describe detailed procedures for making routine and
comprehensive determinations, respectively. The basic procedures are described in a series of
steps that lead to a wetland determination.
12. The manual also describes (PART IV, Section F) methods for delineating wetlands in
which the vegetation, soils, and/or hydrology have been altered by recent human activities or
natural events, as discussed below:
a. The definition of wetlands (paragraph 26a) contains the phrase plunder normal
circumstances, which was included because there are instances in which the vegetation in a
wetland has been inadvertently or purposely removed or altered as a result of recent natural
events or human activities. Other examples of human alterations that may affect wetlands are
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draining, ditching, levees, deposition of fill, irrigation, and impoundments. When such
activities occur, an area may fail to meet the diagnostic criteria for a wetlands Likewise,
positive hydric soil indicators may be absent in some recently created wetlands. In such cases,
an alternative method must be employed in making wetland determinations.
b. Natural events may also result in sufficient modification of an area that indicators of one or
more wetland parameters are absent. For example, changes in river course may significantly
alter hydrology, or beaver dams may create new wetland areas that lack hydric soil conditions.
Catastrophic events (e.g. fires, avalanches, mudslides, and volcanic activities) may also alter or
destroy wetland indicators on a site.
Such atypical situations occur throughout the United States, and all of these cannot be
identified in this manual.
13. Certain wetland types, under the extremes of normal circumstances, may not always meet
all the wetland criteria defined in the manual. Examples include prairie potholes during
drought years and seasonal wetlands that may lack hydrophytic vegetation during the dry
season. Such areas are discussed in PART IV, Section G, and guidance is provided for making
wetland determinations in these areas. However, such wetland areas may warrant additional
research to refine methods for their delineation.
14. Appendix A is a glossary of technical terms used in the manual. Definitions of some
terms were taken from other technical sources, but most terms are defined according to the
manner in which they are used in the
manual.
15. Data forms for methods presented in PART IV are included in Appendix B. Examples of
completed data forms are also provided.
16. Supporting information is presented in Appendices C and D. Appendix C contains lists of
plant species that occur in wetlands. Section 1 consists of regional lists developed by a Federal
interagency panel. Section 2 consists of shorter lists of plant species that commonly occur in
wetlands of each region. Section 3 describes morphological, physiological, and reproductive
adaptations associated with hydrophytic species, as well as a list of some species exhibiting
such adaptations. Appendix D discusses procedures for examining soils for hydric soil
indicators, and also contains a list of hydric soils of the United States.
Use
17. Although this manual was prepared primarily for use by Corps of Engineers (CE) field
inspectors, it should be useful to anyone who makes wetland determinations for purposes of
Section 404 of the Clean Water Act. The user is directed through a series of steps that involve
gathering of information and decisionmaking, ultimately leading to a wetland determination.
A general flow diagram of activities leading to a determination is presented in Figure 1.
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However, not all activities identified in Figure 1 will be required for each wetland
determination. For example, if a decision is made to use a routine determination procedure,
comprehensive determination procedures will not be employed.
PRELIMINARY DATA
GATHERING AND SYNTHESIS
PART IV, SECTION B
I
SELECT METHOD
PART IV,SECTION C
ROUTINE
DETERMINATION
PART IV,SECTION D
COMPREHENSIVE
DETERMINATION
PART IV, SECTION E
JURISDICTIONAL
DETERMINATION
Figure 1. General schematic diagram of activities leading
to a wetland/nonwetland determination
Premise for use of the manual
18. Three key provisions of the CE/EPA definition of wetlands (see paragraph 26a) include:
a. Inundated or saturated soil conditions resulting from permanent or periodic inundation by
ground water or surface water.
b. A prevalence of vegetation typically adapted for life in saturated soil conditions
(hydrophytic vegetation).
c. The presence of "normal circumstances."
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19. Explicit in the definition is the consideration of three environmental parameters:
hydrology, soil, and vegetation. Positive wetland indicators of all three parameters are
normally present in wetlands. Although vegetation is often the most readily observed
parameter, sole reliance on vegetation or either of the other parameters as the determinant of
wetlands can sometimes be misleading. Many plant species can grow successfully in both
wetlands and nonwetlands, and hydrophytic vegetation and hydric soils may persist for
decades following alteration of hydrology that will render an area a nonwetland. The presence
of hydric soils and wetland hydrology indicators in addition to vegetation indicators will
provide a logical, easily defensible, and technical basis for the presence of wetlands. The
combined use of indicators for all three parameters will enhance the technical accuracy,
consistency, and credibility of wetland determinations. Therefore, all three parameters were
used in developing the technical guideline for wetlands and all approaches for applying the
technical guideline embody the multiparameter concept.
Approaches
20. The approach used for wetland delineations will vary, based primarily on t;he complexity
of the area in question. Two basic approaches described in the manual are (a) routine and (b)
comprehensive.
21. Routine approach. The routine approach normally will be used in the vast majority of
determinations. The routine approach requires minimal level of effort, using primarily
qualitative procedures. This approach can be further subdivided into three levels of required
effort, depending on the complexity of the area and the amount and quality of preliminary data
available. The following levels of effort may be used for routine determinations:
a. Level 1 - Onsite inspection unnecessary. (PART IV, Section D, Subsection 1).
b. Level 2 - Onsite inspection necessary. (PART IV, Section D, Subsection 2).
c. Level 3 - Combination of Levels 1 and 2. (PART IV, Section D, Subsection 3).
22. Comprehensive approach. The comprehensive approach requires application of
quantitative procedures for making wetland determinations. It should seldom be necessary,
and its use should be restricted to situations in which the wetland is very complex and/or is the
subject of likely or pending litigation. Application of the comprehensive approach (PART IV,
Section E) requires a greater level of expertise than application of the routine approach, and
only experienced field personnel with sufficient training should use this approach.
Flexibility
23. Procedures described for both routine and comprehensive wetland determinations have
been tested and found to be reliable. However, site-specific conditions may require
modification of field procedures. For example, slope configuration in a complex area may
necessitate modification of the baseline and transect positions. Since specific characteristics
(e.g. plant density) of a given plant community may necessitate the use of alternate methods
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for determining the dominant species, the user has the flexibility to employ sampling
procedures other than those described. However, the basic approach for making wetland
determinations should not be altered (i.e. the determination should be based on the dominant
plant species, soil characteristics, and hydrologic characteristics of the area in question). The
user should document reasons for using a different characterization procedure than described
in the manual. CA UTION: Application of methods described in the manual or the modified
sampling procedures requires that the user be familiar with wetlands of the area and use his
training, experience, and good judgment in making wetland determinations.
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PART II: TECHNICAL GUIDELINES
24. The interaction of hydrology, vegetation, and soil results in the development of
characteristics unique to wetlands. Therefore, the following technical guideline for wetlands is
based on these three parameters, and diagnostic environmental characteristics used in applying
the technical guideline are represented by various indicators of these parameters.
25. Because wetlands may be bordered by both wetter areas (aquatic habitats) and by drier
areas (nonwetlands), guidelines are presented for wetlands, deepwater aquatic habitats, and
nonwetlands. However, procedures for applying the technical guidelines for deepwater aquatic
habitats and nonwetlands are not included in the manual.
Wetlands
26. The following definition, diagnostic environmental characteristics, and technical approach
comprise a guideline for the identification and delineation of wetlands:
a. Definition. The CE (Federal Register 1982) and the EPA (Federal Register 1980) jointly
define wetlands as: Those areas that are inundated or saturated by surface or ground water at a
frequency and duration sufficient to support, and that under normal circumstances do support,
a prevalence of vegetation typically adapted for life in saturated soil conditions. Wetlands
generally include swamps, marshes, bogs, and similar areas.
b. Diagnostic environmental characteristics. Wetlands have the following general diagnostic
environmental characteristics:
(1) Vegetation. The prevalent vegetation consists of macrophytes that are typically adapted to
areas having hydrologic and soil conditions described in a above. Hydrophytic species, due to
morphological, physiological, and/or reproductive adaptations), have the ability to grow,
effectively compete, reproduce, and/or persist in anaerobic soil conditions. Footnote: Species
(e.g. Acer rubrum) having broad ecological tolerances occur in both wetlands and
nonwetlands. Indicators of vegetation associated with wetlands are listed in paragraph 35.
(2) Soil. Soils are present and have been classified as hydric, or they possess characteristics
that are associated with reducing soil conditions. Indicators of soils developed under reducing
conditions are listed in paragraphs 44 and 45.
(3) Hydrology. The area is inundated either permanently or periodically at mean water depths
<6.6 ft, or the soil is saturated to the surface at some time during the growing season of the
prevalent vegetation. The period of inundation or soil saturation varies according to the
hydrologic/soil moisture regime and occurs in both tidal and nontidal situations. Indicators of
hydrologic conditions that occur in wetlands are listed in paragraph 49.
c. Technical approach for the identification and delineation of wetlands. Except in certain
situations defined in this manual, evidence of a minimum of one positive wetland indicator
from each parameter (hydrology, soil, and vegetation) must be found in order to make a
positive wetland determination.
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Deepwater Aquatic Habitats
27. The following definition, diagnostic environmental characteristics, and technical approach
comprise a guideline for deepwater aquatic habitats:
a. Definition. Deepwater aquatic habitats are areas that are permanently inundated at mean
annual water depths >6.6 ft or permanently inundated areas <6.6 ft in depth that do not support
rooted-emergent or woody plant species. Areas <6.6ft mean annual depth that support only
submergent aquatic plants are vegetated shallows, not wetlands.
b. Diagnostic environmental characteristics. Deepwater aquatic habitats have the following
diagnostic environmental characteristics:
(1) Vegetation. No rooted-emergent or woody plant species are present in-these permanently
inundated areas.
(2) Soil. The substrate technically is not defined as a soil if the mean water depth is >6.6 ft or
if it will not support rooted emergent or woody plants.
(3) Hydrology. The area is permanently inundated at mean water depths >6.6 ft.
c. Technical approach for the identification and delineation of deepwater aquatic habitats.
When any one of the diagnostic characteristics identified in b above is present, the area is a
deepwater aquatic habitat.
Nonwetlands
28. The following definition, diagnostic environmental characteristics, and technical approach
comprise a guideline for the identification and delineation of nonwetlands:
a. Definition. Nonwetlands include uplands and lowland areas that are neither deepwater
aquatic habitats, wetlands, nor other special aquatic sites. They are seldom or never inundated,
or if frequently inundated, they have saturated soils for only brief periods during the growing
season, and, if vegetated, they normally support a prevalence of vegetation typically adapted
for life only in aerobic soil conditions.
b. Diagnostic environmental characteristics. Nonwetlands have the following general
diagnostic environmental characteristics:
(1) Vegetation. The prevalent vegetation consists of plant species that are typically adapted
for life only in aerobic soils. These mesophytic and/or xerophytic macrophytes cannot persist
in predominantly anaerobic soil conditions. Some species, due to their broad ecological
tolerances, occur in both wetlands and nonwetlands (e.g. Acer rubrum).
(2) Soil. Soils, when present, are not classified as hydric, and possess characteristics
associated with aerobic conditions.
(3) Hydrology. Although the soil may be inundated or saturated by surface water or ground
water periodically during the growing season of the prevalent vegetation, the average annual
duration of inundation or soil saturation does not preclude the occurrence of plant species
typically adapted for life in aerobic soil conditions.
(L. Technical approach for the identification and delineation of nonwetlands. When any one of
the diagnostic characteristics identified in b above is present, the area is a nonwetland.
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PART III: CHARACTERISTICS AND INDICATORS OF
HYDROPHYTIC VEGETATION, HYDRIC SOILS, AND WETLAND
HYDROLOGY
Hydrophytic Vegetation
Definition
29. Hydrophytic vegetation. Hydrophytic vegetation is defined herein as the sum total of
macrophytic plant life that occurs in areas where the frequency and duration of inundation or
soil saturation produce permanently or periodically saturated soils of sufficient duration to
exert a controlling influence on the plant species present. The vegetation occurring in a
wetland may consist of more than one plant community (species association). The plant
community concept is followed throughout the manual. Emphasis is placed on the assemblage
of plant species that exert a controlling influence on the character of the plant community,
rather than on indicator species. Thus, the presence of scattered individuals of an upland plant
species in a community dominated by hydrophytic species is not a sufficient basis for
concluding that the area is an upland community. Likewise, the presence of a few individuals
of a hydrophytic species in a community dominated by upland species is not a sufficient basis
for concluding that the area has hydrophytic vegetation.
CAUTION: In determining whether an area is "vegetated" for the purpose of Section 404
jurisdiction, users must consider the density of vegetation at the site being evaluated. While it
is not possible to develope a numerical method to determine how many plants or how much
biomass is needed to establish an area as being vegetated or unvegetated, it is intended that
the predominant condition of the site be used to make that characterization. This concept
applies to areas grading from wetland to upland, and from wetland to other waters. This
limitation would not necessarily apply to areas which have been disturbed by man or recent
natural events.
30. Prevalence of vegetation. The definition of wetlands (paragraph 26a) includes the phrase
"prevalence of vegetation." Prevalence, as applied to vegetation, is an imprecise, seldom-used
ecological term. As used in the wetlands definition, prevalence refers to the plant community
or communities that occur in an area at some point in time. Prevalent vegetation is
characterized by the dominant species comprising the plant community or communities.
Dominant plant species are those that contribute more to the character of a plant community
than other species present, as estimated or measured in terms of some ecological parameter or
parameters. The two most commonly used estimates of dominance are basal area (trees) and
percent areal cover (herbs). Hydrophytic vegetation is prevalent in an area when the dominant
species comprising the plant community or communities are typically adapted for life in
saturated soil conditions.
31. Typically adapted. The term "typically adapted" refers to a species being normally or
commonly suited to a given set of environmental conditions, due to some morphological,
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physiological, or reproductive adaptation (Appendix C, Section 3). As used in the CE wetlands
definition, the governing environmental conditions for hydrophytic vegetation are saturated soils
resulting from periodic inundation or saturation by surface or ground water. These periodic events
must occur for sufficient duration to result in anaerobic soil conditions. When the dominant
species in a plant community are typically adapted for life in anaerobic soil conditions, hydrophytic
vegetation is present. Species listed in Appendix C, Section 1 or 2. that have an indicator status of
OBL, FACW, or FAC (Table 1) are considered to be typically adapted for life in anaerobic soil
conditions (see paragraph 35a). Species having a FAC- indicator status are not considered to be
typically adapted for life in anaerobic soil conditions.
Influencing factors
32. Many factors (e.g. light, temperature, soil texture and permeability, man-induced
disturbance, etc.) influence the character of hydrophytic vegetation. However, hydrologic
factors exert an overriding influence on species that can occur in wetlands. Plants lacking
morphological, physiological, and/or reproductive adaptations cannot grow, effectively
compete, reproduce, and/or persist in areas that are subject to prolonged inundation or
saturated soil conditions.
Geographic diversity
33. Many hydrophytic vegetation types occur in the United States due to the diversity of
interactions among various factors that influence the distribution of hydrophytic species.
General climate and flora contribute greatly to regional variations in hydrophytic vegetation.
Consequently, the same associations of hydrophytic species occurring in the southeastern
United States are not found in the Pacific Northwest. In addition, local environmental
conditions (e.g. local climate, hydrologic regimes, soil series, salinity, etc.) may result in broad
variations in hydrophytic associations within a given region. For example, a coastal saltwater
marsh will consist of different species than an inland freshwater marsh in the same region. An
overview of hydrophytic vegetation occurring in each region of the Nation has been published
by the CE in a series of eight preliminary wetland guides (Table 2), and a group of wetland and
estuarine ecological profiles (Table 3) has been published by FWS.
Classification
34. Numerous efforts have been made to classify hydrophytic vegetation. Most systems are
based on general characteristics of the dominant species occurring in each vegetation type.
These range from the use of general physiognomic categories (e.g. overstory, subcanopy,
ground cover, vines) to specific vegetation types (e.g. forest type numbers as developed by the
Society of American Foresters). In other cases, vegetational characteristics are combined with
hydrologic features to produce more elaborate systems. The most recent example of such a
system was developed for the FWS by Cowardin et al. (1979).
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Table 1
Plant Indicator Status Categories*
Indicator Category
OBLIGATE WETLAND
PLANTS
Indicator
Symbol
OBL
FACULTATIVE WETLAND
PLANTS
FACW
FACULTATIVE UPLAND
PLANTS
FACU
OBLIGATE UPLAND
PLANTS
OBL
Definition
Plants that occur almost always (estimated
probability >99%) in wetlands under natural
conditions, but which may also occur rarely
(estimated probability <1%) in nonwetlands.
Examples: Spartina alterniflora, Taxodium
distichum.
Plants that occur usually (estimated probability
>67% to 99%) inwetlands, but may also occur
(estimated probability 1% to 33% in nonwet-
lands). Examples: Fraxinuspennsylvanica,
Cornus stolonifera.
Plants that occur sometimes (estimated prob-
ability 1% to <33%) in wetlands, but occur
more often (estimated probability >67% to
99%) in nonwetlands. Examples: Quercus
rubra, Potentilla arguta.
Plants that occur rarely (estimated probability
<1%) in wetlands, but occur almost always
(estimated probability >99%) in nonwetlands
under natural conditions. Examples: Pinus
echinata, Bromus mollis.
* Categories were originally developed and defined by the USFWS National Wetlands Inventory
and subsequently modified by the National Plant List Panel. The three facultative categories are
subdivided by (+) and (-) modifiers (See Appendix C, Section 1).
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Indicators of hydrophytic vegetation
35. Several indicators may be used to determine whether hydrophytic vegetation is present on
a site. However, the presence of a single individual of a hydrophytic species does not mean
that hydrophytic vegetation is present. The strongest case for the presence of hydrophytic
vegetation can be made when several indicators, such as those in the following list, are present.
However, any one of the following is indicative that hydrophytic vegetation is present:
Indicators are listed in order of decreasing reliability. Although all are valid indicators, some
are stronger than others. When a decision is based on an indicator appearing in the lower
portion of the list, re-evaluate the parameter to ensure that the proper decision was reached.
a. More than 50 percent of the dominant species are OBL. FACW. or FAC-** (Table 1) on
lists of plant species that occur in wetlands. A national interagency panel has prepared a
National List of Plant Species that occur in wetlands. This list categorizes species according to
their affinity for occurrence in wetlands. Regional subset lists of the national list, including
only species having an indicator status of OBL, FACW, or FAC, are presented in Appendix C,
Section 1. The CE has also developed regional lists of plant species that commonly occur in
wetlands (Appendix C, Section 2). Either list may be used. Note: A District that, on a
subregional basis, questions the indicator status of FAC species may use the following option:
When FAC species occur as dominants along with other dominants that are not FAC (either
wetter or drier than FAC), the FAC species can be considered as neutral, and the vegetation
decision can be based on the number of dominant species wetter than FAC as compared to the
number of dominant species drier than FAC. When a tie occurs or all dominant species are
FAC, the nondominant species must be considered. The area has hydrophytic vegetation when
more than 50 percent of all considered species are wetter than FAC. When either all
considered species are FAC or the number of species wetter than FAC equals the number of
species drier than FAC, the wetland determination will be based on the soil and hydrology
parameters. Districts adopting this option should provide documented support to the Corps
representative on the regional plant list panel, so that a change in indicator status of FAC
species of concern can be pursued. Corps representatives on the regional and national plant
list panels will continually strive to ensure that plant species are properly designated on both a
regional and subregional basis.
**FAC+ species are considered to be wetter (i.e., have a greater estimated probability of
occurring in wetlands) than FAC species, while FAC- species are considered to be drier (i.e.,
have a lesser estimated probability of occurring in wetlands) than FAC species.
b. Other indicators. Although there are several other indicators of hydrophytic vegetation, it
will seldom be necessary to use them. However, they may provide additional useful
information to strengthen a case for the presence of hydrophytic vegetation. Additional
training and/or experience may be required to employ these indicators.
(1) Visual observation of plant species growing in areas of prolonged inundation and/or soil
saturation. This indicator can only be applied by experienced personnel who have
accumulated information through several years of field experience and written documentation
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(field notes) that certain species commonly occur in areas of prolonged (>10 percent)
inundation and/or soil saturation during the growing season. Species such as Taxodiun
distichum, Typha latifolia, and Spartina alterniflora normally occur in such areas. Thus,
occurrence of species commonly observed in other wetland areas provides a strong indication
that hydrophytic vegetation is present. CA UTION: The presence of standing water or
saturated soil on a site is insufficient evidence that the species present are able to tozerate long
periods of inundation. The user must rezate the observed species to other simizar situations
and determine whether they are normally found in wet areas, taking into consideration the
season and immediatezy preceding weather conditions.
(2) Morphological adaptations. Some hydrophytic species have easily recognized physical
characteristics that indicate their ability to occur in wetlands. A given species may exhibit
several of these characteristics, but not all hydrophytic species have evident morphological
adaptations. A list of such morphological adaptations and a partial list of plant species with
known morphological adaptations for occurrence in wetlands are provided in Appendix C,
Section 3.
(3) Technical literature. The technical literature may provide a strong indication that plant
species comprising the prevalent vegetation are commonly found in areas where soils are
periodically saturated for long periods. Sources of available literature include:
(a) Taxonomic references. Such references usually contain at least a general description of
the habitat in which a species occurs. A habitat description such as, "Occurs in water of
streams and lakes and in alluvial floodplains subject to periodic flooding," supports a
conclusion that the species typically occurs in wetlands. Examples of some useful taxonomic
references are provided in Table 4.
(b) Botanical journals. Some botanical journals contain studies that define species occurrence
in various hydrologic regimes. Examples of such journals include: Ecology. Ecological
Monographs. American Journal of Botany. Journal of American Forestry, and Wetlands: The
Journal of the Society of Wetland Scientists.
(c) Technical reports. Governmental agencies periodically publish reports (e.g. literature
reviews) that contain information on plant species occurrence in relation to hydrologic
regimes. Examples of such publications include the CE preliminary regional wetland guides
(Table 2) published by the US Army Engineer Waterways Experiment Station (WES) and the
wetland community and estuarine profiles of various habitat types (Table 3) published by the
FWS.
(d) Technical workshops, conferences, and symposia. Publications resulting from periodic
scientific meetings contain valuable information that can be used to support a decision
regarding the presence of hydrophytic vegetation. These usually address specific regions or
wetland types. For example, distribution of bottomland hardwood forest species in relation to
hydrologic regimes was examined at a workshop on bottomland hardwood forest wetlands of
the Southeastern United States (Clark and Benforado 1981).
(e) Wetland plant database. The NWI is producing a Plant Database that contains habitat
information on approximately 5,200 plant species that occur at some estimated probability in
wetlands, as compiled from the technical literature. When completed, this computerized
database will be available to all governmental agencies.
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(4) Physiological adaptations. Physiological adaptations include any features of the metabolic
processes of plants that make them particularly fitted for life in saturated soil conditions.
NOTE: It is impossible to detect the presence of physiological adaptations in plant species
during onsite visits. Physiological adaptations known for hydrophytic species and species
known to exhibit these adaptations are listed and discussed in Appendix C, Section 3.
(5) Reproductive adaptations. Some plant species have reproductive features that enable them
to become established and grow in saturated soil conditions. Reproductive adaptations known
for hydrophytic species are presented in Appendix C, Section 3.
Hvdric Soils
Definition
36. A hydric soil is a soil that is saturated, flooded, or ponded long enough during the growing
season to develop anaerobic conditions that favor the growth and regeneration of hydrophytic
vegetation (US Department of Agriculture (USDA) Soil Conservation Service (SCS) 1985, as
amended by the National Technical Committee for Hydric Soils (NTCHS) in December 1986).
Criteria for hydric soils
37. Based on the above definition, the NTCHS developed the following criteria for hydric
soils:
a. All Histosols except Folists; Soil nomenclature follows USDA-SCS (1975).
b. Soils in Aquic suborders, Aquic subgroups, Albolls suborder, Salorthids great group, or
Pell great groups of Vertisols that are:
(1) Somewhat poorly drained and have a water table less than 0.5 ft
from the surface for a significant period (usually a week or more) during the growing season,
or
(2) Poorly drained or very poorly drained and have either:
(a) A water table at less than 1.0 ft from the surface for a significant period (usually a week or
more) during the growing season if permeability is equal to or greater than 6.0 in/hr in all
layers within 20 inches; or
(b) A water table at less than 1.5 ft from the surface for a significant period (usually a week or
more) during the growing season if permeability is less than 6.0 in/hr in any layer within 20
inches; or
c. Soils that are ponded for long or very long duration during the growing season; or
d. Soils that are frequently flooded for long duration or very long duration during the growing
season."
A hydric soil may be either drained or undrained, and a drained hydric soil may not continue to
support hydrophytic vegetation. Therefore, not all areas having hydric soils will qualify as
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wetlands. Only when a hydric soil supports hydrophytic vegetation and the area has indicators
of wetland hydrology may the soil be referred to as a "wetland" soil.
38. A drained hydric soil is one in which sufficient ground or surface water has been removed
by artificial means such that the area will no longer support hydrophyte vegetation. Onsite
evidence of drained soils includes:
a. Presence of ditches or canals of sufficient depth to lower the water table below the major
portion of the root zone of the prevalent vegetation.
b. Presence of dikes, levees, or similar structures that obstruct normal inundation of an area.
c. Presence of a tile system to promote subsurface drainage.
d. Diversion of upland surface runoff from an area.
Although it is important to record such evidence of drainage of an area, a hydric soil that has
been drained or partially drained still allows the soil parameter to be met. However, the area
will not qualify as a wetland if the degree of drainage has been sufficient to preclude the
presence of either hydrophytic vegetation or a hydrologic regime that occurs in wetlands.
NOTE: the mere presence of drainage structures in an area is not sufficient basis .for
concluding that a hydric soil has been drained; such areas may continue to have wetland
hydrology.
General information
39. Soils consist of unconsolidated, natural material that supports, or is capable of supporting,
plant life. The upper limit is air and the lower limit is either bedrock or the limit of biological
activity. Some soils have very little organic matter (mineral soils), while others are composed
primarily of organic matter (Histosols). The relative proportions of particles (sand, silt, clay,
and organic matter) in a soil are influenced by many interacting environmental factors. As
normally defined, a soil must support plant life. The concept is expanded to include substrates
that could support plant life. For various reasons, plants may be absent from areas that have
well-defined soils.
40. A soil profile (Figure 2) consists of various soil layers described from the surface
downward. Most soils have two or more identifiable horizons. A soil horizon is a layer
oriented approximately parallel to the soil surface, and usually is differentiated from
contiguous horizons by characteristics that can be seen or measured in the field (e.g., color,
structure, texture, etc.). Most mineral soils have A-, B-, and C-horizons, and many have
surficial organic layers (0-horizon). The A-horizon, the surface soil or topsoil, is a zone in
which organic matter is usually being added to the mineral, soil. It is also the zone from which
both mineral and organic matter are being moved slowly downward. The next major horizon is
the B-horizon, often referred to as the subsoil. The B-horizon is the zone of maximum
accumulation of materials. It is usually characterized by higher clay content and/or more
pronounced soil structure development and lower organic matter than the A-horizon. The next
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major horizon is usually the C-horizon, which consists of unconsolidated parent material that
has not been sufficiently weathered to exhibit characteristics of the B-horizon. Clay content
and degree of soil structure development in the C-horizon are usually less than in the B-
horizon. The lowest major horizon, the R-horizon, consists of consolidated bedrock. In many
situations, this horizon occurs at such depths that it has no significant influence on soil
characteristics.
Influencing factors
41. Although all soil-forming factors (climate, parent material, relief, organisms, and time)
affect the characteristics of a hydric soil, the overriding influence is the hydrologic regime.
The unique characteristics of hydric soils result from the influence of periodic or permanent
inundation or soil saturation for sufficient duration to effect anaerobic conditions. Prolonged
anaerobic soil conditions lead to a reducing environment, thereby lowering the soil redox
potential. This results in chemical reduction of some soil components (e.g. iron and
manganese oxides), which leads to development of soil colors and other physical
characteristics that usually are indicative of hydric soils.
Classification
42. Hydric soils occur in several categories of the current soil classification system, which is
published in Soil Taxonomy (USDA-SCS 1975). This classification system is based on
physical and chemical properties of soils that can be seen, felt, or measured. Lower taxonomic
categories of the system (e.g. soil series and soil phases) remain relatively unchanged from
earlier classification systems.
43. Hydric soils may be classified into two broad categories: organic and mineral. Organic
soils (Histosols) develop under conditions of nearly continuous saturation and/or inundation.
All organic soils are hydric soils except Folists, which are freely drained soils occurring on dry
slopes where excess litter accumulates over bedrock. Organic hydric soils are commonly
known as peats and mucks. All other hydric soils are mineral soils. Mineral soils have a wide
range of textures (sandy to clayey) and colors (red to gray). Mineral hydric soils are those
periodically saturated for sufficient duration to produce chemical and physical soil properties
associated with a reducing environment. They are usually gray and/or mottled immediately
below the surface horizon (see paragraph 44d), or they have thick, dark-colored surface layers
overlying gray or mottled subsurface horizons.
Wetland indicators (nonsandy soils)
44. Several indicators are available for determining whether a given soil meets the definition
and criteria for hydric soils. Any one of the following indicates that hydric soils are present:
Indicators are listed in order of decreasing reliability. Although all are valid indicators, some
are stronger indicators than others. When a decision is based on an indicator appearing in the
lower portion of the list, re-evaluate the parameter to ensure that the proper decision was
reached.
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a. Organic soils (Histosols). A soil, is an organic soil when:
(1) more than 50 percent (by volume) of the upper 32 inches of soil is composed of organic
soil material; A detailed definition of organic soil material is available in USDA-SCS (1975),
or
(2) organic soil material of any thickness rests on bedrock. Organic soils (Figure 3) are
saturated for long periods and are commonly called peats or mucks.
b. Histic epipedons. A histic epipedon is an 8- to 16-inch layer at or near the surface of a
mineral hydric soil that is saturated with water for 30 consecutive days or more in most years
and contains a minimum of 20 percent organic matter when no clay is present or a minimum of
30 percent organic matter when clay content is 60 percent or greater. Soils with histic
epipedons are inundated or saturated for sufficient periods to greatly retard aerobic
decomposition of the organic surface, and are considered to be hydric soils.
c. Sulfidic material. When mineral soils emit an odor of rotten eggs, hydrogen sulfide is
present. Such odors are only detected in waterlogged soils that are permanently saturated and
have sulfidic material within a few centimetres of the soil surface. Sulfides are produced only
in a reducing environment.
d. Aquic or peraquic moisture regime. An aquic moisture regime is a reducing one: i.e.. it is
virtually free of dissolved oxygen because the soil is saturated by ground water or by water of
the capillary fringe (USDA-SCS 1975). Because dissolved oxygen is removed from ground
water by respiration of microorganisms, roots, and soil fauna, it is also implicit that the soil
temperature is above biologic zero (5° C) at some time while the soil is saturated. Soils with
peraquic moisture regimes are characterized by the presence of ground water always at or near
the soil surface. Examples include soils of tidal marshes and soils of closed, landlocked
depressions that are fed by permanent streams.
e. Reducing soil conditions. Soils saturated for long or very long duration will usually exhibit
reducing conditions. Under such conditions, ions of iron are transformed from a ferric valence
state to a ferrous valence state. This condition can often be detected in the field by a ferrous
iron test. A simple calorimetric field test kit has been developed for this purpose. When a soil
extract changes to a pink color upon addition of a-a-dipyridil, ferrous iron is present, which
indicates a reducing soil environment. NOTE: This test cannot be used in mineral hydric soils
having low iron content, organic soils, and soils that have been desaturated for significant
periods of the growing season.
f. Soil colors. The colors of various soil components are often the most diagnostic indicator of
hydric soils. Colors of these components are strongly influenced by the frequency and duration
of soil saturation, which leads to reducing soil conditions. Mineral hydric soils will be either
gleyed or will have bright mottles and/or low matrix chroma. These are discussed
below:
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(1) Gleyed soils (gray colors). Gleyed soils develop when anaerobic soil conditions result in
pronounced chemical reduction of iron, manganese, and other elements, thereby producing
gray soil colors. Anaerobic conditions that occur in waterlogged soils result in the
predominance of reduction processes, and such soils are greatly reduced. Iron is one of the
most abundant elements in soils. Under anaerobic conditions, iron in converted from the
oxidized (ferric) state to the reduced (ferrous) state, which results in the bluish, greenish, or
grayish colors associated with the gleying effect (Figure 4). Gleying immediately below the A-
horizon or 10 inches (whichever is shallower) is an indication of a markedly reduced soil, and
gleyed soils are hydric soils. Gleyed soil conditions can be determined by using the gley page
of the Munsell Color Book (Munsell Color 1975).
(2) Soils with bright mottles and/or low matrix chroma. Mineral hydric soils that are saturated
for substantial periods of the growing season (but not long enough to produce gleyed soils) will
either have bright mottles and a low matrix chroma or will lack mottles but have a low matrix
chroma (see Appendix D, Section 1, for a definition and discussion of "chroma" and other
components of soil color). Mottled means "marked with spots of contrasting color." Soils that
have brightlv colored mottles and a low matrix chroma are indicative of a fluctuating water
table. The soil matrix is the portion (usually more than 50 percent) of a given soil layer that
has the predominant color (Figure 5). Mineral hydric soils usually have one of the following
color features in the horizon immediately below the A-horizon or 10 inches (whichever is
shallower):
(a) Matrix chroma of 2 or less* in mottled soils.
(b) Matrix chroma of 1 or less* in unmottled soils.
*FOOTNOTE: Colors should be determined in soils that have been moistened; otherwise,
state that colors are for dry soils.
NOTE: The matrix chroma of some dark (black) mineral hydric soils will not conform to the
criteria described in (a) and (b) above; in such soils, gray mottles occurring at 10 inches or
less are indicative of hydric conditions.
CAUTION: Soils with significant coloration due to the nature of the parent material (e.g. red
soils of the Red River Valley) may not exhibit the above characteristics, in such cases, this
indicator cannot be used.
g. Soil appearing on hydric soils list. Using the criteria for hydric soils (paragraph 37), the
NTCHS has developed a list of hydric soils. Listed soils have reducing conditions for a
significant portion of the growing season in a major portion of the root zone and are frequently
saturated within 12 inches of the soil surface. The NTCHS list of hydric soils is presented in
Appendix D, Section 2. CAUTION: Be sure that the profile description of the mapping unit
conforms to that of the sampled soil.
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h. Iron and manganese concretions. During the oxidation-reduction process, iron and
manganese in suspension are sometimes segregated as oxides into concretions or soft masses
(Figure 6). These accumulations are usually black or dark brown. Concretions >2 mm in
diameter occurring within 7.5 cm of the surface are evidence that the soil is saturated for long
periods near the surface.
Wetland indicators (sandy soils)
45. Not all indicators listed in paragraph 44 can be applied to sandy soils. In particular, soil
color should not be used as an indicator in most sandy soils. However, three additional soil
features may be used as indicators of sandy hydric soils, including:
a. High organic matter content in the surface horizon. Organic matter tends to accumulate
above or in the surface horizon of sandy soils that are inundated or saturated to the surface for
a significant portion of the growing season. Prolonged inundation or saturation creates
anaerobic conditions that greatly reduce oxidation of organic matter.
b. Streaking of subsurface horizons by organic matter. Organic matter is moved downward
through sand as the water table fluctuates. This often occurs more rapidly and to a greater
degree in some vertical sections of a sandy soil containing high content of organic matter than
in others. Thus, the sandy soil appears vertically streaked with darker areas. When soil from a
darker area is rubbed between the fingers, the organic matter stains the fingers.
c. Organic pans. As organic matter is moved downward through ,;andy soils, it tends to
accumulate at the point representing the most commonly occurring depth to the water table.
This organic matter tends to become slightly cemented with aluminum, forming a thin layer of
hardened soil (spodic horizon). These horizons often occur at depths of 12 to 30 inches below
the mineral surface. Wet spodic soils usually have thick dark surface horizons that are high in
organic matter with dull, gray horizons above the spodic horizon.
CAUTION: In recently deposited sandy material (e.g. accreting sandbars), it may be
impossible to find any of these indicators, in such cases., consider this as a natural atypical
situation.
Wetland Hydrology
Definition
46. The term "wetland hydrology" encompasses all hydrologic characteristics of areas that are
periodically inundated or have soils saturated to the surface at some time during the growing
season. Areas with evident characteristics of wetland hydrology are those where the presence
of water has an overriding influence on characteristics of vegetation and soils due to anaerobic
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and reducing conditions, respectively. Such characteristics are usually present in areas that are
inundated or have soils that are saturated to the surface for sufficient duration to develop
hydrl.c soils and support vegetation typically adapted for life in periodically anaerobic soil
conditions. Hydrolpgy is often the least exact of the parameters, and indicators of wetland
hydrology are sometimes difficult to find in the field. However, it is essential to establish that a
wetland area is periodically inundated or has saturated soils during the growing season.
Influencing factors
47. Numerous factors (e.g., precipitation, stratigraphy, topography,!' soil permeability, and
plant cover) influence the wetness of an area. Regardless, the characteristic common to all
wetlands is the presence of an abundant supply of water. The water source may be runoff from
direct precipitation, headwater or backwater flooding, tidal influence, ground water, or some
combination of these sources. The frequency and duration of inundation or soil saturation
varies from nearly permanently inundated or saturated to irregularly inundated or saturated.
Topographic position, stratigraphy, and soil permeability influence both the frequency and
duration of inundation and soil saturation. Areas of lower elevation in a floodplain or marsh
have more frequent periods of inundation and/or greater duration than most areas at higher
elevations. Floodplain configuration may significantly affect duration of inundation. When
the floodplain configuration is conducive to rapid runoff, the influence of frequent periods of
inundation on vegetation and soils maylbe reduced. Soil permeability also influences duration
of inundation and soil saturation. For example, clayey soils absorb water more slowly than
sandy or loamy soils, and therefore have slower permeability and remain saturated much
longer. Type and amount of plant cover affect both degree of inundation and duration of
saturated soil conditions. Excess water drains more slowly in areas of abundant plant cover,
thereby increasing frequency and duration of inundation and/or soil saturation. On the other
hand, transpiration rates are higher in areas of abundant plant cover, which may reduce the
duration of soil saturation.
Classification
48. Although the interactive effects of all hydrologic factors produce a continuum of wetland
hydrologic regimes, efforts have been made to classify wetland hydrologic regimes into
functional categories. These efforts have focused on the use of frequency, timing, and duration
of inundation or soil saturation as a basis for classification. A classification system developed
for nontidal areas is presented in Table 5. This classification system was slightly modified from
the system developed by the Workshop on Bottomland Hardwood Forest Wetlands of the
Southeastern United States (Clark and Benforado 1981). Recent research indicates that
duration of inundation and/or soil saturation during the growing season is more influential on
the plant community than frequency of inundation/saturation during the growing season
(Theriot, in press). Thus, frequency of inundation and soil saturation are not included in Table
5. The WES has developed a computer program that can be used to transform stream gage data
to mean sea level elevations representing the upper limit of each hydrologic zone shown in
Table 5.
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Wetland indicators
49. Indicators of wetland hydrology may include, but are not necessarily limited to: drainage
patterns, drift lines, sediment deposition, watermarks, stream gage data and flood predictions,
historic records, visual observation of saturated soils, and visual observation of inundation.
Any of these indicators may be evidence of wetland hydrologic characteristics. Methods for
determining hydrologic indicators can be categorized according to the type of indicator.
Recorded data include stream gage data, lake gage data, tidal gage data, flood predictions, and
historical records. Use of these data is commonly limited to areas adjacent to streams or other
similar areas. Recorded data usually provide both short- and long-term information about
frequency and duration of inundation, but contain little or no information about soil saturation,
which must be gained from soil surveys or other similar sources. The remaining indicators
require field observations. Field indicators are evidence of present or past hydrologic events
(e.g. location and height of flooding). Indicators for recorded data and field observations
include: (Indicators are listed in order of decreasing reliability. Although all are valid
indicators, some are stronger indicators than others. When a decision is based on an indicator
appearing in the lower portion of the list, re-evaluate the parameter to ensure that the proper
decision was reached.)
cL Recorded data. Stream gage data, lake gage data, tidal gage data, flood predictions, and
historical data may be available from the following sources:
(1) CE District Offices. Most CE Districts maintain stream, lake, and tidal gage records for
major water bodies in their area. In addition, CE planning and design documents often contain
valuable hydrologic information. For example, a General Design Memorandum (GDM)
usually describes flooding frequencies and durations for a project area. Furthermore, the extent
of flooding within a project area is sometimes indicated in the GDM according to elevation
(height) of certain flood frequencies (1-, 2-, 5-, 10-year, etc.).
(2) US Geological Survey (USGS). Stream and tidal gage data are available from the USGS
offices throughout the Nation, and the latter are also available from the National Oceanic and
Atmospheric Administration. CE Districts often have such records.
(3) State, county, and local agencies. These agencies often have responsibility for flood
control/relief and flood insurance.
(4) Soil Conservation Service Small Watershed Projects. Planning documents from this
agency are often helpful, and can be obtained from the SCS district office in the county.
(5) Planning documents of developers.
b. Field data. The following field hydrologic indicators can be assessed quickly, and although
some of them are not necessarily indicative of hydrologic events that occur only during the
growing season, they do provide evidence that inundation and/or soil saturation has occurred:
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(1) Visual observation of inundation. The most obvious and revealing hydrologic indicator
may be simply observing the areal extent of inundation. However, because seasonal conditions
and recent weather conditions can contribute to surface water being present on a nonwetland
site, both should be considered when applying this indicator.
(2) Visual observation of soil saturation. Examination of this indicator requires digging a soil
pit (Appendix D, Section 1) to a depth of 16 inches and observing the level at which water
stands in the hole after sufficient time has been allowed for water to drain into the hole. The
required time will varv depending on soil texture. In some cases, the upper level at which
water is flowing into the pit can be observed by examining the wall of the hole. This level
represents the depth to the water table. The depth to saturated soils will always be nearer the
surface due to the capillary fringe. For soil saturation to impact vegetation, it must occur
within a major portion of the root zone (usually within 12 inches of the surface) of the
prevalent vegetation. The major portion of the root zone is that portion of the soil profile in
which more than one half of the plant roots occur. CAUTION: In some heavy clay soils, water
may not rapidly accumulate in the hole even when the soil is saturated. If water is observed at
the bottom of the hole but has notfiLLed to the 12-inch depth, examine the sides of the hole
and determine the shallowest depth at which water is entering the hole. When applying this
indicator, both the season of the year and preceding weather conditions must be considered.
(3) Watermarks. Watermarks are most common on woody vegetation. They occur as stains
on bark (Figure 7) or other fixed objects (e.g. bridge pillars, buildings, fences, etc.). When
several watermarks are present, the highest reflects the maximum extent of recent inundation.
(4) Drift lines. This indicator is most likely to be found adjacent to streams or other sources of
water flow in wetlands, but also often occurs in tidal marshes. Evidence consists of deposition
of debris in a line on the surface (Figure 8) or debris entangled in aboveground vegetation or
other fixed objects. Debris usually consists of remnants of vegetation (branches, stems, and
leaves), sediment, litter, and other waterborne materials deposited parallel to the direction of
water flow. Drift lines provide an indication of the minimum portion of the area inundated
during a flooding event; the maximum level of inundation is generally at a higher elevation
than that indicated by a drift line.
(5) Sediment deposits. Plants and other vertical objects often have thin layers, coatings, or
depositions of mineral or organic matter on them after inundation (Figure 9). This evidence
may remain for a considerable period before it is removed by precipitation or subsequent
inundation. Sediment deposition on vegetation and other objects provides an. indication of the
minimum inundation level. When sediments are primarily organic (e.g. fine organic material,
algae), the detritus may become encrusted on or slightly above the soil surface after dewatering
occurs (Figure 10).
(6) Drainage patterns within wetlands. This indicator, which occurs primarily in wetlands
adjacent to streams, consists of surface evidence of drainage flow into or through an area
(Figure 11). In some wetlands, this evidence may exist as a drainage pattern eroded into the
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soil, vegetative matter (debris) piled against thick vegetation or woody stems oriented
perpendicular to the direction of water flow, or the absence of leaf litter (Figure 8). Scouring is
often evident around roots of persistent vegetation. Debris may be deposited in or along the
drainage pattern (Figure 12). CAUTION: Drainage patterns also occur in upland areas after
periods of considerable precipitation; therefore, topographic position must also be considered
when applying this indicator.
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PART IV: METHODS
Section A. Introduction
50. PART IV contains sections on preliminary data gathering, method selection, routine
determination procedures, comprehensive determination procedures, methods for
determinations in atypical situations, and guidance for wetland determinations in natural
situations where the three-parameter approach may not always apply.
51. Significant flexibility has been incorporated into PART IV. The user is presented in
Section B with various potential sources of information that may be helpful in making a
determination, but not all identified sources of information may be applicable to a given
situation. The user is not required to obtain information from all identified sources. Flexibility
is also provided in method selection (Section C). Three levels of routine determinations are
available, depending on the complexity of the required determination and the quantity and
quality of existing information. Application of methods presented in both Section D (routine
determinations) and Section E (comprehensive determinations) may be tailored to meet site-
specific requirements, especially with respect to sampling design.
52. Methods presented in Sections D and E vary with respect to the required level of technical
knowledge and experience of the user. Application of the qualitative methods presented in
Section D (routine determinations) requires considerably less technical knowledge and
experience than does application of the quantitative methods presented in Section E
(comprehensive determinations). The user must at least be able to identify the dominant,plant
species in the project area when making a routine determination (Section D), and should have
some basic knowledge of hydric soils when employing routine methods that require soils
examination. Comprehensive determinations require a basic understanding of sampling
principles and the ability to identify all commonly occurring plant species in a project area, as
well as a good understanding of indicators of hydric soils and wetland hydrology. The
comprehensive method should only be employed by experienced field inspectors.
Section B. Preliminary Data Gathering and Synthesis
53. This section discusses potential sources of information that may be helpful in making a
wetland determination. When the routine approach is used, it may often be possible to make a
wetland determination based on available vegetation, soils, and hydrology data for the area.
However, this section deals only with identifying potential information sources, extracting
pertinent data, and synthesizing the data for use in making a determination. Based on the
quantity and quality of available information and the approach selected for use (Section C), the
user is referred to either Section D or Section E for the actual determination. Completion of
Section B is not required, but is recommended because the available information may reduce or
eliminate the need for field effort and decrease the time and cost of making a determination.
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However, there are instances in small project areas in which the time required to obtain the
information may be prohibitive. In such cases PROCEED to paragraph 55, complete STEPS 1
through 3, and PROCEED to Section D or E.
Data sources
54. Obtain the following information, when available and applicable:
a. USGS quadrangle maps. USGS quadrangle maps are available at different scales. When
possible, obtain maps at a scale of 1:24,000; otherwise, use maps at a scale of 1:62,500. Such
maps are available from USGS in Reston, Va., and Menlo Park, Calif., but they may already be
available in the CE District Office. These maps provide several types of information:
(1) Assistance in locating field sites. Towns, minor roads, bridges, streams, and other
landmark features (e.g. buildings, cemeteries, water bodies, etc.) not commonly found on road
maps are shown on these maps.
(2) Topographic details, including contour lines (usually at 5- or 10-ft contour intervals).
(3) General delineation of wet areas (swamps and marshes). The actual wet area may be
greater than that shown on the map because USGS generally maps these areas based on the
driest season of the year.
(4) Latitude, longitude, townships, ranges, and sections. These provide legal descriptions of
the area.
(5) Directions, including both true and magnetic north.
(6) Drainage patterns.
(7) General land uses, such as cleared (agriculture or pasture), forested, or urban.
CA UTION: Obtain the most recent USGS maps. Older maps may show features that no
longer exist and will not show new features that have developed since the map was
constructed. Also, USGS is currently changing the mapping scale from 1:24,000 to 1:25,000.
IL National Wetlands Inventory products.
(1) Wetland maps. The standard NWI maps are at a scale of 1:24,000 or, where USGS base
maps at this scale are not available, they are at 1:62,500 (1:63,350 in Alaska). Smaller scale
maps ranging from 1:100,000 to 1:500,000 are also available for certain areas. Wetlands on
NWI maps are classified in accordance with Cowardin et al. (1979). CAUTION: Since not all
delineated areas on NWI maps are wetlands under Department of Army jurisdiction, NWI
maps should not be used as the sole basis for determining whether wetland vegetation is
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present. NWI "User Notes" are available that correlate the classification system with local
wetland community types. An important feature of this classification system is the water
regime modifier, which describes the flooding or soil saturation characteristics. Wetlands
classified as having a temporarily flooded or intermittently flooded water regime should be
viewed with particular caution since this designation is indicative of plant communities that are
transitional between wetland and nonwetland. These are among the most difficult plant
communities to map accurately from aerial photography. For wetlands "wetter" than
temporarily flooded and intermittently flooded, the probability of a designated map unit on
recent NWI maps being a wetland (according to Cowardin et al. 1979) at the time of the
photography is in excess of 90 percent. CAUTION: Due to the scale of aerial photography
used and other factors, all NWI map boundaries are approximate. The optimum use of NWI
maps is to plan field review (i.e. how wet, big, or diverse is the area?) and to assist during field
review, particularly by showing the approximate areal extent of the wetland and its association
with other communities. NWI maps are available either as a composite with, or an overlay for,
USGS base maps and may be obtained from the NWI Central Office in St. Petersburg, Fla., the
Wetland Coordinator at each FWS regional office, or the USGS.
(2) Plant database. This database of approximately 5,200 plant species that occur in wetlands
provides information (e.g., ranges, habitat, etc.) about each plant species from the technical
literature. The database served as a focal point for development of a national list of plants that
occur in wetlands (Appendix C, Section 1).
c. Soil surveys. Soil surveys are prepared by the SCS for political units (county, parish, etc.)
in a state. Soil surveys contain several types of information:
(1) General information (e.g. climate, settlement, natural resources, farming, geology, general
vegetation types).
(2) Soil maps for general and detailed planning purposes. These maps are usually generated
from fairly recent aerial photography. CAUTION: The smallest mapping unit is 3 acres, and a
given soil series as mapped may contain small inclusions of other series.
(3) Uses and management of soils. Any wetness characteristics of soils will be mentioned
here.
(4) Soil properties. Soil and water features are provided that may be very helpful for wetland
investigations. Frequency, duration, and timing of inundation (when present) are described for
each soil type. Water table characteristics that provide valuable information about soil
saturation are also described. Soil permeability coefficients may also be available.
(5) Soil classification. Soil series and phases are usually provided. Published soil surveys will
not always be available for the area. If not, contact the county SCS office and determine
whether the soils have been mapped.
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d. Stream and tidal gage data. These documents provide records of tidal and stream flow
events. They are available from either the USGS or CE District office.
e. Environmental impact assessments (EIAsX environmental impact statements (EISsX general
design memora da (GDMX and other similar publications. These documents may be available
from Federal agencies for an area that includes the project area. They may contain some
indication of the location and characteristics of wetlands consistent with the required criteria
(vegetation, soils, and hydrology), and often contain flood frequency and duration data.
f. Documents and maps from State, county, or local governments. Regional maps that
characterize certain areas (e.g., potholes, coastal areas, or basins) may be helpful because they
indicate the type and character of wetlands.
g. Remote sensing. Remote sensing is one of the most useful information sources available for
wetland identification and delineation. Recent aerial photography, particularly color infrared,
provides a detailed view of an area; thus, recent land use and other features (e.g. general type
and areal extent of plant communities and degree of inundation of the area when the
photography was taken) can be determined. The multiagency cooperative National High
Altitude Aerial Photography Program (HAP) has l:59,000-scale color infrared photography for
approximately 85 percent (December 1985) of the coterminous United States from 1980 to
1985. This photography has excellent resolution and can be ordered enlarged to 1:24,000 scale
from USGS. Satellite images provide similar information as aerial photography, although the
much smaller scale makes observation of detail more difficult without sophisticated equipment
and extensive training. Satellite images provide more recent coverage than aerial photography
(usually at 18-day intervals). Individual satellite images are more expensive than aerial
photography, but are not as expensive as having an area flown and photographed at low
altitudes. However, better resolution imagery is now available with remote sensing equipment
mounted on fixed-wing aircraft.
h. Local individuals and experts. Individuals having personal knowledge of an area may
sometimes provide a reliable and readily available source of information about the area,
particularly information on the wetness of the area.
i. USGS land use and land cover maps. Maps created by USGS using remotely sensed data
and a geographical information system provide a systematic and comprehensive collection and
analysis of land use and land cover on a national basis. Maps at a scale of 1:250,000 are
available as overlays that show land use and land cover according to nine basic levels. One
level is wetlands (as determined by the FWS), which is further subdivided into forested and
nonforested areas. Five other sets of maps show political units, hydrologic units, census
subdivisions of counties, Federal land ownership, and State land ownership. These maps can
be obtained from any USGS mapping center.
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I, Applicant's survey plans and engineering designs. In many cases, the permit applicant will
already have had the area surveyed (often at 1-ft contours or less) and will also have
engineering designs for the proposed activity.
Data synthesis
55. When employing Section B procedures, use the above sources of information to complete
the following steps:
• STEP 1 - Identify the Project Area on a Map. Obtain a USGS quadrangle map (1:24,000) or
other appropriate map, and locate the area identified in the permit application. PROCEED TO
STEP 2.
* STEP 2 - Prepare a Base Map. Mark the project area boundaries on the map. Either use the
selected map as the base map or trace the area on a mylar overlay, including prominent
landscape features (e.g., roads, buildings, drainage patterns, etc.). If possible, obtain diazo
copies of the resulting base map. PROCEED TO STEP 3.
• STEP 3 - Determine Size of the Project Area. Measure the area boundaries and calculate the
size of the area. PROCEED TO STEP 4 OR TO SECTION D OR E IF SECTION B IS NOT
USED.
• STEP 4 - Summarize Available Information on Vegetation. Examine available sources that
contain information about the area vegetation. Consider the following:
a. USGS quadrangle maps. Is the area shown as a marsh or swamp? CAUTION: Do not use
this as the sole basis for determining that hydrophytic vegetation is present.
b. NWI overlays or maps. Do the overlays or maps indicate that hydrophytic vegetation
occurs in the area? If so, identify the vegetation type(s).
c. EIAs, EISs, or GDMs that include the project area. Extract any vegetation data that pertain
to the area.
d. Federal, State, or local government documents that contain information about the area
vegetation. Extract appropriate data.
e. Recent (within last 5 years) aerial photography of the area. Can the area plant community
type(s) be determined from the photography? Extract appropriate data.
f. Individuals or experts having knowledge of the area vegetation. Contact them and obtain
any appropriate information. CAUTION: Ensure that the individual providing the information
has firsthand knowledge of the area.
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g. Any published scientific studies of the area plant communities. Extract any appropriate
data.
h. Previous wetland determinations made for the area. Extract any pertinent vegetation data.
When the above have been considered, PROCEED TO STEP 5.
• STEP 5 - Determine Whether the Vegetation in the Project Area Is Adequately Characterized.
Examine the summarized data (STEP 4) and determine whether the area plant communities are
adequately characterized. For routine determinations, the plant community type(s) and the
dominant species in each vegetation layer of each community type must be known. Dominant
species are those that have the largest relative basal area (overstory) (This term is used because
species having the largest individuals may not be dominant when only a few are present. To
use relative basal area, consider both the size and number of individuals of a species and
subjectively compare with other species present), height (woody understory), number of stems
(woody vines), or greatest areal cover (herbaceous understory). For comprehensive
determinations, each plant community type present in the project area area must have been
quantitatively described within the past 5 years using accepted sampling and analytical
procedures, and boundaries between community types must be known. Record information on
DATA FORM 1. A separate DATA FORM 1 must be used for each plant community type. In
either case, PROCEED TO Section F if there is evidence of recent significant vegetation
alteration due to human activities or natural events. Otherwise, PROCEED TO STEP 6.
* STEP 6. - Summarize Available Information on Area Soils. Examine available information
and describe the area soils. Consider the following:
a. County soil surveys. Determine the soil series present and extract characteristics for each.
CA UTION: Soil mapping units sometimes include more than one soil series.
b. Unpublished county soil maps. Contact the local SCS office and determine whether soil
maps are available for the area. Determine the soil series of the area, and obtain any available
information about possible hydric soil indicators (paragraph 44 or 45) for each soil series.
CL Published EIAs, EISs, or GDMs that include soils information. Extract any pertinent
information.
d. Federal, State, and/or local government documents that contain descriptions of the area
soils. Summarize these data.
e. Published scientific studies that include area soils data. Summarize these data.
f. Previous wetland determinations for the area. Extract any pertinent soils data.
When the above have been considered, PROCEED TO STEP 7.
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• STEP 7 - Determine Whether Soils of the Project Area Have Been Adequately Characterized.
Examine the summarized soils data and determine whether the soils have been adequately
characterized. For routine determinations, the soil series must be known. For comprehensive
determinations, both the soil series and the boundary of each soil series must be known.
Record information on DATA FORM 1. In either case, if there is evidence of recent significant
soils alteration due to human activities or natural events, PROCEED TO Section F. Otherwise,
PROCEED TO STEP 8.
• STEP 8 - Summarize Available Hydrology Data. Examine available information and
describe the area hydrology. Consider the following:
a. USGS quadrangle maps. Is there a significant, well-defined drainage through the area? Is
the area within a maior floodplain or tidal area? What range of elevations occur in the area,
especially in relation to the elevation of the nearest perennial watercourse?
b. NWI overlays or maps. Is the area shown as a wetland or deepwater aquatic habitat? What
is the water regime modifier?
c. El As, EISs, or GDMs that describe the project area. Extract any pertinent hydrologic data.
d. Floodplain management maps. These maps may be used to extrapolate elevations that can
be expected to be inundated on a 1-, 2-, 3-year, etc., basis. Compare the elevations of these
features with the elevation range of the project area to determine the frequency of inundation.
e. Federal, State, and local government documents (e.g. CE floodplain management maps and
profiles) that contain hydrologic data. Summarize these data.
f. Recent (within past 5 years) aerial photography that shows the area to be inundated. Record
the date of the photographic mission.
1. Newspaper accounts of flooding events that indicate periodic inundation of the area.
h. SCS County Soil Surveys that indicate the frequency and duration of inundation and soil
saturation for area soils. CA UTION: Data provided only represent average conditions for a
particular soil series in its natural undrained state, and cannot be used as a positive
hydrologic indicator in areas that have significantly altered hydrology.
\. Tidal or stream gage data for a nearby water body that apparently influences the area.
Obtain the gage data and complete (1) below if the routine approach is used, or (2) below if
the comprehensive approach is used (OMIT IF GAGING STATION DATA ARE
UNAVAILABLE):
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(1) Routine approach. Determine the highest water level elevation reached during the growing
season for each of the most recent 10 years of gage data. Rank these elevations in descending
order and select the fifth highest elevation. Combine this elevation with the mean sea level
elevation of the gaging station to produce a mean sea level elevation for the highest water level
reached every other year. Stream gage data are often presented as flow rates in cubic feet per
second. In these cases, ask the CE District's Hydrology Branch to convert flow rates to
corresponding mean sea level elevations and adjust gage data to the site. Compare the
resulting elevations reached biennially with the project area elevations. If the water level
elevation exceeds the area elevation, the area is inundated during the growing season on
average at least biennially.
(2) Comprehensive approach. Complete the following:
(a) Decide whether hydrologic data reflect the apparent hydrology. Data available from the
gaging station may or may not accurately reflect the area hydrology. Answer the following
questions:
• Does the water level of the area appear to fluctuate in a manner that differs from that of the
water body on which the gaging station is located? (In ponded situations, the water level of the
area is usually higher than the water level at the gaging station.)
• Are less than 10 years of daily readings available for the gaging station?
• Do other water sources that would not be reflected by readings at the gaging station appear to
significantly affect the area? For example, do major tributaries enter the stream or tidal area
between the area and gaging station?
If the answer to any of the above questions is YES, the area hydrology cannot be determined
from the gaging station data. If the answer to all of the above questions is NO, PROCEED TO
(b).
(b) Analyze hydrologic data. Subject the hydrologic data to appropriate analytical procedures.
Either use duration curves or a computer program developed by WES (available from the
Environmental Laboratory upon request) for determining the mean sea level elevation
representing the upper limits of wetland hydrology. In the latter case, when the site elevation is
lower than the mean sea level elevation representing a 5-percent duration of inundation and
saturation during the growing season, the area has a hydrologic regime that may occur in
wetlands. NOTE: Duration curves do not reflect the period of soil saturation following
dewatering.
When all of the above have been considered, PROCEED TO STEP 9.
* STEP 9 - Determine Whether Hydrology Is Adequately Characterized. Examine the
summarized data and determine whether the hydrology of the project area is adequately
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characterized. For routine determinations, there must be documented evidence of frequent
inundation or soil saturation during the growing season. For comprehensive determinations,
there must be documented quantitative evidence of frequent inundation or soil saturation
during the growing season, based on at least 10 years of stream or tidal gage data. Record
information on DATA FORM 1. In either case, if there is evidence of recent significant
hydrologic alteration due to human activities or natural events, PROCEED TO Section F.
Otherwise, PROCEED TO Section C.
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Section C. Selection of Method
56. All wetland delineation methods described in this manual can be grouped into two general
types: routine and comprehensive. Routine determinations (Section D) involve simple, rapidly
applied methods that result in sufficient qualitative data for making a determination.
Comprehensive methods (Section E) usually require significant time and effort to obtain the
needed quantitative data. The primary factor influencing method selection will usually be the
complexity of the required determination. However, comprehensive methods may sometimes
be selected for use in relatively simple determinations when rigorous documentation is
required.
57. Three levels of routine wetland determinations are described below. Complexity of the
project area and the quality and quantity of available information will influence the level
selected for use.
a. Level 1 - Onsite Inspection Unnecessary. This level may be employed when the
information already obtained (Section B) is sufficient for making a determination for the entire
project area (see Section D., Subsection 1).
b. Level 2 - Onsite Inspection Necessary. This level must be employed when there is
insufficient information already available to characterize the vegetation, soils, and hydrologyof
the entire project area (see Section D, Subsection 2).
c. Level 3 - Combination of Levels 1 and 2. This level should be used when there is sufficient
information already available to characterize the vegetation, soils, and hydrology of a portion,
but not all, of the project area. Methods described for Level 1 may be applied to portions of
the area for which adequate information already exists, and onsite methods (Level 2) must be
applied to the remainder of the area (see Section D, Subsection 3).
58. After considering all available information, select a tentative method (see above) for use,
and PROCEED TO EITHER Section D or E, as appropriate. NOTE: Sometimes it may be
necessary to change to another method described in the manuaz, depending on the quality of
available information and/or recent changes in the prospect area.
Section D. Routine Determinations
59. This section describes general procedures for making routine wetland determinations. It is
assumed that the user has already completed all applicable steps in Section B, (If it has been
determined that it is more expedient to conduct an onsite inspection than to search for
available information, complete STEPS 1 through 3 of Section B. and PROCEED TO
Subsection 2.) and a routine method has been tentatively selected for use (Section C).
Subsections 1-3 describe steps to be followed when making a routine determination using one
of the three levels described in Section C. Each subsection contains a flowchart that defines the
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relationship of steps to be used for that level of routine determinations. The selected method
must be considered tentative because the user may be required to change methods during the
determination.
Subsection 1 - Onsite Inspection Unnecessary
60. This subsection describes procedures for making wetland determinations when sufficient
information is already available (Section B) on which to base the determination. A flowchart
of required steps to be completed is presented in Figure 13, and each step is described below.
Equipment and materials
61. No special equipment is needed for applying this method. The following materials will be
needed:
a. Map of project area (Section B, STEP 2).
b. Copies of DATA FORM 1 (Appendix B).
c. Appendices C and D to this manual.
Procedure
62. Complete the following steps, as necessary:
• STEP 1 - Determine Whether Available Data Are Sufficient for Entire Project Area.
Examine the summarized data (Section B, STEPS 5, 7, and 9) and determine whether the
vegetation, soils, and hydrology of the entire project area are adequately characterized. If so,
PROCEED TO STEP 2. If all three parameters are adequately characterized for a portion, but
not all, of the project area, PROCEED TO Subsection 3. If the vegetation, soils, and hydrology
are not adequately characterized for any portion of the area, PROCEED TO Subsection 2. &
STEP 2 - Determine Whether Hydrophytic Vegetation Is Present. Examine the vegetation data
and list on DATA FORM 1 the dominant plant species found in each vegetation layer of each
community type. NOTE: A separate DATA FORM 1 will be required for each community
type. Record the indicator status for each dominant species (Appendix C, Section 1 or 2).
When more than 50 percent of the dominant species in a plant community have an indicator
status of OBL, FACW, and/or FAC, hydrophytic vegetation is present (For the FAC-neutral
option, see paragraph 35a). If one or more plant communities comprise of hydrophytic
vegetation, PROCEED TO STEP 3. If none of the plant communities comprise hydrophytic
vegetation, none of the area is a wetlands Complete the vegetation section for each DATA
FORM 1.
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40
STEP 1 - DETERMINE WHETHER
AVAILABLE DATA ARE SUFFICIENT
FOR ENTIRE PROJECT AREA
1
YES
I
/
\
ONE OR MORE PARAMETERS MUST
BE CHARACTERIZED OVER ENTIRE
PROJECT AREA
ALL PARAMETERS ADEQUATELY
CHARACTERIZED IN PART, BUT NOT
ALL OF AREA
PROCEED TO
SUBSECTION 2
PROCEED TO
SUBSECTION 3
STEP 2 - DETERMINE WHETHER
HYDROPHYTIC VEGETATION IS
PRESENT
STEP 3 - DETERMINE WHETHER
WETLAND HYDROLOGY IS PRESENT
STEP 4 - DETERMINE WHETHER
SOILS PARAMETER MUST BE
CONSIDERED
STEP 5 - DETERMINE WHETHER
HYDRIC SOILS ARE PRESENT
i
STEP 6-WETLAND DETERMINATION
STEP 7 - DETERMINE WETLAND
BOUNDARY (IF NECESSARY)
Figure 13. Flowchart of steps involved in making
a wetland determination when an
onsite inspection is unnecessary
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* STEP 3 - Determine Whether Wetland Hydrology Is Present. When one of the following
conditions applies (STEP 2), it is only necessary to confirm that there has been no recent
hydrologic alteration of the area:
a. The entire project area is occupied by a plant community or communities in which all
dominant species are OBL (Appendix C, Section 1 or 2).
b. The project area contains two or more plant communities, all of which are dominated by
OBL and/or FACW species, and the wetland-nonwetland boundary is abrupt (e.g. a Spartina
alternifzora marsh bordered by a road embankment). * There must be documented evidence of
periodic inundation or saturated soils when the project area:
a. Has plant communities dominated by one or more FAC species;
b. Has vegetation dominated by FACW species but no adjacent community dominated by OBL
species;
c. Has a gradual, nondistinct boundary between wetlands and nonwetlands; and/or
d. Is known to have or is suspected of having significantly altered hydrology.
If either a or b applies, look for recorded evidence of recently constructed dikes, levees,
impoundments, and drainage systems, or recent avalanches, mudslides, beaver dams, etc., that
have significantly altered the area hydrology. If any significant hydrologic alteration is found,
determine whether the area is still periodically inundated or has saturated soils for sufficient
duration to support the documented vegetation (a or b above). When a or b applies and there is
no evidence of recent hydrologic alteration, or when a or b do not applv and there is
documented evidence that the area is periodically inundated or has saturated soils, wetland
hydrology is present. Otherwise, wetland hydrology does not occur on the area. Complete the
hydrology section of DATA FORM 1 and PROCEED TO STEP 4.
• STEP 4 - Determine Whether the Soils Parameter Must Be Considered. When either a or b
of STEP 3 applies and there is either no evidence of recent hydrologic alteration of the project
area or if wetland hydrology presently occurs on the area, hydric soils can be assumed to be
present. If so, PROCEED TO STEP 6. Otherwise PROCEED TO STEP 5. o STEP 5^
Determine Whether Hydric Soils Are Present. Examine the soils data (Section B, STEP 7) and
record the soil series or soil phase on DATA FORM 1 for each community type. Determine
whether the soil is listed as a hydric soil (Appendix D, Section 2). If all community types have
hydric soils, the entire project area has hydric soils. (CAUTION: If the soil series description
makes reference to inclusions of other soil types, data must be field verified). Any portion of
the area that lacks hydric soils is a nonwetland. Complete the soils section of each DATA
FORM 1 and PROCEED TO STEP 6.
• STEP 6 - Wetland Determination. Examine the DATA FORM 1 for each community type.
Any portion of the project area is a wetland that has:
a. Hydrophytic vegetation that conforms to one of the conditions identified in STEP 3a or 3b
and has either no evidence of altered hydrology or confirmed wetland hydrology.
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b. Hydrophytic vegetation that does not conform to STEP 3a or 3b. has hydric soils, and has
confirmed wetland hydrology.
If STEP 6a or 6b_ applies to the entire project area, the entire area is a wetlands Complete a
DATA FORM 1 for all plant community types. Portions of the area not qualifying as a
wetland based on an office determination might or might not be wetlands. If the data used for
the determination are considered to be highly reliable, portions of the area not qualifying as
wetlands may properly be considered nonwetlands. PROCEED TO STEP 7. If the available
data are incomplete or questionable, an onsite inspection (Subsection 2) will be required.
• STEP 7 - Determine Wetland Boundary. Mark on the base map all community types
determined to be wetlands with a W and those determined to be nonwetlands with an N.
Combine all wetland community types into a single mapping unit. The boundary of these
community types is the interface between wetlands and nonwetlands.
Subsection 2 - Onsite Inspection Necessary
63. This subsection describes procedures for routine determinations in which the available
information (Section B) is insufficient for one or more parameters. If only one or two
parameters must be characterized, apply the appropriate steps and return to Subsection 1 and
complete the determination. A flowchart of steps required for using this method is presented
in Figure 14, and each step is described below.
Equipment and materials
64. The following equipment and materials will be needed:
a. Base map (Section B, STEP 2).
b. Copies of DATA FORM 1 (one for each community type and additional copies for
boundary determinations).
c. Appendices C and D.
d. Compass.
e^ Soil auger or spade (soils only).
f. Tape (300 ft).
g. Munsell Color Charts (Munsell Color 1975) (soils only).
Procedure
65. Complete the following steps, as necessary:
• STEP 1 - Locate the Project Area. Determine the spatial boundaries of the project area using
information from a USGS quadrangle map or other appropriate map, aerial photography,
and/or the project survey plan (when available). PROCEED TO STEP 2.
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• STEP 2 - Determine Whether an Atypical Situation Exists. Examine the area and determine
whether there is evidence of sufficient natural or human-induced alteration to significantly
alter the area vegetation, soils, and/or hydrology. NOTE: Include possible of/site modifications
that may affect the area hydrology. If not, PROCEED TO STEP 3.
If one or more parameters have been significantly altered by an activity that would normally
require a permit, PROCEED TO Section F and determine whether there is sufficient evidence
that hydrophytic vegetation, hydric soils, and/or wetland hydrology were present prior to this
alteration. Then, return to this subsection and characterize parameters not significantly
influenced by human activities. PROCEED TO STEP 3.
• STEP 3 - Determine the Field Characterization Approach to be Used. Considering the size
and complexity of the area, determine the field characterization approach to be used. When
the area is equal to or less than 5 acres in size (Section B,-STEP 3) and the area is thought to
be relatively homogeneous with respect to vegetation, soils, and/or hydrologic regime,
PROCEED TO STEP 4. When the area is greater than 5 acres in size (Section B, STEP 3) or
appears to be highly diverse with respect to vegetation, PROCEED TO STEP 18.
Areas Equal to or Less Than 5 Acres in Size
• STEP 4 - Identify the Plant Community Type(s). Traverse the area and determine the
number and locations of plant community types. Sketch the location of each on the base map
(Section B, STEP 2), and give each community type a name. PROCEED TO STEP 5.
• STEP 5 - Determine UThether Normal Environmental Conditions Are Present. Determine
whether normal environmental conditions are present by considering the following:
a. Is the area presently lacking hydrophytic vegetation or hydrologic indicators due to annual
or seasonal fluctuations in precipitation or ground-water levels?
b. Are hydrophytic vegetation indicators lacking due to seasonal fluctuations in temperature?
If the answer to either of these questions is thought to be YES, PROCEED TO Section G. If
the answer to both questions is NO, PROCEED TO STEP 6.
• STEP 6 - Select Representative Observation Points. Select a representative observation
point in each community type. A representative observation point is one in which the apparent
characteristics (determine visually) best represent characteristics of the entire community.
Mark on the base map the approximate location of the observation point. PROCEED TO
STEP 7.
• STEP 7 - Characterize Each Plant Community Type. Visually determine the dominant plant
species in each vegetation layer of each community type and record them on DATA FORM 1
(use a separate DATA FORM 1 for each community type). Dominant species are those having
the greatest relative basal area (woody overstory) (This term is used because species having
the larsest individuals may not be dominant when only a few are present. To determine
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relative basal area, consider both the size and number of individuals of a species and subjectly
compare with other species present.) ,greatest height (woody understory), greatest percentage
of areal cover (herbaceous understory), and/or greatest number of stems (woody vines).
PROCEED TO STEP 8.
• STEP 8 - Record Indicator Status of Dominant Species. Record on DATA FORM 1 the
indicator status (Appendix C, Section 1 or 2) of each dominant species in each community
type. PROCEED TO STEP 9.
• STEP 9 - Determine Whether Hydrophytic Vegetation Is Present. Examine each DATA
FORM 1. When more than 50 percent of the dominant species in a community type have an
indicator status (STEP 8) of OBL, FACW, and/or FAC (for the FAC-neutral option, see
paragraph 35a.), hydrophytic vegetation is present. Complete the vegetation section of each
DATA FORM 1. Portions of the area failing this test are not wetlands. PROCEED TO STEP
10.
• STEP 10 - Apply Wetland Hydrologic Indicators. Examine the portion of the area occupied
by each plant community type for positive indicators of wetland hydrology (PART III,
paragraph 49). Record findings on the appropriate DATA FORM 1. PROCEED TO STEP 11.
• STEP 11 - Determine Whether Wetland Hydrology Is Present. Examine the hydrologic
information on DATA FORM 1 for each plant community type. Any portion of the area
having a positive wetland hydrology indicator has wetland hydrology. If positive wetland
hydrology indicators are present in all community types, the entire area has wetland hydrology.
If no plant community type has a wetland hydrology indicator, none of the area has wetland
hydrology. Complete the hydrology portion of each DATA FORM 1. PROCEED TO STEP
12.
This term is used because species having the largest individuals may not be dominant when
only a few are present. To determine relative basal area, consider both the size and number of
individuals of a species and subjectively compare with other species present. For the FAC-
neutral option, see paragraph 3 5a.
• STEP 12 - Determine Whether Soils Must Be Characterized. Examine the vegetation section of
each DATA FORM 1. Hydric soils are assumed to be present in any plant community type in
which:
a. All dominant species have an indicator status of OBL.
b. All dominant species have an indicator status of OBL or FACW, and the wetland boundary
(when present) is abrupt. The soils parameter must be considered in any plant community in
which:
a. The community is dominated by one or more FAC species.
b. No community type dominated by OBL species is present.
c. The boundary between wetlands and nonwetlands is gradual or nondistinct.
d. The area is known to or is suspected of having significantly altered hydrology.
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When either a or b occurs and wetland hydrology is present, check the hydric soils blank as
positive on DATA FORM 1 and PROCEED TO STEP 16. If neither a nor b applies,
PROCEED TO STEP 13.
• STEP 13 - Dig a Soil Pit. Using a soil auger or spade, dig a soil pit at the representative
location in each community type. The procedure for digging a soil pit is described in
Appendix D, Section 1. When completed, approximately 16 inches of the soil profile will be
available for examination. PROCEED TO STEP 14.
• STEP 14 - Apply Hydric Soil Indicators. Examine the soil at each location and compare its
characteristics immediately below the A-horizon or 10 inches (whichever is shallower) with
the hydric soil indicators described in PART III, paragraphs 44 and/or 45. Record findings on
the appropriate DATA FORM 1's. PROCEED TO STEP 15.
• STEP 15 - Determine Whether Hydric Soils Are Present. Examine each DATA FORM 1
and determine whether a positive hydric soil indicator was found. If so, the area at that
location has hydric soil. If soils at all sampling locations have positive hydric soil indicators,
the entire area has hydric soils. If soils at all sampling locations lack positive hydric soil
indicators, none of the area is a wetlands Complete the soil section of each DATA FORM 1.
PROCEED TO STEP 16.
• STEP 16 - Make Wetland Determination. Examine DATA FORM 1. If the entire area presently or
normally has wetland indicators of all three parameters (STEPS 9, 11, and 15), the entire area is a
wetlands If the entire area presently or normallv lacks wetland indicators of one or more parameters,
the entire area is a nonwetland. If only a portion of the area presently or normally has wetland
indicators for all three parameters, PROCEED TO STEP 17.
• STEP 17 - Determine Wetland-Nonwetland Boundary. Mark each plant community type on the
base map with a W if wetland or an N if nonwetland. Combine all wetland plant communities into
one mapping unit and all nonwetland plant communities into another mapping unit. The wetland-
nonwetland boundary will be represented by the interface of these two mapping units.
Areas Greater Than 5 Acres in Size
• STEP 18 - Establish a Baseline. Select one project boundary as a baseline. The baseline should
parallel the major watercourse through the area or should be perpendicular to the hydrologic gradient
(Figure 15). Determine the approximate baseline length. PROCEED TO STEP 19.
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46
• STEP 19 - Determine the Required Number and Position of Transects. Use the following to
determine the required number and position of transacts (specific site conditions may necessitate
changes in intervals):
Number of
Baseline length, miles Required Transects
<0.25
>0.25-0.50
>0. 50-0. 75
>0. 75-1. 00
>1. 00-2. 00
>2. 00-4. 00
>4.00
3
3
3
3
3-5
5-8
8 or more*
*Transect intervals should not exceed 0.5 mile.
BASELINE
SEGMENT
B
C
D
STARTING POINT
OF BASELINE
A
\
STREAM
TRANSECT 1
Figure 15. General Orientation of baseline and transects (dotted lines) in a hypothetical project area.
Alpha characters represent different plant communities. All transects start at the midpoint of a
basline segment except the first, which may be repositioned to include community type A.
Divide the baseline length by the number of required transacts. Establish one transect in each
resulting baseline increment. Use the midpoint of each baseline increment as a transect
starting point. For example, if the baseline is 1,200 ft in length, three transacts would be
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established—one at 200 ft, one at 600 ft, and one at 1,000 ft from the baseline starting point.
CAUTION: All plant community types must be included. This may necessitate relocation of
one or more transect lines. PROCEED TO STEP 20.
• STEP 20 - Sample Observation Points Along the First Transect. Beginning at the starting
point of the first transect, extend the transect at a 90-deg angle to the baseline. Use the
following procedure as appropriate to simultaneously characterize the parameters at each
observation point. Combine field-collected data with information already available and make
a wetland determination at each observation point. A DATA FORM 1 must be completed for
each observation point.
cL Determine whether normal environmental conditions are present. Determine whether
normal environmental conditions are present by considering the following:
(1) Is the area presently lacking hydrophytic vegetation and/or hydrologic indicators due to
annual or seasonal fluctuations in precipitation or ground-water levels?
(2) Are hydrophytic vegetation indicators lacking due to seasonal fluctuations in temperature?
If the answer to either of these questions is thought to be YES, PROCEED TO Section G. If
the answer to both questions is NO, PROCEED TO STEP 20b.
b. Establish an observation point in the first plant community type encountered. Select a
representative location along the transect in the first plant community type encountered. When
the first plant community type is large and covers a significant distance along the transect,
select an area that is no closer than 300 ft to a perceptible change in plant community type.
PROCEED TO STEP 20c.
c. Characterize parameters. Characterize the parameters at the observation point by
completing (1), (2), and (3) below:
(1) Vegetation. Record on DATA FORM 1 the dominant plant species in each vegetation
layer occurring in the immediate vicinity of the observation point. Use a 5-ft radius for herbs
and saplings/shrubs, and a 30-ft radius for trees and woody vines (when present). Subjectively
determine the dominant species by estimating those having the largest relative basal area
(woody overstory) This term is used because species having the largest individuals may not be
dominant when only a few are present. To use relative basal area, consider both the size and
number of individuals of a species and subjectively compare with other species present.,
greatest height (woody understory), greatest percentage of areal cover (herbaceous
understory), and/or greatest number of stems (woody vines). NOTE: Plot size may be
estimated, and plot size may also be varied when site conditions warrant. Record on DATA
FORM 1 any dominant species observed to have morphological adaptations (Appendix C,
Section 3) for occurrence in wetlands, and determine and record dominant species that have
known physiological adaptations for occurrence in wetlands (Appendix C, Section 3). Record
on DATA FORM 1 the indicator status (Appendix C, Section 1 or 2) of each dominant species.
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Hydrophytic vegetation is present at the observation point when more than 50 percent of the
dominant species have an indicator status of OBL, FACW, and/or FAC (For the FAC-neutral
option, see paragraph 35a); when two or more dominant species have observed
morphological or known physiological adaptations for occurrence in wetlands; or when other
indicators of hydrophytic vegetation (PART III, paragraph 35) are present. Complete the
vegetation section of DATA FORM 1. PROCEED TO (2).
(2) Soils. In some cases, it is not necessary to characterize the soils. Examine the vegetation
of DATA FORM 1. Hydric soils can be assumed to be present when:
(a) All dominant plant species have an indicator status of OBL.
(b) All dominant plant species have an indicator status of OBL and/or FACW (at least one
dominant species must be OBL). Soils must be characterized when any dominant species has
an indicator status of FAC.
When either (a) or (b) applies, check the hydric soils blank as positive and PROCEED TO (3).
If neither (a) nor (b) applies but the vegetation qualifies as hydrophytic, dig a soil pit at the
observation point using the procedure described in Appendix D, Section 1. Examine the soil
immediately below the A-horizon or 10-inches (whichever is shallower) and compare its
characteristics (Appendix D, Section 1) with the hydric soil indicators described in PART III,
paragraphs 44 and/or 45. Record findings on DATA FORM 1. If a positive hydric soil
indicator is present, the soil at the observation point is a hydric soil. If no positive hydric soil
indicator is found, the area at the observation point does not have hvdric soils and the area at
the observation point is not a wetland. Complete the soils section of DATA FORM 1 for the
observation point. PROCEED TO (3) if hydrophytic vegetation (1) and hydric soils (2) are
present. Otherwise, PROCEED TO STEP 20d.
(3) Hydrology. Examine the observation point for indicators of wetland hydrology (PART III,
paragraph 49), and record observations on DATA FORM 1. Consider the indicators in the
same sequence as presented in PART III, paragraph 49. If a positive wetland hydrology
indicator is present, the area at the observation point has wetland hydrology. If no positive
wetland hydrologic indicator is present, the area at the observation point is not a wetland.
Complete the hydrology section of DATA FORM 1 for the observation point. PROCEED TO
STEP 20d.
d. Wetland determination. Examine DATA FORM 1 for the observation point. Determine
whether wetland indicators of all three parameters are or would normally be present during a
significant portion of the growing season. If so, the area at the observation point is a wetland.
If no evidence can be found that the area at the observation point normally has wetland
indicators for all three parameters, the area is a nonwetland. PROCEED TO STEP 20e.
e. Sample other observation points along the first transect. Continue along the first transect
until a different community type is encountered. Establish a representative observation point
within this community type and repeat STEP 20c - 20d. If the areas at both observation points
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are either wetfands -or nonwetlands, continue along the transect and repeat STEP 20c - 20d for
the next community type encountered. Repeat for all other community types along the first
transect. If the area at one observation point is wetlands and the next observation point is
nonwetlands (or vice versa), PROCEED TO STEP 20f.
f. Determine wetland-nonwetland boundary. Proceed along the transect from the wetland
observation point toward the nonwetland observation point. Look for subtle changes in the
plant community (e.g. the first appearance of upland species, disappearance of apparent
hydrology indicators, or slight changes in topography). When such features are noted,
establish an observation point and repeat the procedures described in STEP 20c - 20d. NOTE:
A new DATA FORM 1 must be completed for this observation point, and all three parameters
must be characterized by field observation. If the area at this observation point is a wetlands
proceed along the transect toward the nonwetland observation point until upland indicators are
more apparent. Repeat the procedures described in STEP 20c - 20d. If the area at this
observation point is a nonwetland, move Halfway back along the transect toward the last
documented wetland observation point and repeat the procedure described in STEP 20c - 20d.
Continue this procedure until the wetland-nonwetland boundary is found. It is not necessary to
complete a DATA FORM 1 for all intermediate points, but a DATA FORM 1 should be
completed for the wetland-nonwetland boundary. Mark the position of the wetland boundary
on the base map, and continue along the first transect until all community types have been
sampled and all wetland boundaries located. CAUTION: In areas where wetlands are
interspersed among nonwetlands (or vice versa), several boundary determinations will be
required. When all necessary wetland determinations have been completed for the first
transect, PROCEED TO STEP 21.
• STEP 21 - Sample Other Transects. Repeat procedures described in STEP 21 for all other
transacts. When completed, a wetland determination will have been made for one observation
point in each community type along each transect, and all wetland-nonwetland boundaries
along each transect will have been determined. PROCEED TO STEP 22.
• STEP 22 - Synthesize Data. Examine all completed copies of DATA FORM 1, and mark
each plant community type on the base map. Identify each plant community type as either a
wetland (W) or nonwetland (N). If all plant community types are identified as wetlands, the
entire area is wetlands. If all plant community types are identified as nonwetlands, the entire
area is nonwetlands. If both wetlands and nonwetlands are present, identify observation points
that represent wetland boundaries on the base map. Connect these points on the map by
generally following contour lines to separate wetlands from nonwetlands. Walk the contour
line between transacts to confirm the wetland boundary. Should anomalies be encountered, it
will be necessary to establish short transacts in these areas, apply the procedures described in
STEP 20f, and make any necessary adjustments on the base map.
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Subsection 3 - Combination of Levels 1 and 2
66. In some cases, especially for large projects, adequate information may already be available
(Section B) to enable a wetland determination for a portion of the project area, while an onsite
visit will be required for the remainder of the area. Since procedures for each situation have
already been described in Subsections 1 and 2, they will not be repeated. Apply the following
steps:
• STEP 1 - Make Wetland Determination for Portions of the Project Area That Are Already
Adequately Characterized. Apply procedures described in Subsection 1. When completed, a
DATA FORM 1 will have been completed for each community type, and a map will have been
prepared identifying each community type as wetland or nonwetland and showing any wetland
boundary occurring in this portion of the project area. PROCEED TO STEP 2.
• STEP 2 - Make Wetland Determination for Portions of the Project Area That Require an
Onsite Visit. Apply procedures described in Subsection 2. When completed, a DATA FORM
1 will have been completed for each plant community type or for a number of observation
points (including wetland boundary determinations). A map of the wetland (if present) will
also be available. PROCEED TO STEP 3.
• STEP 3 - Synthesize Data. Using the maps resulting from STEPS 1 and 2, prepare a
summary map that shows the wetlands of the entire project area. CAUTION: Wetland
boundaries for the two maps will not always match exactly. When this occurs, an additional
site visit will be required to refine the wetland boundaries. Since the degree of resolution of
wetland boundaries will be greater when determined onsite, it may be necessary to employ
procedures described in Subsection 2 in the vicinity of the boundaries determined from
Subsection 1 to refine these boundaries.
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Section E. Comprehensive Determinations
67. This section describes procedures for making comprehensive wetland determinations.
Unlike procedures for making routine determinations (Section D), application of procedures
described in this section will result in maximum information for use in making determinations,
and the information usually will be quantitatively expressed. Comprehensive determinations
should only be used when the project area is very complex and/or when the determination
requires rigorous documentation. This type of determination may be required in areas of any
size, but will be especially useful in large areas. There may be instances in which only one
parameter (vegetation, soil, or hydrology) is disputed. In such cases, only procedures
described in this section that pertain to the disputed parameter need be completed. It is
assumed that the user has already completed all applicable steps in Section B. NOTE:
Depending on site characteristics, it may be necessary to alter the sampling design and/or
data collection procedures.
68. This section is divided into five basic types of activities. The first consists of preliminary
field activities that must be completed prior to making a determination (STEPS 1-5). The
second outlines procedures for determining the number and locations of required
determinations (STEPS 6-8). The third describes the basic procedure for making a
comprehensive wetland determination at any given point (STEPS 9-17). The fourth describes
a procedure for determining wetland boundaries (STEP 18). The fifth describes a procedure
for synthesizing the collected data to determine the extent of wetlands in the area (STEPS 20-
21). A flowchart showing the relationship of various steps required for making a
comprehensive determination is presented in Figure 16.
Equipment and material
69. Equipment and materials needed for making a comprehensive determination include:
a. Base map (Section B, STEP 2).
b. Copies of DATA FORMS 1 and 2.
c. Appendices C and D.
d. Compass.
e. Tape (300 ft).
f. Soil auger or spade.
g. Munsell Color Charts (Munsell Color 1975).
h. Quadrat (3.28 ft by 3.28 ft).
i. Diameter or basal area tape (for woody overstory).
Field procedures
70. Complete the following steps:
• STEP I - Identify the Project Area. Using information from the USGS quadrangle or other
appropriate map (Section B), locate and measure the spatial boundaries of the project area.
Determine the compass heading of each boundary and record on the base map (Section B,
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STEP 2). The applicant's survey plan may be helpful in locating the project boundaries.
PROCEED TO STEP 2.
• STEP 2 - Determine Whether an Atypical Situation Exists. Examine the area and determine
whether there is sufficient natural or human-induced alteration to significantly change the area
vegetation, soils, and/or hydrology. If not, PROCEED TO STEP 3. If one or more parameters
have been recently altered significantly.- PROCEED TO Section F and determine whether
there is sufficient evidence that hydrophytic vegetation, hydric soils, and/or wetland hydrology
were present on the area prior to alteration. Then return to this section and characterize
parameters not significantly influenced by human activities. PROCEED TO STEP 3. o STEP
3 - Determine Homogeneity of Vegetation. While completing STEP 2, determine the number
of plant community types present. Mark the approximate location of each community type on
the base map. The number and locations of required wetland determinations will be strongly
influenced by both the size of the area and the number and distribution of plant community
types; the larger the area and greater the number of plant community types, the greater the
number of required wetland determinations. It is imperative that all plant community types
occurring in all portions of the area be included in the investigation. PROCEED TO STEP 4.
• STEP 4 - Determine the Type and Number of Layers in Each Plant Community. Examine
each identified plant community type and determine the type(s) and number of layers in each
community. Potential layers include trees (woody overstory), saplings/shrubs (woody
understory), herbs (herbaceous understory), and/or woody vines. PROCEED TO STEP 5. o
• STEP 5 - Determine Whether Normal Environmental Conditions Are Present. Determine
whether normal environmental conditions are present
at the observation point by considering the following:
a. Is the area at the observation point presently lacking hydrophytic vegetation and/or
hydrologic indicators due to annual or seasonal fluctuations in precipitation or groundwater
levels?
b. Are hydrophytic vegetation indicators lacking due to seasonal fluctuations in temperature?
If the answer to either of these questions is thought to be YES,
PROCEED TO Section G. If the answer to both questions is NO, PROCEEDTO STEP 6.
• STEP 6 - Establish a Baseline. Select one project boundary area as a baseline. The baseline
should extend parallel to any major watercourse and/or perpendicular to a topographic gradient
(see Figure 17). Determine the baseline length and record on the base map both the baseline
length and its compass heading. PROCEED TO STEP 7.
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• STEP 7. Establish Transect Locations. Divide the baseline into a number of equal segments
(Figure 17). Use the following as a guide to determine the appropriate number of baseline
segments:
Length of
Baseline Length, ft Number of Segments Baseline Segment ft
>50 - 500
>500 - 1,000
>1,000- 5,000
>5,000 - 10,000
>10,000*
3
3
5
7
variable
18-
167
200
700
-2,
167
-333
- 1,000
- 1,400
000
*If the baseline exceeds 5 miles, baseline segments should be 0.5 mile in length.
Use a random numbers table or a calculator with a random numbers generation feature to
determine the position of a transect starting point within each baseline segment. For example,
when the baseline is 4,000 ft, the number of baseline segments will be five, and the baseline
segment length will be 4,000/5 = 800 ft. Locate the first transect within the first 800 ft of the
baseline. If the random numbers table yields 264 as the distance from the baseline starting
point, measure 264 ft from the baseline starting point and establish the starting point of the first
transect. If the second random number selected is 530, the starting point of the second
transect will be located at a distance of 1,330 ft (800 + 530 ft) from the baseline starting point.
CA UTION: Make sure that each plant community type is included in at
least one transect. If not, modify the sampling design accordingly. When the starting point
locations for all required transacts have been determined, PROCEED TO STEP 8.
• STEP 8 - Determine the Number of Required Observation Points Along Transects. The
number of required observation points along each transect will be largely dependent on
transect length. Establish observation points along each transect using the following as a
guide:
Transect Number of Interval Between
Length, ft Observation Points Observation Points, ft
<1,000 2-10 100
1,000-<5,000 10 100-500
5,000-<10,000 10 500-1,000
>10,000 >10 1,000
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Establish the first observation point at a distance of 50 ft from the baseline (Figure 17). When
obvious nonwetlands occupy a long portion of the transect from the baseline starting point,
establish the first observation point in the obvious nonwetland at a distance of approximately
300 ft from the point that the obvious nonwetland begins to intergrade into a potential wetland
community type. Additional observation points must also be established to determine the
wetland boundary between successive regular observation points when one of the points is a
wetland and the other is a nonwetland. CA UTION: In large areas having a mosaic of plant
community types, several wetland boundaries may occur along the same transect. PROCEED
TO STEP 9 and apply the comprehensive wetland determination procedure at each required
observation point. Use the described procedure to simultaneously characterize the vegetation,
soil, and hydrology at each required observation point along each transect, and use the
resulting characterization to make a wetland determination at each point. NOTE: All required
wetland boundary determinations should be made while proceeding along a transect.
• STEP 9 - Characterize the Vegetation at the First Observation Point Along the First Transect.
There is no single best procedure for characterizing vegetation. Methods described in STEP 9
afford standardization of the procedure. However, plot size and descriptors for determining
dominance may vary. Record on DATA FORM 2 the vegetation occurring at the first
observation point along the first transect by completing the following (as appropriate):
a. Trees. Identify each tree occurring within a 3 0-ft radius (A larger sampling plot may be
necessary when trees are large and widely spaced.) of the observation point, measure its basal
area (square inches) or diameter at breast height (DBH) using a basal area tape or diameter
tape, respectively, and record. NOTE: If DBH is measured, convert values to basal area by
applying the formula A = pi r2. This must be done on an individual basis. A tree is any
nonclimbing, woody plant that has a DBH of >3.0 in., regardless of height.
b. Saplings/shrubs. Identify each sapling/shrub occurring within a 10-ft radius of the
observation point, estimate its height, and record the midpoint of its class range using the
following height classes (height is used as an indication of dominance; taller individuals exert
a greater influence on the plant community):
Height Height Class Midpoint of
Class Range, ft Range, ft
113 2
235 4
357 6
479 8
5 9 11 10
6 >11 12
A sapling/shrub is any woody plant having a height >3.2 ft but a stem diameter of <3.0 in.,
exclusive of woody vines.
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(L, Herbs. Place a 3.28- by 3.28-ft quadrat with one corner touching the observation point and
one edge adjacent to the transect line. As an alternative, a 1.64-ft-radius plot with the center of
the plot representing the observation point position may be used. Identify each plant species
with foliage extending into the quadrat and estimate its percent cover by applying the
following cover classes:
Cover Class Midpoint of
Class Range. % Class Range. %
1 0-5 2.5
2 >5-25 15.0
3 >25 -50 37.5
4 >50 -75 62.5
5 >75 -95 85.0
6 >95 - 100 97.5
Include all nonwoody plants and woody plants <3.2 ft in height.
NOTE: Total percent cover for all species will often exceed 100 percent.
d. Woody vines (lianas). Identify species of woody vines climbing each tree and
sapling/shrub sampled in STEPS 9a and 9b above, and record the number of stems of each.
Since many woody vines branch profusely, count or estimate the number of stems at the
ground surface. Include only individuals rooted in the 10-ft radius plot. Do not include
individuals <3.2 ft in height. PROCEED TO STEP 10.
• STEP 10 - Analyze Field Vegetation Data. Examine the vegetation data (STEP 9) and
determine the dominant species in each vegetation layer (The same species may occur as a
dominant in more than one vegetation layer) by completing the following:
a. Trees. Obtain the total basal area (square inches) for each tree species identified in STEP
9a by summing the basal area of all individuals of a species founi in the sample plot. Rank the
species in descending order of dominance based on total basal area. Complete DATA FORM
2 for the tree layer.
b. Saplings/shrubs. Obtain the total height for each sapling/ shrub species identified in STEP
9b_. Total height, which is an estimate of dominance, is obtained by summing the midpoints of
height classes for all individuals of a species found in the sample plot. Rank the species in
descending order of dominance based on sums of midpoints of height class ranges. Complete
DATA FORM 2 for the sapling/shrub layer.
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c. Herbs. Obtain the total cover for each herbaceous and woody seedling species identified in
STEP 9c. Total cover is obtained by using the midpoints of the cover class range assigned to
each species (only one estimate of cover is made for a species in a given plot). Rank herbs and
woody seedlings in descending order of dominance based on percent cover. Complete DATA
FORM 2 for the herbaceous layer.
d. Woody vines (lianas). Obtain the total number of individuals of each species of woody
vine identified in STEP 9d. Rank the species in descending order of dominance based on
number of stems. Complete DATA FORM 2 for the woody vine layer. PROCEED TO STEP
11.
• STEP 11 - Characterize Soil. If a soil survey is available (Section B), the soil type may
already be known. Have a soil scientist confirm that the soil type is correct, and determine
whether the soil series is a hydric soil (Appendix D, Section 2). CAUTION: Mapping units on
soil surveys sometimes have inclusions of soil series or phases not shown on the soil survey
map, If a hydric soil type is confirmed, record on DATA FORM I and PROCEED TO STEP
12. If not, dig a soil pit using a soil auger or spade (See Appendix D, Section 1) and look for
indicators of hydric soils immediately below the A-horizon or 10 inches (whichever is
shallower) (PART III, paragraphs 44 and/or 45). Record findings on DATA FORM 1.
PROCEED TO STEP 12.
• STEP 12 - Characterize Hydrology. Examine the observation point for indicators of wetland
hydrology (PART III, paragraph 49), and record observations on DATA FORM 1. Consider
indicators in the same sequence as listed in paragraph 49. PROCEED TO STEP 13.
• STEP 13 - Determine Whether Hydrophytic Vegetation Is Present. Record the three
dominant species from each vegetation layer (five species if only one or two layers are present)
on DATA FORM 1.* Record all dominant species when less than three are present in a
vegetation layer. Determine whether these species occur in wetlands by considering the
following:
a. More than 50 percent of the dominant plant species are OBL. FACW. and/or FAC** on
lists of plant species that occur in wetlands. For the FAC-neutral option, see paragraph 35a.
Record the indicator status of all dominant species (Appendix C, Section I or 2) on DATA
FORM 1. Hydrophytic vegetation is present when the majority of the dominant species have
an indicator status of OBL, FACW, or FAC. CA UTION: Not necessarily all plant communities
composed of only FAC species are hydrophytic commnities. They are hydrophytic
communities only when positive indicators of hydric soils and wetland hydrology are also
found. If this indicator is satisfied, complete the vegetation portion of DATA FORM 1 and
PROCEED TO STEP 14. If not, consider other indicators of hydrophytic vegetation.
b. Presence of adaptations for occurrence in wetlands. Do any of ihe-species listed on DATA
FORM I have observed morphological or known physiological adaptations (Appendix C,
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Section 3) for occurrence in wetlands? If so, record species having such adaptations on DATA
FORM 1. When two or more dominant species have observed morphological adaptations or
known physiological adaptations for occurrence in wetlands, hydrophytic vegetation is present.
If so, complete the vegetation portion of DATA FORM I and PROCEED TO STEP 14. If not,
consider other indicators of hydrophytic vegetation.
c. Other indicators of hydrophytic vegetation. Consider other indicators (see PART III,
paragraph 35) that the species listed on DATA FORM I are commonly found in wetlands. If
so, complete the vegetation portion of DATA FORM I bv recording sources of supporting
information, and PROCEED TO STEP 14. If no indicator of hydrophytic vegetation is
present, the area at the observation point is not a wetlands In such cases, it is unnecessary to
consider soil and hydrology at that observation point. PROCEED TO STEP 17.
• STEP 14 - Determine Whether Hydric Soils Are Present. Examine DATA FORM 1 and
determine whether any indicator of hydric soils is present. If so, complete the soils portion of
DATA FORM I and PROCEED TO STEP 15. If not, the area at the observation point is not a
wetlands. PROCEED TO STEP 17.
• STEP 15 - Determine Whether Wetland Hydrology Is Present. Examine DATA FORM 1
and determine whether any indicator of wetland hydrology is present. Complete the hydrology
portion of DATA FORM I and PROCEED TO STEP 16.
• STEP 16 - Make Wetland Determination. When the area at the observation point presently
or normally has wetland indicators of all three parameters, it is a wetlands When the area at the
observation point presently or normally lacks wetland indicators of one or more parameters, it
is a nonwetland. PROCEED TO STEP 17.
• STEP 17 - Make Wetland Determination at Second Observation Point. Locate the second
observation point along the first transect and make a wetland determination by repeating
procedures described in STEPS 9-16. When the area at the second observation point is the
same as the area at the first observation point (i.e. both wetlands or both nonwetlands),
PROCEED TO STEP 19. When the areas at the two observation points are different (i.e. one
wetlands, the other nonwetlands), PROCEED TO STEP 18.
• STEP 18 - Determine the Wetland Boundary Between Observation Points. Determine the
position of the wetland boundary by applying the following procedure:
a. Look for a change in vegetation or topography. NOTE: The changes may sometimes be
very subtle. If a change is noted, establish an observation point and repeat STEPS 9-16.
Complete a DATA FORM 1. If the area at this point is a wetlands proceed toward the
nonwetland observation point until a more obvious change in vegetation or topography is
noted and repeat the procedure. If there is no obvious change, establish the next observation
point approximately halfway between the last observation point and the nonwetland
observation point and repeat STEPS 9-16.
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b. Make as many additional wetland determinations as necessary to find the wetland
boundary. NOTE: The completed DATA FORM 1's for the original two observation points
often will provide a clue as to the parameters that change between the two points.
c. When the wetland boundary is found, mark the boundary location on the base map and
indicate on the DATA FORM 1 that this represents a wetland boundary. Record the distance
of the boundary from one of the two regular observation points. Since the regular observation
points represent known distances from the baseline, it will be possible to accurately pinpoint
the boundary location on the base map. PROCEED TO STEP 19.
• STEP 19 - Make Wetland Determinations at All Other Required Observation Points Along
All Transects. Continue to locate and sample all required observation points along all
transacts. NOTE: The procedure described in Step 18 must be applied at every position where
a wetzand boundary occurs between successive observation points. Complete a DATA FORM
1 for each observation point and PROCEED TO STEP 20.
• STEP 20 - Synthesize Data to Determine the Portion of the Area Containing Wetlands.
Examine all completed copies of DATA FORM 1 (STEP 19), and mark on a copy of the base
map the locations of all-observation points that are wetlands with a W and all observation
points that are nonwetlands with an N. Also, mark all wetland boundaries occurring along
transacts with an X. If all the observation points are wetlands, the entire area is wetlands. If all
observation points are nonwetlands, none of the area is wetlands. If some wetlands and some
nonwetlands are present, connect the wetland boundaries (X) by following contour lines
between transacts. CAUTION: -If the determination is considered to be highly controversial, it
may be necessary to be more precise in determining the wetland boundary between transacts.
This is also true for very large areas where the distance between transacts is greater. If this is
necessary, PROCEED TO STEP 21.
• STEP 21 - Determine Wetland Boundary Between Transects. Two procedures may be used
to determine the wetland boundary between transacts, both of which involve surveying:
a. Survey contour from wetland boundary along transacts. The first method involves
surveying the elevation of the wetland boundaries along transacts and then extending the
survey to determine the same contour between transacts. This procedure will be adequate in
areas where there is no significant elevational change between transacts. However, if a
significant elevational change occurs between transacts, either the surveyor must adjust
elevational readings to accommodate such changes or the second method must be used.
NOTE: The surveyed wetland boundary must be examined to ensure that no anomalies exist.
If these occur, additional wetland determinations will be required in the portion of the area
where the anomalies occur, and the wetland boundary must be adjusted accordingly.
b. Additional wetland determinations between transacts. This procedure consists of traversing
the area between transacts and making additional wetland determinations to locate the wetland
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boundary at sufficiently close intervals (not necessarily standard intervals) so that the area can
be surveyed. Place surveyor flags at each wetland boundary location. Enlist a surveyor to
survey the points between transacts. From the resulting survey data, produce a map that
separates wetlands from nonwetlands.
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Section F. Atypical Situations
71. Methods described in this section should be used only when a determination has already
been made in Section D or E that positive indicators of hydrophytic vegetation, hydric soils,
and/or wetland hydrology could not be found due to effects of recent human activities or
natural events. This section is applicable to delineations made in the following types of
situations:
a. Unauthorized activities. Unauthorized discharges requiring Tnf-orcement actions may
result in removal or covering of indicators of one or more wetland parameters. Examples
include, but are not limited to: (1) alteration or removal of vegetation; (2) placement of
dredged or fill material over hydric soils; and/or (3) construction of levees, drainage systems,
or dams that significantly alter the area hydrology. NOTE: This section should not be used for
activities that have been previously authorize 3-or those that are exempted from CE regulation.
For example, this section is not applicable to areas that have been drained under CE
authorization or that did not require CE authorization. Some of these areas may still be
wetlands, but procedures described in Section D or E must be used in these cases.
b. Natural events. Naturally occurring events may result in either creation or alteration of
wetlands. For example, recent beaver dams may impound water, thereby resulting in a shift of
hydrology and vegetation to wetlands. However, hydric soil indicators may not have
developed due to insufficient time having passed to allow their development. Fire, avalanches,
volcanic activity, and changing river courses are other examples. NOTE: It is necessary to
determine whether alterations to an area have resulted in changes that are now the "normal
circumstances. " The relative permanence of the change and whether the area is now
functioning as a wetland must be considered.
c. Man-induced wetlands. Procedures described in Subsection 4 are for use in delineating
wetlands that have been purposely or incidentally created by human activities, but in which
wetland indicators of one or more parameters are absent. For example, road construction may
have resulted in impoundment of water in an area that previously was nonwetland, thereby
effecting hydrophytic vegetation and wetland hydrology in the area. However, the area may
lack hydric soil indicators. NOTE: Subsection D is not intended to bring into CE jurisdiction
those manmade wetlands that are exempted under CE regulations or policy. It is also
important to consider whether the man-induced changes are now the "normal circumstances"
for the area. Both the relative permanence of the change and the functioning of the area as a
wetland are implied.
72. When any of the three types of situations described in paragraph 71 occurs, application of
methods described in Sections D and/or E will lead to the conclusion that the area is not a
wetland because positive wetland indicators for at least one of the three parameters will be
absent. Therefore, apply procedures described in one of the following subsections (as
appropriate) to determine whether positive indicators of hydrophytic vegetation, hydric soils,
and/or wetland hydrology existed prior to alteration of the area. Once these procedures have
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been employed, RETURN TO Section D or E to make a wetland determination. PROCEED
TO the appropriate subsection.
Subsection 1 - Vegetation
73. Employ the following steps to determine whether hydrophytic vegetation previously
occurred:
• STEP 1 - Describe the Type of Alteration. Examine the area and describe the type of
alteration that occurred. Look for evidence of selective harvesting, clear cutting, bulldozing,
recent conversion to agriculture, or other activities (e.g., burning, discing, or presence of
buildings, dams, levees, roads, parking lots, etc.). Determine the approximate date* when the
alteration occurred. Record observations on DATA FORM 3, and PROCEED TO STEP 2.
• STEP 2 - Describe Effects on Vegetation. Record on DATA FORM 3 a general description
of how the activities (STEP 1) have affected the plant communities. Consider the following:
a. Has all or a portion of the area been cleared of vegetation?
b. Has only one layer of the plant community (e.g. trees) been removed?
c. Has selective harvesting resulted in removal of some species?
d. Has all vegetation been covered by fill, dredged material, or structures?
e. Have increased water levels resulted in the death of some individuals?
It is especially important to determine whether the alteration occurred prior to implementation
of Section 404. PROCEED TO STEP 3.
• STEP 3 - Determine the Type of Vegetation That Previously Occurred. Obtain all possible
evidence of the type of plant communities that occurred in the area prior to alteration.
Potential sources of such evidence include:
a. Aerial photography. Recent (within 5 years) aerial photography
an often be used to document the type of previous vegetation. The general type of plant
communities formerly present can usually be determined, and species identification is
sometimes possible.
b. Onsite inspection. Many types of activities result in only partial removal of the previous
plant communities, and remaining species may be indicative of hydrophytic vegetation. In
other cases, plant fragments (e.g. stumps, roots) may be used to reconstruct the plant
community types that occurred prior to site alteration. Sometimes, this can be determined by
examining piles of debris resulting from land-clearing operations or excavation to uncover
identifiable remains of the previous plant community.
c. Previous site inspections. Documented evidence from previous inspections of the area may
describe the previous plant communities, particularly in cases where the area was altered after
a permit application was denied.
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d. Adjacent vegetation. Circumstantial evidence of the type of plant communities that
previously occurred may sometimes be obtained by examining the vegetation in adjacent
areas. If adjacent areas have the same topographic position, soils, and hydrology as the altered
area, the plant community types on the altered area were probably similar to those of the
adjacent areas.
e. SCS records. Most SCS soil surveys include a description of the plant community types
associated with each soil type. If the soil type on the altered area can be determined, it may be
possible to generally determine the type of plant communities that previously occurred.
f. Permit applicant. In some cases, the permit applicant may provide important information
about the type of plant communities that occurred prior to alteration.
g. Public. Individuals familiar with the area may provide a good general description of the
previously occurring plant communities.
h. NWI wetland maps. The NWI has developed wetland type maps for many areas. These
may be useful in determining the type of plant communities that occurred prior to alteration.
To develop the strongest possible record, all of the above sources should be considered. If the
plant community types that occurred prior to alteration can be determined, record them on
DATA FORM 3 and also record the basis used for the determination. PROCEED TO STEP 4.
If it is impossible to determine the plant community types that occurred on the area prior to
alteration, a determination cannot be made using all three parameters. In such cases, the
determination must be based on the other two parameters. PROCEED TO Subsection 2 or 3 if
one of the other parameters has been altered, or return to the appropriate Subsection of Section
D or to Section E, as appropriate.
• STEP 4 - Determine Whether Plant Community Types Constitute Hydrophytic Vegetation.
Develop a list of species that previously occurred on the site (DATA FORM 3). Subject the
species list to applicable indicators of hydrophytic vegetation (PART III, paragraph 35). If
none of the indicators are met, the plant communities that previously occurred did not
constitute hydrophytic vegetation. If hydrophytic vegetation was present and no other
parameter was in question, record appropriate data on the vegetation portion of DATA FORM
3, and return to either the appropriate subsection of Section D or to Section E. If either of the
other parameters was also in question, PROCEED TO Subsection 2 or 3.
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Subsection 2 - Soils
74. Employ the following steps to determine whether hydric soils previously occurred:
• STEP 1 - Describe the Type of Alteration. Examine the area and describe the type of
alteration that occurred. Look for evidence of:
a. Deposition of dredged or fill material or natural sedimentation. In many cases the presence
of fill material will be obvious. If so, it will be necessary to dig a hole to reach the original soil
(sometimes several feet deep). Fill material will usually be a different color or texture than the
original soil (except when fill material has been obtained from like areas onsite). Look for
decomposing vegetation between soil layers and the presence of buried organic or hydric soil
layers. In accreting or recently formed sandbars in riverine situations, the soils may support
hydrophytic vegetation but lack hydric soil characteristics.
b. Presence of nonwoody debris at the surface. This can only be applied in areas where the
original soils do not contain rocks.
Nonwoody debris includes items such as rocks, bricks, and concrete fragments.
c. Subsurface plowing. Has the area recently been plowed below he A-horizon or to depths of
greater than 10 in.?
d. Removal of surface layers. Has the surface soil layer been removed by scraping or natural
landslides? Look for bare soil surfaces with exposed plant roots or scrape scars on the surface.
e. Presence of man-made structures. Are buildings, dams, levees, roads, or parking lots
present?
Determine the approximate date (It is especially important to determine whether the alteration
occurred prior to implementation of Section 404.} when the alteration occurred. This may
require checking aerial photography, examining building permits, etc. Record on DATA
FORM 3, and PROCEED TO STEP 2.
• Step 2 - Describe Effects on Soils. Record on DATA FORM 3 a general description of how
identified activities in STEP 1 have affected the soils. Consider the following:
a. Has the soil been buried? If so, record the depth of fill material and determine whether the
original soil is intact.
b. Has the soil the original been mixed at a depth below the A-horizon or greater than 10
inches? If so, it will be necessary to examine soil at a depth immediately below the plowed
zone. Record supporting evidence.
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c. Has the soil been sufficiently altered to change the soil phase? Describe these changes.
PROCEED TO STEP 3.
• STEP 3 - Characterize Soils That Previously Occurred. Obtain all possible evidence that
may be used to characterize soils that previously occurred on the area. Consider the following
potential sources of information:
a. Soil surveys. In many cases, recent soil surveys will be available. If so, determine the soil
series that were mapped for the area, and compare these soil series with the list of hydric soils
(Appendix D, Section 2). If all soil series are listed as hydric soils, the entire area had hydric
soils prior to alteration.
b. Characterization of buried soils. When fill material has been placed over the original soil
without physically disturbing the soil, examine and characterize the buried soils. To
accomplish this, dig a hole through the fill material until the original soil is encountered.
Determine the point at which the original soil material begins. Remove 12 inches of the
original soil from the hole and look for indicators of hydric soils (PART III, paragraphs 44
and/or 45) immediately below the A-horizon or 10 inches (whichever is shallower). Record on
DATA FORM 3 the color of the soil matrix, presence of an organic layer, presence of mottles
or gleying, and/or presence of manganese concretions. If the original soil is motthe chroma of
the soil matrix is 2 or less, (The matrix chroma must be 1 or less if no mottles are present (see
paragraph 44). The soil must be moist when colors are determined.), a hydric formerly
present on the site. If any of these indicafound, the original soil was a hydric soil. (NOTE: fill
material is a thick layer, it might be necessary to use backhoe or posthole digger to excavate
the soil pit.) If USGS quadrangle maps indicate distinct variation in area topography, this
procedure must be applied in each portion of the area that originally had a different surface
elevation. Record findings on DATA FORM 3.
c. Characterization of plowed soils. Determine the depth to which the soil has been disturbed
by plowing. Look for hydric soil characteristics (PART III, paragraphs 44 and/or 45)
immediately below this depth. Record findings on DATA FORM 3.
d. Removal of surface layers. Dig a hole (Appendix D, Section 1) and determine whether the
entire surface layer (A-horizon) has been removed. If so, examine the soil immediately below
the top of the subsurface layer (B-horizon) for hydric soil characteristics. As an alternative,
examine an undisturbed soil of the same soil series occurring in the same topographic position
in an immediately adjacent area that has not been altered. Look for hydric soil indicators
immediately below the A-horizon or 10 inches (whichever is shallower), and record findings
on DATA FORM 3.
If sufficient data on soils that existed prior to alteration can be obtained to determine whether a
hydric soil was present, PROCEED TO STEP 4. If not, a determination cannot be made using
soils. Use the other parameters (Subsections 1 and 3) for the determination.
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• STEP 4 - Determine Whether Hydric Soils Were Formerly Present. Examine the available
data and determine whether indicators of hydric soils (PART III, paragraphs 44 and/or 45)
were formerly present. If no indicators of hydric soils were found, the original soils were not
hydric soils. If indicators of hydric soils were found, record the appropriate indicators on
DATA FORM 3 and PROCEED TO Subsection 3 if the hydrology of the area has been
significantly altered or return either to the appropriate subsection of Section D or to Section E
and characterize the area hydrology.
Subsection 3 - Hydrology
75. Apply the following steps to determine whether wetland hydrology previously occurred:
• STEP 1 - Describe the Type of Alteration. Examine the area and describe the type of
alteration that occurred. Look for evidence of:
a. Dams. Has recent construction of a dam or some natural event (e.g. beaver activity or
landslide) caused the area to become increasingly wetter or drier? NOTE: This activity could
have occurred a considerable distance away from the site in question.
b. Levees, dikes, and similar structures. Have levees or dikes recently been constructed that
prevent the area from becoming periodically inundated by overbank flooding?
c. Ditching. Have ditches been constructed recently that cause the area to drain more rapidly
following inundation?
d. Filling of channels or depressions (land-leveling). Have natural channels or depressions
been recently filled?
e. Diversion of water. Has an upstream drainage pattern been altered that results in water
being diverted from the area?
f. Ground-water extraction. Has prolonged and intensive pumping of ground water for
irrigation or other purposes significantly lowered the water table and/or altered drainage
patterns?
g. Channelization. Have feeder streams recently been channelized sufficiently to alter the
frequency and/or duration of inundation?
Determine the approximate date* It is especially important to determine whether the alteration
occurred prior to implementation of Section 404. when the alteration occurred. Record
observations on DATA FORM 3 and PROCEED TO STEP 2.
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• STEP 2 - Describe Effects of Alteration on Area Hydrology. Record on DATA FORM 3 a
general description of how the observed alteration (STEP 1) has affected the area. Consider
the following:
a. Is the area more frequently or less frequently inundated than prior to alteration? To what
degree and why?
b. Is the duration of inundation and soil saturation different than prior to alteration? How
much different and why? PROCEED TO STEP 3.
• STEP 3 - Characterize the Hydrology That Previously Existed in the Area. Obtain all
possible evidence that may be used to characterize the hydrology that previously occurred.
Potential sources of information include:
a. Stream or tidal gage data. If a stream or tidal gaging station is located near the area, it may
be possible to calculate elevations representing the upper limit of wetlands hydrology based on
duration of inundation. Consult hydrologists from the local CE District Office for assistance.
The resulting mean sea level elevation will represent the upper limit of inundation for the area
in the absence of any alteration. If fill material has not been placed on the area, survey this
elevation from the nearest USGS benchmark. Record elevations representing zone boundaries
on DATA FORM 3. If fill material has been placed on the area, compare the calculated
elevation with elevations shown on a USGS quadrangle or any other survey map that predated
site alteration.
b. Field hydrologic indicators. Certain field indicators of wetland hydrology (PART III,
paragraph 49) may still be present. Look for watermarks on trees or other structures, drift
lines, and debris deposits. Record these on DATA FORM 3. If adjacent undisturbed areas are
in the same topographic position and are similarly influenced by the same sources of
inundation, look for wetland indicators in these areas.
c. Aerial photography. Examine any available aerial photography and determine whether the
area was inundated at the time of the photographic mission. Consider the time of the year that
the aerial photography was taken and use only photography taken during the growing season
and prior to site alteration.
d. Historical records. Examine any available historical records for evidence that the area has
been periodically inundated. Obtain copies of any such information and record findings on
DAT A FORM 3.
e. Floodplain Management Maps. Determine the previous frequency of inundation of the area
from Floodplain Management Maps (if available). Record flood frequency on DATA FORM
O
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f. Public or local government officials. Contact individuals who might have knowledge that
the area was periodically inundated.
If sufficient data on hydrology that existed prior to site alteration can be obtained to determine
whether wetland hydrology was previously present, PROCEED TO STEP 4. If not, a
determination involving hydrology cannot be made. Use other parameters (Subsections 1 and
2) for the wetland determination. Return to either the appropriate subsection of Section D or to
Section E and complete the necessary data forms. PROCEED TO STEP 4 if the previous
hydrology can be characterized. *
• STEP 4 - Determine Whether Wetland Hydrology Previously Occurred. Examine the
available data and determine whether indicators of wetland hydrology (PART III, paragraph
49) were present prior to site alteration. If no indicators of wetland hydrology were found, the
original hydrology of the area was not wetland hydrology. If indicators of wetland hydrology
were found, record the appropriate indicators on DATA FORM 3 and return either to the
appropriate subsection of Section D or to Section E and complete the wetland determination.
Subsection 4 - Man-Induced Wetlands
76. A man-induced wetland is an area that has developed at least some characteristics of
naturally occurring wetlands due to either intentional or incidental human activities. Examples
of man-induced wetlands include irrigated wetlands, wetlands resulting from impoundment
(e.g. reservoir shorelines), wetlands resulting from filling of formerly deepwater habitats,
dredged material disposal areas, and wetlands resulting from stream channel realignment.
Some man-induced wetlands may be subject to Section 404. In virtually all cases, man-
induced wetlands involve a significant change in the hydrologic regime, which may either
increase or decrease the wetness of the area. Although wetland indicators of all three
parameters (i.e. vegetation, soils, and hydrology) may be found in some man-induced
wetlands, indicators of hydric soils are usually absent. Hydric soils require long periods
(hundreds of years) for development of wetness characteristics, and most man-induced
wetlands have not been in existence for a sufficient period to allow development of hydric soil
characteristics. Therefore, application of the multiparameter approach in making wetland
determinations in man-induced wetlands must be based on the presence of hydrophytic
vegetation and wetland hydrology. (Uplands that support hydrophytic vegetation due to
agricultural irrigation and that have an obvious hydrologic connection to other "waters of the
United States" should not be delineated as wetlands under this subsection). There must also
be documented evidence that the wetland resulted from human activities. Employ the
following steps to determine whether an area consists of wetlands resulting from human
activities:
• STEP I - Determine Whether the Area Represents a Potential Man-Induced Wetland.
Consider the following questions:
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a. Has a recent man-induced change in hydrology occurred that caused the area to become
significantly wetter?
b. Has a major man-induced change in hydrology that occurred in the past caused a former
deepwater aquatic habitat to become significantly drier?
c. Has man-induced stream channel realignment significantly altered the area hydrology?
d. Has the area been subjected to long-term irrigation practices? If the answer to any of the
above questions is YES, document the approximate time during which the change in
hydrology occurred, and PROCEED TO STEP 2. If the answer to all of the questions is NO,
procedures described in Section D or E must be used.
• STEP 2 - Determine Whether a Permit Will be Needed if the Area is Found to be a Wetland.
Consider the current CE regulations and policy regarding man-induced wetlands. If the type of
activity resulting in the area being a potential man-induced wetland is exempted by regulation
or policy, no further action is needed. If not exempt, PROCEED TO STEP 3.
• STEP 3 - Characterize the Area Vegetation. Soils, and Hydrology. Apply procedures
described in Section D (routine determinations) or Section E (comprehensive determinations)
to the area. Complete the appropriate data forms and PROCEED TO STEP 4.
• STEP 4 - Wetland Determination. Based on information resulting from STEP 3, determine
whether the area is a wetland. When wetland indicators of all three parameters are found, the
area is a wetland. When indicators of hydrophytic vegetation and wetland hydrology are
found and there is documented evidence that the change in hydrology occurred so recently that
soils could not have developed hydric characteristics, the area is a wetland. In such cases, it is
assumed that the soils are functioning as hydric soils. CAUTION: if hydrophytic vegetation is
being-maintained only because of man-induced wetland hydrology that would no longer exist
if the activity (e.g. irrigation) were to be terminated., the area should not be considered a
wetland.
Section G - Problem Areas
77. There are certain wetland types and/or conditions that may make application of indicators
of one or more parameters difficult, at least at certain times of the year. These are not
considered to be atypical situations. Instead, they are wetland types in which wetland
indicators of one or more parameters may be periodically lacking due to normal seasonal or
annual variations in environmental conditions that result from causes other than human
activities or catastrophic natural events.
Types of problem areas
78. Representative examples of potential problem areas, types of variations that occur, and
their effects on wetland indicators are presented in the following subparagraphs. Similar
situations may sometimes occur in other wetland types. Note: This section is not intended to
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bring nonwetland areas having wetzand indicators of two, but not all three, parameters into
Section 404 jurisdiction.
a. Wetlands on drumlins. Slope wetlands occur in glaciated areas Tn-which thin soils cover
relatively impermeable glacial till or in which layers of glacial till have different hydraulic
conditions that produce a broad zone of ground-water seepage. Such areas are seldom, if ever,
flooded, but downslope groundwater movement keeps the soils saturated for a sufficient
portion of the growing season to produce anaerobic and reducing soil conditions. This fosters
development of hydric soil characteristics and selects for hydrophytic vegetation. Indicators of
wetland hydrology may be lacking during the drier portion of the growing season.
b. Seasonal wetlands. In many regions (especially in western states), depression areas occur
that have wetland indicators of all three parameters during the wetter portion of the growing
season, but normally lack wetland indicators of hydrology and/or vegetation during the drier
portion of the growing season. Obligate hydrophytes and facultative wetland plant species
(Appendix C, Section 1 or 2) normally are dominant during the wetter portion of the growing
season, while upland species (annuals) may be dominant during the drier portion of the
growing season. These areas may be inundated during the wetter portion of the growing
season, but wetland hydrology indicators may be totally lacking during the drier portion of the
growing season. It is important to establish that an area truly is a water body. Water in a
depression normally must be sufficiently persistent to exhibit an ordinary high-water mark or
the presence of wetland characteristics before it can be considered as a water body potentially
subject to Clean Water Act jurisdiction. The determination that an area exhibits wetland
characteristics for a sufficient portion of the growing season to qualify as a wetland under the
Clean Water Act must be made on a case-by-case basis. Such determinations should consider
the respective length of time that the area exhibits upland and wetland characteristics, and the
manner in which the area fits into the overall ecological system as a wetland. Evidence
concerning the persistence of an area's wetness can be obtained from its history, vegetation,
soil, drainage characteristics, uses to which it has been subjected, and weather or hydrologic
records.
c. Prairie potholes. Prairie potholes normally occur as shallow depressions in glaciated
portions of the north-central United States. Many are landlocked, while others have a drainage
outlet to streams or other potholes. Most have standing water for much of the growing season
in years of normal or above normal precipitation, but are neither inundated nor have saturated
soils during most of the growing season in years of below normal precipitation. During dry
years, potholes often become incorporated into farming plans, and are either planted to row
crops (e.g. soybeans) or are mowed as part of a haying operation. When this occurs, wetland
indicators of one or more parameters may be lacking. For example, tillage would eliminate
any onsite hydrologic indicator, and would make detection of soil and vegetation indicators
much more difficult.
d. Vegetated flats. In both coastal and interior areas throughout the Nation, vegetated flats are
often dominated by annual species that are categorized as OBL. Application of procedures
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described in Sections D and E during the growing season will clearly result in a positive
wetland determination. However, these areas will appear to be unvegetated mudflats when
examined during the nongrowing season, and the area would not qualify at that time as a
wetland due to an apparent lack of vegetation.
Wetland determinations in problem areas
79. Procedures for making wetland determinations in problem areas are presented below.
Application of these procedures is appropriate only when a decision has been made in Section
D or E that wetland indicators of one or more parameters were lacking, probably due to normal
seasonal or annual variations in environmental conditions. Specific procedures to be used will
vary according to the nature of the area, site conditions, and parameters) affected by the
variations in environmental conditions. A determination must be based on the best evidence
available to the field inspector, including:
a. Available information (Section B).
b. Field data resulting from an onsite inspection.
c. Basic knowledge of the ecology of the particular community
type(s) and environmental conditions associated with the community type.
NOTE: The procedures described below should only be applied to parameters not adequately
characterized in Section D or E. Complete the following steps:
• STEP 1 - Identify the Parameter(s) to be Considered. Examine the DATA FORM 1 (Section
D or E) and identify the parameter(s) that must be given additional consideration. PROCEED
TO STEP 2.
• STEP 2 - Determine the Reason for Further Consideration. Determine the reason why the
parameters) identified in STEP 1 should be given further consideration. This will require a
consideration and documentation of:
a. Environmental condition(s) that have impacted the parameters).
b. Impacts of the identified environmental condition(s) on the parameter(s) in question.
Record findings in the comments section of DATA FORM 1. PROCEED TO STEP 3.
• STEP 3 - Document Available Information for Parameter(s) in Question. Examine the
available information and consider personal ecological knowledge of the range of normal
environmental conditions of the area. Local experts (e.g. university personnel) may provide
additional information. Record information on DATA FORM 1. PROCEED TO STEP 4.
• STEP 4 - Determine Whether Wetland Indicators are Normally Present During a Portion of
the Growing Season. Examine the information resulting from STEP 3 and determine whether
wetland indicators are normally present during part of the growing season. If so, record on
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DATA FORM 1 the indicators normally present and return to Section D or Section E and make
a wetland determination. If no information can be found that wetland indicators of all three
parameters are normally present during part of the growing season, the determination must be
made using procedures described in Section D or Section E.
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REFERENCES
Clark, J. R., and Benforado, J., eds. 1981. Wetlands of Bottomland Hardwood Forests.
Proceedings of a Workshop on Bottomland Hardwood Forest Wetlands of the Southeastern
United States. Elsevier Scientific Publishing Company, New York.
Correll, D. S., and Correll, H. B. 1972.. Aquatic and Wetland Plants of the Southwestern
United States. Publ. No. 16030 DNL 01/72, Environmental Protection Agency, Washington,
D.C.
Cowardin, L. M., Carter, V., Golet, F. C., and LaRoe, E. T. 1979. "Classification of Wetlands
and Deepwater Habitats of the United States," FWS/OBS79/31, US Fish and Wildlife Service,
Office of Biological Services, Washington, D.C.
Cronquist, A., Holmgren, A. H., Holmgren, N. H., and Reveal, J. L. 1972. Intermountain Flora
- Vascular Plants of the Intermountain West. USA. Vols I and II, Hafner Publishing Company,
New York.
Davis, R. J. 1952. Flora of Idaho. William C. Brown Company, Dubuque, Iowa.
Federal Register. 1980. "40 CFR Part 230: Section 404(b)(l) Guidelines for Specification of
Disposal Sites for Dredged or Fill Material," Vol 45, No. 249, pp 85352-85353, US
Government Printing Office, Washington, D.C.
Federal Register. 1982. "Title 33: Navigation and Navigable Waters; Chapter II, Regulatory
Programs of the Corps of Engineers," Vol 47, No. 138, p 31810, US Government Printing
Office, Washington, D.C.
Fernald, M. L. 1950. Gray's Manual of Botany. 8th ed., American Book Company, New York.
Gleason, H. A., and Cronquist, A. 1963. Manual of Vascular Plants of Northeastern United
States and Adjacent Canada. Van Nostrand, Princeton, N. J.
Godfrey, R. K., and Wooten, J. W. 1979. Aquatic and Wetland Plants of the Southeastern
United States. Vols I and II, University of Georgia Press, Athens, Ga.
Harrington, H. D. 1979. Manual of the Plants of Colorado. 2nd ed., Sage Books, Denver,
Colo.
Hitchcock, A. S. 1950. Manual of Grasses of the United States. US Department of Agriculture
Miscellaneous Publication No. 200, US Government Printing Office, Washington, D.C.
Hitchcock, C. L., and Cronquist, A. 1973. Flora of the Pacific Northwest. University of
Washington Press, Seattle, Wash.
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Kearney, T. H., and Peebles, R. H. 1960. Arizona Flora. 2nd ed., University of California
Press, Berkeley, Calif.
Long, R. W., and Lakela, 0. 1976. A Flora of Tropical Florida. Banyan Books, Miami, Fla.
Munsell Color. 1975. Munsell Soil Color Charts. Kollmorgen Corporation, Baltimore, Md.
Munz, P. A., and Keck, D. D. 1959. A California Flora. University of California Press,
Berkeley, Calif.
Radford, A. E., Ahles, H. E., and Bell, C. R. 1968. Manual of the Vascular Flora of the
Carolinas. The University of North Carolina Press, Chapel Hill, N. C.
Small, J. K. 1933. Manual of the Southeastern Flora. The University of North Carolina Press,
Chapel Hill, N. C.
Steyermark, J. A. 1963. Flora of Missouri. The Iowa State University Press, Ames, Iowa.
Theriot, R. F. In Review. "Flood Tolerance Indices of Plant Species of Southeastern
Bottomland Forests," Technical Report, US Army Engineer Waterways Experiment Station,
Vicksburg, Miss.
US Department of Agriculture - Soil Conservation Service. 1975. Soil Taxonomy. Agriculture
Handbook No. 436, US Government Printing Office, Washington, D.C.
US Department of Agriculture - Soil Conservation Service. 1983. "List of Soils with Actual
or High Potential for Hydric Conditions," National Bulletin No. 430-3-10, Washington, D.C.
US Department of Agriculture - Soil Conservation Service. 1985. "Hydric Soils of the United
States," USDA-SCS National Bulletin No. 430-5-91 Washington, D.C.
US Department of the Interior. 1970. National Atlas of the United States. US Geological
Survey, US Government Printing Office, Washington, D.C., pp 110-111.
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BIBLIOGRAPHY
Copeland, B. I, Hodson, R. G., and Riggs, S. R. 1984. "The Ecology of the Pamlico River,
North Carolina: An Estuarine Profile," FWS/OBS-82/06, US Fish and Wildlife Service,
Washington, D.C.
Foster, M. S., and Schiel, D. R. 1985. "The Ecology of Giant Kelp Forests in California: A
Community Profile," FWS/OB8-85(7.2), US Fish and Wildlife Service, Washington, D.C.
Gosselink, J. G. 1984. "The Ecology of Delta Marshes of Coastal Louisiana: A Community
Profile," FWS/OBS-84/09, US Fish and Wildlife Service, Washington, D.C.
Hobbie, J. E. 1984. "The Ecology of Tundra Ponds of the Arctic Coastal Plain: A Community
Profile," FWS/OBS-83/25, US Fish and Wildlife Service, Washington, D.C.
Huffman, R. T., and Tucker, G. E. 1984. "Preliminary Guide to the Onsite Identification and
Delineation of the Wetlands of Alaska," Technical Report Y-78-9, US Army Engineer
Waterways Experiment Station, Vicksburg, Miss.
Huffman, R. T., Tucker, G. E, Wooten, J. W., Kilmas, C. V., Freel, M. W., Forsythe, S. W.,
and Wilson, J. S. 1982. "Preliminary Guide to the Onsite Identification and Delineation of the
Wetlands of the South Atlantic United States," Technical Report Y-78-7, US Army Engineer
Waterways Experiment Station, Vicksburg, Miss.
. 1982. "Preliminary Guide to the Onsite Identification and Delineation of the
Wetlands of the North Atlantic United States," Technical Report Y-78-8, US Army Engineer
Waterways Experiment Station, Vicksburg, Miss.
Japp, W. C. 1984. "The Ecology of the South Florida Coral Reefs: A Community Profile,"
FWS/OBS-82/08, US Fish and Wildlife Service, Washington, D.C.
Josselyn, M. 1983. "The Ecology of San Francisco Bay Tidal Marshes: A Community
Profile," FWS/OBS-83/23, US Fish and Wildlife Service, Washington, D.C.
Livingston, R. J. 1984. "The Ecology of the Apalachicola Bay System: An Estuarine Profile,"
FWS/OBS-82/05, US Fish and Wildlife Service, Washington, D.C.
Nixon, S. W. 1982. "The Ecology of New England High Salt Marshes: A Community
Profile," FWS/OBS-81/55, US Fish and Wildlife Service, Washington, D.C.
Odum, W. E., Mclvor, C. C., and Smith, T. J., III. 1982. "The Ecology of the Mangroves of
South Florida: A Community Profile," FWS/OB S-81/24, US Fish and Wildlife Service,
Washington, D.C.
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Odum, W. E., Smith, T. I, III, Hoover, J. K., and Mclvor, C. C. 1984. "The Ecology of
Tidal Freshwater Marshes of the United States East Coast: A Community Profile," FWS/OBS-
83/17, US Fish and Wildlife Service, Washington, D.C.
Peterson, C. H., and Peterson, N. M. 1979. "The Ecology of Intertidal Flats of North Carolina:
A Community Profile," FWS/OBS-79/39, US Fish and Wildlife Service, Washington, D.C.
Phillips, R. C. 1984. "The Ecology of Eelgrass Meadows in the Pacific Northwest: A
Community Profile," FWS/OBS-84/24, US Fish and Wildlife Service, Washington, D.C.
Seliskar, D. M., and Gallagher, J. L. 1983. "The Ecology of Tidal Marshes of the Pacific
Northwest Coast: A Community Profile," FWS/OBS-82/32, US Fish and Wildlife Service,
Washington, D.C.
Sharitz, R. R., and Gibbons, J. W. 1982. "The Ecology of Southeastern Shrub Bogs (Pocosins)
and Carolina Bays: A Community Profile," FWS/OBS-82/04, US Fish and Wildlife Service,
Washington, D.C.
Thayer, G. W., Kenworthy, W. J., and Fonseca, M. S. 1984. "The Ecology of Eelgrass
Meadows of the Atlantic Coast: A Community Profile," FWS/OBS-84/02, US Fish and
Wildlife Service, Washington, D.C.
US Army Corps of Engineers. 1978. "Preliminary Guide to Wetlands of Peninsular Florida,"
Technical Report Y-78-2, US Army Engineer Waterways Experiment Station, Vicksburg,
Miss.
. 1978. "Preliminary Guide to Wetlands of Puerto Rico," Technical Report Y-78-3,
US Army Engineer Waterways Experiment Station, Vicksburg, Miss.
. 1978. "Preliminary Guide to Wetlands of the West Coast States," Technical Report
Y-78-4, US Army Engineer Waterways Experiment Station, Vicksburg, Miss.
. 1978. "Preliminary Guide to Wetlands of the Gulf Coastal Plain," Technical
Report Y-78-5, US Army Engineer Waterways Experiment Station, Vicksburg, Miss.
. 1982. "Preliminary Guide to the Onsite Identification and Delineation of the
Wetlands of the Interior United States," Technical Report Y-78-6, US Army Engineer
Waterways Experiment Station, Vicksburg, Miss.
Witlatch, R. B. 1982. "The Ecology of New England Tidal Flats: A Community Profile,"
FWS/OBS-81/01, US Fish and Wildlife Service, Washington, D.C.
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Wharton, C. H., Kitchens, W. M., and Sipe, T. W. 1982. "The Ecology of Bottomland
Hardwood Swamps of the Southeast: A Community Profile," FWS/OBS-81/37, US Fish and
Wildlife Service, Washington, D.C.
Zedler, J. B. 1984. "The Ecology of Southern California Coastal Salt Marshes: A Community
Profile," FWS/OBS-81/54, US Fish and Wildlife Service, Washington, D.C.
Zieman, J. C. 1982. "The Ecology of the Seagrasses of South Florida: A Community Profile,"
FWS/OBS-82/25, US Fish and Wildlife Service, Washington, D.C.
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APPENDIX A: GLOSSARY
Active water table - A condition in which the zone of soil saturation fluctuates, resulting in
periodic anaerobic soil conditions. Soils with an active water table often contain bright mottles
and matrix chromas of 2 or less.
Adaptation - A modification of a species that makes it more fit for existence under the
conditions of its environment. These modifications are the result of genetic selection
processes.
Adventitious roots Roots found on plant stems in positions where they normally do not occur.
Aerenchymous tissue A type of plant tissue in which cells are unusually large and arranged in
a manner that results in air spaces in the plant organ. Such tissues are often referred to as
spongy and usually provide increased buoyancy.
Aerobic - A situation in which molecular oxygen is a part of the environment.
Anaerobic - A situation in which molecular oxygen is absent (or effectively so) from the
environment.
Aquatic roots - Roots that develop on stems above the normal position occupied by roots in
response to prolonged inundation.
Aguic moisture regime - A mostly reducing soil moisture regime nearly free of dissolved
oxygen due to saturation by ground water or its capillary fringe and occurring at periods when
the soil temperature at 19.7 in. is greater than 50 C.
Arched roots - Roots produced on plant stems in a position above the normal position of roots,
which serve to brace the plant during and following periods of prolonged inundation.
Areal cover - A measure of dominance that defines the degree to which aboveground portions
of plants (not limited to those rooted in a sample plot) cover the ground surface. It is possible
for the total areal cover in a community to exceed 100 percent because (a) most plant
communities consist of two or more vegetative strata; (b) areal cover is estimated by vegetative
layer; and (c) foliage within a single layer may overlap.
Atypical situation - As used herein, this term refers to areas in which one or more parameters
(vegetation, soil, and/or hydrology) have been sufficiently altered by recent human activities or
natural events to preclude the presence of wetland indicators of the parameter.
Backwater flooding - Situations in which the source of inundation is overbank flooding from a
nearby stream.
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Basal area - The cross-sectional area of a tree trunk measured in square inches, square
centimetres, etc. Basal area is normally measured at 4.5 ft above the ground level and is used
as a measure of dominance. The most easily used tool for measuring basal area is a tape
marked in square inches. When plotless methods are used, an angle gauge or prism will
provide a means for rapidly determining basal area. This term is also applicable to the
crosssectional area of a clumped herbaceous plant, measured at 1.0 in. above the soil surface.
Bench mark - A fixed, more or less permanent reference point or object, the elevation of which
is known. The US Geological Survey (USGS) installs brass caps in bridge abutments or
otherwise permanently sets bench marks at convenient locations nationwide. The elevations
on these marks are referenced to the National Geodetic Vertical Datum (NGVD), also
commonly known as mean sea level (MSL). Locations of these bench marks on USGS
quadrangle maps are shown as small triangles. However, the marks are sometimes destroyed
by construction or vandalism. The existence of any bench mark should be field verified before
planning work that relies on a particular reference point. The USGS and/or local state
surveyor's office can provide information on the existence, exact location, and exact elevation
of bench marks.
Biennial - An event that occurs at 2-year intervals.
Buried soil - A once-exposed soil now covered by an alluvial, loessal, or other deposit
(including man-made).
Canopy layer - The uppermost layer of vegetation in a plant community. In forested areas,
mature trees comprise the canopy layer, while the tallest herbaceous species constitute the
canopy layer in a marsh.
Capillary fringe - A zone immediately above the water table (zero gauge pressure) in which
water is drawn upward from the water table by capillary action.
Chemical reduction - Any process by which one compound or ion acts as an electron donor.
In such cases, the valence state of the electron donor is decreased.
Chroma - The relative purity or saturation of a color; intensity of distinctive hue as related to
grayness; one of the three variables of color.
Comprehensive wetland determination - A type of wetland determination that is based on the
strongest possible evidence, requiring the collection of quantitative data.
Concretion - A local concentration of chemical compounds (e.g. calcium carbonate, iron
oxide) in the form of a grain or nodule of varving size, shape, hardness, and color.
Concretions of significance in hydric soils are usually iron and/or manganese oxides occurring
at or near the soil surface, which develop under conditions of prolonged soil saturation.
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Contour - An imaginary line of constant elevation on the ground surface. The corresponding
line on a map is called a "contour line."
Criteria - Standards, rules, or tests on which a judgment or decision may be based.
Deepwater aquatic habitat - Any open water area that has a mean annual water depth >6.6 ft,
lacks soil, and/or is either unvegetated or supports only floating or submersed macrophytes.
Density - The number of individuals of a species per unit area.
Detritus - Minute fragments of plant parts found on the soil surface. When fused together by
algae or soil particles, this is an indicator that surface water was recently present.
Diameter at breast height (DBH) - The width of a plant stem as measured at 4.5 ft above the
ground surface.
Dike - A bank (usually earthen) constructed to control or confine water.
Dominance - As used herein, a descriptor of vegetation that is related to the standing crop of a
species in an area, usually measured by height, areal cover, or basal area (for trees).
Dominant species - As used herein, a plant species that exerts a controlling influence on or
defines the character of a community.
Drained - A condition in which ground or surface water has been reduced or eliminated from
an area by artificial means.
Drift line An accumulation of debris along a contour (parallel to the water flow) that represents
the height of an inundation event.
Duration (inundation/soil saturation) - The length of time during which water stands at or
above the soil surface (inundation), or during which the soil is saturated. As used herein,
duration refers to a period during the growing season.
Ecological tolerance - The range of environmental conditions in which a plant species can
grow.
Emergent plant - A rooted herbaceous plant species that has parts extending above a water
surface.
Field capacity - The percentage of water remaining in a soil after it has been saturated and after
free drainage is negligible.
Fill material - Any material placed in an area to increase surface elevation.
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Flooded - A condition in which the soil surface is temporarily covered with flowing water from
any source, such as streams overflowing their banks, runoff from adjacent or surrounding
slopes, inflow from high tides, or any combination of sources.
Flora - A list of all plant species that occur in an area.
Frequency (inundation or soil saturation) - The periodicity of coverage of an area by surface
water or soil saturation. It is usually expressed as the number of years (e.g. 50 years) the soil is
inundated or saturated at least once each year during part of the growing season per 100 years
or as a 1-, 2-, 5-year, etc., inundation frequency.
Frequency (vegetation) - The distribution of individuals of a species in an area. It is
quantitatively expressed as
Number of samples containing species A X 100
Total number of samples
More than one species may have a frequency of 100 percent within the same area.
Frequently flooded - A flooding class in which flooding is likely to occur often under normal
weather conditions (more than 50-percent chance of flooding in any year or more than 50
times in 100 years).
Gleyed - A soil condition resulting from prolonged soil saturation, which is manifested by the
presence of bluish or greenish colors through the soil mass or in mottles (spots or streaks)
among other colors. Gleying occurs under reducing soil conditions resulting from soil
saturation, by which iron is reduced predominantly to the ferrous state.
Ground water - That portion of the water below the ground surface that is under greater
pressure than atmospheric pressure.
Growing season - The portion of the year when soil temperatures at 19.7 inches below the soil
surface are higher than biologic zero (5' C) (US Department of Agriculture - Soil Conservation
Service 1985). For ease of determination this period can be approximated by the number of
frost-free days (US Department of the interior 1970).
Habitat - The environment occupied by individuals of a particular species, population, or
community.
Headwater flooding - A situation in which an area becomes inundated directly by surface
runoff from upland areas.
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Herb - A nonwoody individual of a macrophytic species. In this manual, seedlings of woody
plants (including vines) that are less than 3.2 ft in height are considered to be herbs.
Herbaceous layer - Any vegetative stratum of a plant community that is composed
predominantly of herbs.
Histic epipedon - An 8- to 16-in. soil layer at or near the surface that is saturated for 30
consecutive days or more during the growing season in most years and contains a minimum of
20 percent organic matter when no clay is present or a minimum of 30 percent organic matter
when 60 percent or greater clay is present.
Histosols - An order in soil taxonomy composed of organic soils that have organic soil
materials in more than half of the upper 80 cm or that are of any thickness if directly overlying
bedrock.
Homogeneous vegetation - A situation in which the same plant species association occurs
throughout an area.
Hue - A characteristic of color that denotes a color in relation to red, yellow, blue, etc; one of
the three variables of color. Each color chart in the Munsell Color Book (Munsell Color 1975)
consists of a specific hue.
Hydric soil - A soil that is saturated, flooded, or ponded long enough during the growing
season to develop anaerobic conditions that favor the growth and regeneration of hydrophytic
vegetation (US Department of Agriculture-Soil Conservation Service 1985). Hydric soils that
occur in areas having positive indicators of hydrophytic vegetation and wetland hydrology are
wetland soils.
Hydric soil condition - A situation in which characteristics exist that are associated with soil
development under reducing conditions.
Hydrologic regime - The sum total of water that occurs in an area on average during a given
period.
Hydrologic zone - An area that is inundated or has saturated soils within a specified range of
frequency and duration of inundation and soil saturation.
Hydrology - The science dealing with the properties, distribution, and circulation of water.
Hydrophyte - Any macrophyte that grows in water or on a substrate that is at least periodically
deficient in oxygen as a result of excessive water content; plants typically found in wet
habitats.
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Hydrophytic vegetation - The sum total of macrophytic plant life growing in water or on a
substrate that is at least periodically deficient in oxygen as a result of excessive water content.
When hydrophytic vegetation comprises a community where indicators of hydric soils and
wetland hydrology also occur, the area has wetland vegetation.
Hypertrophied lenticels - An exaggerated (oversized) pore on the surface of stems of woody
plants through which gases are exchanged between the plant and the atmosphere. The
enlarged lenticels serve as a mechanism for increasing oxygen to plant roots during periods of
inundation and/or saturated soils.
Importance value - A quantitative term describing the relative influence of a plant species in a
plant community, obtained by summing any combination of relative frequency, relative
density, and relative dominance.
Indicator - As used in this manual, an event, entity, or condition that typically characterizes a
prescribed environment or situation; indicators determine or aid in determining whether or not
certain stated circumstances exist.
Indicator status - One of the categories (e.g. OBL) that describes the estimated probability of a
plant species occurring in wetlands.
Intercellular air space - A cavity between cells in plant tissues, resulting from variations in cell
shape and configuration. Aerenchymous tissue (a morphological adaptation found in many
hydrophytes) often has large intercellular air spaces.
Inundation - A condition in which water from any source temporarily or permanently covers a
land surface.
Levee - A natural or man-made feature of the landscape that restricts movement of water into
or through an area.
Liana - As used in this manual, a layer of vegetation in forested plant communities that
consists of woody vines. The term may also be applied to a given species.
Limit of biological activity - With reference to soils, the zone below which conditions preclude
normal growth of soil organisms. This term often is used to refer to the temperature (5' C) in a
soil below which metabolic processes of soil microorganisms, plant roots, and animals are
negligible.
Long duration (flooding) - A flooding class in which the period of inundation for a single
event ranges from 7 days to 1 month.
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Macrophyte Any plant species that can be readily observed without the aid of optical
magnification. This includes all vascular plant species and mosses (e.g., Sphagnum spp.), as
well as large algae (e.g. Chora spp., kelp).
Macrophytic - A term referring to a plant species that is a macrophyte.
Major portion of the root zone. The portion of the soil profile in which more than 50 percent
of plant roots occur. In wetlands, this usually constitutes the upper 12 in. of the profile.
Man-induced wetland - Any area that develops wetland characteristics due to some activity
(e.g., irrigation) of man.
Mapping unit - As used in this manual, some common characteristic of soil, vegetation, and/or
hydrology that can be shown at the scale of mapping for the defined purpose and objectives of
a survey.
Mean sea level - A datum, or "plane of zero elevation," established by averaging all stages of
oceanic tides over a 19-year tidal cycle or "epoch." This plane is corrected for curvature of the
earth and is the standard reference for elevations on the earth's surface. The correct term for
mean sea level is the National Geodetic Vertical Datum (NGVD).
Mesophytic - Any plant species growing where soil moisture and aeration conditions lie
between extremes. These species are typically found in habitats with average moisture
conditions, neither very dry nor very wet.
Metabolic processes - The complex of internal chemical reactions associated with life-
sustaining functions of an organism.
Method - A particular procedure or set of procedures to be followed.
Mineral soil - A soil consisting predominantly of, and having its properties determined
predominantly by, mineral matter usually containing less than 20-percent organic matter.
Morphological adaptation - A feature of structure and form that aids in fitting a species to its
particular environment (e.g. buttressed base, adventitious roots, aerenchymous tissue).
Mottles - Spots or blotches of different color or shades of color interspersed within the
dominant color in a soil layer, usually resulting from the presence of periodic reducing soil
conditions.
Muck - Highly decomposed organic material in which the original plant parts are not
recognizable.
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Multitrunk - A situation in which a single individual of a woody plant species has several
stems.
Nonhydric soil - A soil that has developed under predominantly aerobic soil conditions. These
soils normally support mesophytic or xerophytic species.
Nonwetland - Any area that has sufficiently dry conditions that indicators of hydrophytic
vegetation, hydric soils, and/or wetland hydrology are lacking. As used in this manual, any
area that is neither a wetland, a deepwater aquatic habitat, nor other special aquatic site.
Organic pan - A layer usually occurring at 12 to 30 inches below the soil surface in coarse-
textured soils, in which organic matter and aluminum (with or without iron) accumulate at the
point where the top of the water table most often occurs. Cementing of the organic matter
slightly reduces permeability of this layer.
Organic soil - A soil is classified as an organic soil when it is: (1) saturated for prolonged
periods (unless artificially drained) and has more than 30-percent organic matter if the mineral
fraction is more than 50-percent clay, or more than 20-percent organic matter if the mineral
fraction has no clay; or (2) never saturated with water for more than a few days and having
more than 34-percent organic matter.
Overbank flooding - Any situation in which inundation occurs as a result of the water level of
a stream rising above bank level.
Oxidation-reduction process - A complex of biochemical reactions in soil that influences the
valence state of component elements and their ions. Prolonged soil saturation during the
growing season elicits anaerobic conditions that shift the overall process to a reducing
condition.
Oxygen pathway - The sequence of cells, intercellular spaces, tissues, and organs, through
which molecular oxygen is transported in plants. Plant species having pathways for oxygen
transport to the root system are often adapted for life in saturated soils.
Parameter - A characteristic component of a unit that can be defined. Vegetation, soil, and
hydrology are three parameters that may be used to define wetlands.
Parent material - The unconsolidated and more or less weathered mineral or organic matter
from which a soil profile develops.
Ped - A unit of soil structure (e.g. aggregate, crumb, prism, block, or granule) formed by
natural processes.
Peraquic moisture regime - A soil condition in which a reducing environment always occurs
due to the presence of ground water at or near the soil surface.
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Periodically - Used herein to define detectable regular or irregular saturated soil conditions or
inundation, resulting from ponding of ground water, precipitation, overland flow, stream
flooding, or tidal influences that occur(s) with hours, days, weeks, months, or even years
between events.
Permeability- A soil characteristic that enables water or air to move through the profile,
measured as the number of inches per hour that water moves downward through the saturated
soil. The rate at which water moves through the least permeable layer governs soil
permeability.
Physiognomy - A term used to describe a plant community based on the growth habit (e.g.,
trees, herbs, lianas) of the dominant species.
Physiological adaptation - A feature of the basic physical and chemical activities that occurs in
cells and tissues of a species, which results in it being better fitted to its environment (e.g.
ability to absorb nutrients under low oxygen tensions).
Plant community - All of the plant populations occurring in a shared habitat or environment.
Plant cover - See areal cover.
Pneumatophore - Modified roots that may function as a respiratory organ in species subjected
to frequent inundation or soil saturation (e.g., cypress knees).
Ponded - A condition in which water stands in a closed depression. Water may be removed
only by percolation, evaporation, and/or transpiration.
Poorly drained - Soils that commonly are wet at or near the surface during a sufficient part of
the year that field crops cannot be grown under natural conditions. Poorly drained conditions
are caused by a saturated zone, a layer with low hydraulic conductivity, seepage, or a
combination of these conditions.
Population - A group of individuals of the same species that occurs in a given area.
Positive wetland indicator - Any evidence of the presence of hydrophytic vegetation, hydric
soil, and/or wetland hydrology in an area.
Prevalent vegetation - The plant community or communities that occur in an area during a
given period. The prevalent vegetation is characterized by the dominant macrophytic species
that comprise the plant community.
Quantitative - A precise measurement or determination expressed numerically.
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Range - As used herein, the geographical area in which a plant species is known to occur.
Redox potential - A measure of the tendency of a system to donate or accept electrons, which
is governed by the nature and proportions of the oxidizing and reducing substances contained
in the system.
Reducing environment - An environment conducive to the removal of oxygen and chemical
reduction of ions in the soils.
Relative density - A quantitative descriptor, expressed as a percent, of the relative number of
individuals of a species in an area; it is calculated by
Number of individuals of species A X 100
Total number of individuals of all species
Relative dominance - A quantitative descriptor, expressed as a percent, of the relative size or
cover of individuals of a species in an area; it is calculated by
Amount* of species A X 100
Total amount of all species
*The amount of a species may be based on percent areal cover, basal area, or height.
Relative frequency - A quantitative descriptor, expressed as a percent, of the relative
distribution of individuals of a species in an area; it is calculated by
Frequency of species A X 100
Total frequency of all species
Relief - The change in elevation of a land surface between two points; collectively, the
configuration of the earth's surface, including such features as hills and valleys.
Reproductive adaptation - A feature of the reproductive mechanism of a species that results in
it being better fitted to its environment (e.g. ability for seed germination under water).
Respiration - The sum total of metabolic processes associated with conversion of stored
(chemical) energy into kinetic (physical) energy for use by an organism.
Rhizosphere - The zone of soil in which interactions between living plant roots and
microorganisms occur.
Root zone - The portion of a soil profile in which plant roots occur.
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Routine wetland determination - A type of wetland determination in which office dat and/or
relatively simple, rapidly applied onsite methods are employed to determine whether or not an
area is a wetland. Most wetland determinations are of this type, which usually does not require
collection of quantitative data.
Sample plot - An area of land used for measuring or observing existing conditions.
Sapling/shrub - A layer of vegetation composed of woody plants <3.0 in. in diameter at breast
height but greater than 3.2 ft in height, exclusive of woody vines.
Saturated soil conditions - A condition in which all easily drained voids (pores) between soil
particles in the root zone are temporarily or permanently filled with water to the soil surface at
pressures greater than atmospheric.
Soil - Unconsolidated mineral and organic material that supports, or is capable of supporting,
plants, and which has recognizable properties due to the integrated effect of climate and living
matter acting upon parent material, as conditioned by relief over time.
Soil horizon - A layer of soil or soil material approximately parallel to the land surface and
differing from adjacent genetically related layers in physical, chemical, and biological
properties or characteristics (e.g. color, structure, texture, etc.).
Soil matrix - The portion of a given soil having the dominant color. In most cases, the matrix
will be the portion of the soil having more than 50 percent of the same color.
Soil permeability - The ease with which gases, liquids, or plant roots penetrate or pass through
a layer of soil.
Soil phase - A subdivision of a soil series having features (e.g. slope, surface texture, and
stoniness) that affect the use and management of the soil, but which do not vary sufficiently to
differentiate it as a separate series. These are usually the basic mapping units on detailed soil
maps produced by the Soil Conservation Service.
Soil pore - An area within soil occupied by either air or water, resulting from the arrangement
of individual soil particles or peds.
Soil profile - A vertical section of a soil through all its horizons and extending into the parent
material.
Soil series - A group of soils having horizons similar in differentiating characteristics and
arrangement in the soil profile, except for texture of the surface horizon.
Soil structure - The combination or arrangement of primary soil particles into secondary
particles, units, or peds.
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Soil surface - The upper limits of the soil profile. For mineral soils, this is the upper limit of
the highest (Al) mineral horizon. For organic soils, it is the upper limit of undecomposed.
dead organic matter.
Soil texture - The relative proportions of the various sizes of particles in a soil.
Somewhat poorly drained - Soils that are wet near enough to the surface or long enough that
planting or harvesting operations or crop growth is markedly restricted unless artificial
drainage is provided. Somewhat poorly drained soils commonly have a layer with low
hydraulic conductivity, wet conditions high in the profile, additions of water through seepage,
or a combination of these conditions.
Stilted roots - Aerial roots arising from stems (e.g., trunk and branches), presumably providing
plant support (e.g., Rhizophora mangle).
Stooling - A form of asexual reproduction in which new shoots are produced at the base of
senescing stems, often resulting in a multitrunk growth habit.
Stratigraphy - Features of geology dealing with the origin, composition, distribution, and
succession of geologic strata (layers).
Substrate - The base or substance on which an attached species is growing.
Surface water - Water present above the substrate or soil surface.
Tidal - A situation in which the water level periodically fluctuates due to the action of lunar
and solar forces upon the rotating earth.
Topography - The configuration of a surface, including its relief and the position of its natural
and man-made features.
Transect - As used herein, a line on the ground along which observations are made at some
interval.
Transition zone - The area in which a change from wetlands to nonwetlands occurs. The
transition zone may be narrow or broad.
Transpiration - The process in plants by which water vapor is released into the gaseous
environment, primarily through stomata.
Tree - A woody plant >3.0 in. in diameter at breast height, regardless of height (exclusive of
woody vines).
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Typical - That which normally, usually, or commonly occurs.
Typically adapted - A term that refers to a species being normally or commonly suited to a
given set of environmental conditions, due to some feature of its morphology, physiology, or
reproduction.
Unconsolidated parent material - Material from which a soil develops, usually formed by
weathering of rock or placement in an area by natural forces (e.g. water, wind, or gravity).
Under normal circumstances - As used in the definition of wetlands, this term refers to
situations in which the vegetation has not been substantially altered by man's activities.
Uniform vegetation - As used herein, a situation in which the same group of dominant species
generally occurs throughout a given area.
Upland - As used herein, any area that does not qualify as a wetland because the associated
hydrologic regime is not sufficiently wet to elicit development of vegetation, soils, and/or
hydrologic characteristics associated with wetlands. Such areas occurring within floodplains
are more appropriately termed nonwetlands.
Value (soil color) - The relative lightness or intensity of color, approximately a function of the
square root of the total amount of light reflected from a surface; one of the three variables of
color.
Vegetation - The sum total of macrophytes that occupy a given area.
Vegetation layer - A subunit of a plant community in which all component species exhibit the
same growth form (e.g., trees, saplings/shrubs, herbs).
Very long duration (flooding) - A duration class in which the length of a single inundation
event is greater than I month.
Very poorly drained - Soils that are wet to the surface most of the time. These soils are wet
enough to prevent the growth of important crops (except rice) unless artificially drained.
Watermark - A line on a tree or other upright structure that represents the maximum static
water level reached during an inundation event.
Water table - The upper surface of ground water or that level below which the soil is saturated
with water. It is at least 6 in. thick and persists in the soil for more than a few weeks.
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Wetlands - Those areas that are inundated or saturated by surface or ground water at a
frequency and duration sufficient to support, and that under normal circumstances do support,
a prevalence of vegetation typically adapted for life in saturated soil conditions. Wetlands
generally include swamps, marshes, bogs, and similar areas.
Wetland boundary - The point on the ground at which a shift from wetlands to nonwetlands or
aquatic habitats occurs. These boundaries usually follow contours.
Wetland determination - The process or procedure by which an area is ad udged a wetland or
nonwetland.
Wetland hydrology - The sum total of wetness characteristics in areas that are inundated or
have saturated soils for a sufficient duration to support hydrophytic vegetation.
Wetland plant association - Any grouping of plant species that recurs wherever certain wetland
conditions occur.
Wetland soil - A soil that has characteristics developed in a reducing atmosphere, which exists
when periods of prolonged soil saturation result in anaerobic conditions. Hydric soils that are
sufficiently wet to support hydrophytic vegetation are wetland soils.
Wetland vegetation - The sum total of macrophytic plant life that occurs in areas where the
frequency and duration of inundation or soil saturation produce permanently or periodically
saturated soils of sufficient duration to exert a controlling influence on the plant species
present. As used herein, hydrophytic vegetation occurring in areas that also have hydric soils
and wetland hydrology may be properly referred to as wetland vegetation.
Woody vine - See liana.
Xerophytic - A plant species that is typically adapted for life in conditions -where a lack of
water is a limiting factor for growth and/or reproduction. These species are capable of growth
in extremely dry conditions as a result of morphological, physiological, and/or reproductive
adaptations.
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APPENDIX C: VEGETATION
1. This appendix contains three sections. Section 1 is a subset of the regional list of plants
that occur in wetlands, but includes only those species having an indicator status of OBL,
F ACW, or FAC. Section 2 is a list of plants that commonly occur in wetlands of a given
region. Since many geographic areas of Section 404 responsibility include portions of two or
more plant list regions, users will often need more than one regional list; thus, Sections 1 and 2
will be published separately from the remainder of the manual. Users will be furnished all
appropriate regional lists.
2. Section 3, which is presented herein, describes morphological, physiological, and
reproductive adaptations that can be observed or are known to occur in plant species that are
typically adapted for life in anaerobic soil conditions.
Section 3 - Morphological, Physiological, and Reproductive
Adaptations of Plant Species for Occurrence in Areas
Having Anaerobic Soil Conditions
Morphological adaptations
3. Many plant species have morphological adaptations for occurrence in wetlands. These
structural modifications most often provide the plant with increased buoyancy or support. In
some cases (e.g. adventitious roots), the adaptation may facilitate the uptake of nutrients and/or
gases (particularly oxygen). However., not all species occurring in areas having anaerobic soil
conditions exhibit morphological adaptations for such conditions. The following is a list of
morphological adaptations that a species occurring in areas having anaerobic soil conditions
may possess (a partial list of species with such adaptations is presented in Table Cl):
a. Buttressed tree trunks. Tree species (e.g. Taxodium distichum) may develop enlarged
trunks (Figure Cl) in response to frequent inundation. This adaptation is a strong indicator of
hydrophytic vegetation in nontropical forested areas.
b. Pneumatophores. These modified roots may serve as respiratory organs in species
subjected to frequent inundation or soil saturation. Cypress knees (Figure C2) are a classic
example, but other species (e.g., Nyssa aquatics, Rhizophora mangze) may also develop
pneumatophores.
c. Adventitious roots. Sometimes referred to as "water roots," Adventitious roots occur
on plant stems in positions where roots normally are not found. Small fibrous roots protruding
from the base of trees (e.g. Salix nigra) or roots on stems of herbaceous plants and tree
seedlings in positions immediately above the soil surface (e.g. Ludwigia spp.) occur in
response to inundation or soil saturation (Figure C3). These usually develop during periods of
sufficiently prolonged soil saturation to destroy most of the root system. CAUTION: Not all
adventitious roots develop as a result of inundation or soil saturation. For example, aerial
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roots on woofty vines are not normally produced as a response to inundation or soil
saturation.
d. Shallow root systems. When soils are inundated or saturated for long periods during
the growing season, anaerobic conditions develop in the zone of root growth. Most species
with deep root systems cannot survive in such conditions. Most species capable of growth
during periods when soils are oxygenated only near the surface have shallow root systems. In
forested wetlands, windthrown trees (Figure C4) are often indicative of shallow root systems.
e. Inflated leaves, stems, or roots. Many hydrophytic species, particularly herbs (e.g.
Limnobium spongia, Ludwigia spp.), have or develop spongy (aerenchymous) tissues in
leaves, stems, and/or roots that provide buoyancy or support and serve as a reservoir or
passageway for oxygen needed for metabolic processes. An example of inflated leaves is
shown in Figure C5.
f. Polymorphic leaves. Some herbaceous species produce different types of leaves,
depending on the water level at the time of leaf formation. For example, Alisma spp. produce
strap-shaped leaves when totally submerged, but produce broader, floating leaves when plants
are emergent. CA UTION: Many upland species also produce polymorphic leaves.
g. Floating leaves. Some species (e.g. Nymphaea spp.) produce leaves that are uniquely
adapted for floating on a water surface (Figure C6). These leaves have stomata primarily on
the upper surface and a thick waxy cuticle that restricts water penetration. The presence of
species with floating leaves is strongly indicative of hydrophytic vegetation.
h. Floating stems. A number of species (e.g., Alternantheraphiloxeroides) produce
matted stems that have large internal air spaces when occurring in inundated areas. Such
species root in shallow water and grow across the water surface into deeper areas. Species
with floating stems often produce adventitious roots at leaf nodes.
i. Hypertrophied lenticels. Some plant species (e.g. GZeditsia aquatica) produce enlarged
lenticels on the stem in response to prolonged inundation or soil saturation. These are thought
to increase oxygen uptake through the stem during such periods.
j.. Multitrunks or stooling. Some woody hydrophytes characteristical.ly produce several
trunks of different ages (Figure C7) or produce new stems arising from the base of a senescing
individual (e.g. Forestiera acuminata, Nyssa ogechee) in response to inundation.
k. Oxygen pathway to roots. Some species (e.g. Spartina alterniflora) have a specialized
cellular arrangement that facilitates diffusion of gaseous oxygen from leaves and stems to the
root system.
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Physiological adaptations
4. Most, if not all, hydrophytic species are thought to possess physiological adaptations
for occurrence in areas that have prolonged periods of anaerobic soil conditions. However,
relatively few species have actually been proven to possess such adaptations, primarily due to
the limited research that has been conducted. Nevertheless, several types of physiological
adaptations known to occur in hydrophytic species are discussed below, and a list of species
having one or more of these adaptations is presented in Table C2. NOTE: Since it is
impossible to detect these adaptations in the field, use of this indicator will be limited to
observing the species in the field and checking the list in Table C2 to determine whether the
species is known to have a physiological adaptation for occurrence in areas having anaerobic
soil conditions):
a. Accumulation of malate. Malate, a nontoxic metabolite, accumuYat-es in roots of
many hydrophytic species (e.g. Glyceria maxima, Nyssa sylvatica var. biflord). Nonwetland
species concentrate ethanol, a toxic by-product of anaerobic respiration, when growing in
anaerobic soil conditions. Under such conditions, many hydrophytic species produce high
concentrations of malate and unchanged concentrations of ethanol, thereby avoiding
accumulation of toxic materials. Thus, species having the ability to concentrate malate instead
of ethanol in the root system under anaerobic soil conditions are adapted for life in such
conditions, while species that concentrate ethanol are poorly adapted for life in anaerobic soil
conditions.
b. Increased levels of nitrate reductase. Nitrate reductase is an enzyme involved in
conversion of nitrate nitrogen to nitrite nitrogen, an intermediate step in ammonium
production. Ammonium ions can accept electrons as a replacement for gaseous oxygen in
some species, thereby allowing continued functioning of metabolic processes under low soil
oxygen conditions. Species that produce high levels of nitrate reductase (e.g. Larix Zaricina)
are adapted for life in anaerobic soil conditions.
c. Slight increases in metabolic rates. Anaerobic soil conditions effect short-term
increases in metabolic rates in most species. However, the rate of metabolism often increases
only slightly in wetland species, while metabolic rates increase significantly in nonwetland
species. Species exhibiting only slight increases in metabolic rates (e.g. Larix laricina,
Senecio vulgaris) are adapted for life in anaerobic soil conditions.
d. Rhizosphere oxidation. Some hydrophytic species (e.g. Nyssa aquatica, Myrica gale)
are capable of transferring gaseous oxygen from the root system into soil pores immediately
surrounding the roots. This adaptation prevents root deterioration and maintains the rates of
water and nutrient absorption under anaerobic soil conditions.
e. Ability for root growth in low oxygen tensions. Some species (e.g. Typha angustifotia,
Juncus effusus) have the ability to maintain root growth under soil oxygen concentrations as
low as 0.5 percent. Although prolonged (>1 year) exposure to soil oxygen concentrations
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lower than 0.5 percent generally results in the death of most individuals, this adaptation
enables some species to survive extended periods of anaerobic soil conditions.
f. Absence of alcohol dehydrogenase (ADH) activity. ADH is an enzyme associated with
increased ethanol production. When the enzyme is not functioning, ethanol production does
not increase significantly. Some hydrophytic species (e.g. Potentilla anserina, Polygonum
amphibium) show only slight increases in ADH activity under anaerobic soil conditions.
Therefore, ethanol production occurs at a slower rate in species that have low concentrations of
ADH.
Reproductive adaptations
5. Some plant species have reproductive features that enable them to become established
and grow in saturated soil conditions. The following have been identified in the technical
literature as reproductive adaptations that occur in hydrophytic species:
a. Prolonged seed viability. Some plant species produce seeds that may remain viable for
20 years or more. Exposure of these seeds to atmospheric oxygen usually triggers
germination. Thus, species (e.g., Taxodium distichum) that grow in very wet areas may
produce seeds that germinate only during infrequent periods when the soil is dewatered.
NOTE: Many upland species also have prolonged seed viability, but the trigger mechanism for
germination is not exposure to atmospheric oxygen.
b. Seed germination under low oxygen concentrations. Seeds of some hydrophytic
species germinate when submerged. This enables germination during periods of early-spring
inundation, which may provide resulting seedlings a competitive advantage over species
whose seeds germinate only when exposed to atmospheric oxygen.
c. Flood-tolerant seedlings. Seedlings of some hydrophytic species (e.g. Fraxinus
pennsylvanica) can survive moderate periods of total or partial inundation. Seedlings of these
species have a competitive advantage over seedlings of flood-intolerant species.
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Tabled
Partial List of Species With Known Morphological Adaptations for
Occurrence in Wetlands*
Species
Acer negundo
Acer rubrum
Acer saccharinum
Alisma spp.
Alternanthera philoxeroides
Avicennia nitida
Brasenia schreberi
Cladium mariscoides
Cyperus spp. (most species)
Eleocharis spp. (most species)
Forestiera acuminata
F-raxinus pennsylvanica
Gleditsia aquatics
Juncus spp.
Limnobium spongia
Ludwigia spp.
Menyanthes trifoliata
Myrica gale
Nelumbo spp.
Nuphar spp.
Nymphaea spp.
Nyssa aquatics
Nyssa ogechee
Common Name
Box elder
Red maple
Silver maple
Water plantain
Alligatorweed
Black mangrove
Watershield
Twig rush
Flat sedge
Spikerush
Swamp privet
Green ash
Water locust
Rush
Frogbit
Waterprimrose
Buckbean
Sweetgale
Lotus
Cowlily
Waterlily
Water tup el o
Ogechee tupelo
Adaptation
Adventitious roots
Hypertrophied lenticels
Hypertrophied lenticels:
adventitious roots
(juvenile plants)
Polymorphic leaves
Adventitious roots; inflated,
floating stems
Pneumatophores; hypertrophied
lenticels
Inflated, floating leaves
Inflated stems
Inflated stems and leaves
Inflated stems and leaves
Multi-trunk, stooling
Buttressed trunks; adventi-
tious roots
Hypertrophied lenticels
Inflated stems and leaves
Inflated, floating leaves
Adventitious roots; inflated
floating stems
Inflated stems (rhizome)
Hypertrophied lenticels
Floating leaves
Floating leaves
Floating leaves
Buttressed trunks; pneuma-
tophores; adventitious
roots
Buttressed trunks; multi-
trunk; stooling
* Many other species exhibit one or more morphological adaptations for occurrence in
wetlands. However, not all individuals of a species will exhibit these adaptations under field
conditions, and individuals occurring in uplands characteristically may not exhibit them.
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Tabled (Continued)
Partial List of Species With Known Morphological Adaptations for
Occurrence in Wetlands*
Species Common Name Adaptation
Nyssa sylvatica Swamp blackgum Buttressed trunks
var. biflora
Platanus occidentalis Sycamore Adventitious roots
Populus deltoides Cottonwood Adventitious roots
Quercus laurifolia Laurel oak Shallow root system
Quercus palustris Pin oak Adventitious roots
Rhizophora mangle Red mangrove Pneumatophores
Sagittaria spp. Arrowhead Polymorphic leaves
Salix spp. Willow Hypertrophied lenticels;
adventitious roots; oxygen
pathway to roots
Scirpus spp. Bulrush Inflated stems and leaves
Spartina alterniflora Smooth Oxygen pathway to roots
cordgrass
Taxodium distichum Bald cypress Buttressed trunks;
pneumatophores
* Many other species exhibit one or more morphological adaptations for occurrence in
wetlands. However, not all individuals of a species will exhibit these adaptations under field
conditions, and individuals occurring in uplands characteristically may not exhibit them.
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Species
Alnus incana
Alnus rubra
Baccharis viminea
Betula pubescens
Carex arenaria
Carexflacca
Carex lasiocarpa
Deschampsia cespitosa
Filipendula ulmaria
Fraxinus pennsyzvanica
Glyceria maxima
Juncus effusus
Larix laricina
Lobelia dortmanna
Lythrum salicaria
Molinia caerulea
Myrica gale
Nuphar lutea
Nyssa aquatica
Nyssa sylvatica var. biflora
Phalaris arundinacea
Phragmites australis
Pinus contorta
Polygonum amphibium
Potentilla anserina
Ranunculus flammula
Salix cinerea
Salix fragilis
Salix lasiolepis
Scirpus maritimus
Senecio vulgaris
Spartina alterniflora
Trifoliun subterraneum
Typha angustifolia
Table C2
Species Exhibiting Physiological Adaptations for
Occurrence in Wetlands
Physiological Adaptation
Increased levels of nitrate reductase; malate accum.
Increased levels of nitrate reductase
Ability for root growth in low oxygen tensions
Oxidizes the rhizosphere; malate accumulation
Malate accumulation
Absence of ADH activity
Malate accumulation
Absence of ADH activity
Absence of ADH activity
Oxidizes the rhizosphere
Malate accumulation; absence of ADH activity
Ability for root growth in low oxygen tensions;
absence of ADH activity
Slight increases in metabolic rates; increased
levels of nitrate reductase
Oxidizes the rhizosphere
Absence of ADH activity
Oxidizes the rhizosphere
Oxidizes the rhizosphere
Organic acid production
Oxidizes the rhizosphere
Oxidizes the rhizosphere; malate accumulation
Absence of ADH activity; ability for root
growth in low oxygen tensions
Malate accumulation
Slight increases in metabolic rates; increased
levels of nitrate reductase
Absence of ADH activity
Absence of ADH activity; ability for root
Malate accumulation; absence of ADH activity
Malate accumulation
Oxidizes the rhizosphere
Ability for root growth in low oxygen tensions
Ability for root growth in low oxygen tensions
Slight increases in metabolic rates
Oxidizes the rhizosphere
Low ADH activity
Ability for root growth in low oxygen tensions
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APPENDIX D: HYDRIC SOILS
1. This appendix consists of two sections. Section 1 describes the basic procedure for
digging a soil pit and examining for hydric soil indicators. Section 2 is a list of hydric soils of
the United States.
Section 1 - Procedures for Digging a Soil Pit and Examining
for Hydric Soil Indicators
Digging a soil pit
2. Apply the following procedure: Circumscribe a 1-ft-diameter area, preferably with a tile
spade (sharpshooter). Extend the blade vertically downward, cut all roots to the depth of the
blade, and lift the soil from the hole. This should provide approximately 16 inches of the soil
profile for examination. Note: Observations are usually made immediately below the A-
horizon or 10 inches (whichever is shallower). In many cases, a soil auger or probe can be
used instead of a spade. If so, remove successive cores until 16 inches of the soil profile have
been removed. Place successive cores in the same sequence as removed from the hole. Note:
An auger or probe cannot be effectively used when the soil profile is loose, rocky, or contains
a large volume of water (e.g. peraquic moisture regime).
Examining the soil
3. Examine the soil for hydric soils indicators (paragraphs 44 and/or 45 of main text (for
sandy soils)). Note: It may not be necessary to conduct a classical characterization (e.g.
texture, structure, etc.) of the soil. Consider the hydric soil indicators in the following
sequence (Note: THE SOIL EXAMINATION CAN BE TERMINATED WHEN A POSITIVE
HYDRIC S01-LINDICA TOR IS FOUND) :
Nonsandy soils.
a. Determine whether an organic soil is present (see paragraph 44 of the
main text). If so, the soil is hydric.
b. Determine whether the soil has a histic epipedon (see paragraph 44 of the
main text). Record the thickness of the histic epipedon on DATA FORM 1.
c. Determine whether sulfidic materials are present by smelling the soil.
The presence of a "rotten egg" odor is indicative of hydrogen sulfide, which
forms only under extreme reducing conditions associated with prolonged
inundation/soil saturation.
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d. Determine whether the soil has an aquic or peraquic moisture regime
(see paragraph 44 of the main text). If so, the soil is hydric.
e. Conduct a ferrous iron test. A calorimetric field test kit has been
developed for this purpose. A reducing soil environment is present when the
soil extract turns pink upon addition of a-a-dipyridil.
f. Determine the color(s) of the matrix and any mottles that may be present.
Soil color is characterized by three features: hue, value, and chroma. Hue refers
to the soil color in relation to red, yellow, blue, etc. Value refers to the lightness
of the hue. Chroma refers to the strength of the color (or departure from a
neutral of the same lightness). Soil colors are determined by use of a Munsell
Color Book (Munsell Color 1975). Each Munsell Color Book has color charts
of different hues, ranging from 10R to 5Y. Each page of hue has color chips
that show values and chromas. Values are shown in columns down the page
from as low as 0 to as much as 8, and chromas are shown in rows across the
page from as low as 0 to as much as 8. In writing Munsell color notations, the
sequence is always hue, value, and chroma (e.g. 10YR5/2). To determine soil
color, place a small portion of soil (moistened) in the openings behind the color
page and match the soil color to the appropriate color chip. Note: Match the soil
to the nearest color chip. Record on DATA FORM 1 the hue, value, and
chroma of the best matching color chip. CAUTION: Never place soil on the
face or front of the color page because this might smear the color chips.
Mineral hydric soils usually have one of the following color features
immediately below the A-horizon or 10 inches (whichever is shallower):
(1) Gleyed soil.
Determine whether the soil is gleyed. If the matrix color best fits a color chip
found on the gley page of the Munsell soil color charts, the soil is gleyed. This
indicates prolonged soil saturation, and the soil is highly reduced.
(2) Nongleyed soil.
(a) Matrix chroma of 2 or less in mottled soils, (moistened)
(b) Matrix chroma of 1 or less in unmottled soils, (moistened)
(c) Gray mottles within 10 inches of the soil surface in dark (black) mineral
soils (e.g., Mollisols) that do not have characteristics of (a) or (b) above.
Soils having the above color characteristics are normally saturated for
significant duration during the growing season. However, hydric soils with
significant coloration due to the nature of the parent material (e.g. red soils of
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the Red River Valley) may not exhibit chromas within the range indicated
above. In such cases, this indicator cannot be used.
g. Determine whether the mapped soil series or phase is on the national list
of hydric soils (Section 2). CAUTION: It will often be necessary to compare
the profile description of the soil with that of the soil series or phase indicated
on the soil map to verify that the soil was correctly mapped. This is especially
true when the soil survey indicates the presence of inclusions or when the soil is
mapped as an association of two or more soil series.
h. Look for iron and manganese concretions. Look for small (>0.08-inch)
aggregates within 3 inches of the soil surface. These are usually black or dark
brown and reflect prolonged saturation near the soil surface.
Sandy soils.
Look for one of the following indicators in sandy soils:
a. A layer of organic material above the mineral surface or high organic
matter content in the surface horizon (see paragraph 45a of the main text). This
is evidenced by a darker color of the surface layer due to organic matter
interspersed among or adhering to the sand particles. This is not observed in
upland soils due to associated aerobic conditions.
h. Streaking of subsurface horizons (see paragraph 45c of the main text).
Look for dark vertical streaks in subsurface horizons. These streaks represent
organic matter being moved downward in the profile. When soil is rubbed
between the fingers, the organic matter will leave a dark stain on the fingers.
c. Organic pans (see paragraph 45b of the main text). This is evidenced by
a thin layer of hardened soil at a depth of 12 to 30 inches below the mineral
surface.
Section 2. Hydric Soils of the United States
(Note: not included here as the list from 1987 is out of date)
(Contact your County or State NRCS office)
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Modifications and Clarifications
to the 1987 Wetland Delineation Manual
These modifications and clarifications were issued after Congress removed the 1989 Manual
from use in making wetland determinations.
CECW-OR 23 August 1991
MEMORANDUM
SUBJECT: Wetlands Delineation and the 1992 Energy and Water Development Appropriations
Act
1. The 1992 Energy and Water Development Appropriations Act (Act) contains the following
provisions:
a. None of the funds of the Act shall be used to identify or delineate any land as a "water of the
United States" under the Federal Manual for Identifying and Delineating Jurisdictional Wetlands
that was adopted in January 1989 (1989 Manual) or any subsequent manual not adopted in
accordance with the requirements for notice and public comment of the rule-making process of
the Administrative Procedure Act.
b. In addition, regarding Corps of Engineers ongoing enforcement actions and permit
applications involving lands which the Corps or the Environmental Protection Agency (EPA)
has delineated as "waters of the United States" under the 1989 Manual, and which have not yet
been completed on the date of enactment of the Act (i.e., August 17, 1991), the landowner or
permit applicant shall have the option to elect a new delineation under the Corps 1987 Wetlands
Delineation Manual (1987 Manual) (Technical Report Y-87-1, Waterways Experiment Station
(WES), January 1987) or completion of the permit process or enforcement action based on the
1989 Manual delineation, unless the Corps of Engineers determines, after investigation and
consultation with other appropriate parties, including the landowner or permit applicant, that the
delineation would be substantially the same under either the 1987 or the 1989 Manual.
c. None of the funds in the Act shall be used to finalize or implement the proposed regulations to
amend the fee structure for the Corps of Engineers regulatory program which were published in
Federal Register, Vol. 55, No. 197, Thursday, October 11, 1991.
The provisions of the Act make it necessary for the Corps to change some regulatory procedures,
as described below. This guidance has been reviewed and approved by the Office of the
Assistant Secretary of the Army for Civil Works.
2. After August 17, 1991, initial delineations will be made using the Corps 1987 Manual. WES
will provide field of floes copies of the 1987 Manual. Supplementary guidance will be provided
by CECW-OR under separate cover.
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3. The following general guidance will apply:
a. For the purposes of this guidance, ongoing permit applications are defined as: formal
individual permit applications (ENG FORM 4345), letters requesting verification of
authorization under regional or nationwide permits, or predischarge notices as required by
nationwide permits, received before August 17, 1991, where no permit has been issued, verified
or denied, and existing jurisdictional delineations where no permit has been requested.
Individual permit decisions are considered final when the District Engineer signs the decision
document. The 20 day predischarge notification clock (nationwide permit 26) will be stopped
and the information considered incomplete until the options are explained and the general
permitted responds, at which time the clock restarts.
b. Only applications involving section 404, where the jurisdictional delineation (Corps or EPA)
involved wetlands identified as waters of the United States using the 1989 Manual are subject to
the options provided by the Act.
4. Landowners/applicants involved in ongoing applications, as defined in 3. a. & b., will be
notified of their options, as follows:
a. The district may investigate and determine, for some or all of the district's area, on a generic
or case-by-case basis, whether delineations using the 1989 Manual are substantially the same or
substantially different than would have been made using the 1987 Manual. In every case the
Corps must notify the landowner/applicant by letter of the Corps initial determination and
provide the landowner/applicant an opportunity for consultation (not to exceed 30 days), before
proceeding with the final determination in accordance with paragraph b. or c. below. In the
interest of timeliness, districts are encouraged to use generic initial determinations and form
letters requesting the landowner's/applicant's consultation.
b. Where, after considering all applicable information (including any obtained during
consultation with the landowner/applicant and other appropriate parties), the Corps determines
that a delineation using the 1989 Manual is substantially the same as would have been made
using the 1987 Manual, each landowner/applicant will be notified by letter of the Corps'
determination. The Corps letter will inform the landowner/applicant that the delineation will be
considered binding and the evaluation of any formal application will proceed based on this
delineation.
c. Where the Corps determines that delineations using the 1989 Manual are substantially
different than would have been made using the 1987 Manual, each landowner/applicant will be
notified by letter of the Corps determination. The letter will provide the landowner/applicant the
following options: (1) proceeding with the ongoing application using the delineation made under
the 1989 Manual; or (2), electing to have the area redelineated using the 1987 Manual. The
Corps letter will also include an indication of the approximate time required by the district to
complete a redelineation of the area using the 1987 Manual and a statement that the
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landowner/applicant must reply in writing concerning their choice of the above options.
Evaluation, including a redelineation if necessary, of any application will proceed upon receipt
of the landowner's/applicant's letter.
5. Where an ongoing permit application, involving wetlands delineations made using the 1989
Manual, does not fit the circumstances described above, districts will compare the old (1989
Manual) delineation with the provisions of the 1987 Manual to determine if there would be a
substantial change in the area delineated. If there would be a substantial change, the
landowner/applicant will be provided the opportunity to have a new delineation made using the
1987 Manual.
6. The final decision concerning whether a substantial difference exists between delineations
based on the use of the 1989 and the 1987 Manuals rests solely with the Corps. The Corps may
consult with other experts in delineating wetlands, such as the Waterways Experiment Station,
EPA, the Fish and Wildlife Services or the Soil Conservation Service, in reaching a decision
Landowners/applicants and other appropriate parties may provide the Corps documentation for
use in tile decision making process, but in all cases the Corps district decision will be final.
7. The Office of Counsel (CECC-K) is providing through Counsel channels guidance concerning
the steps to be taken for ongoing enforcement actions that fall within the provision of the Act. A
copy of that guidance is enclosed for reference and use.
8. The Army will take no further action, at this time, on its proposal to amend regulatory fees, as
published in the Federal Register, Vol. 55, No. 197, Thursday, October 11,1990. No decision
has been made concerning future actions to change the fee structure.
FOR THE DIRECTOR OF CIVIL WORKS
SIGNED
JOHN P. ELMORE Chief
Operations, Construction and Readiness Division
Directorate of Civil Works
CECW-OR 27 August 1991
MEMORANDUM
SUBJECT: Implementation of the 1987 Corps Wetland Delineation Manual
1. The purpose of this memorandum is to provide guidance concerning the implementation of
the 1987 Corps of Engineers Wetlands Delineation Manual (1987 Manual)(Waterways
Experiment Station Technical Report Y-87-1, January 1987). This guidance supersedes the
guidance provided in John Studt's 21 August memorandum to the field. Further, this guidance is
to be used in conjunction with memoranda dated 23 August, 1991, concerning wetlands
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delineation and the 1992 Energy and Water Development Appropriations Act (Act). In
accordance with the Act and the 23 August memoranda, the 1987 Manual is now used to
delineate potentially jurisdictional wetlands in place of the 1989 Federal Manual for Identifying
and Delineating Jurisdictional Wetlands.
2. The guidance in paragraph 3 will be followed in the application of the 1987 Manual.
3. Use of the 1987 Manual is mandatory, however, the Appendices are modified as discussed
below:
a. Appendix A: The definition of "under normal circumstances" provided in this glossary is
modified pursuant to Regulatory Guidance Letter (RGL) #90-7;
b. Appendix B: Use of the data sheets provided is recommended, but is not mandatory;
c. Appendix C: Sections 1 and 2 - These sections are replaced with the May 1988 National List
of Plant Species That Occur in Wetlands and associated regional lists (U.S. Fish and Wildlife
Service, Summary 88(24) and Biological Reports 88(26.1- 26.13). The referenced lists will be
used to determine the wetland indicator status of plant species and any subsequent updates will
be adopted;
d. Appendix D: Section 2 - The most recent Hydric Soils of the United States list developed by
the U.S. Department of Agriculture, Soil Conservation Service (SCS), will be used to determine
if a particular soil has been designated as hydric by the National Technical Committee for
Hydric Soils. The current hydric soils list was published by SCS in December 1987, and any
subsequent updates will be adopted.
4. All other current policy considerations concerning wetlands in general (e.g., RGLs) remain in
effect during interim use of the 1987 Corps Manual.
5. The Waterways Experiment Station will provide each division and district copies of the 1987
Manual. In addition, a copy of the Environmental Effects of Dredging Technical Notes (EEDP-
04-7) dated January 1988, an article which summarizes the methods for delineating wetlands as
presented in the 1987 Manual, will follow under separate cover. The article does not reflect the
guidance contained in this memorandum; however, it does provide a general overall summary of
the 1987 Manual.
JOHN P. ELMORE
Chief, Operations, Construction and Readiness Division
Directorate of Civil Works
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SUBJECT: Questions & Answers on 1987 Manual
1. In response to questions from the field, the Qs & As on the 1987 Corps of Engineers Wetland
Delineation Manual (1987 Manual) have been further clarified (in particular, questions #7 and
#8). We clarified that for saturated-only systems, the saturation must be to the surface for the
appropriate number of days during the growing season. Furthermore, we clarified that the
number of days for inundation or saturation to the surface are consecutive, not cumulative. The
enclosed Qs and As dated 7 October, 1991 supersede those previously distributed under the
cover memorandum of 16 September, 1991.
2.1 want to again emphasize that the 1987 Manual stresses the need to verify that all three
parameters exist prior to identifying and delineating an area as a wetland. Further, the 1987
Manual focuses on hydrology (i.e., inundation and/or saturation to the surface). In situations
where hydrology is questionable, the 1987 Manual requires stronger evidence regarding the
hydrophytic nature of the vegetation. The 1987 Manual also stresses the need to use sound
professional judgment, providing latitude to demonstrate whether an area is a wetland or not
based on a holistic and careful consideration of evidence for all three parameters. As indicated in
the 1987 Manual and the attached Qs and As, careful professional judgment must be used in
situations where indicators of hydrology are not clear and the dominant vegetation is facultative.
JOHNF. STUDT
Chief, Regulatory Branch
Operations, Construction and Readiness Division
Directorate of Civil Works
October 7, 1991
Questions and Answers on 1987 Corps of Engineers Manual
l.Q. What is the definition and practical interpretation of the growing season which should be
used in the application of the 1987 Manual?
A. The 1987 Manual defines the growing season as "the portion of the year when soil
temperatures at 19.7 inches below the soil surface are higher than biological zero (5 degrees C)".
This is the definition found in Soil Taxonomy, and growing season months can be assumed
based on temperature regimes (e.g., mesic: March- October). The 1987 Manual further states this
period can be approximated by the number of frost-free days. The Waterways Experiment
Station (WES) indicates that the county soil surveys, which utilize 32 degrees, provide the
growing season for each county. There is some flexibility in the determination of the growing
season in the 1987 Manual. The growing season, based on air temperature in the county soil
surveys can be approximated as the period of time between the average date of the first killing
frost to average date of the last killing frost, which sometimes does not accurately reflect the
period of time when the soil temperatures are higher than biological zero. The source of the
information may vary, however, the growing season generally is to be determined by the number
of killing frost-free days. In certain parts of the country where plant communities in general have
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become more adapted to regional conditions, local means of determining growing season may be
more appropriate and can be used.
2.Q. Should the determination of hydric soils be based on the presence of an indicator listed in
the 1987 Manual higher on the list than the soils list or on the fact that a soil appears on the
Hydric Soils of the United States, an indicator which is listed as less reliable in the hierarchy of
hydric soil indicators in the 1987 Manual?
A. The order of soil indicators reliability as listed in the 1987 Manual remains valid and will be
used. The reliability of the indicators is based on the fact that field verification of a soil's hydric
characteristics is more accurate than mapping or soils lists. Soils listed on the most recent Hydric
Soils list have been determined by the National Technical Committee for Hydric Soils to meet
the criteria for hydric soils. When in the field, verification that mapped hydric soils actually
exhibit indicators identified in me 1987 Manual for hydric soils is recommended. Although a
soil may appear on the list of hydric soils, inclusions or disturbances may alter this designation
to some degree, so the list alone may not always be reliable. In obvious wetlands, if the soil is on
the list and the area meets the hydrology and vegetation criteria, the area is a wetland. As found
with me 1989 Manual, one cannot rely solely on the fact that a soil is mapped as hydric in
making the wetland delineation, all cases, best professional judgment should be used. The
county lists provide valuable information, but again should not solely be relied on to make a
final determination as to whether hydric soils are present. Verification of the presence of at least
one of the indicators for hydric soils on the list (pages. 30-34) is required in conjunction with the
use of a county soils list. The national soils list to be used has recently been updated by the
NTCHS (June 1991), and this list will be used by the Corps in conjunction with the 1987
Manual.
3.Q. How should the 1987 Manual be applied with respect to the definition of "normal
circumstances"?
A. The definition of "under normal circumstances" in the 1987 Manual states briefly that "this
term refers to situations in which the vegetation has not been substantially altered by man's
activities". As stated in item #3 of the memorandum of 27 August, 1991, the definition of
normal circumstances used in the 1987 Manual has been clarified by Regulatory Guidance Letter
(RGL) 90-7. Although this RGL deals primarily with agricultural activities in wetlands,
paragraphs #3 & #4 discuss normal circumstances with respect to all areas potentially subject to
404. Further guidance on normal circumstances is found in RGL 86-9 regarding construction
sites and irrigated wetlands. The guidance should be followed in preferential sequence of; 1)
RGL 90-7, 2) RGL 86-9, and 3) 1987 Manual.
4.Q. Does the vegetation criteria in the 1987 manual require the use of the facultative(FAC) -
neutral vegetation test (i.e., count the dominant species wetter and drier than FAC, and ignore all
of the FACs in the vegetation determination)?
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A. While the 1987 Manual mentions use of the FAC - neutral test for determining the presence
of wetland vegetation in several places, the first indicator of wetland vegetation criteria is the
presence of more than 50% of the dominant plant species FAC or wetter (not including FAC-
species, which are considered non-wetland indicators under the 1987 manual). The indicator
status of each of the dominant species is determined by consulting the current regional plant list
published by the FWS. The 1987 Manual provides an option in this determination of applying
the FAC - neutral test in cases where the delineator questions the status designation of a
particular plant species on a subregional basis (see page 233. As always, any deviation from
established protocol requires documentation. The FAC - neutral option may also prove useful in
questionable areas or when the determination relies on the vegetation call in an area that is not
otherwise an obvious wetland. Specifically, the 1987 Manual is replete with cautions and
guidance that the Corps regulators must be confident that the area is wetland when the area has a
FAC-dominated plant community. Uncertainty regarding the status of an area as a wetland
where the dominant vegetation is FAC would be a valid reason to use the FAC - neutral option.
Situations exist where use of the FAC - neutral method will not serve to provide any additional
information as to the hydrophytic nature of the plant community (e.g., all species are FAC or
there is an equal number of species wetter and drier than FAC such that they cancel each other
out). In these cases, it is appropriate to consider the + and - modifiers associated with some FAC
species, which indicate the species frequency of occurrence in a wetter or drier environment, in
the overall assessment of the vegetation parameter. Documentation supporting reasons for using
the FAC - neutral option must always be provided and acceptance of delineations, as always,
remains up to the discretion of the District.
5.Q. Can indicators for any of the criteria in the 1989 Manual be used as indicators for
verification of the same or other criteria presented in the 1987 Manual?
A. The indicators of hydrology in the 1987 Manual differ from those of the 1989 Manual, and
are not interchangeable. In particular, the hydrology determination in the 1989 Manual often
relied on evidence of properties from the soil and/or vegetation parameters. Indicators provided
in the 1989 Manual for field verification of a certain criterion that are not presented in the 1987
Manual for application with the same criterion cannot be used except as additional information
in support of the verification. It is unlikely that an area which is a wetland will fail to meet a
criteria utilizing the indicators which are listed in the 1987 Manual.
6.Q. Will the other Federal agencies be utilizing the 1987 Manual in their wetland
determinations as well as the Corps of Engineers?
A. EPA has concurred with the Corps using the 1987 Manual for all actions. Further, we
understand that EPA will likely use the 1987 Manual for EPA's delineations as well. The other
agencies (SCS & FWS) typically do not make delineations for purposes of Section 404.
7.Q. To what depth should one look in the soil to find indicators of hydrology?
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A. In accordance with the 1987 Manual's guidance on reading soil color (D2), after digging a
16" soil pit observations should be made immediately below the A-horizon or within 10" of the
soil surface (whichever is shallower). This guidance pertains to observations of indicators of the
soil criterion. For indicators of saturation to the surface in the hydrology criterion, observations
are made within a major portion of the root zone (usually within 12"), again in the 16" pit.
Visual observation of standing water within 12" of the surface may, under certain circumstances,
be considered a positive indicator of wetland hydrology (i.e., saturation to the surface) as stated
on page 38. When using water table within 12" of the surface as an indicator of hydrology, care
must be used to consider conditions and the soil types (i.e., to ensure that the capillary ability of
the soil texture is considered in regard to the water table depth). Vegetation and soil properties
used in the determination of hydrology in the 1989 Manual, are typically not available for field
verification of this criterion in the 1987 Manual. However, the 1987 Manual allows for some
flexibility with regards to indicators of wetland hydrology, and states that indicators are not
limited to those listed on pages 37-41. Other indicators, such as some type of recorded data (e.g.,
soil surveys which provide specific and strong information about the soil series' hydrology) may
be used to verify a wetland hydrology call in a saturated but not inundated area. Appropriate
documentation to support the call is necessary in all cases.
8.Q. What length of time must wetland hydrology be present for an area to be determined a
wetland under the 1987 Manual?
A. In the hydrology section of Part III, the 1987 Manual discusses the hydrologic zones which
were developed through research at WES to indicate the duration of inundation and/or soil
saturation during the growing season. Wetland hydrology is defined in the 1987 Manual as the
sum total of wetness characteristics in areas that are inundated or have saturated soils for a
sufficient duration to support hydrophytic vegetation. The 1987 Manual discusses hydrology in
terms of a percent of the growing season when an area is wet (page 36). Generally speaking,
areas which are seasonally inundated and/or saturated to the surface for more than 12.5% of the
growing season are wetlands. Areas saturated to the surface between 5% and 12.5% of the
growing season are sometimes wetlands and sometimes uplands. Areas saturated to the surface
for less than 5% of the growing season are non-wetlands. The percent of growing season
translates to a number of days, depending on the length of the growing season in any particular
area (e.g., 12.5% of a 170-day growing season is 21 consecutive days). This system for
classification of hydrologic zones based on stream gauge data transformed to mean sea level
elevations is useful as a guide to time frames of wetness sufficient to create wetlands. The length
of time an area is wet for hydrology is based on consecutive days during the growing season. If
an area is only saturated to the surface for a period of between 5% and 12.5% of the growing
season and no clear indicators of wetland hydrology exist (i.e., recorded or field data; also see
answer #7 above), then the vegetation test should be critically reviewed. Specifically, in such
cases a vegetative community dominated by FAC species would generally indicate that the area
is not a wetland (unless the FAC- neutral test was indicative of wetlands). The actual number of
days an area is inundated and/or saturated to the surface for an area to be called a wetland
varies; the identification of an indicator of recorded or field data is necessary to document that
an area meets the wetland hydrology criterion of the 1987 Manual (i.e., the list of hydrology
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indicators on pages 37-41, which are to be used in the preferential order shown; also see
question #7). The number of days specified in the June 1991 Hydric Soils of the United States
(i.e., usually more than 2 weeks during the growing season) as the criteria for hydric soils
pertains to hydric soils and not the hydrology criterion of the 1987 Manual, which varies with
the growing season as previously discussed.
9.Q. Will delineations made now under the 1987 Corps Manual be subject to redelineation under
the revised 1989 Manual after it is finalized?
A. Wetland determinations made after 17 August, 1991, are made following the guidance
provided in the 1987 Corps Manual and memoranda of 23 and 27 August, 1991 and these
questions and answers. These delineations are subject to and remain valid for the period of time
described in RGL 90-6. As discussed in Issue #4 of the preamble to the proposed revisions to the
1989 Federal Manual for Identifying and Delineating Jurisdictional Wetlands issued 14 August
in the Federal Register, wetland calls made after the issuance date of these revisions but prior to
finalization of the revised manual may be subject to redelineation under the new manual at the
request of the landowner. Final actions will generally not be reopened. Wetland calls made
under the 1989 Manual are already subject to redelineation under the 1987 Manual in
accordance with the guidance issued 23 August. Until such time as the proposed revisions to the
1989 Manual are finalized, it is unclear as to what effect, if any, the equity provision in the
preamble to the proposed revisions will have on the 404 program. Therefore, written
delineations made with the 1987 Manual will explicitly state they are final for a period of three
years as specified in RGL 90-6, subject to any equity provisions that may be adopted as part of
implementation of the final revisions to the 1989 Manual.
10.Q. How does the 1987 Manual compare to the 1989 Manual or its proposed revisions?
A. The various manuals have been compared by WES and the side-by-side comparison is
available for your information.
1 l.Q. Will applicants be subject to delay with use of the 1987 Manual?
A. During the initial transition to use of the 1987 Manual for wetland delineations as of 17
August, some delays are unavoidable. The Corps field offices must adhere to the procedures
provided in the 23 August memorandum, while striving to expedite the review process to the
extent possible. No offices should indicate that they cannot operate due to lack of guidance
during this transition period. HQUSACE recognizes that there will be delays associated with
implementing the Corps 1987 Manual and we will take these delays into account when
reviewing district application performance data (e.g., % of IPs evaluated in 60 days). Districts
should not stop the permit clock, but should indicate where substantial impacts to permit
evaluation performance have resulted from implementation of the 1987 Manual.
Field testing questions:
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l.Q. Which procedure should be followed for the field testing since the guidance provided
initially states both the routine and comprehensive?
A. Since the criteria for determining the presence of hydrophytic vegetation is proposed to be the
prevalence index, the comprehensive method must be used as neither the routine nor
intermediate on-site methods will yield a prevalence index. The point intercept sampling
procedure on pages 40471-40473 yields a PI directly and is stated that it is the procedure to be
used in the determination of the PI on page 40455 of the Federal Register. Routine or
intermediate on-site determinations methods can be used for determination of the limits of hydric
soils (Step 1, page 40471) and wetland hydrology (Step 16, page 40473) necessary to be
considered in conjunction with the vegetation assessment as described on pages. 40472-40473
(Steps 6-15). Documentation of deviations from the prescribed methodology should be noted in
the field testing reports.
2.Q. Must all plants or just the dominants be looked at to determine the prevalence index (data
sheets say dominants while procedure looks at individuals)?
A. All plant species, not just dominant plants, are considered in the prevalence index
determination of hydrophytic vegetation. However, the point intercept sampling procedure in the
comprehensive methods which is used to yield a PI requires the identification of only those
species which are intercepted by an imaginary line extended from the points every 2 feet along
the transect.
3.Q. How is one to compare site determinations made using the 1989 Manual and the proposed
revisions when different methods (e.g., routine vs. comprehensive) were used?
A. Comparing the data from delineations previously performed under the 1989 Manual
(regardless of the method employed at the time) with the data recorded during the field testing of
the proposed revisions of the 1989 Manual, should be done to the level of detail possible. The
resultant delineations from these different manuals and/or procedures are what these tests are
comparing; some discussion of any differences and why they exist (e.g., changes in the criteria,
indicators, and methodologies utilized) should be provided based on the teams or individual's
observations, experience and best professional judgment.
4.Q. Are the regional indicators listed in the proposed revisions to the 1989 Manual the same for
soils and hydrology?
A. Yes, they are proposed for use as an indicator of the soil criterion as well as corroborative
information for use with the secondary indicators of hydrology.
5.Q. Do you always need to prove 21 days saturation?
A. Yes, but identification of one or more of the primary indicators of hydrology is proof that the
area meets the 21 day hydrology criterion.
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Wetland Delination Manual, 1987 111
6.Q. Is there a map that goes with the regional indicators?
A. Yes, the regional indicators of soil saturation continue to be developed and are being
proposed for use with the manual for the four Department of Agriculture, Soil Conservation
Service main regions; Northeast, South, Midwest and West (see enclosed map).
7.Q. Can the soils list be used as corroborative information with a secondary indicator for
verification that the hydrology criterion is met?
A. No, only the information listed can be used. Comments on the appropriateness of the
information and suggestions for others should be provided during the comment period.
8.Q. Can blackened leaves be used as hydrology indicator?
A. No, only those listed in the list of hydrology indicators.
9.Q. Must three transects always be evaluated for the methodology described to determine the
prevalence index?
A. No, in most cases, only 1 transect is required for purposes of these tests. The following
guidance provides the exact circumstances under which options concerning the number of
transects are permitted before proceeding to Step 11 and repeating the two transects: - If the
prevalence index value determined for the first transect is less than 2.5 or more than 3.5, and, in
the professional judgment of the investigators repeated transects are likely to provide the same
result, the investigators need not repeat the transect. In cases where this shortened method is
used, this should be reflected in the results. While the agencies recognize that this change in
protocol does not strictly adhere to the method contained in the revised manual, it is nevertheless
a practical step to reduce the demands of using the comprehensive method.
10.Q. How are the frost-free dates determined under the proposed revisions which the field
testing should utilize?
A. The frost-free dates are to be determined by using the summary of growing season data
developed by NOAA referred to on the first page of the Interagency Protocol. This information
is available for each state and will be provided to the Divisions for distribution to the field in the
near future.
1 l.Q. Must sites visited and data collected prior to the recently released revised data sheets be
redone using the new forms?
A. No, both versions collect essentially the same data. Sections I (Background) and VI (Wetland
Evaluation) of the revised data sheets are identified as sections that will generally apply to the
entire site, and in most cases will not have to be repeated for each individual transect. Besides
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Wetland Delination Manual, 1987 112
the reorganization of these forms, revisions have been made to collect data on the 1987 Manual,
include questions on the regional indicators, and clarify the functions and values assessments.
12.Q. How should the Corps provide comments on the proposed revisions to the 1989 manual?
A. Each District is to submit comments to their Division office for consolidation into one
response to EPA (in accordance with the Federal Register issued 14 August) prior to the end of
the comment period (currently 15 October); a copy of these comments are to be provided to
CECW-OR. Each Division is encouraged to provide comments to HQUSACE at least one week
prior to the end of the comment period so that key issues and concerns raised may be stressed in
the comments which will come from Headquarters. In addition, the Corps has the opportunity to
comment through the interagency field testing reports, which are due to HQUSACE by 1
November. HQUSACE and WES will continue to participate in interagency efforts to finalize
revisions to the 1989 Manual until such time as the revisions become final. There is
consideration being given as to the need for a time extension to the comment period (and
therefore the field testing). If this becomes final, we will let the Divisions know as soon as
possible.
13.Q. Is the information gathered during the inter agency field testing available for release to the
public and may the public accompany the teams in the field?
A. The interagency field testing of the proposed revisions to the 1989 Federal Manual for
Identifying and Delineating Jurisdictional Wetlands published in the 14 August, 1991 Federal
Register is being performed to evaluate the technical validity, practical utility, and clarity of
understanding of the proposed revisions. The results of the field testing will be taken into
consideration during the final revisions to the 1989 Manual. The information gathered during the
field testing is considered to be predecisional by HQUSACE and inappropriate for release prior
to the finalization of the proposed revisions. Counsel is preparing written guidance on this issue
and it will be distributed to the Divisions as soon as possible.
14.Q. Will delineations made now under the 1987 Corps Manual be subject to redelineation
under the revised 1989 Manual after it is finalized?
A. Wetland determinations made after 17 August, 1991, are made following the guidance
provided in the 1987 Corps Manual and memoranda of 23 and 27 August, 1991. These
delineations are subject to and remain valid for the period of time described in RGL 9-6. As
discussed in Issue #4 of the preamble to the proposed revisions to the 1989 Federal Manual for
Identifying and Delineating Jurisdictional Wetlands issued 14 August in the Federal Register,
wetland calls made after the issuance date of these revisions but prior to finalization of the
revised manual may be subject to redelineation under the new manual at the request of the
landowner. Final actions will generally not be reopened. Wetland calls made under the 1989
Manual are already subject to redelineation under the 1987 Manual in accordance with the
guidance issued 23 August. Until such time as the proposed revisions to the 1989 Manual are
finalized, it is unclear as to what effect, if any, the equity provision in the preamble to the
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Wetland Delination Manual, 1987 113
proposed revisions will have on the 4U4 program. Therefore, written delineations made with the
1987 Manual will explicitly state they are final for a period of three years as specified in RGL
9-6, subject to any equity provisions that may be adopted as part of implementation of the final
revisions to the 1989 Manual.
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Wetland Delination Manual, 1987 114
CECW-OR20Feb 1992
MEMORANDUM FOR ALL MAJOR
SUBORDINATE COMMANDS,
DISTRICT COMMANDS
SUBJECT: Regional Interpretation of the 1987 Manual
1. The purpose of this memorandum is to provide clarification to the divisions and districts
concerning application of the 1987 Corms Wetlands Delineation Manual (1987 Manual). As you
are aware, we have been using the 1987 Manual since 17 August 1991. All indications are that
the 1987 Manual, when used in conjunction with the guidance provided in the 7 October 1991
Questions and Answers, is working well for the identification and delineation of wetlands. While
the Administration's effort to finalize a revised Manual continues, the Corps of Engineers will
continue to use the 1987 Manual.
2. Local procedures on the implementation of the 1987 Manual must be fully consistent with
both the 1987 Manual and the Questions and Answers issued 7 October 1991. Any efforts to
provide additional guidance regarding the use of the 1987 Manual must be reviewed and
approved by HQUSACE (CECW-OR) prior to regional implementation. The data forms
provided in the 1987 Manual are to be used, however, additional fields may be added to collect
more detailed site specific information when taken from the list of indicators in the 1987
Manual. As pointed out in the 7 October 1991 Questions and Answers, there is flexibility in the
1987 Manual which can be applied on a case-by-case basis only. Local procedures must not add
indicators of any of the three wetland parameters to the data sheets. We recognize that the
indicators of hydrology in the 1987 Manual are sometimes difficult to demonstrate. However,
additional regional indicators must only be used on a case-by-case basis to demonstrate that a
parameter is met. For example, blackened leaves and oxidized rhizospheres could be used
together to support a delineation where the listed indicators in the 1987 Manual are not directly
observed, but where the FAC neutral test is met.
3. All guidance on the use of the 1987 Manual must come from HQUSACE to ensure the a
consistent national approach is taken in the Corps application of the 1987 Manual.
FOR THE COMMANDER:
Signed
ARTHUR E. WILLIAMS
Major General, USA
Director of Civil Works
CECW-OR March 6, 1992
SUBJECT: Clarification and Interpretation of the 1987 Manual
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Wetland Delination Manual, 1987 115
1. The purpose of this memorandum is to provide additional clarification and guidance
concerning the application of the Corps of Engineers Wetlands Delineation Manual, Technical
Report Y-87-1, January 1987, Final Report (1987 Manual). As discussed in my 20 February
1992 memorandum, procedures for the identification and delineation of wetlands must be fully
consistent with both the 1987 Manual and the Questions and Answers issued 7 October 1991.
The technical and procedural guidance contained in paragraphs 2 thru 6 below has been
prepared by the Waterways Experiment Station (WES) and is provided as further guidance. The
following guidance is considered to be consistent with the 1987 Manual and the 7 October
Questions and Answers. Further, this guidance will be presented in the upcoming Regulatory IV
wetlands delineation training sessions in FY 92. The alternative technical methods of data
gathering discussed below are acceptable as long as the basic decision rules (i.e., criteria and
indicators) established in the 1987 Manual are applied. Also enclosed is a revised data form
which may be used in lieu of the routine data sheet provided with the 1987 Manual, if desired.
As discussed in my 20 February 1992 memorandum to the field, regional approaches and/or
alternative data sheets must be reviewed and approved by HQUSACE (CECW-OR) prior to
regional implementation. Notwithstanding this requirement, we encourage interagency
coordination and cooperation on implementation of the 1987 Manual. Such cooperation can
facilitate the continued success of our use of the 1987 Manual
2. Vegetation:
a. Basic rule: More than 50 percent of dominant species from all strata are OBL, FACW, or FAC
(excluding FAC-) on the appropriate Fish and Wildlife Service regional list of plant species that
occur in wetlands.
b. The 1987 Manual provides that the 3 most dominant species be selected from each stratum
(select 5 from each stratum if only 1-2 strata are present). However, alternative ecologically
based methods for selecting dominant species from each stratum are also acceptable. The
dominance method described in the 1989 interagency manual is an appropriate alternative
method. (1989 Manual, p. 9, para.3.3)
c. The 4 vegetation strata (tree, sapling/shrub, herb, and woody vine) described in the 1987
Manual are appropriate. However, a 5-stratum approach (tree, sapling, shrub, herb, and woody
vine) is an acceptable alternative.
d. The 1987 Manual states on page 79 that hydrophytic vegetation is present if 2 or more
dominant species exhibit morphological adaptations or have known physiological adaptations
for wetlands. This rule should be used only after the basic rule is applied; use caution with
adaptations (e.g., shallow roots) that can develop for reasons other than wetness. Furthermore,
the morphological adaptations must be observed on most individuals of the dominant species.
e. In areas where the available evidence of wetlands hydrology or hydric soil is weak (e.g., no
primary indicators of hydrology), the Facultative Neutral (FAC neutral) option may be used to
help clarify a wetland delineation. Use of the FAC neutral option is explained in paragraph
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Wetland Delination Manual, 1987 116
3 5 (a), page 23, of the 1987 Manual. Use of the FAC neutral option is at the discretion of the
District. Further, the FAC neutral option cannot be used to exclude areas that meet the "basic
vegetation rule" and the hydrology and hydric soil requirements.
3. Hydrology:
a. Areas which are seasonally inundated and/or saturated to the surface for a consecutive number
of days for more than 12.5 percent of the growing season are wetlands, provided the soil and
vegetation parameters are met. Areas wet between 5 percent and 12.5 percent of the growing
season in most years (see Table 5, page 36 of the 1987 Manual) may or may not be wetlands.
Areas saturated to the surface for less than 5 percent of the growing season are non-wetlands.
Wetland hydrology exists if field indicators are present as described herein and in the enclosed
data sheet.
b. To evaluate hydrologic data (e.g., from stream gages or groundwater wells) growing season
dates are required. Soil temperature regime (i.e., period of the year when soil temperature at 20
inches below the surface is above 5 degrees C) is the primary definition of growing season, but
data are rarely available for individual sites. Broad regions based on soil temperature regime
(e.g., mesic, thermic) are not sufficiently site-specific. For wetland determinations, growing
season can be estimated from climatological data given in most SCS county soil surveys (usually
in Table 2 or 3 of modern soil surveys). Growing season starting and ending dates will generally
be determined based on the "28 degrees F or lower" temperature threshold at a frequency of "5
years in 10". In the south, at the discretion of the district, it may be more appropriate to use the
32 degree F threshold.
c. In groundwater-driven systems, which lack surface indicators of wetland hydrology, it is
acceptable to use local Soil Conservation Service (SCS) soil survey information to evaluate the
hydrology parameter (p. 37 in the Manual) in conjunction with other information, such as the
FAC neutral test. Use caution in areas that may have been recently drained.
d. Oxidized rhizospheres surrounding living roots are acceptable hydrology indicators on a case-
by-case basis and may be useful in groundwater systems. Use caution that rhizospheres are not
relicts of past hydrology. Rhizospheres should also be reasonably abundant and within the upper
12 inches of the soil profile. Oxidized rhizospheres must be supported by other indicators of
hydrology such as the FAC neutral option if hydrology evidence is weak.
4. Soil:
a. The most recent version of National Technical Committee for Hydric Soils hydric soil criteria
will be used. At this writing, criteria published in the June 1991 Hydric Soils of the United States
are current. These criteria specify at least 15 consecutive days of saturation or 7 days of
inundation during the growing season in most years.
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Wetland Delination Manual, 1987 117
b. Local Lists of Hydric Soil Mapping Units recently developed by SCS and available from
county or State SCS offices give local information about presence of hydric soils on a site.
When available, these local lists take precedence over the national list for hydric soil
determinations.
c. SCS is currently developing regional indicators of significant soil saturation. Until finalized
and adopted, these indicators may not be used for hydrology or hydric soil determinations.
d. The statement (p.31 of the 1987 Manual) that gleyed and low-chroma colors must be observed
"immediately below the A-horizon or 10 inches (whichever is shallower)" is intended as general
guidance. Certain problem soils may differ.
5. Methods:
a. As stated in the 1987 Manual (footnote p. 76), alternative plot sizes and dominance measures
are acceptable.
h. For comprehensive determinations involving a patchy or diverse herb layer, a single, centrally
located 3.28 x 3.28-foot quadrat may not give a representative sample. As an alternative, the
multiple-quadrat procedure presented in the 1989 Manual (p. 42) is recommended.
6. Problem Areas
a. Page 93, paragraph 78 of the 1987 Manual states that similar problem situations may occur in
other wettand types, therefore, problem areas are not limited this list.
b. Problem soil situations mentioned elsewhere in the Manual include soils derived from red
parent materials, some Entisols, Mollisols, and Spodosols.
7. Questions concerning this information should be directed to Ms. Karen A. Kochenbach,
HQUSACE (CECW-OR), at (202) 272-1784, or Mr. James S. Wakeley, WES, at (601) 634-
3702.
Signed by,:
Hugh F. Boyd III
for
ARTHUR E. WILLIAMS
Major General, USA
Director of Civil Works
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United States
Environmental Protection
Agency
Office of Environmental
Information
Washington, DC 20460
EPA/600/R-96/055
August 2000
f/EPA
Guidance for the Data Quality
Objectives Process
EPA QA/G-4
-------
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Foreword
The U.S. Environmental Protection Agency (EPA) has developed the Data Quality
Objectives Process as the Agency's recommended planning process when environmental data are
used to select between two opposing conditions. The Data Quality Objectives Process is used to
develop Data Quality Objectives that clarify study objectives, define the appropriate type of data,
and specify tolerable levels of potential decision errors that will be used as the basis for
establishing the quality and quantity of data needed to support decisions. When this Process is
not immediately applicable (i.e., the objective of the program is estimation, research, or any other
objective that does not select between two distinct conditions), the Agency requires the use of a
systematic planning method for defining performance criteria. This document, Guidance for the
Data Quality Objectives Process (EPA QA/G-4) provides a standard working tool for project
managers and planners to develop Data Quality Objectives for determining the type, quantity, and
quality of data needed to reach defensible decisions.
As required by EPA Manual 5360 (May 2000), this document is valid for a period of up to
five years from the official date of publication. After five years, this document will be reissued
without change, revised, or withdrawn from the EPA Quality System series documentation.
This document is one of the EPA Quality System Series documents which describe EPA
policies and procedures for planning, implementing, and assessing the effectiveness of a quality
system. Questions regarding this document or other EPA Quality System Series documents
should be directed to:
U.S. EPA
Quality Staff (2811R)
1200 Pennsylvania Ave, NW
Washington, DC 20460
Phone: (202)564-6830
Fax: (202)565-2441
e-mail: qualityfg), epa.gov
Copies of EPA Quality System Series documents may be obtained from the Quality Staffer by
downloading them from the Quality Staff Home Page:
www.epa.gov/quality
Final
EPA QA/G-4 i August 2000
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Final
EPA QA/G-4 ii August 2000
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TABLE OF CONTENTS
Pas
CHAPTER 0.
0.1
0.2
0.3
0.4
0.5
CHAPTER 1.
1.1
1.2
1.3
1.4
CHAPTER 2.
2.1
2.2
2.3
2.4
CHAPTER 3.
3.1
3.2
o o
J.J
3.4
CHAPTER 4.
4.1
4.2
4.3
4.4
CHAPTER 5.
5.1
5.2
5.3
5.4
INTRODUCTION 0-1
EPA Program Requirements for Environmental Data and Technology . 0-1
Systematic Planning and the Data Quality Objectives Process 0-4
Benefits of Using the DQO Process 0-9
Organization of this Document 0-12
Background for the Three Examples 0-13
STEP 1. STATE THE PROBLEM 1-1
Background 1-1
Activities 1-2
Outputs 1-5
Examples 1-5
STEP 2. IDENTIFY THE DECISION 2-1
Background 2-1
Activities 2-2
Outputs 2-4
Examples 2-4
STEP 3. IDENTIFY THE INPUTS TO THE DECISION 3-1
Background 3-1
Activities 3-2
Outputs 3-4
Examples 3-4
STEP 4. DEFINE THE BOUNDARIES OF THE STUDY 4-1
Background 4-1
Activities 4-3
Outputs 4-7
Examples 4-8
STEP 5. DEVELOP A DECISION RULE 5-1
Background 5-1
Activities 5-2
Outputs 5-4
Examples 5-5
EPA QA/G-4
in
Final
August 2000
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TABLE OF CONTENTS (continued)
Pa
CHAPTER 6. STEP 6. SPECIFY TOLERABLE LIMITS ON DECISION
ERRORS 6-1
6.1 Background 6-1
6.2 Activities 6-2
6.3 Outputs 6-12
6.4 Examples 6-14
CHAPTER 7. OPTIMIZE THE DESIGN FOR OBTAINING DATA 7-1
7.1 Background 7-1
7.2 Activities 7-2
7.3 Outputs 7-6
7.4 Examples 7-6
CHAPTER 8. BEYOND THE DATA QUALITY OBJECTIVES PROCESS ... 8-1
8.1 Planning 8-1
8.2 Implementation 8-3
8.3 Assessment 8-3
APPENDIX A. DERIVATION OF SAMPLE SIZE FORMULA FOR TESTING
MEAN OF NORMAL DISTRIBUTION VERSUS AN ACTION
LEVEL A-l
APPENDIX B. BIBLIOGRAPHY AND REFERENCES B-l
APPENDIX C. DQO EXAMPLE: USE OF THE MEDIAN C-l
APPENDIX D. GLOSSARY OF TERMS D-l
EPA QA/G-4
IV
Final
August 2000
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LIST OF FIGURES
Page
Figure 0-1. The EPA Quality System 0-2
Figure 0-2. The Scientific Method 0-4
Figure 0-3. The Data Quality Objectives Process 0-6
Figure 0-4. Repeated Application of the DQO Process Throughout the Life
Cycle of a Project 0-8
Figure 2-1. An Example of the DQO Process Applied to Multiple Decisions
for a Hazardous Site Investigation 2-3
Figure 4-1. Geographic Boundaries Delineated Using a Map 4-4
Figure 6-1. An Example of How Total Study Error Can Be Broken Down
by Components 6-3
Figure 6-2. An Example of a Decision Performance Curve 6-7
Figure 6-3. An Example of a Decision Performance Goal Diagram (Baseline Condition:
Parameter exceeds the Action Level) 6-9
Figure 6-4. An Example of a Decision Performance Goal Diagram (Baseline Condition:
Parameter is less than the Action Level) 6-13
Figure 6-5. Decision Performance Goal Diagram for Example 1 6-13
Figure 6-6. Decision Performance Goal Diagram for Example 2 6-15
Figure 8-1. The Data Quality Assessment Process 8-4
Figure C-l. Decision Performance Goal Diagram for Dust Lead Loading C-4
Final
EPA QA/G-4 v August 2000
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LIST OF TABLES
Page
Table 0-1. Elements of the Systematic Planning Process 0-5
Table 2-1. An Example of a Principal Study Question and Alternative Actions 2-2
Table 5-1. Population Parameters and Their Applicability to a Decision Rule 5-3
Table 6-1. False Acceptance and False Rejection Decisions 6-5
Table 7-1. False Acceptance Decision Error Rates and Alternative
Sampling Frequencies 7-8
Table 8-1. Elements of a Quality Assurance Project Plan 8-2
Table C-l. Number of Samples Required for Determining
if the True Median Dust Lead Loading is Above the Standard C-5
EPA QA/G-4
VI
Final
August 2000
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CHAPTER 0
INTRODUCTION
After reading this chapter you should understand the structure and
function of EPA 's Quality System, the kinds of programs that are apart
of this System, and the benefits of using the Data Quality Objectives
Process.
When data are being used to select between two alternative conditions (e.g., compliance
or non-compliance with a standard), the Agency's recommended systematic planning tool is the
Data Quality Objectives (DQO) Process. The DQO Process is a systematic planning process that
is part of the EPA's Quality System.
Who can use this guidance document! This guidance is intended for project managers,
technical staff, regulators, stakeholders, and others who wish to use the DQO Process to plan
data collection efforts and develop an appropriate data collection design to support decision
making.
0.1 EPA Quality System Requirements
EPA Order 5360.1 A2 (EPA 2000a) and the applicable Federal regulations establish a
mandatory Quality System that applies to all EPA organizations and organizations funded by
EPA. Components of the Quality System are presented in Figure 0-1. Organizations must ensure
that data collected for the characterization of environmental processes and conditions are of the
appropriate type and quality for their intended use and that environmental technologies are
designed, constructed, and operated according to defined expectations. Systematic planning is a
key project-level component of the EPA Quality System (see Figure 0-1).
EPA policy is based on the national consensus standard, ANSI/ASQC E4-1994,
Specifications and Guidelines for Environmental Data Collection and Environmental
Technology Programs, developed by the American National Standards Institute and the American
Society for Quality. This document describes the necessary management and technical area
elements for developing and implementing a quality system by using a tiered approach to a quality
system. The standard recommends first documenting each organization-wide quality system in a
Quality Management Plan or Quality Manual (to address requirements of Part A: Management
Systems of the standard), and then documenting the applicability of the quality system to technical
activity-specific efforts in a Quality Assurance Project Plan or similar document (to address the
requirements of Part B: Collection and Evaluation of Environmental Data of the standard).
EPA has adopted this tiered approach for its mandatory Agency-wide Quality System. This
document addresses Part B requirements of the standard for systematic planning for
environmental data operations.
Final
EPA QA/G-4 0 - 1 August 2000
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o
LLJ
O
or
Q.
o
o
Q.
Z
g
i
z
o
o
O
_l
O
Q.
Consensus Standards
ANSI/ASQC E4
ISO 9000 Series
Internal EPA Policies
EPA Order 5360.1
EPA Manual 5360
External Policies
Contracts - 48 CFR 46
Assistance Agreements -
40 CFR 30, 31, and 35
EPA Program &
Regional Policy
Quality System
Documentation
(e.g., Quality Management Plan)
Supporting System Elements
(e.g., Procurements,
Computer Hardware/Software)
Training/Communication
(e.g., Training Plan,
Conferences)
Annual Review and Planning
(e.g., QA Annual Report
and Work Plan)
System Assessment
(e.g., Quality System Audit)
Systematic
Planning
(e.g., DQO Process)
Acquire Data
1
Standard
Operating
Procedures
i
Technical
Assessments
PLANNING
i
IMPLEMENTATION
ASSESSMENT
Defensible Products and Decisions
Figure 0-1. EPA Quality System Components and Tools
EPA QA/G-4 0 - 2
Final
August 2000
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In accordance with EPA Order 5360.1 A2, the Agency requires that:
• Environmental programs performed for or by the Agency be supported by data of
the type and quality appropriate to their expected use. EPA defines environmental
data as information collected directly from measurements, produced from models,
or compiled from other sources such as databases or literature.
Decisions involving the design, construction, and operation of environmental
technology be supported by appropriate quality assured engineering standards and
practices. Environmental technology includes treatment systems, pollution control
systems and devices, waste remediation, and storage methods.
EPA Order 5360.1 A2 is supported by the EPA Quality Manual for Environmental
Programs (U.S. EPA, 2000b) that defines requirements for implementing EPA's Quality System.
The Order defines the quality requirements and the Manual presents the mandatory "how to" for
implementing these requirements.
EPA's Quality System (presented in Figure 0-1) comprises three levels - Policy,
Organization/Program, and Project:
• Policy - this level addresses Agency-wide quality policies and regulations that both
EPA organizations and external EPA-funded organizations must address;
Organization/Program - this level addresses the management and implementation
component of the individual Quality System; and
• Project - this level addresses the project-specific components that are applied to
individual projects to ensure that the needs of the organization are met.
EPA has developed a Quality System Series of documents that provide guidelines to help
organizations ensure that data collected for the characterization of environmental processes and
conditions are of the appropriate type and quality for their intended use. Documents useful in
planning for data collection include:
• Decision Error Feasibility Trials (DEFT) Software for the Data Quality
Objectives Process (EPA QA/G-4D),
• Guidance for the Data Quality Objectives Process for Hazardous Waste Sites
(EPA QA/G-4HW),
• Guidance on Quality Assurance Project Plans (EPA QA/G-5),
• Guidance for the Preparation of Standard Operating Procedures for Quality-
Related Documents (EPA QA/G-6), and
• Guidance for Data Quality Assessment: Practical Me thods for Data Analysis
(EPA QA/G-9).
Final
EPA QA/G-4 0 - 3 August 2000
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0.2 Systematic Planning and the DQO Process
EPA Order 5360.1 A2 requires that all EPA organizations (and organizations with
extramural agreements with EPA) follow a systematic planning process to develop acceptance or
performance criteria for the collection, evaluation, or use of environmental data. A systematic
planning process is the first component in the planning phase of the project tier, while the actual
data collection activities are in the implementation phase of this tier (Figure 0-1).
1. Observe some aspect of the universe.
2. Invent a tentative theory or hypothesis
consistent with what is observed.
3. Use this hypothesis to make predictions.
4. Test these predictions by planned
experiment or the collection of further observations.
Draw the conclusion
that the theory is true.
5. Modify the theory or hypothesis
in light of the results.
Figure 0-2. The Scientific Method
What is systematic planning? Systematic planning is a planning process that is based on the
scientific method and includes concepts such as objectivity of approach and acceptability of
results (Figure 0-2). Systematic planning is based on a common sense, graded approach to ensure
that the level of detail in planning is commensurate with the importance and intended use of the
work and the available resources. This framework promotes communication between all
organizations and individuals involved in an environmental program. Through a systematic
planning process, a team can develop acceptance or performance criteria for the quality of the
data collected and for the quality of the decision. When these data are being used in decision
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making by selecting between two clear alternative conditions (e.g., compliance/non-compliance
with a standard), the Agency's recommended systematic planning tool is called the DQO Process.
Elements of the systematic planning process (from Section 3.3.8 of the EPA Quality Manual) and
relationship to the DQO Process are shown in Table 0-1.
Table 0-1. Elements of the Systematic Planning Process
Elements of Systematic Planning Process
Identifying and involving the project
manager/decision maker, and project personnel
Identifying the project schedule, resources,
milestones, and requirements
Describing the project goal(s) and objective(s)
Identifying the type of data needed
Identifying constraints to data collection
Determining the quality of the data needed
Determining the quantity of the data needed
Describing how, when, and where the data will be
obtained
Specifying quality assurance and quality control
activities to assess the quality performance criteria
Describing methods for data analysis, evaluation,
and assessment against the intended use of the data
and the quality performance criteria
Corresponding Step in the DQO Process
Step 1 . Define the problem
Step 1 . Define the problem
Step 2. Identify the problem
Step 3. Identify information needed for the
decision
Step 4. Define the boundaries of the study
Step 5. Develop a decision rule
Step 6. Specify limits on decision errors
Step 7. Optimize the design for obtaining data
Step 7. Optimize the design for obtaining data
Part B of QA Project Plan
Part D of QA Project Plan; DQA Process
What are acceptance or performance criteria? Acceptance or performance criteria are based on
the ultimate use of the data to be collected and needed quality assurance (QA) and quality control
(QC) practices required to support the decision. In the decision making process, these criteria
allow a user to limit decision errors to a fixed level for determining whether or not an Action
Level (regulatory or risk-based) has been exceeded.
What is the DQO Process? The DQO Process is a seven-step planning approach to develop
sampling designs for data collection activities that support decision making. This process uses
systematic planning and statistical hypothesis testing to differentiate between two or more clearly
defined alternatives. A summary of the seven steps is presented in Figure 0-3.
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Step 1. State the Problem
Define the problem; identify the planning team;
examine budget, schedule.
1
Step 2. Identify the Decision
State decision; identify study question; define
alternative actions.
1
Step 3. Identify the Inputs to the Decision
Identify information needed for the decision (information
sources, basis for Action Level, sampling/analysis method).
1
Step 4. Define the Boundaries of the Study
Specify sample characteristics; define
spatial/temporal limits, units of decision making.
1
Step 5. Develop a Decision Rule
Define statistical parameter (mean, median); specify Action
Level; develop logic for action.
1
Step 6. Specify Tolerable Limits on Decision Errors
Set acceptable limits for decision errors relative to
consequences (health effects, costs).
1
Step 7. Optimize the Design for Obtaining Data
Select resource-effective sampling and analysis plan that
meets the performance criteria.
Figure 0-3. The Data Quality Objectives Process
The DQO Process is iterative and allows the planning team to incorporate new
information and modify outputs from previous steps as inputs for a subsequent step. Although
the principles of systematic planning and the DQO Process are applicable to all scientific studies,
the DQO Process is particularly designed to address problems that require making a decision
between two clear alternatives. The final outcome of the DQO Process is a design for collecting
data (e.g., the number of samples to collect, and when, where, and how to collect samples),
together with limits on the probabilities of making decision errors.
What are DQOs? DQOs are qualitative and quantitative statements, developed using the DQO
Process, that clarify study objectives, define the appropriate type of data, and specify tolerable
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levels of potential decision errors that will be used as the basis for establishing the quality and
quantity of data needed to support decisions. DQOs define the performance criteria that limit the
probabilities of making decision errors by considering the purpose of collecting the data; defining
the appropriate type of data needed; and specifying tolerable probabilities of making decision
errors.
What projects are covered by the DQO Process? The DQO Process may be applied to all
programs involving the collection of environmental data used in decision making. The principles
used in the DQO Process are also applicable to programs with objectives other than decision
making (e.g., estimation and research studies).
Who should be included in the DQO Process? When applying the DQO Process, a planning
team of senior program staff, technical experts, managers, data users (usually with some statistical
expertise), a quality assurance specialist, regulators, and stakeholders are usually involved. It is
important that the key persons participate (or stay informed) throughout the DQO Process so that
each individual understands the problem/decision and objectives of the decision-making process.
Individuals with specific areas of technical expertise may decide to be involved only in the steps of
the DQO Process that require technical input.
When should the DQO Process be used? The DQO Process should be used during the planning
stage of any study that requires data collection, before the data are collected. As the DQO
Process is iterative by nature, steps within the process can be revisited before a final decision is
reached. As shown in Figure 0-4, the planning team may choose to revisit selected parts of the
DQO Process or to investigate the entire process cyclically.
Is the DQO Process only applicable to large studies or studies that require multiple decisions?
The DQO Process applies to any study, regardless of its size. However, the depth and detail of
DQO development will depend on the study objectives. The DQO Process is particularly
applicable to a study in which multiple decisions must be reached because, by using this planning
process, the planning team can clearly separate and delineate data requirements for each
problem/decision. For projects that require multiple decisions or answers to more than one
question, it is likely that the resolution of one decision will lead to the evaluation of subsequent
decisions. In these cases, the DQO Process can be used repeatedly throughout the life cycle of a
project. Often, the decisions that are made early in the project will be preliminary in nature; they
might require only a limited planning and evaluation effort. As the study nears conclusion and the
consequences of making a decision error become more critical, however, the level of effort
needed to resolve a decision generally will become greater. Figure 0-4 illustrates this point.
What are the outputs of the DQO Process? The DQO Process leads to the development of
acceptance or performance criteria based on the ultimate use of the data to be collected and define
the quality required for the decision in terms of acceptance limits on the probabilities of
committing a decision error. Each step of the DQO Process defines criteria that will be used to
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STUDY PLANNING
COMPLETED
START
DEVELOPING
DQOs
STUDY PLANNING
COMPLETED
STUDY PLANNING
COMPLETED
APPROACH
INCREASING LEVEL OF EFFORT
Figure 0-4. Repeated Application of the DQO Process throughout the Life Cycle of
a Project
establish the final data collection design. The first five steps of the DQO Process are primarily
focused on identifying qualitative criteria, such as:
the nature of the problem that has initiated the study and a conceptual model of the
environmental hazard to be investigated;
the decisions that need to be made and the order of priority for resolving them;
• the type of data needed (i.e., geographic area, environmental medium, overall
timing of data collection, etc.); and
• a decision rule that defines how the data will be used to choose among alternative
actions.
The sixth step defines quantitative criteria, expressed as limits on the probability or chance
(risk) of making a decision error, that the decision maker can tolerate. The seventh step is used to
develop a data collection design based on the criteria developed in the first six steps. In this step
the planning team considers the final product of the DQO Process, a data collection design that
meets the quantitative and qualitative needs of the study using a specified number of samples that
can be accommodated by the budget available. The outputs of the DQO Process are used to
develop a QA Project Plan and for performing Data Quality Assessment (Chapter 8).
What is a data collection design? A data collection design specifies the number, location,
physical quantity, and type of samples that should be collected to satisfy the DQOs. The sampling
design designates where, when, and under what conditions samples should be collected; what
variables are to be measured; and the QA and QC activities that will ensure that sampling design
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and measurement errors are managed sufficiently to meet the tolerable decision error rates
specified in the DQOs. These QA and QC activities together with details of the data collection
design are documented in the QA Project Plan.
Can existing data be used in the DQO Process to support your decision making? Existing data
can be very useful. For example, pilot studies are often performed to provide a preliminary
assessment of variability. In these cases, the existing data may provide valuable information to
help develop a design for collecting data. It is critical to examine the existing data to ensure that
their quality is acceptable for use, or for integration into a new data set. Some considerations
include:
• determining if the existing data were collected within approximately the same
spatial and temporal boundaries as the new data;
• examining the existing data to determine if this data set includes identical media
and analytes;
• examining the performance of the analytical methods for the existing data
(accuracy, precision, detection limits) and comparing this to the specifications in
Step 3 of the DQO Process for new data to be collected; and
examining the variability among samples in the existing and new data sets.
Combining existing data and new data can be a very complex operation and you should
undertake this with great care. In many cases, statistical expertise is required to evaluate both
data sets before they can be combined with confidence.
Will you always develop statistical/probabilistic sampling designs for data collection if you use
the DQO Process? No. Although statistical methods for developing the data collection design
are strongly encouraged, this guidance recognizes that not every sampling problem can be
resolved with probabilistic sampling designs. However, the DQO Process can and should be used
as a planning tool for studies even if a statistical data collection design ultimately will not be used.
In these cases, the planning team is encouraged to seek expert advice on how to develop a non-
statistical data collection design and how to evaluate the results of the data collection. When
nonprobabilistic, judgmental, or quota sampling methods are used, be sure to consult with an EPA
representative to ensure that program-specific QA requirements are satisfied.
How should you use this guidance? You should use this guidance as a tool to structure the
planning activities for collecting environmental data. It should be used to organize meetings,
focus the collection of background information, and facilitate communication between a team that
includes technical experts, program managers, stakeholders, regulators, and decision makers.
0.3 Benefits of Using the DQO Process
The DQO Process integrates a multidisciplinary team and offers the advantages of using
experience and resources of individuals who have different backgrounds, different kinds of
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knowledge, and who can collectively focus on achieving a successful project conclusion. During
the initial planning stages, the planning team can concentrate on developing requirements for
collecting the data and work to reach consensus on the type, quantity, and quality of data needed
to support Agency decisions. This interaction results in a clear understanding of the problem and
the options available for addressing it, the development of acceptance or performance criteria for
decision making, a consensus-based approach to understanding the problem, and data being
collected of appropriate quality. Organizations that have used the DQO Process have observed
that:
• The structure of the DQO Process provides a convenient way to document
activities and decisions and to communicate the data collection design to others.
This documentation facilitates rapid review and approval by regulators and
stakeholders.
• The DQO Process enables data users and relevant technical experts to participate
collectively in data collection planning and to specify their particular needs prior to
data collection. The DQO process fosters communication among all participants,
one of the central tenets of quality management practices, and directs efforts to
achieving consensus between decision makers, stakeholders, and regulators.
• The DQO Process helps to focus studies by encouraging data users to clarify
vague objectives and to limit the number of decisions that will be made. Due to
this clarification, the consequences of decision errors are examined and correct
decisions will be made most frequently when the DQO Process is employed.
• The DQO Process is a planning tool that can save resources by making data
collection operations more resource-effective. Good planning will streamline the
study process and increase the likelihood of efficiently collecting appropriate and
useful data.
• The DQO Process provides a method for defining decision performance
requirements that are appropriate for the intended use of the data. This is done by
considering the consequences of decision errors and then placing tolerable limits
on the chance that the data will mislead the decision maker into committing a
decision error. A statistical sampling design can then be generated to provide the
most efficient method for managing decision errors and satisfying the DQOs.
Upon implementing the DQO Process, your environmental programs may be strengthened by:
• focused data requirements and optimized design for data collection,
• use of clearly developed work plans for collecting data in the field,
• uniformly documented data collection, evaluation, and use,
• clearly developed analysis plans,
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• sound, comprehensive quality assurance project plans, and
up-front buy-in by stakeholders to the sampling design and data collection process.
This can lead to:
• rapid review by regulators and other stakeholders,
• defensible results on which to base decisions,
• increased credibility with regulators and stakeholders, and
• a better use of resources.
Where else can the DQO Process be applied? The DQO Process is widely applicable. For
example, the Department of Energy Environmental Management program considers the following
potential applications for the DQO Process (Grumley, 1994):
• Waste management
S Characterizing waste, using process knowledge verified by minimal
sampling/ analysis data to meet acceptance criteria for treatment, storage,
and disposal.
S Designing optimal monitoring networks for ground water and surface
water discharges, and air emissions.
• Environmental restoration
S Focusing regulatory and public concerns associated with remediation.
S Identifying target analytes of concern for remedial activities.
S Determining when remediation has met cleanup levels.
• Facility transition and management
S Performing characterization assessments, using existing information or
collecting new data, to verify facilities for environmental management
acceptance.
S Evaluating alternative end-state conditions and planning facility
deactivation in preparation for eventual decontamination and
decommi ssioning.
S Designing optimized short- and long-term environmental monitoring.
• Decontamination and decommissioning
S Determining the location and levels of facility contamination.
S Determining when decontamination and decommissioning is complete.
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• Technology development
S Determining what constitutes and acceptably demonstrates success in
technology development and evaluation.
0.4 Organization of This Document
This document provides EPA's guidance specific to the design plans for collecting data for
decision-making activities. EPA recognizes that by using systematic planning and the DQO
Process to design environmental data collection efforts, the effectiveness, efficiency, and
defensibility of decisions will be improved. This document presents:
• the advantages of using systematic planning for data collection,
the seven steps of the DQO Process, including activities and outputs for each step,
and
three scenarios that each use a different statistical parameter (mean, median, and
upper percentile) to develop a design for collecting data.
The objective of this guidance document is to describe how a planning team can use the DQO
Process to generate a plan to collect data of appropriate quality and quantity for defensible
decision making. This guidance replaces in its entirety EPA's September 1994 document,
Guidance for the Data Quality Objectives Process (EPA QA/G-4), (U.S. EPA, 1994a), and is
consistent with the Data Quality Objectives Process for Hazardous Waste Site Investigations
(EPA QA/G-4HW) (U.S. EPA, 1999).
This document contains an introductory chapter that is followed by seven chapters that
correspond to the seven steps of the DQO Process. Each chapter is divided into four sections:
1. Background — Provides background information on the DQO Process step,
including the rationale for the activities in that step and the objective(s) of the
chapter.
2. Activities — Describes the activities recommended for completing the DQO
Process step, including how inputs to the step are used.
3. Outputs — Identifies the results that may be achieved by completing the DQO
Process step.
4. Examples — Presents outputs from two different DQO scenarios for
environmental contamination.
Chapter 8 shows how outputs of the DQO Process are used to develop a QA Project Plan.
Appendix A shows the derivation of the formula used to calculate sample size, and
Appendix B gives a Bibliography of referenced books, papers, and publications. Appendix C
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shows a complete DQO example using the median as the parameter of interest and Appendix D
contains a glossary of terms used in this document.
0.5 Background for the Two Examples
The following examples have been derived from real-life DQO development efforts to
illustrate the use of mean and percentile in planning for decision making:
Example 1 - Use of the mean to make a decision about waste disposal of material.
Example 2 - Use of the percentile to make a decision relative to a regulatory limit value.
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
Cadmium is primarily used for corrosion protection on metal parts of cars, electrical
appliances, and in some batteries. Cadmium and cadmium salts have been shown to be
toxic to humans through both ingestion and inhalation. Ingestion of concentrations as
low as 0.1 mg/kg/day causes mild to severe irritation of the gastrointestinal tract.
Exposure from chronic (long-term) inhalation can cause increased incidence of
emphysema and chronic bronchitis, as well as kidney damage.
A waste incineration facility located in the Midwest routinely removes fly ash from its
flue gas scrubber system and disposes of it in a municipal landfill. Previously the waste
fly ash was determined not to be hazardous according to RCRA program regulations.
The incinerator, however, recently began accepting and treating a new waste stream.
The representatives of the incineration company are concerned that the waste fly ash in a
new waste stream could contain hazardous levels of cadmium from new waste sources.
They have decided to test the ash to determine whether it should be sent to a hazardous
waste landfill or continue to be sent to the municipal landfill.
As a precursor to the DQO Process, the incineration company has conducted a pilot
study of the fly ash to determine the variability in the concentration of cadmium within
loads of waste fly ash leaving the facility and has determined that each load is fairly
homogeneous. There is considerable variability between loads, however, due to the
nature of the waste stream. The company has decided that testing each container load
before it leaves the facility would be an economical approach to evaluating the potential
hazard. They could then send containers of ash that exceeded the regulated standards to
the higher cost RCRA landfills and continue to send the other containers to the
municipal landfill. This example demonstrates use of the mean as the population
parameter of concern. (The derivation of a sampling design using the mean is provided
in Appendix A).
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Example 2. Making Decisions About Urban Air Quality Compliance
In July 1997, the EPA established new ambient air quality standards for PM25,
paniculate matter smaller than 2.5 microns (40 CFR 50). PM25 is comprised of fine
particles about I/30th the thickness of a human hair that are a complex mixture of acids,
metals, and carbon. Because the health risks of the chemical components ofPM25 are
not fully understood, EPA is implementing PM25 standards and investigating scientific
uncertainties associated with these components.
This example involves monitoring urban air for the presence ofPM25. Representatives
of a primary metropolitan statistical area (PMSA) in the northeast wish to determine
whether their PMSA is in attainment for PM25 according to the National Ambient Air
Quality Standards (NAAQS). If determined to be in nonattainment, control strategies
will be implemented for the PMSA, as defined in its associated State Implementation
Plan (SIP). This example uses an upper percentile as the primary population parameter
of concern as it is specified in the Standards. Additionally, this example highlights DQO
activities and outputs for the case when a data collection design (i.e., number of
samples) already has been determined, but not necessarily in accordance with the DQO
Process.
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CHAPTER 1
STEP 1. STATE THE PROBLEM
1.
State
the
Problem
2. Identify the Decision"
3. Identify the Inputs to
4. Define the Boundaries of the
5. Develop a Decision Rule
6. Specify Tolerable Limits on Decision
Errors
7. Optimize the Design for Obtaining Data
1. State the Problem
Identify the planning team members
including decision makers.
Describe the problem; develop a
conceptual model of the
environmental hazard to be
investigated.
Determine resources - budget,
personnel, and schedule.
After reading this chapter you should understand how to assemble an
effective planning team and how to describe the problem and examine
your resources for investigating it.
1.1 Background
The first step in any systematic planning process is to define the problem that has initiated
the study. As environmental problems are often complex combinations of technical, economic,
social, and political issues, it is critical to the success of the process to separate each problem,
define it completely, and express it in an uncomplicated format. A proven effective approach to
solving a problem is to use a planning team composed of experts and stakeholders who have
multidisciplinary backgrounds. A team of individuals with diverse backgrounds offers:
the ability to develop a complete, concise description of complex problems,
multilateral experience and awareness of potential data uses.
and
If there is a potential that the data collected in the current investigation could be used in future
studies (secondary uses of the data), it is important to consult, if possible, potential data users
during the planning process.
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1.2 Activities
The most important activities in this step are to:
• establish the planning team including the decision makers;
describe the problem and develop a conceptual model of the environmental hazard
to be investigated; and
identify available resources, constraints, and deadlines.
How do you establish the planning team and decision makers? The DQO planning team is
usually composed of the project manager, technical staff, data users (including those with a
statistical background), and stakeholders. It is important to carefully select the planning team and
leaders (or decision makers) because this team will work together through all seven steps of the
planning process. The development of DQOs does not necessarily require a large planning team,
particularly if the problem appears to be straightforward. The size of the planning team is usually
directly proportional to the complexity and importance of the problem. As the DQO Process is
iterative, team members may be added to address areas of expertise not initially considered.
Prior to or during the first meeting of the DQO team, members should identify the
decision makers. The decision maker may be one or more individuals familiar with the problem,
or with a vested interest in it. As the technical project manager is familiar with the problem and
the budgetary/time constraints the team is facing, he or she will usually serve as one of the
decision makers and will actively participate in all steps of DQO development. The decision
makers will have the ultimate authority for making final decisions based on the recommendations
of the planning team. In cases where the decision makers cannot attend DQO planning meetings,
alternate staff members should attend and keep the decision makers informed of important
planning issues.
The technical staff and data users should include individuals who are knowledgeable
about technical issues (such as geographical layout, sampling constraints, analysis, statistics, and
data interpretation). The planning team of multidisciplinary experts may include quality assurance
managers, chemists, modelers, soil scientists, engineers, geologists, health physicists, risk
assessors, field personnel, regulators, and data users with statistical experience.
Stakeholders are individuals or organizations who are directly affected by a decision,
interested in a problem, and want to be involved, offer input, or seek information. Usually
stakeholders will have multiple perspectives about a problem. The involvement of stakeholders
early on in the DQO Process can provide a forum for communication as well as foster trust in the
decision making process. An environmental example is the Common Sense Initiative Council, a
group of stakeholders convened to offer EPA advice and recommendations on a number of topics.
The Common Sense Initiative Council recognizes that involving stakeholders improves
communication and assists in analyzing situations to determine the tools and expertise needed to
address problems and maintain lasting agreements.
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The identification of stakeholders is influenced by the issues under consideration, as well
as the ability of stakeholders to articulate their interests. Because EPA is organized into multiple
program areas that are concerned with different environmental media that address different
regulatory areas (e.g., Clean Air Act, Resource Conservation and Recovery Act), stakeholder
involvement activities are not centralized. EPA has developed a web page Introduction to
Stakeholder Involvement (http://www.epa.gov/ooaujeag/stakeholders/people.htm) that identifies
individuals in various EPA program offices who can offer assistance in stakeholder involvement
activities. EPA provides additional information/resources on stakeholder involvement, including:
• EPA Resources for Non-Profit Organizations,
• Children's Health Protection Advisory Committee,
EPA Voluntary Programs, and
• Partners of Wetlands, Oceans, and Watersheds.
Information for stakeholder involvement and consensus building processes for other federal
agencies is also provided at this website. At the state level, information on potential stakeholders
is often available. For example, the State of California has developed a directory of citizen
groups, government agencies, and environmental education programs concerned with California
environmental issues (Harbinger Communications, 1996).
You should identify the roles of team members and group members that have key and secondary
roles, then consider the roles of the planning team members when coordinating meetings. While it
is important for key members (e.g., decision makers and members involved in policy decisions) to
either attend all meetings, or designate a representative to attend meetings that are missed,
technical members (e.g., technical managers, field and laboratory personnel, data users,
statisticians) may decide to be involved only in meetings where technical input is required.
Stakeholders and regulators may elect to attend initial meetings, but miss meetings that address
technical issues (e.g., sampling and analysis). When possible, the use of a facilitator or recorder at
these meetings is encouraged.
How do you describe the problem and the environmental hazard to be investigated? In Step 1,
the planning team describes the conditions or circumstances that are causing the problem and the
reasons for undertaking the study. Typical examples for environmental problems include
conditions that may pose a threat to human health or the environment or circumstances of
potential noncompliance with regulations.
The team may be able to describe the problem as it is currently understood by briefly
summarizing existing information, or they may conduct literature searches and examine past or
ongoing studies. This will ensure that the problem is correctly defined and has not been solved
previously. As you define the problem, you should consider similar studies and document
information about the performance of sampling and analytical methods observed in these studies.
This information may prove to be particularly valuable later in the DQO Process. You should
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organize and review all relevant information, indicate the source of the information, and evaluate
its reliability.
The planning team should:
examine the study objectives from a regulatory standpoint as necessary;
• identify individuals or organizations who are involved or have an interest in the
study;
• examine political issues associated with the study;
look at results of similar studies performed previously from the standpoint of:
S study parameters,
S regulatory or other constraints on sampling designs,
S variability and quality of data collected;
consider non-technical issues that may influence the sample design; and
• examine possible future uses of the data to be collected (e.g., the data to be
collected may be eventually linked to an existing database).
It is critical to carefully develop an accurate conceptual model of the environmental
problem in this step of the DQO Process, as this model will serve as the basis for all subsequent
inputs and decisions. Errors in the development of the conceptual model will be perpetuated
throughout the other steps of the DQO Process and are likely to result in developing a sampling
and analysis plan that may not achieve the data required to address the relevant issues.
The conceptual model of the potential environmental hazard developed at the beginning of
the DQO Process is often a diagram that shows:
known or expected locations of contaminants,
• potential sources of contaminants,
media that are contaminated or may become contaminated, and
• exposure scenarios (location of human health or ecological receptors).
If the problem is complex, the team may consider breaking it into more manageable pieces,
which might be addressed by separate studies. Priorities may be assigned to individual segments
of the problem and the relationship between the segments examined.
How do you identify available resources, constraints, and deadlines? You should examine
limitations on resources and time constraints for collecting data. This estimate should include
developing acceptance or performance criteria, preparing the QA Project Plan, collecting and
analyzing samples, and interpreting data. At this time the planning team should also examine
available personnel, and contracts (if applicable) and identify intermediate and final deadlines for
collecting data.
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1.3 Outputs
The major outputs of this step are:
• a list of the planning team members and their roles,
identification of decision makers,
• a concise description of the problem and a conceptual model of the environmental
problem to be investigated, and
• a summary of available resources and relevant deadlines for the study including
budget, availability of personnel, and schedule.
1.4 Examples
Given the background of the three examples as outlined in Section 0.5, the following
DQO Step 1 outputs were derived.
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
How were the planning team members selected? The planning team included the
incineration plant manager, a plant engineer, a quality assurance specialist with
statistical experience, and a chemist with sampling experience in the RCRA program.
The plant manager was the decision maker.
How was the problem described and a conceptual model of the potential hazard
developed? The problem was described as determining which container loads of waste
fly ash from a new waste stream needed to be sent to a RCRA landfill as a result of a
change in an incinerator process that possibly increased the levels of cadmium in waste
fly ash. The plant manager wanted to avoid expensive RCRA disposal of waste, if
possible, but also needed to comply with regulations and permits.
The conceptual model described fly ash that was created from industrial waste
incineration and is a potential source of toxic metals that include cadmium. Ash is
transferred to large containers via a conveyer belt. Containers are filled and trucked to
a disposal site. If the waste fly ash is hazardous but disposed in a municipal (sanitary)
landfill, then metals can leach into ground water and create runoff to streams, and other
surface water bodies, which could pose a hazard to human health and ecological
receptors. If such waste is disposed in a RCRA approved landfill, the hazards are
contained.
What were the available resources and relevant deadlines? Although the project was
not constrained by cost, the waste generator (the incineration company) wished to hold
sampling costs below $2,500. The incineration company also requested that the testing
of the waste fly ash in each container be completed within one week.
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Example 2. Making Decisions About Urban Air Quality Compliance
How were the planning team members selected? The planning team included senior
program staff, technical experts, senior managers, a QA specialist, and an individual
with statistical experimental design expertise. The most senior program staff member
served as the decision maker.
How was the problem described and a conceptual model of the potential hazard
developed? EPA had setNAAQSfor fine paniculate matter (PM2^) and other air
pollutants (40 CFR 50). The problem was described as determining whether the primary
metropolitan statistical area (PMSA) of concern was in attainment for fine paniculate
matter.
The conceptual model of the potential hazard was considering the concentration of fine
particulates in urban air that were primarily combustion products from point and mobile
sources. The particulates posed potential sources of exposure from inhalation. As a
rule, the PMSA was not concerned with long-term transport because over time
particulates aggregated or became deposited on other materials such that the particles
came within the purview of the Pmlorule. The PMSA developed a Cartesian map
indicating local PM25 point sources, main roadways, and predominant wind patterns to
identify areas of maximum potential exposure.
What were the available resources and relevant deadlines? The monitoring network
was already in place. It consisted of three fixed-site multiple filter gravimetric devices
for measuring daily concentrations (24-hr average) once every 3 days. Thus, about 365
readings were obtained each year.
Looking Ahead to other DQO Steps:
• Careful description of the problem will assist in Step 3, Identify the
Inputs to the Decision, when considering additional use of data (link to
databases, etc.).
• The conceptual model will assist in Step 4, Define the Boundaries of the
Study, when
S establishing spatial boundaries, and
S considering regulatory and practical constraints for sampling.
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CHAPTER 2
STEP 2. IDENTIFY THE DECISION
The DQO Process
1. State the Problem
2. Identify the Decision
3. Identify the Inputs to the Decision
4. Define the Boundaries of the StuC
5. Develop a Decision Rule
6. Specify Tolerable Limits on Decision
Errors
7. Optimize the Design for Obtaining Data
2. Identify the Decision
• Identify the principal study question.
• Define alternative actions.
• Develop a decision statement.
• Organize multiple decisions.
After reading this chapter you should know how to identify the principal study
question and how to define options for addressing it (alternative actions).
2.1 Background
This step builds on the output of the previous step where you have:
• identified members of a planning team, including decision makers;
concisely described the problem; and
• developed a conceptual model of the environmental problem to be investigated.
In Step 2 of the DQO Process, you should identify the key question that the study
attempts to address and alternative actions that may be taken, depending on the answer to the key
study question. Then you are able to combine these two elements to develop a decision
statement. The decision statement is critical for defining decision performance criteria later in
Step 6 of the Process.
In cases of multiple or complex problems, you should identify multiple decisions, organize
the decisions sequentially (or logically), and examine the decisions to ensure consistency with the
statement of the problem in Step 1. If the principal study question is not obvious and specific
alternative actions cannot be identified, then the study may fall in the category of exploratory
research, in which case this particular step of the DQO Process may not be needed.
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2.2 Activities
In this step you should:
• identify the principal study question;
define alternative actions;
• combine the principal study question and alternative actions into a decision
statement and state each decision in terms of whether to take action. In some
cases, this decision statement will be based on regulatory guidelines; and
organize multiple decisions into an order of priority.
How do you identify the principal study question? Based on a review of the problem described
in Step 1, you should identify the principal study question and state it as specifically as possible.
A specific statement of the principal study question focuses the search for information needed to
address the problem. The principal study question identifies key unknown conditions or
unresolved issues that reveal the solution to the problem being investigated. EPA recommends
that initially you should concentrate on only one principal study question and expand to other
issues later. The following are examples of typical principal study questions:
• Does the concentration of contaminants in ground water exceed acceptable levels?
Does the pollutant concentration exceed the National Ambient Air Quality
Standard?
Does a contaminant pose a human health or ecological risk?
• Is the contaminant concentration significantly above background levels (suggesting
a release)?
In each case, the answer to the principal study question will provide the basis for determining the
course of action that should be taken to solve the problem.
What are alternative actions and how should you define them? During this step, the planning
team should identify the possible actions that may be taken to solve the problem, including an
alternative that requires no action. The team should confirm that the alternative actions can
resolve the problem (if it exists) and determine whether the actions satisfy regulations. An
example of a principal study question and alternative actions is given in Table 2-1.
Table 2-1. An Example of a Principal Study Question and Alternative Actions
Principal Study Question
Are there significant levels of lead in floor dust at a
children's residence.
Alternative Actions
Remove the children from the residence.
Initiate a clean-up removal of lead -based
paint.
Take no action.
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How do you develop a decision statement? After examining the alternative actions, you should
combine the principal study question and alternative actions into a decision statement that
expresses a choice among alternative actions. The following template may be helpful in drafting
decision statements:
Determine whether or not [unknown environmental conditions/issues/criteria from
the principal study question] require (or support) [taking alternative actions].
Does the DQO Process address multiple decisions? If several separate decision statements must
be defined to address the problem, you should examine how the decisions relate to one another
and prioritize them in the order of the importance and sequence for resolving them. It may be
helpful to document the prioritizing process proposed to resolve the problem using a diagram or a
flow chart. An example is presented in Figure 2-1.
Figure 2-1. An Example of the DQO Process Applied to
Multiple Decisions for a Hazardous Waste Investigation
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2.3 Outputs
The output for this step is a decision statement that links the principal study question to
possible actions that will solve the problem.
2.4 Examples
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
What was the Decision Statement? The decision statement was determining whether
waste fly ash was hazardous under RCRA regulations.
What were the alternative actions? If the waste was hazardous, disposal in a RCRA
landfill was required. If it was not, the team decided that disposal in a sanitary landfill
was acceptable.
Example 2. Making Decisions About Household Dust for Lead Hazard Assessment
What was the Decision Statement? The decision statement was determining if there
were significant levels of lead in floor dust at the residence.
What were the alternative actions? If yes, the team planned follow-up testing to
determine whether immediately dangerous contamination existed and the location of the
contamination in the property. If no, the team decided there was not a potential lead
hazard, and testing was discontinued.
Example 3. Making Decisions About Urban Air Quality Compliance
What was the decision statement? The decision statement was determining if the PMSA
of concern was in attainment for PM25.
What were the alternative actions? If yes, monitoring was continued. If no, monitoring
was continued and the PM2i control strategies outlined in the State Implementation Plan
(SIP) were implemented.
Looking Ahead to other DQO steps:
The principal study question will be used in constructing the baseline and
alternative conditions in Step 6.
• Alternative actions will form the basis for determining the potential
consequences of committing a decision error as addressed in Step 6.
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CHAPTER 3
STEP 3. IDENTIFY THE INPUTS TO THE DECISION
6.
7.
The
1. State the Problem
2. Identify the Decision
13. Identify the Inputs to the Decision
4. Define the Boundaries of the Study
5. Develop a Decision Rule
Specify Tolerable Limits on Decision
Errors
Optimize the Design for Obtaining Data
3. Identify the Inputs
to the Decision
Identify the information needed.
Determine sources for this information.
Determine the basis for determining the
Action Level.
Identify sampling and analysis methods
that can meet the data requirements.
After reading this chapter you should know the kinds of information
that are required to investigate the problem and whether appropriate
sampling and analytical methods are available.
3.1 Background
This step builds on the previous steps where you have:
• identified members of a planning team, including decision makers;
concisely described the problem;
• developed a conceptual model of the environmental problem to be investigated;
and
• identified the decision that needs to be made.
In Step 3 of the DQO Process you should identify the kind of information that is needed to
resolve the decision statement and potential sources of this information (new data or existing
data). This information should include the decision values (e.g., concentration of contaminants)
information about its derivation. You should also determine if the appropriate analytical
methodology exists to measure the environmental characteristics. Once you have determined
what needs to be measured, you may refine the specifications and criteria for these measurements
in later steps of the DQO Process.
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3.2 Activities
In this step you should:
• identify the kinds of information needed;
identify the sources of information;
• determine the basis for setting the Action Level; and
confirm the appropriateness of proposed sampling and analyses methods.
How do you identify the kinds of information that you will need? You may identify
information needs by asking the following questions:
Is information on the physical properties of the media required?
• Is information on the chemical or radiological characteristics of the matrix needed?
Can existing data be used to make the decision?
• Do we need to make new measurements of environmental characteristics?
If you decide that new measurements are needed, you should develop a list of characteristics that
need to be measured to make the decision. For example, if the information can be obtained as an
output from an environmental model (e.g., ground water transport), then the list of characteristics
should include the inputs required for the model.
If the decision can be based on existing data, then the sources of these data should be
examined to the extent possible to ensure that they are acceptable. If you consider integrating
new data with existing data, parameters in the existing database need to be examined so that new
samples can be collected (or analyzed) in a similar way and that the databases for new and existing
data include common parameters. In some cases, statistical expertise is required to evaluate
databases for possible aggregation because data collected for different purposes may not be
compatible. For example, studies that model exposure to environmental contaminants may link
environmental, lexicological, biological, geological, and census data. In these cases, issues such
as physical properties of contaminants, environmental media, ingestion and inhalation rates,
cancer slope factors, plant uptake rates, meteorological conditions, latitude, longitude, location of
population centers and water bodies, and population density are inputs for evaluating exposure to
the contaminant. Meta-data analysis offers the planning team options for using existing databases
in conjunction with newly collected data. Existing data will also be evaluated quantitatively in
Step 7, Optimize the Design for Obtaining Data.
How should you identify the source of the information? You should identify and document the
sources for the information needed to resolve the decision. These sources may include results of
previous data collections, historical records, regulatory guidance, professional judgment, scientific
literature, or new data collections.
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How do you determine the basis for the Action Level? The value for action is the threshold
value (chosen in Step 5 of the DQO Process) that provides the criterion for choosing among
alternative actions (e.g., whether to take action or not to take action or whether to choose action
1 versus action 2). Action Levels are concentrations of contaminants that are either based on
regulatory requirements, based on risk assessments, based on performance criteria for analytical
methodology (limitations of technology), or based on a reference standard. In this step, it is
important for you to understand how the Action Level will be derived. In other words, you need
to understand what information will be used to determine the Action Level, such as a promulgated
regulation or a project-specific risk assessment. The actual numerical value of the Action Level
need not be specified until DQO Process Step 5, Develop a Decision Rule, but a potential Action
Level should be established. If the Action Level is based on a regulatory requirement, then the
planning team will know the numerical value of the Action level at this step. However, if the
Action Level is based on a risk assessment or other performance criterion, it may be best to defer
the specification of the numerical value until after the study boundaries have been specified in
DQO Process Step 4.
If the decision will be made relative to background concentrations (rather than a
quantitative limit), then you should determine what constitutes background. Characteristics of the
background need to be consistent with the characteristics of the area to be investigated. The
actual numerical value of the Action Level will be established in Step 5, Develop a Decision Rule.
How should you identify that sampling and analysis methods that can meet the data
requirements? Using the list of environmental characteristics that pertain to the decision, you
should develop a list of sampling and analytical methods that may be appropriate for the problem
being investigated. For example, you should specify sampling considerations (e.g., quantities)
required for detecting analytes at low concentrations and procedures required to collect these
sample quantities. You should also identify analytical methods that have appropriate detection
limits (the minimum concentration that can be measured and reported with a specific confidence
that the analyte concentration is greater than zero). Detection limits are analyte-, matrix- and
instrument-specific. For example, atomic absorption spectroscopy or inductively coupled plasma
emission spectrometry may not be sensitive enough to measure lead levels in water samples;
however, graphite furnace atomic absorption spectroscopy would be capable of making these
measurements.
Great importance should be given to the problem of minimizing bias as this is an important
performance characteristic of sampling and analysis. The decision error rates to be established in
Step 6 of the DQO Process rely on bias being kept to a minimum. Six major causes of bias have
been identified for environmental sampling and analysis (1) non-representative sampling; (2)
instability of samples between sampling and analysis; (3) interferences and matrix effects in
analysis; (4) inability to determine the relevant forms of the parameter being measured; (5)
calibration; and (6) failure to blank-correct. Some of the EPA methods are particularly subject to
bias in calibration. For example, EPA methods for analyses of phenols in water exhibit around
50% bias due to calibration. Methods known to exhibit large biases should be avoided if possible.
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Additional considerations include requirements for certification of personnel, and
laboratory accreditation or Performance-Based Measurement Systems (PBMS). Laboratories
analyzing environmental samples should follow standard protocols and procedures or use
performance-based methods. When measurement requires the analysis of chemical, biological, or
radioactive samples, it is advisable to select a laboratory that is accredited to perform the analyses.
Requirements for accreditation include having qualified personnel, appropriate instrumentation,
standard operating procedures, and proficiency in the analysis of samples for specific analytes or
programs. For example, laboratories analyzing lead in paint, dust, and soil samples must be
accredited through the National Lead Laboratory Accreditation Program (NLLAP) to become
"EPA recognized." According to the Department of Housing and Urban Development's
Guidelines (HUD, 1995), "property owners, risk assessors, inspector technicians, and contractors
should ensure that laboratory analyses are performed by an 'EPA-recognized' laboratory;" a
requirement also of EPA and many States.
3.3 Outputs
The outputs from Step 3 are:
• a list of environmental characteristics that will be measured to enable the planning
team to make the decision;
• a list of information sources or methods that indicate how each Action Level will
be derived;
• a list of information that may be applicable to uses of the data in future
investigations [e.g., inputs to models, associated meta-data analysis (e.g., using
latitude, longitude, census data) that may be appropriate to use for combining
existing databases with newly collected data]; and
• confirmation that sampling and analytical methods exist (or can be developed) to
meet the detection limit criteria required for collecting data, given the appropriate
magnitude of the Action Level.
3.4 Examples
It is in this step that numerical quantities start making their appearance in the DQO
Process.
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
Identify the kind of information. To resolve the decision statement, the planning team
decided to measure the cadmium concentration in the leachate resulting from Toxicity
Characteristic Leaching Procedure (TCLP) extraction. Existing pilot study data
provided information about variability, but there was not enough information to resolve
the decision statement.
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Identify the source of information. The Action Level was based on RCRA toxicity
regulations for cadmium in TCLP leachate which is specified as 1.0 mg L.
What sampling and analytical methods were appropriate? Cadmium was measured
in the leachate according to the method specified in 40 CFR 261, App. II. The
detection limit was well below the Action Level.
Example 2. Making Decisions About Urban Air Quality Compliance
Identify the kind of information. To resolve the decision statement, the planning
team obtained three years ofPM2,5 concentration measurements from the existing
monitoring network within the PMSA of concern.
Identify the source of information. The 24-hr PM2,5 federal standard of 65 ^g/m3 is
attained when 98 percent of the daily concentrations, measured over three years,
are equal to or less than the standard.
What sampling and analytical methods were appropriate? The existing network
consisted of three IMPROVE samplers, each equipped with a
polytetrafluoroethylene (PTFE) membrane filter to collect aerosols for mass
measurement. Gravimetry (electro-microbalance) was used as the method of
quantitative analysis. The detection limit was well below the standard used for the
Action Level.
Looking Ahead to other DQO Steps:
• The effect of sampling methods (e.g., compositing) may affect the required
detection limit and should be considered relative to analytical measurement
methods. These issues are also considered in Steps 5 and Step 7.
• Criteria for existing data will be examined in Step 7, Optimize the Design for
Collecting Data.
Method detection limit and method quantitation limits identified in this step will
be revisited in Step 7, Optimizing the Design for Collecting Data.
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CHAPTER 4
STEP 4. DEFINE THE BOUNDARIES OF THE STUDY
The DQO Process
1. State the Problem
2. Identify the Decision
3. Identify the Inputs to the Decision
14. Define the Boundaries of the Study
5. Develop a Decision Rule
6. Specify Tolerable Limits on Decision
Errors
7. Optimize the Design for Obtaining Data
4. Define the Boundaries
of the Study
• Define the target population of interest.
• Specify the spatial boundaries that
clarify what the data must represent.
• Determine the time frame for collecting
data and making the decision.
• Determine the practical constraints on
collecting data.
• Determine the smallest subpopulation,
area, volume, or time for which
separate decisions must be made.
After reading this chapter you should understand how to define the
geographic and temporal boundaries of the problem, how to examine any
practical constraints to collecting data, and factors that affect your
selection of the unit for decision making.
4.1 Background
This step builds on the previous steps where you have:
identified members of the planning team, including decision makers;
• concisely described the problem;
developed a conceptual model of the environmental problem to be investigated;
• identified the decision that needs to be made; and
identified sources of information, potential Action Levels, and possible
measurement methods that are appropriate.
In Step 4 of the DQO Process, you should identify the target population of interest and specify
the spatial and temporal features of that population that are pertinent for decision making.
It is difficult to interpret data that have not been drawn from a well-defined target
population. The term "target population" refers to the total collection or universe of objects, or
sampling units, to be studied and from which samples will be drawn. (In this context, the term
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"sample" means the individual member, or unit, of the target population that is selected and
measured or observed, such as a 100-gram scoop of soil, a cubic meter of air, a single fish, or
single radiation measurement.) The term "sampling unit" is used in the more general and
theoretical context when defining how the target population will be broken down into elementary
components or members that can be selected and measured or observed. When the target
population is made up of "natural units," such as people, plants, or fish, then the definition of a
sampling unit is straightforward. However, many environmental studies involve target
populations made up of continuous media, such as air, water, or soil. In this context, the
sampling unit must be defined as some volume or mass to be selected which is often called the
sample support (Myers, 1997). The actual determination of the optimal size of a sampling unit
for environmental data collection efforts can be complicated, and usually will be addressed as a
part of the sampling design in Step 7. Here in Step 4, the planning team should be able to provide
a first approximation of the sampling unit definition when specifying the target population.
Quite often in environmental studies the target population is the set of all possible
environmental samples (e.g., volume of soil, water, or air) that, taken together, constitute the
geographic area of interest. The purpose of this step is to unambiguously define the spatial and
temporal features of each environmental medium within a specific area or time period covered in
the decision. A clear definition of the target population and its characteristics to the decision
maker will make data interpretation more straightforward. The boundaries of the population
include:
• spatial boundaries that define the physical area to be studied and generally where
samples will be collected, and
• temporal boundaries that describe the time frame that the study will represent and
when the samples should be taken.
You should use boundaries to ensure that the data collection design incorporates the time
periods in which the study and decision should be implemented, areas where samples will be
collected, and the time period to which the decision should apply. This should help you collect
data that are representative of the population being studied. Defining boundaries before the data
are collected can also prevent inappropriate combining of data sets in a way that masks useful
information. The conceptual model that you developed in Step 1 of the DQO Process should
provide essential input into defining the spatial boundaries.
Practical constraints that could interfere with sampling should also be identified in this
step. A practical constraint is any hindrance or obstacle (such as fences, property access, water
bodies) that may interfere with collecting a complete data set. These constraints may limit the
spatial and/or temporal boundaries or regions that will be included in the study population and
hence, the inferences (conclusions) that can be made with the study data.
As the final decision depends on data that are aggregated, you should carefully identify the
size of "decision" units within which the data will be combined to make the decision. Factors
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such as areas of potential risk, limits of remediation technology, future land uses, and activity
patterns, may impact the size of the decision unit selected.
4.2 Activities
In this step you should:
define the target population,
• determine the spatial and temporal boundaries,
identify practical constraints, and
• define the scale of decision making.
How do you define the target population? It is important for you to clearly define the target
population to be sampled. The target population is usually the set of all environmental samples
about which the decision maker wants to draw conclusions. In a number of cases, defining the
target population for an environmental study requires specifying the medium, such as ground
water, ambient air, surface soil, etc. It may be helpful to "work backwards" and think of how you
would define an individual sampling unit when trying to develop a clear definition of the target
population.
How do you determine the spatial boundaries of the decision statement?
1. Define the geographic area applicable for the decision making.
You should define the entire geographic area where data are to be collected using
distinctive physical features such as volume, length, width, or boundaries. Some examples
of geographic areas are the metropolitan city limits, the soil within the property boundaries
down to a depth of 6 inches, a specific water body, length along a shoreline, or the natural
habitat range of a particular animal species. It is important to state as definitively as
possible the media and geographic area; this statement may include soil depth, water
depth, or distance inside a fence line. You should be careful when designating areas that
are on the periphery of the geographic area because peripheral samples are subject to edge
effects and contamination that is not associated with the spatial boundaries designated for
the decision making. In Figure 4-1 the geographic area of the study has been indicated on
a map in the area with a grid.
2. Divide the population into subsets that have relatively homogeneous
characteristics.
You may consider dividing the target population into subpopulations that are relatively
homogeneous within each area or subunit. When combined with an appropriate sampling
design in Step 7, Optimize the Design for Obtaining Data, this approach can reduce the
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Figure 4-1. Geographic Boundaries Delineated Using a Map
number of samples required to meet the tolerable limits on decision errors (Step 6), and,
thus, allow more efficient use of resources. It is often helpful to consider subdividing the
target population in this way at this step because the planning team is focused on their
understanding of how the target population's features and characteristics relate to the
decision. The planning team can use its knowledge of the conceptual model (developed in
Step 1, State the Problem) to consider how the characteristics of interest for the target
population vary or change over space and time. This information will be useful when
completing the subsequent activities in this step, and when considering alternative
sampling designs (such as stratified random sampling) in Step 7, Optimize the Design for
Collecting Data.
How do you determine the temporal boundaries of the decision statement?
1.
Determine when to collect data.
Conditions may vary over the course of a study because of time-related phenomena such
as weather conditions, seasons, operation of equipment under different environmental
conditions, or activity patterns. Examples of these variations include seasonal ground
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water levels, daily or hourly airborne contaminant levels in metropolitan areas, and
fluctuations in pollutant discharges from industrial sources. These variations may impact
the success of your data collection and the interpretation of data results. You should
determine when conditions are most favorable for collecting data and select the most
appropriate time period to collect data. For example, you may consider the measurement
stability of the following:
• measurement of lead in dust on window sills may show higher concentrations
during the summer when windows are raised and paint/dust accumulates on the
window sill;
terrestrial background radiation levels may change due to shielding effects related
to soil dampness;
• measurement of pesticides on surfaces may show greater variations in the summer
because of higher temperatures and volatilization;
instruments may not give accurate measurements when temperatures are colder; or
measurements of airborne paniculate matter may not be accurate if the sampling is
conducted in the wetter winter months rather than the drier summer months.
2. Determine the time frame for decision making.
It may not be possible to collect data over the full time period to which the decision will
apply. This is particularly true for decisions that project future uses, such as
"Brownfields" (an inactive property being put back into productive economic use after the
relevant environmental agencies agree that contaminants once present at the property no
longer pose an unacceptable risk to human health or to the environment). You should
evaluate the population and determine the optimum time frame for collecting data, given
that the medium may change over time, or the time constraints of the study relative to the
decision making. You should specify if you are making a decision on whether the current
medium meets a criterion, or if the medium will meet the criterion for some future time
periods. You should define time frames for the overall population and for any
subpopulation of interest; then address discrepancies that may arise from the short time
frame of data collection relative to the long time periods for implementing decisions. For
example, you may develop a statement for the decision to be based on:
• the condition of contaminant leaching into ground water over a period of a
hundred years, or
the risk conditions of an average resident over their average length of residence,
which is estimated to be 8 years.
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What kinds of practical constraints on collecting data should you identify? You should discuss
the proposed data collection activities in light of any practical constraints that are related to the
spatial or temporal boundaries of the study, to the availability of personnel, or to time and
budgetary constraints (identified in Step 1 of the DQO Process). These constraints could include
access to the property, availability and operation of equipment, and environmental conditions
when sampling is not possible (high humidity, freezing temperatures). For example:
• it may not be possible to take surface soil samples beyond the east boundaries of a
property under investigation because permission has not been granted by the
owner of the adjacent property, or
• it may not be possible to collect dust wipe samples (for lead) if certified risk
assessors are not available to supervise the sampling.
How do you define the scale of decision making? The scale of decision making refers to the
way the planning team has delineated decision units and identified the smallest unit of area,
volume, or time where data will be collected, analyzed, aggregated, and interpreted to make a
decision and control decision error. The consequences of making incorrect decisions (Step 6) are
associated with the size, location, and shape of the decision unit. It is important to consider
present and future uses for the decision unit, where the decision unit is located (remote area
versus densely populated area) and requirements for potential remediation. The consequences of
a wrong decision (even if quite small) should be carefully considered. For example, if a decision,
based on the data collected, results in a large land area being cleaned (soil removed to a certified
disposal area) when the true conditions would not warrant a cleanup action, then the decision
maker may have to incur a large cost unnecessarily. The area of land being sampled (decision
unit) should be appropriate to the potential risk of an incorrect decision. When establishing the
scale of decision making, take care that this is not so large that an incorrect decision could result
in either an unacceptable resource expense or unacceptable threat to human health or the
environment.
The question of using one large decision unit versus a number of small decision units is
also an important consideration for the planning team. If there are many decision units and
multiple decisions are made, then the team needs to consider whether they want to limit the
probability of leaving at least one contaminated unit unremediated (rather than just any one unit).
The chance of at least one incorrect decision increases exponentially. This is known as
"comparison-wise" versus "experiment-wise" error rates. If multiple decisions are expected, and
the planning team determines that the overall probability of making at least one decision error
must be controlled, then consultation with a statistician is advisable.
The planning team may establish decision units based on several considerations:
Risk - The scale of decision making based on risk is determined by the potential
exposure an area presents; an individual unit of risk is called an exposure unit. For
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example, in a study where the decision statement is, "Determine whether or not the
concentration of lead in soil poses an unacceptable health risk to children and
requires remediation," the geographic area is the top 6 inches of soil within the
property boundaries, and the population is the collection of individual volumes of
soil that could be selected for inclusion in a sample. The scale of decision making
could be the size that corresponds to the area where children derive the majority of
their exposure (such as a play area or an average residential lot size if the future
land use will be residential). Studying the area at this scale will be protective of
children, a sensitive population in risk assessment.
• Technological Considerations - A technological scale for decision making is
defined as the most efficient area or volume that can be remediated with a selected
technology. An example of a remediation unit would be the area of soil that can be
removed by available technology under estimated working conditions if the
decision will be made on the basis of bulldozer-pass-volume.
• Temporal Considerations - A temporal scale of decision making is based on
exposure from constituents in media that change over time. For example, in order
to regulate water quality, it would be useful to set a scale of decision making that
reduces the time between sampling events. Using this scale the planning team
could minimize the potential adverse effects in case the water quality changed
between sampling events.
Financial Scale - The financial scale is based on the actual cost to remediate a
specified decision unit. For example, if a large exposure unit is identified, the costs
of remediation could be prohibitive. In this case, the planning team may want to
develop a different scale to narrow the data collection process and identify the
distinct areas of contamination.
Other - The possibility of "hot spots" (areas of high concentration of a
contaminant) may be apparent to the planning team from the history of the
property. In cases where previous knowledge (or planning team judgment)
includes identification of areas that have a higher potential for contamination, a
scale may be developed to specifically represent these areas.
Further information on sampling designs and associated definitions on methods may be
obtained from Gilbert (1987) and Thompson (1992).
4.3 Outputs
The outputs of this step are:
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• detailed descriptions of the characteristics that define the population to be
sampled,
• detailed descriptions of geographic limits (spatial boundaries) that are appropriate
for the data collection and decision making,
• time frame appropriate for collecting data and making the decision,
list of practical constraints that may interfere with the data collection, and
• appropriate scale for decision making.
4.4 Examples
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
What population was sampled? Individual samples of fly ash that comprise the
container were sampled and analyzed. The fly ash was not mixed with any other
constituents except water (used for dust control). Each container of ash filled at least
70% of the waste container. In cases where the container was less than 70% full, the
container was kept on-site until more ash was produced and the container was filled to
capacity.
What were the spatial boundaries? Decisions applied to each container load of fly ash
waste as the actual container made a natural physical boundary.
What was an appropriate time frame for sampling? The decision was to be based on
the current concentration of cadmium in the waste fly ash. Contained in the containers,
the waste did not pose a threat to humans or the environment. Additionally, since the fly
ash was not subject to change, disintegration, or alteration, the decision about the waste
characteristics was not influenced by temporal constraints. To expedite decision
making, however, the planning team placed deadlines on sampling and reporting. The
waste fly ash was tested within 48 hours of being loaded onto waste containers. The
analytical results from each sampling round were completed and reported within 5
working days of sampling. The container was not used again until analysis had been
completed and evaluated.
What were the practical constraints for collecting data? The most important practical
constraint was the ability to take samples from the waste fly ash stored in the containers.
Although the containers had open access, special procedures and methods based on EPA
protocols were implemented so that samples were representative of the entire depth of
the waste fly ash.
What was the scale for decision making? The decision unit was each container of waste
fly ash.
Example 2. Making Decisions about Urban Air Quality Compliance
What population was sampled? The volume from samplers that represented fine
paniculate matter from urban air was sampled and analyzed.
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What were the spatial boundaries? The spatial boundary was defined by the region
represented by the PMSA of concern.
What was an appropriate time frame for sampling? The temporal boundaries had two
components. Individual observations (i.e., daily concentrations) were based on 24-hour
averages obtained each day of monitoring. The standard required that a decision be
made, and subsequent action taken, after 3 years of data collection. Monitoring results
were assumed to characterize both the near past (i.e., previous 3 years) and current air
quality, unless substantial upward or downward trends were observed in daily PM2.5
concentrations.
What were the practical constraints for collecting data? Given that the monitoring
network and sampling plan were already established, the only potential practical
constraint was the continual operation of the monitoring network. If a monitor became
defective, the planning team decided to either collect a smaller sample size (number of
samples) over the 3-year period, or to extend the period for collecting data to obtain the
required number of samples.
What was the scale for decision making? The decision unit was the geographic region
represented by the PMSA over the 3-year period of data collection.
Looking ahead to other DQO steps:
The way in which you divide the problem into strata may affect the
number of samples required to meet the tolerable limits for decision
errors specified in Step 6.
The scale of decision making may have an impact on the performance
criteria and the consequences of decision errors in Step 6.
• Outputs from Step 4 may potentially affect the sampling design
developed in Step 7.
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CHAPTER 5
STEP 5. DEVELOP A DECISION RULE
The DQO Process
1. State the Problem
2. Identify the Decision
3. Identify the Inputs to the Decisi
4. Define the Boundaries of t
15. Develop a Decision Rule |
6. Specify Tolerable Limits
Errors
7. Optimize the Design for Obtaining Data
5. Develop a Decision Rule
• Specify an appropriate population
parameter (mean, median, percentile).
• Confirm the Action Level exceeds
measurement detection limits.
• Develop a decision rule
(If... then... statement).
After reading this chapter you should know how to construct a
theoretical "If...then... " decision rule that defines how the decision
maker would choose among alternative actions if the true state of
nature could be known with certainty.
5.1 Background
This step builds on the previous steps where you have:
identified members of the planning team, including decision makers;
concisely described the problem;
developed a conceptual model of the environmental problem to be investigated;
identified the decision that needs to be made;
identify sources of information, potential Action Levels, and possible measurement
methods that are appropriate; and
decided on the spatial/temporal boundaries of the decision.
In Step 5 of the DQO Process, you should imagine that perfect information will be
available for making decisions. Under the assumption that there is no uncertainty in the decision
making process, the planning team integrates the outputs from previous steps with inputs
developed in Step 5 into an unambiguous "If...then..." statement (theoretical decision rule). This
rule describes the conditions under which possible alternative actions would be chosen.
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You need to conduct the following activities in this step:
• specify the population parameter (e.g., mean, median, percentile, or total amount)
that the DQO planning team considers to be important to make decisions about the
target population;
• choose an Action Level (if not already established) that sets the boundary between
one outcome of the decision process and another outcome;
• select the measurement and analysis methods capable of performing over the
expected rate of values and verify that the Action Level is greater than the
detection limit of the measurement method that will be used; and
• construct the theoretical "If...then..." decision rule by combining the true value of
the selected population parameter and the Action Level (from above) with the
scale of decision making (from Step 4) and the alternative actions (from Step 2).
This decision rule will state the alternative actions that would be taken depending
on the true value of the parameter relative to the Action Level.
Note that the "If... then..." decision rule is a theoretical rule because it is stated in terms of
the true value of the population parameter, even though in reality the true value is never known.
In practice, the decision is made by using an operational decision rule that uses an estimate (based
on the actual data) of the true value of the population parameter. The reason for specifying the
theoretical rule is to focus the attention of the DQO planning team on how they would make
decisions if they had perfect knowledge of the population. This helps clarify what the team really
wants to know to support the decision. In Step 7 of the DQO Process, the planning team will
select the operational decision rule they believe will most efficiently meet the requirements
specified in the first six steps of the DQO process.
5.2 Activities
In this step you should:
• define the population parameter;
determine what action is needed; and
• confirm that the Action Level exceeds minimum detection limits.
What population parameter best characterizes the population of interest? In this step you
should select a population parameter (such as the true mean, median, percentile, or total amount)
that summarizes the critical characteristic or feature of the population that will be compared to the
Action Level to make a decision. In some cases, the parameter that must be used may be
specified in a regulation. In other cases, the DQO planning team will select the parameter based
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on specific needs and considerations. A comparison of the different population parameters and
their application to a decision rule is presented in Table 5-1.
Table 5-1. Population Parameters and Their Applicability to a Decision Rule
Parameter
Definition
Example of Use
Mean
Average
Central tendency: Comparison of middle part of population to
Action Level. Appropriate for chemical that could cause cancer
after a long-term chronic exposure. Use of the mean and the total
amount of media (e.g., mass of soil or water) allows a planning
team to estimate the total amount of a contaminant contained in
the soil or water body. The mean is greatly influenced by
extremes in the contaminant distribution, and not very useful if a
large proportion of values are below the detection limit.
Median
Middle observation of
distribution; 50th
percentile; half of data
is above and half is
below
Better estimate of central tendency for a population that is highly
skewed (nonsymmetrical). Also may be preferred if the population
contains many values that are less than the measurement detection
limit. The median is not a good choice if more than 50% of the
population is less than the detection limit because a true median
does not exist in this case. The median is not influenced by the
extremes of the contaminant distribution.
Percentile
Specifies percent of
sample that is below the
given value; e.g., the
80th percentile should be
chosen if you are
interested in the value
that is greater than 80%
of the population.
For cases where only a small portion of the population can be
allowed to exceed the Action Level. Sometimes selected if the
decision rule is being developed for a chemical that can cause
acute health effects. Also useful when a large part of the
population contains values less than the detection limit. Often
requires larger sample sizes than mean or median.
It must be noted, however, that the more complex the parameter chosen, the more
complex will be the decision rule and accompanying data collection design. The most common
parameter used in decision making is the population mean because the mean is frequently used to
model random exposure to environmental contamination. Aside from scientific or policy
considerations, the mathematical and statistical properties of the mean are well understood. You
should consult a statistician if you are uncertain as to the choice of an appropriate parameter.
What Action Level is needed for the decision? In addition to specifying the population
parameter, you will need to specify the Action Level that will be used to choose between courses
of action. For example, the decision maker may take one action if the true value of the parameter
exceeds a specified value (Action Level) and a different action otherwise. There are basically two
kinds of Action Levels - those predetermined and those determined during the DQO Process.
Examples of predetermined Action Levels are fixed standards such as drinking water
standards or technology-based standards. For example, in the area of childhood lead poisoning
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prevention, EPA's Office of Pollution Prevention and Toxics has proposed hazard levels for lead
in residential dust and soil to protect children from significant lead exposures (40 CFR 745).
Also, in the area of air quality control, EPA's Office of Air and Radiation has promulgated
National Ambient Air Quality Standards for priority pollutants such as carbon monoxide, lead,
nitrogen dioxide, ozone, particulate matter (PM10), and sulfur dioxide, as well as other pollutants
that include fine particulate matter (PM2 5) (40 CFR 50).
Examples of investigation-specific Action Levels are background standards or specific
risk-based standards. For the case of investigation-specific Action Levels, one consideration in
selecting the Action Level is its degree of conservatism, i.e., whether the level is a very low value
or a higher value. You will need to decide whether to set the Action Level at a threshold of real
concern, or at a lower (more conservative) value that, if exceeded to some degree, may not
necessarily pose a serious risk. A more conservative Action Level may require a more sensitive
analytical method that has appropriate detection limits.
Does the Action Level exceed measurement detection limits? You will need to determine the
detection limit for each potential measurement method identified in Step 3. If the detection limit
for a measurement method exceeds the Action Level, then a more sensitive method should be
specified or a different approach should be used.
Detection limits are defined specific to an intended purpose. The DQO planning team
should choose the definition that is most appropriate to the "If...then..." decision rule being used.
For example, if the decision rule is used to decide if a contaminant exists at the study site, then the
detection limit should be one that provides for a high probability of positive identification and
presence in the matrix and a low probability of false confirmation. However, if the decision rule is
used to compare a mean to a threshold action level, then the detection limit should be defined in
terms of the reliability of quantitation.
5.3 Outputs
After you have completed the above activities, you can construct the theoretical
"If...then..." decision rule by combining the selected population parameter and Action Level with
the scale of decision making (from Step 4) and the alternative actions (from Step 2). An example
of a theoretical decision rule is:
//"the true mean dioxin concentration in the surface 2 inches of soil
of a decision unit (20 ft by 100 ft) exceeds 1 ppb, then remove a 6
inch layer of soil, //"the true mean is not greater than 1 ppb, then
do nothing.
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5.4 Examples
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
What was the decision rule and Action Level? The planning team was interested in the
true mean concentration of cadmium in the TCLP leachate for each container. If the
true mean concentration of cadmium from the fly ash leachate in each container load
was greater than 1.0 mg/L, then the waste was considered hazardous and disposed of at
a RCRA landfill. If the true mean concentration of cadmium from the waste fly ash
leachate was less than 1.0 mg/L, then the waste was considered nonhazardous and
disposed of in a sanitary landfill.
Example 2. Making Decisions About Urban Air Quality Compliance
What was the decision rule and Action Level? The population parameter of interest
that characterizes PM25 air quality was the true long-run proportion of daily
concentrations falling below the 24-hr PM25 federal standard of 65 ^g/m3. If the true
proportion of daily concentrations less than or equal to 65 jug/m3 was greater than or
equal to 0.98, then the local region was considered in attainment for PM25, so
monitoring was continued, but no other action was taken. If the true proportion of daily
concentrations less than or equal to 65 ^g/m3 was less than 0.98, then the local region
was considered in nonattainment for PM25, so monitoring was continued and the PM25
control strategies outlined in the State Implementation Plan were implemented.
Looking ahead to other DQO steps:
Step 6 provides key information that will be used with the outputs of this
step to select the sampling and analysis methods.
• Step 6 addresses the questions of what risk of an incorrect decision can
be tolerated.
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CHAPTER 6
STEP 6. SPECIFY TOLERABLE LIMITS ON DECISION ERRORS
The DQO Process
1. State the Problem
2. Identify the Decision
3. Identify the Inputs to the Decision
4. Define the Boundaries of the Study
5. Develop a Decision Rule
6. Specify Tolerable Limits on Decision
Errors
7. Optimize the Design for Obtaining Data
6. Specify Tolerable Limits
on Decision Errors
• Determine the range of the parameter
of interest.
• Choose a null hypothesis.
• Examine consequences of making an
incorrect decision.
• Specify a range of values where
consequences are minor (gray region).
• Assign probability values to points
above and below the Action Level that
reflect tolerable probability for
potential decision errors.
After reading this chapter you should under stand why specifying
tolerable limits on decision errors is required to continue the DQO
Process and the meaning of the concepts and terms used in completing
this task. You should be able to specify tolerable limits on decision
errors for your problem.
6.1 Background
This step builds on the previous steps where you have:
• identified members of the planning team, including decision makers;
concisely described the problem;
• developed a conceptual model of the environmental problem to be investigated;
identified the decision that needs to be made;
• determined the type of information required, the Action Level, and probable
measurement methods;
• decided on the spatial/temporal boundaries of the decision; and
• decided on the theoretical "if... then" decision rule.
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In Step 6 of the DQO Process you no longer imagine that perfect information on unlimited
data will be available for making decisions as you did in Step 5. You now face the reality that you
will not have perfect information upon which to base your decisions. Instead you will be making
decisions based on a set of sample data subject to various errors which is only part of the much
larger population of interest. Inherent in the use of sampled data for making decisions is the fact
that those decisions can, and occasionally will, be wrong. In this step of the DQO Process,
numerical values will be considered in an attempt to keep the possibility of a decision error to a
minimum.
The purpose of Step 6 is to specify quantitative performance goals for choosing between
the two alternative actions decision rule. These goals are expressed as probabilities of making
errors in your decision at selected true values of the parameter of interest specified in Step 5.
These decision performance goal probabilities are a statement of the amount of uncertainty you
are willing to tolerate in your decisions at a few specific critical true values of the parameter of
interest.
6.2 Activities
You should conduct the following activities in Step 6:
• determine the sources of error in the sample data set;
establish a plausible range of values for the parameter of interest;
• define the two types of potential decision errors and the consequences of making
those errors;
• determine how to manage potential decision errors;
• select the baseline condition of the environment that will be assumed to be true in
the absence of overwhelming evidence to the contrary;
specify a range of possible parameter values where the consequences of a false
acceptance decision error are considered tolerable (gray region); and
assign probability values at several true value points above and below the Action
Level that reflect your tolerable probability for the occurrence of decision errors.
What are sources of error in the sample data set? A decision error occurs when the sample data
set misleads you into making the wrong decision and, therefore, taking the wrong response action.
The possibility of a decision error exists because your decision is based on sample data that are
incomplete and never perfect. Even though the data collection method and analysis method may
be unbiased, the sample data are subject to random and systematic errors at different stages of
acquisition, from field collection to sample analysis. The combination of all these errors is called
"total study error." There can be many contributors to total study error, but there are typically
two main components:
• Sampling design error - This error is influenced by the inherent variability of the
population over space and time, the sample collection design, and the number of
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samples. It is usually impractical to measure the entire decision unit, and limited
sampling may miss some features of the natural variation of the measurement of
interest. Sampling design error occurs when the data collection design does not
capture the complete variability within the decision unit to the extent appropriate
for the decision of interest. Sampling design error can lead to random error (i.e.,
variability or imprecision) and systematic error (bias) in estimates of population
parameters.
Measurement error - This error (variability) is influenced by imperfections in the
measurement and analysis system. Random and systematic measurement errors are
introduced in the measurement process during physical sample collection, sample
handling, sample preparation, sample analysis, data reduction, transmission, and
storage.
Total study error directly affects the
probability of making decision errors.
Therefore, it is essential for you to manage
total study error by your choice of sample
design and measurement system. This will
enable you to control the possibility of making
decision errors to acceptable levels. Figure 6-1
shows an example of how total study error
(also known as Total Variability) can be
broken down further into components that will
relate to the data collection process.
How do you establish a plausible range of
values for the parameter of interest? You
should establish a plausible range of values for
the parameter of interest by approximating its
upper and lower bounds based on currently
available information, professional judgment,
or historical data. This helps focus the process
of defining probability limits on decision errors
only on the relevant values of the parameter.
For example, if the parameter of interest is a
mean, the range might be defined using the
lowest and highest concentrations at which the
contaminant is thought to exist at the property.
This range of values is useful when discussing
the Decision Performance Goal Diagram (to be
discussed later).
1— Homogenization
Sampling Frame Selection
Sampling Unit Definition
- Selection Probabilities
Number of Samples
— Support Volume/mass
— Sample Delineation
1—Sample Extraction
Sample Handling
"Preservation
—Packaging
—Labeling
—Transport
—Storage
— Preparation
— Subsampling
— Extraction
— Analytical
Determination
— Data Reduction
Figure 6-1. An Example of How Total Study
Error Can Be Broken Down by Components
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How are decision errors defined? If perfect knowledge of the true value of the parameter of
interest in a decision unit were available to you, you could simply apply the theoretical decision
rule from Step 5 to the known true value, make your decision, and not be concerned with decision
errors. However, in real life you use sample data to make the decision and, consequently, the
chance of a decision error becomes reality.
Due to the uncertainty inherent in decisions based on sample data, it is possible to get
results that will not clearly tell you if the true value is below the Action Level or above the Action
Level. It becomes necessary to label one of these two possibilities as the baseline condition so
that a decision can still be made in these situations. The baseline condition then becomes the de
facto decision outcome when there is insufficient evidence to refute it and the other condition then
becomes the alternative decision. For example, in legal decisions on human behavior, the baseline
condition is "innocent until proven guilty."
In environmental decisions affecting human health and the environment, the baseline
condition is more flexible and may depend on your situation. In certain instances, the baseline
condition for your problem may be prescribed for you in regulations. For example the baseline
condition in RCRA facility monitoring is that the concentrations in ground water are less than or
equal to the background concentrations. If the baseline condition is not specified for you, you
must select it based on careful consideration of the consequences of making decision errors and
taking the wrong actions. This selection may be based on your conceptual model for the decision
unit, i.e., based on prior information, you have good cause to think that the true value for the
decision unit is above the Action Level.
The probabilities of making decision errors with sample data can be quantified through the
use of a statistical decision procedure known as hypothesis testing. When hypothesis testing is
applied to decision making, the sample data are used to choose between a baseline condition of
the environment and an alternative condition. The test can then be used to show either that there
is insufficient evidence to indicate that the baseline condition is false (and therefore you accept the
default that the baseline condition is presumed to be true), or that the baseline condition is
probably false (and therefore the alternative condition is probably true). The burden of proof is
placed on rejecting the baseline condition. This approach is taken because the test-of-hypothesis
structure maintains the baseline condition as being true until overwhelming evidence is presented
to indicate that the baseline condition is not true. It is critical to understand that selection of the
baseline condition is important to the outcome of the decision process. The exact same set of
sample data from a decision unit can lead to different decisions depending on which possibility
was chosen as the baseline condition.
A false rejection decision error1 occurs when the limited amount of sample data lead you
to decide that the baseline condition is probably false when it is really true. In the reverse case, a
In previous editions of Guidance for Data Quality Objectives Process (EPA QA/G-4) (U.S. EPA, 1994a),
false rejection was called "false positive" and false acceptance was called "false negative."
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false acceptance decision occurs when the sample data lead you to decide that the baseline
condition is probably true when it is really false. To understand these definitions you may find it
helpful to note that an acceptance decision is to decide the baseline condition is true and a
rejection decision is to decide the alternative condition is true. Hence, a false rejection decision
incorrectly decides that the alternative is true, and a false acceptance decision incorrectly decides
that the baseline is true (see Table 6-1). For example, suppose you strongly believe that the true
value of the parameter of interest exceeds the Action Level (i.e., the baseline condition states that
the true value of the parameter of interest exceeds the Action Level). If your baseline assumption
is actually correct and the sample data, by chance, contain an abnormally large proportion of low
values, you would conclude that the true value of the parameter of interest does not exceed the
Action Level. In reality, the true value did exceed the Action Level; therefore, you would then be
making a false rejection decision error.
Table 6-1. False Acceptance and False Rejection Decisions
Decision Based on Sample
Data
Decide baseline is true
Decide alternative is true
True Condition
Baseline is True
Correct Decision
Decision Error (False Rejection)
Alternative is True
Decision Error (False Acceptance)
Correct Decision
Another example would be a regulatory situation in which an effluent discharge should not
exceed the permitted level. Your baseline condition would be that the true parameter value of the
effluent is less than or equal to the permitted level; your alternative would be that the true
parameter exceeds the permitted level. If the baseline condition was actually correct, but your
sample data happened to have a preponderance of high values, you could conclude the effluent
exceeds the permitted level. This would be a false rejection decision error and is sometimes called
a false positive decision error. The reverse (a false acceptance decision error) is sometimes called
a false negative decision error.
In the statistical language of hypothesis testing, the baseline condition is called the null
hypothesis (H0) and the alternative condition is called the alternative hypothesis (Ha). A false
rejection decision error occurs when the decision maker rejects the null hypothesis when it is
really true; a false acceptance decision error occurs when the decision maker fails to reject the null
hypothesis when it is really false. Statisticians label a false rejection decision error as a Type I
error and the measure of the size of this error (probability) is labeled alpha (a), the hypothesis
test's level of significance. Statisticians label a false acceptance decision error as a Type II error;
the measure of the size of this error (probability) is labeled beta (P). Both alpha and beta are
expressed numerically as probabilities. The statistical power of a test of hypothesis is equal to
How can you manage potential decision errors? Although the possibilities of making decision
errors can never be eliminated totally, you can manage them. To manage the possibilities of
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decision errors, your planning team focuses mostly on the largest components of total study error.
If the sampling design error is believed to be relatively large, you can manage the chance of
making a decision error by collecting a larger number of samples or developing a better sampling
design, i.e., a better way of deciding where and when to sample. If the analytical component of
the measurement error is believed to be relatively large, you can manage it by analyzing multiple
individual samples, or by using more precise and accurate analytical methods. In some instances
your planning team will actually be able to address both components of total error.
In some cases, placing a stringent (i.e., very small) limit on the possibility of both types of
decision errors is unnecessary for making a defensible decision. If the consequences of one
decision error are relatively minor, it may be possible for you to make a defensible decision based
on relatively imprecise data or on a small amount of data. For example, in the early phases of a
hazardous site assessment, the consequences of deciding that an area of a site is hazardous, when
in reality it is not, may be relatively minor. In this case, you may make a decision during this stage
of the investigation by using a moderate amount of data, analyzed using a field screening
analytical method, and only using a limited number of confirmatory analyses.
Conversely, if the consequences of decision errors are severe (i.e., human health effects),
you will want to develop a data collection design that exercises more control over sampling
design and measurement error. For example, in a waste discharge investigation, deciding that a
discharge is not hazardous when it truly is hazardous may have serious consequences because the
discharge may pose a risk to human health and to the environment. Therefore, the decision made
during this phase of the investigation may need to be supported by a large amount of data and
analyzed using very precise and accurate analytical methods.
You will need to balance the consequences of decision errors against the cost of limiting
the possibility of these errors. It may be necessary to iterate between Step 6 and Step 7 several
times before this balance between limits on decision errors and costs of data collection design is
achieved. This is not an easy part of the DQO Process. The balancing of the risk of incorrect
decisions with potential consequences should be explored fully by your planning team. Resorting
to arbitrary values such as "false rejection = 0.05, false acceptance = 0.20" is not recommended.
The circumstances of the investigation may allow for a less stringent option, or possibly a more
stringent requirement. In the early stages of DQO development, it is recommended that a very
stringent choice be made and the consequences of that choice be investigated by your planning
team during their activities under Step 7 of the DQO Process.
Decision errors can also occur that are independent of the use of statistical hypothesis
testing. An example could be that the data were manipulated prior to use in decision making by
an outside agent censoring the reported values. This is sometimes found in the collection of
screening data where insufficient training on the importance of adherence to QA protocol and
practice has resulted in data being recorded in an erroneous fashion. If data has been manipulated
prior to use in decision making, the assumed false rejection and false acceptance error rates
become invalid.
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How can you represent the quality of a decision process? There is a graphical construct called a
Decision Performance Curve that represents the quality of a decision process. In statistical
hypothesis testing usage, an operating characteristic curve or a power curve serve similar
purposes. Figure 6-2 depicts an example Decision Performance Curve and shows the range of
possible true values of the parameter of interest (including the Action Level) decided in Step 6, on
the x-axis and the range of probabilities (0 to 1) of deciding that the parameter of interest exceeds
the Action Level along the y-axis. Intuitively, the probability of deciding the parameter of interest
exceeds the Action Level is small for low true values and increases as the true value increases. A
full Decision Performance Curve is actually a continuous curve from the lowest true value to the
highest true value. If you had perfect knowledge of the true value of the parameter of interest, a
Decision Performance Curve would have a probability of 0 for any true value less than the Action
Level and jump to a probability of 1 for any true value above the Action Level. Since you are
dealing with sampled data (containing error), the probabilities will more realistically increase
gradually from near 0 for true values far below the Action Level, to near 1 for true values far
above the Action Level. The shape and steepness of this curve is a consequence of the sample
design and number of samples taken.
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Figure 6-2. An Example of a Decision Performance Curve
The following subsections describe the process of selecting a baseline condition, defining a
gray region, and establishing Decision Performance Goals (DPGs) by stating tolerable decision
error probabilities at a few critical true values of the parameter of interest. The combined
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information from these activities can then be displayed graphically as a Decision Performance
Goal Diagram (DPGD) that approximates the Decision Performance Curve. This DPGD
stipulates your tolerable risks of decision errors and allows you to communicate them to others,
including your sample design team and all stakeholders. The Decision Performance Curve is then
the overlay on the diagram and can be used to assess performance.
How do you select the baseline condition? If your baseline is not established by regulatory
considerations, your planning team should define the baseline condition based on the relative
consequences of the decision errors.
The baseline condition is the one that will be kept until overwhelming evidence (in the
form of data to be collected) is presented to make you reject the baseline condition in favor of the
alternative. You should use your evaluation of the potential consequences of the decision errors
to establish which decision error has the more severe consequences near the Action Level. For
example, you would judge the threat to public health against spending unnecessary resources.
Define the baseline condition and the alternative condition and assign the terms "false
rejection" and "false acceptance" to the appropriate decision error. An alternative name for "false
rejection" is "false positive" or Type I Error (by statisticians principally). The alternative name for
"false acceptance" is "false negative" or Type II Error. A false rejection decision error
corresponds to the more severe decision error, and a false acceptance decision error corresponds
to the less severe decision error.
You should designate the areas above and below the Action Level as the range where the
two types of decision errors may occur. This activity has two steps:
1. Define both types of decision errors and establish the "true state of nature" for
each decision error. The "true state of nature" is the actual condition of the
parameter of interest in the decision unit which is unknown to the decision maker.
You should state both decision errors in terms of the parameter of interest, the
Action Level, and the alternative actions.
2. Specify and evaluate the potential consequences of each decision error. For
example, the consequences of incorrectly deciding that the parameter is below the
Action Level (when in fact it is above the Action Level) include potential threats to
human health and to the environment. Conversely, the consequences of incorrectly
deciding that the value of the parameter of interest is above the Action Level
(when in fact it does not exceed the Action Level) include spending unnecessary
resources for further study.
You should evaluate the potential consequences of decision errors at several points
within the false rejection and false acceptance ranges. For example, the
consequences of a decision error when the true parameter value is only 10% above
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EPA QA/G-4 6 - 8 August 2000
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the Action Level may be minimal because it may cause only a moderate increase in
the risk to human health. Conversely, the consequences of a decision error when
the true parameter is an order of magnitude above the Action Level may be severe
because it could significantly increase the risk to human health and threaten the
local ecosystem.
How do you specify a range of possible true parameter values where the consequences of a
false acceptance decision error are considered tolerable (gray region)? The gray region is one
component of the quantitative decision performance criteria that is specifically used to limit
impractical and nonfeasible number of samples. The gray region is a range of true parameter
values within the alternative condition near the Action Level where it is "too close to call." This
gray region is where sampled data may correctly reject the baseline condition, but the sampled
data frequently do not provide sufficient evidence to be overwhelming. In essence, the gray
region is an area where it is not considered feasible to control the false acceptance decision error
limits to lower levels because the high costs of sampling and analysis outweigh the potential
consequences of choosing the wrong course of action (see Figure 6-3 for example).
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Tolerable False
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Action Level
True Value of the Parameter (Mean Concentration, ppm)
200
Figure 6-3. An Example of a Decision Performance Goal Diagram
(Baseline Condition: Parameter Exceeds the Action Level)
The first boundary of the gray region is the Action Level itself. Your planning team
establishes the other boundary of the gray region by evaluating the consequences of a false
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acceptance decision error over the range of possible parameter values in which this error may
occur. This boundary corresponds to the parameter value at which the consequences of a false
acceptance decision error are significant enough to have to set a low limit on the probability of
this decision error occurring.
For example, suppose the baseline condition is that the true mean level of contaminant
does not exceed 1.0 mg/L and the result of a sample of five observations reveals a sample mean of
1.05 mg/L. Is this sufficient evidence to reject the baseline condition? If the natural variability of
the contaminant was low, then probably this would be enough evidence. If the natural variability
was quite high (i.e., a coefficient of variation of 50%), then the evidence would not be
overwhelming, as a result of this happening quite naturally. On the other hand, if the sample mean
had been 1.50 mg/L, even high variability could not hide the fact that the baseline condition had
been exceeded. The second boundary of the gray region is that value that you decide represents
overwhelming evidence to reject the baseline condition.
In general, the narrower the gray region, the greater the number of samples needed to
meet the criteria because the area of uncertainty has been reduced. The width of the gray region
may be wide during early phases of the study process, but narrowed at later stages to determine if
the parameter of interest is only slightly different than the Action Level.
In statistical hypothesis testing language, the width of the gray region is called the
"minimum detectable difference" and is often expressed as the Greek letter delta (A). This value
is an essential part of many calculations for determining the number of samples that need to be
collected so that you will have your stated confidence in decisions made based on the data
collected.
How do you assign probability values to points above and below the action level that reflect
the tolerable probability for the occurrence of decision errors? A decision error limit is the
probability that a decision error may occur for a specific value of the parameter of interest when
making the decision using sampled data. This probability is an expression of the decision maker's
tolerance for uncertainty but does not imply that a decision error will occur. Instead it is only a
measure of the risk a decision maker is willing to assume of making an incorrect decision.
At a minimum, you should specify a false rejection decision error limit at the Action Level
and a false acceptance decision error limit at the other end of the gray region based on the
consequences of the respective errors. Severe consequences (such as extreme risks to human
health) should have stringent limits (small probabilities), whereas moderate consequences may
have less stringent limits (large probabilities). In general, the tolerable limits for making a
decision error should decrease as the consequences of a decision error become more severe
farther away from the Action Level.
The most stringent limits on decision errors that are typically encountered for
environmental data are 0.01 (1%) for both the false rejection and false acceptance decision errors.
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EPA QA/G-4 6 - 10 August 2000
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This guidance recommends using 0.01 as the starting point for setting decision error rates. If the
consequences of a decision error are not severe enough to warrant this stringent decision error
limit, this value may be relaxed (a larger probability may be selected). However, if this limit is
relaxed from a value of 0.01 for either the decision error rate at the Action Level or the other
bound of the gray region, your planning team should document the rationale for relaxing the
decision error rate. This rationale may include regulatory guidelines; potential impacts on cost,
human health, and ecological conditions; and sociopolitical consequences.
The value of 0.01 should not be considered a prescriptive value for setting decision error
rates, nor should it be considered EPA policy to encourage the use of any particular decision error
rate. Some programs, for example Superfund, give guidance on alternative values for starting
points. In the Soil Screening Guidance: User's Guide (U.S. EPA, 1996), the starting value for
false rejection is 0.05, and for false acceptance, 0.20. The actual values finally selected by the
planning team will depend on the specific characteristics of the problem being investigated.
Figures 6-3 and 6-4 illustrate some key outputs of Step 6 of the DQO Process for an
example, but with opposite baseline conditions and different project specific-considerations. The
DPGD is a special schematic representation of a Decision Performance Curve. While the
Decision Performance Curve is a continuous curve, the schematic DPGD depicts only a few
critical points on that curve. These few points represent your tolerable error limits, or DPGs, at a
few critical values. Your sampling design team will use this as the criteria for any sampling plan
they design. As the explanation progresses, it may be helpful to keep in mind that the DPGD
represents a set of "what if?" conditions in the following sense. You are answering the question
at several selected true values of the parameter of interest:
If the true value of the parameter of interest were at this level, how
strong of an aversion would I have if the data misled me into
making the wrong decision and taking action?
Figure 6-3 shows the case where a decision maker considers the more severe decision
error to occur above the Action Level and has labeled that as baseline. Figure 6-4 shows the
reverse, the case where the decision maker considers the more severe decision error to occur
below the Action Level.
Consider Figure 6-3 where the baseline condition is that the parameter exceeds the Action
Level (in statistical terms, H0: the parameter equals or exceeds the Action Level and HA: the
parameter is less than the Action Level). The plausible range of values based on professional
judgment was from the Detection Limit (as the Detection Limit was 0.01, it is essentially zero for
purposes of the DPGD) to 200 ppm. The Action Level was 100 ppm (from the permit for this
investigation). A false rejection would be saying the parameter is less than the Action Level,
when, in fact, it is really greater. A false acceptance would be saying the parameter level is above
the Action Level, when, in reality, it is below the Action Level. The gray region is the area where
you consider it is tolerable to make a decision error as it is "too close to call." For example,
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EPA QA/G-4 6-11 August 2000
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suppose you decided the true parameter level was above the Action Level (100 ppm) when in
reality it was 99 ppm. Although an error has occurred (false acceptance), it is not particularly
severe because the difference of 1 ppm on human health and financial resources is minimal. On
the other hand, suppose you decided the true parameter level was above the Action Level (100
ppm) when in reality it was 80 ppm. Again, an error has occurred (false acceptance), but it is
severe because a difference of 20 ppm is quite considerable. In this particular case the planning
team chose 80 ppm as the edge of their gray region because it represented the case where errors
in decision making have a great impact on resources. The planning team then assigned risk
probabilities to the chance of making decision errors for various true values of the parameter.
They agreed that, if the true value was 80 ppm and they decided (from the data yet to be
collected) that the true value exceeded 100 ppm, they were only willing to accept a 10% risk of
this happening. The team then considered the implications of what adverse effect would occur if
the true value was 60 ppm, but they decided the parameter was greater than 100 ppm. The
analysis showed a huge expenditure of resources, so the planning team elected to take only a 5%
risk of this happening. They did a similar exercise with the tolerable false rejection error rates.
Now consider Figure 6-4 where the baseline condition is that the parameter is less than the
Action Level (in statistical terms, H0: the parameter is less than or equal to the Action Level and
HA: the parameter is greater than the Action Level). Notice how the DPGD looks very similar to
that of Figure 6-3, except that the gray region is on the other side of the Action Level, and false
rejection and false acceptance have now been switched. In statistical terms, this is because a false
rejection is defined as rejecting H0 when H0 is really true, and false acceptance to be accepting H0
when H0 is really false.
Figure 6-4 shows that at the Action Level the decision maker will tolerate a 10% chance
of deciding that the true value is below the Action Level when it is really above the Action Level.
If the true value is 140 ppm, the decision maker will tolerate only a 1% chance of deciding the
true value is below the Action Level when it is really above the Action Level. At the edge of the
gray region, 120 ppm, the decision maker is willing to tolerate a 10% risk of saying it is above the
Action Level when it is really below the Action Level. At 60 ppm, the decision maker is only
willing to tolerate a 5% risk of a decision error. These probabilities represent the risk to the
decision maker of making an incorrect decision for the selected true values.
6.3 Outputs
The outputs from this step are your baseline condition, your gray region, and your set of
tolerable decision error limits at selected true values of the parameter of interest. These selections
are based on a consideration of the consequences of making incorrect decisions. The baseline
condition, the gray region, and your tolerable limits on decision errors are summarized in a
Decision Performance Goal Diagram.
Final
EPA QA/G-4 6 - 12 August 2000
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Example 1
EPA QA/G-4
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6.4 Examples
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
How was the baseline condition set? The baseline condition [i.e., the null hypothesis
(HJ] was established as "the waste is hazardous. " The consequences of deciding that
the waste was not hazardous when it truly was hazardous were that the incinerator
company disposed of the waste in a sanitary landfill, possibly endangering human health
and the environment. In this situation, the incinerator company could be held liable for
future damages and environmental cleanup costs. Additionally, the consequences of this
decision error were to compromise the reputation of the incinerator company,
jeopardizing its future profitability. The planning team concluded that this decision
error (false rejection) had the more severe consequences near the Action Level since the
risk of jeopardizing human health outweighed the consequences of having to pay more
for disposal.
How was the gray region specified? The gray region was designated as that area
adjacent to the Action Level where the planning team considered that the consequences
of a false acceptance decision error were minimal. The planning team specified a width
of 0.25 mg/Lfor this gray region based on their preferences to guard against false
acceptance decision errors at a concentration ofO. 75 mg/L (the lower bound of the gray
region).
How were tolerable decision error limits set? RCRA regulations specify a 5% decision
error rate at the Action Level. Below the Action Level, the planning team set the
maximum tolerable probability of making a false acceptance error at 20% when the true
parameter was from 0.25 to 0.75 mg/L and 10% when it was below 0.25 mg/L. These
limits were based on both experience and an economic analysis that showed that these
decision error rates reasonably balanced the cost of sampling versus the consequence of
sending clean ash to the RCRA facility.
Example 2. Making Decisions About Urban Air Quality Compliance
How was the baseline condition set? In most applications of the DQO Process, when,
where, and how many samples to collect is not determined until Step 7. However, given
that the monitoring network and sampling frequency were already established, the DQO
Process in this case was conducted to establish the quality and quantity of data needed
for making attainment decisions and to determine if the present network design achieved
those quality and quantity specifications. As the planning team was most concerned
about protecting public health, the baseline condition in this case was that the 98th
percentile of daily concentrations was above 65 jug/m3 (i.e., less than 98% of daily
concentrations are below 65 ^g/m3). That is, the null hypothesis was set as the state of
nature the planning team found evidence against, and, to protect public health, carefully
guarded against the false rejection decision error of incorrectly rejecting the baseline
condition.
Final
EPA QA/G-4 6 - 14 August 2000
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How was the gray region specified? The gray region, in this case, was specified in
terms of proportions. The planning team decided that the gray region should be from
0.98 to 0.995.
How were tolerable decision error limits set? The planning team determined that the
tolerable false rejection decision error rate should be 10% or less. While lowering the
tolerable bound on such error was desirable, the planning team, based on observed
PM25 daily concentration variability in other parts of the country, believed that
significantly smaller false rejection error rates were unobtainable for all but the most
extensive and costly network designs. The team also wished to protect against
implementing unnecessary and costly control strategies (i.e., incorrectly failing to reject
the baseline condition), but was willing to tolerate a somewhat larger probability of
making this false acceptance decision error. The planning team decided that the false
acceptance decision error rate should be not larger than 30%. These are shown in Figure
6.6.
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Figure 6-6. Decision Performance Goal Diagram for Example 2
Looking ahead to other DQO steps:
The information developed in Step 6 is then translated into the
requirements for a sampling plan in Step 7.
EPA QA/G-4
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Final
EPA QA/G-4 6 - 16 August 2000
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CHAPTER 7
STEP 7. OPTIMIZE THE DESIGN FOR OBTAINING DATA
5.
the Problem
Identify the Decision
Identify the Inputs to the Decision
Define the Boundaries of the Study
Develop a Decision Rule
6. Specify Tolerable Limits on Decision
Errors
7. Optimize the Design for Obtaining
Data
7. Optimize the Design for
Obtaining Data
• Review the DQO outputs.
• Develop data collection design
alternatives.
• Formulate mathematical expressions
for each design.
• Select the sample size that satisfies the
DQOs.
• Decide on the most resource-effective
design, or agreed alternative.
• Document details in the QA Project
Plan
After reading this chapter you should have a broad understanding of
the steps that are needed to develop a sampling and analysis design to
generate data that meet the Data Quality Objectives and Decision
Performance Goals developed in Steps 1 through 6 of the DQO
Process.
7.1 Background
This step builds on the previous steps where you have:
identified members of the planning team, including decision makers;
• concisely described the problem;
developed a conceptual model of the environmental problem to be investigated;
• identified the decision that needs to be made;
determined the type of information required, the Action Level, and probable
measurement methods;
decided on the spatial/temporal boundaries of the decision and the scale of the
decision making;
decided on the theoretical "if...then" decision rule; and
• specified tolerable limits on decision errors.
EPA QA/G-4
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The purpose of Step 7 is to develop a resource-effective sampling and analysis design for
generating data that are expected to satisfy the DQOs and DPGs developed in Steps 1 through 6
of the DQO Process.
7.2 Activities
In this final step you should:
• review existing environmental data;
evaluate operational decision rules;
• develop general data collection design alternatives;
calculate the number of samples to be taken; and
• select the most resource-effective data collection design.
Why should you review existing environmental data? Review existing data in more detail if it
appears that they can be used to support the data collection design (e.g., analyze the variability in
existing data if they appear to provide good information about the variance for the new data). If
no existing data are available, it may be cost-effective to conduct a limited field investigation to
acquire preliminary estimates of variability for determining the number of samples. If existing
data are going to be combined with new data to support the decision, then determine if there are
any gaps that can be filled or deficiencies that might be mitigated by including appropriate features
in the new data collection design. The existing data should also be reviewed for indications of
analytical problems, such as detection limits, that may rule out using certain statistical techniques.
Prior knowledge of the probability distribution (characteristics) exhibited by the data may also
have an effect on the choice of statistical tests.
How do you evaluate operational decision rules? The theoretical decision rule you developed in
Step 5 was based on the assumption that you knew the true value of the parameter of interest
(e.g., the true mean or median). Since you will be using measurements made on samples to make
your decision, an operational decision rule will be needed to replace the theoretical decision rule.
This operational decision rule will most likely be in the form of a statistical hypothesis test which
may involve some form of a statistical interval such as a confidence interval or tolerance interval.
The design team should evaluate the possible operational decision rules and choose one that best
matches the intent of the theoretical decision rule with the statistical assumptions. Each
operational decision rule will have a different formula for determining the number of samples
needed to meet your DPGs.
Some common statistical hypothesis tests and their sample size formulas are described in
detail in Guidance for Data Quality Assessment: Practical Methods for Data Analysis (EPA
QA/G-9), (U.S. EPA, 1997a). Most tests applied to environmental data can be broadly classified
as one-sample (single-site) tests or two-sample (two-site) tests. In one-sample cases, data from a
site are compared with an absolute criterion such as a regulatory threshold or an Applicable or
Relevant and Appropriate Requirement. In the two-sample cases, data from a site are compared
Final
EPA QA/G-4 7- 2 August 2000
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with data from another site or background (reference) area or from another time period at the
same site. In this case, the parameter of interest is usually the difference between the two means,
two medians, two proportions, or two percentiles, and the Action Level is often zero (i.e., no
difference).
How do you develop data collection design alternatives? A full explanation of the procedures
for developing a data collection design is beyond the scope of this guidance document. This
document provides a broad overview of the steps that need to be accomplished to reach a final
sampling plan. This section provides a general description of the activities necessary to generate
sampling design options and select the one that optimally satisfies the DPGs defined in Step 6. In
addition, it contains information about how outputs from the previous six steps of the DQO
Process are used in developing the most resource-effective sampling and analysis design.
The design team should develop alternative data collection and analysis designs based on
the DQO outputs and other relevant information, such as historical patterns of contaminant
deposition, estimates of variance, and technical characteristics of the contaminants and media.
The most important element of this step is to reduce the total variability through judicious choice
of a spatial and temporal sampling design and analytical measurement technique (see also Figure
6-1). If the total variability can be reduced to a value less than that specified in Step 6, the result
will be either a reduction in decision error rates (given a fixed number of samples) or reduction in
the number of samples (and, hence, resource expenditure) for a given set of decision error rates.
In general, the more complex the sampling design, the lower the total variability of the sample will
be.
Generally, the goal is to find cost-effective design alternatives that balance the number of
samples and the measurement performance, given the feasible choices for spatial and temporal
sample designs and measurement methods. In cases where there is relatively high spatial or
temporal variability, it may be more cost-effective to use less expensive and less precise analytical
methods so that a relatively large number of samples over space and time can be taken, thereby
controlling the sampling design error component of total study error. In other cases, where the
contaminant distribution over space and time is relatively homogeneous, or the Action Level is
very near the method detection limit, it may be more cost-effective to use more expensive more
precise and/or more sensitive analytical methods and collect fewer samples, thereby reducing the
analytical measurement error component of total study error. These alternatives should, at a
minimum, include the sample selection technique, the sample type, the number of samples, and the
number of analyses per sample. To generate alternative designs, the planning team may vary the
number and spatial/temporal locations of samples, the type of samples collected, the field
sampling or analytical methods used, or the number of replicate analyses performed on samples.
It should be remembered that the objective of the design is to estimate the parameter (mean,
median, percentile) with as much precision as possible such that the DPGs can be achieved.
How do you calculate the number of samples that satisfy the DPGs for each design
alternative and determine the cost for each design? You should use the formulas identified in
Final
EPA QA/G-4 7- 3 August 2000
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the previous activity to calculate the number of samples needed to meet the DPGs for each data
collection design alternative. You should then determine the associated cost for each design
alternative.
To assist the design team in their development of alternative designs and evaluation of
costs for a few select sampling designs and operational decision rules, EPA has developed the
software, Data Quality Objectives Decision Error Feasibility Trials (DEFT) Software (EPA
QA/G-4D), (U.S. EPA, 1994b). DEFT is a personal computer software package developed to
assist your planning team in evaluating whether the DQOs are feasible (i.e., can be achieved
within resource constraints) before the development of the final data collection design is started.
DEFT uses the outputs generated in Steps 1 through 6 of the DQO Process to evaluate several
basic data collection designs and determines the associated cost. DEFT presents the results in the
form of a Decision Performance Goal Diagram that overlays the desired Decision Performance
Curve of the sampling design.
If the DQOs are not feasible or not achievable within resource constraints, the DEFT
software allows you to relax some of the DQOs and DPGs until a feasible alternative is achieved.
The software allows the user to change the action level, the baseline condition, the width of the
gray region, the decision error rates, the estimate of the standard deviation, and the sample
collection and analysis costs. For each change, the software computes a new sample size and
total cost and shows the corresponding Decision Performance Curve in the Decision Performance
Goal Diagram.
How do you select the most resource-effective data collection design that satisfies all of the
DQOs? You should evaluate the design options based on cost and ability to meet the DQO
constraints and DPGs. The design that provides the best balance between cost (or expected
cost) and ability to meet the DQOs, given the non-technical, economic, and health factors
imposed on the project, is the most resource-effective (or the optimum design).
The statistical concept of a power function is extremely useful in investigating the
performance of alternative designs. The power function is the probability of rejecting the null
hypothesis (H0) when the null hypothesis is false (i.e., the alternative condition is true). If there
was no error associated with a decision, the ideal power function would be 0 if H0 were true, and
1 if H0 were false. Since decisions are based on imperfect data, however, it is impossible to
achieve this ideal power function. Instead, the power function will most likely yield values that
are small when H0 is true and large when H0 is false. A performance curve is based on the graph
of the power function.2 The performance curve can be overlaid into the Decision Performance
Goal Diagram to assess how well a test performs or to compare competing test. A design that
produces a very steep performance curve is preferred over one that is relatively flat.
In this guidance, the performance curve is based on either the power curve or the complement of the
power curve. This ensures that the performance curve always rises from left to right.
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One simple method to improve the power of the statistical design is the use of
stratification to reduce the total variability in the data. Stratification is done by dividing the target
population into strata that are relatively homogeneous. The planning team may have made an
initial attempt at this in Step 4, Define the Boundaries of the Study. The strata may be physically
based (areas proximal to an incinerator, septic tanks, receptor wells, underground storage tanks)
or based on other factors (potential exposure, activity patterns, residences, ecological habitats,
agricultural sectors, historical or future use).
The advantages of stratification are:
• reducing the complexity of the problem by dividing it into manageable segments;
reducing the variability in subsets; and
• improving the efficiency of sampling.
Disadvantages of stratification include:
• difficulty in determining the basis for selecting strata (prior estimates of variability,
estimates of strata area may be needed);
• overstratifying may require more samples so increasing costs; and
stratifying areas that are not approximately homogeneous may result in developing
a design for collecting data that is inefficient or does not accurately reflect the
characteristics of the population.
If none of the data collection designs satisfies all of the DQOs and DPGs within the
resource constraints of the project, the planning team will need to review the outputs from the
entire DQO Process and alter one or more of the steps. Examples of adjustments that could be
made are:
• increasing the tolerable limits on decision errors;
increasing the width of the gray region;
• increasing the funding for sampling and analysis;
changing the boundaries (it may be possible to reduce sampling and analysis costs
by changing or eliminating subgroups that will require separate decisions); and
relaxing other project constraints.
For other sampling designs and/or operational decision rules, it will be necessary for the design
team to evaluate the design alternatives by other methods (perhaps computer simulation) and
possibly involve a statistical expert on sampling design and analysis.
Application of the DQO Process to remediation problems and integration with
geostratistical approaches to the analysis of soil contamination scenarios may be found in Myers
(1997). Once the final data collection design has been selected, it is important to ensure the
design and operational decision rule are properly documented. This improves efficiency and
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effectiveness of later stages of the data collection and analysis process, such as the development
of field sampling procedures, QC procedures, and statistical procedures for data analysis. The
key to successful design documentation is in drawing the link between the statistical assumptions
on which the design and operational decision rule are based and the practical activities that ensure
these assumptions generally hold true.
For EPA programs, the operational requirements for implementing the data collection
design are documented in the Field Sampling Plan, Sampling and Analysis Plan, QA Project Plan
or other required document. Design elements that should be documented include:
• number of samples,
sample type (e.g., composite vs. grab samples),
• general collection techniques (e.g., split spoon vs. core drill, or activated charcoal
media vs. evacuated canister),
• physical sample (i.e., the amount of material to be collected for each sample),
sample support (i.e., the area, volume, or quantity that each individual sample
represents),
sample locations (surface coordinates and depth) and how locations were selected,
• timing issues for sample collection, handling, and analysis,
analytical methods (or performance-based measurement standards), and
• statistical sampling scheme.
Note that proper documentation of the model, operational decision rule, and associated
assumptions used for collecting and statistically analyzing data is essential to maintain the overall
validity of the study in the face of unavoidable deviations from the original design. Additionally,
the documentation will serve as a valuable resource for Data Quality Assessment (DQA) activities
after the data have actually been collected and the subsequent decision making process has been
completed.
7.3 Outputs
The outputs from this step are the full documentation of the final sampling design and
discussion of the key assumptions supporting the sampling design.
7.4 Examples
The examples presented here represent the initial final output of the DQO Process.
Example 1. Making Decisions About Incinerator Fly Ash for RCRA Waste Disposal
What was the selected sampling design ? The planning team 's statistician performed an
initial cost/benefit analysis that indicated a composite sample design was the best
sampling option to use to determine whether a container of ash should be sent to a
RCRA landfill or to a municipal landfill. Eight composite samples, each consisting of
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eight grab samples, were taken from each container; and two subsamples from each
composite were sent to the laboratory for analysis. To form the composite samples, the
containers were divided into eight equal size areas and grab samples were taken
randomly within each area and composited. Each grab sample was a core that was
extracted, then mixed together to form the composite sample. From this composite
sample, two subsamples were sent to the laboratory for analysis.
What were the key assumptions supporting the selected design? The cost of this design
was based on the cost of collecting ($10) and analyzing ($150) a sample. Eight grab
samples were collected for each composite sample, for a sampling cost of $80; two
subsamples were analyzed from each composite sample for a cost of $300. Therefore,
each composite sample cost $380. The total cost of collecting and analyzing the eight
composite samples in one container was eight times the cost of one composite, for a total
of $3,040. The assumption that composite measurements were normally distributed was
made. This assumption was evaluated after the measurements were obtained. If the
assumption was not viable, then the planning team would recommend that additional
grab samples per composite be taken, or that a revised compositing process be used to
achieve normally distributed data. Based on the pilot study, the incineration company
determined that each load of waste fly ash was fairly homogeneous and estimated the
standard deviation in the concentration of cadmium among grab samples within loads of
ash to be 0.6 mg/L. It was assumed that the variability among sub-samples within a
composite sample was negligible. Data from the subsamples was used to test this
assumption and to collect additional subsamples, if necessary.
Example 2. Making Decisions about Urban Air Quality Compliance
What was the selected sampling design? Information from Step 6 indicated that
sampling everyday, regardless of the false rejection decision error rate tolerated, was
probably an inefficient use of resources and was unnecessary. This conclusion was
reached because sampling daily resulted in false acceptance decision error rates that
were far below those required in Step 6. In contrast, l-in-6-day or l-in-3-day sampling
could not satisfy the false acceptance decision error rate of 30% when the rather
restrictive constraint of a 1% false rejection decision error rate was used. The current
sampling scheme (1 in 3 days) performed at a satisfactory level as long as the false
rejection decision error rate allowed was in the range of 5% to 10%. If the planning
team decided that up to 10% false rejection decision error truly could be tolerated, then
information in Table 7-1 indicated it was possible to reduce sampling frequency from the
current rate to l-in-6-day sampling, thereby reducing costs while maintaining an
acceptable false acceptance decision error rate around 23%.
What were the key assumptions supporting the selected design? The monitoring
network was already in place, so the goal at this stage was to determine the performance
of the existing design, and to change the design, if needed, to achieve better
performance. Information in Table 7-1 showed the design performance (false
acceptance decision error rate) as a function of different false rejection error rate
allowances and alternative sampling frequencies (sample sizes) over a 3-year period of
data collection. In general, data in Table 7-1 indicated that the false acceptance
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decision error rate decreased when a higher false rejection decision error rate was
tolerated. Similarly, false acceptance decision error rates decreased when sampling
intensity was increased from one-in-six-days sampling to every-day sampling.
Table 7-1. False Acceptance Decision Error Rates and Alternative Sampling Frequencies
Tolerable
False Rejection
Decision Error
Rates
1%
5%
10%
Sampling Frequency At Each Of Three Monitors
1 in 6 Days
>50%
>50%
23%
1 in 3 Days
(Current)
>50%
28%
11%
Every Day
1%
<1%
<1%
EPA QA/G-4
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CHAPTER 8
BEYOND THE DATA QUALITY OBJECTIVES PROCESS
After reading this chapter you should understand the kinds of
information that will be necessary to develop a QA Project Plan and the
role of Data Quality Assessment.
A project's life cycle consists of three principal phases: planning, implementation, and
assessment (described in Chapter 0 as the project tier of EPA's Quality System). Quality
assurance activities that are associated with each of these phases are illustrated in Figure 0-1.
Systematic planning (e.g., the DQO Process) and developing the sampling design comprises the
planning phase; the actual data collection process is the implementation phase; and an evaluation
(Data Quality Assessment) that the collected data met the performance criteria specified in the
DQOs is the final phase of a project.
8.1 Planning
During the planning stage, investigators specify the intended use of the data to be
collected and plan the management and technical activities (such as sampling) that are needed to
generate the data. Systematic planning and the DQO Process are the foundation for the planning
stage and lead to a sampling design, the generation of appropriate data quality indicators, and
standard operating procedures, which are all finally documented in the Agency's mandatory QA
Project Plan or similar document.
Environmental data for EPA programs may not be collected without having an approved
QA Project Plan in place (EPA Order 5360.1 A2). The mandatory QA Project Plan (EPA, 1998)
documents four main groups - project management, data generation and acquisition,
assessment/oversight, and data validation and usability (shown in Table 8-1).
Group A - Project Management
These elements address project management, project history and objectives, and roles and
responsibilities of the participants. These elements help ensure that project goals are
clearly stated, that all participants understand the project goals and approach, and that the
planning process is documented.
Group B - Data Generation and Acquisition
These elements cover all aspects of the project design and implementation (including the
key parameters to be estimated, the number and type of samples expected, and a
description of where, when, and how samples will be collected). They ensure that
appropriate methods for sampling, analysis, data handling, and QC activities are employed
and documented.
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Table 8-1. Elements of a Quality Assurance Project Plan
QA Project Plan Elements
A. Project Management
Al Title and Approval Sheet
A2 Table of Contents
A3 Distribution List
A4 Project/Task Organization
A5 Problem Definition/Background
A6 Project/Task Description
A7 Quality Objectives and Criteria
A8 Special Training /Certification
A9 Documents and Records
B. Data Generation and Acquisition
B1 Sampling Process Design
(Experimental Design)
B2 Sampling Methods
B3 Sample Handling and Custody
B4 Analytical Methods
B5 Quality Control
B6 Instrument/Equipment Testing, Inspection,
and Maintenance
B7 Instrument/Equipment Calibration and
Frequency
B8 Inspection/Acceptance of
Supplies and Consumables
B9 Nondirect Measurements
B1 OData Management
C. Assessment and Oversight
Cl Assessments and Response Actions C2 Reports to Management
D. Data Validation and Usability
Dl Data Review, Verification, and Validation D2 Verification and Validation Methods
D3 Reconciliation with User Requirements
Group C - Assessment and Oversight
These elements address activities for assessing the effectiveness of project implementation
and associated QA and QC requirements; they help to ensure that the QA Project Plan is
implemented as prescribed.
Group D - Data Validation and Usability
These elements address QA activities that occur after data collection or generation is
complete; they help to ensure that data meet the specified criteria.
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Additional information on the preparation of QA Project Plans is provided in EPA's guidance
document, Guidance on Quality Assurance Project Plans (EPA QA/G-5) (U.S. EPA, 1998).
8.2 Implementation
During the implementation phase of the project, data are collected and samples are
analyzed according to the specifications of the QA Project Plan or the Sampling and Analysis Plan
depending on specific program requirements. These provide detailed specific objectives, QA and
QC specifications, and procedures for conducting a successful field investigation that is intended
to produce data of the quality needed to satisfy the performance criteria. QA and QC activities
(e.g., technical systems audits and performance evaluations) are conducted to ensure that data
collection activities are conducted correctly and in accordance with the QA Project Plan.
8.3 Assessment
During the final phase (assessment) of a project, data are verified and validated in
accordance with the QA Project Plan, and DQA is performed to determine if the performance
criteria have been satisfied.
DQA is built on a fundamental premise: data quality, as a concept, is meaningful only
when it relates to the intended use of the data. Data quality does not exist without some frame of
reference; you really should know the context in which the data will be used in order to establish a
yardstick for judging whether or not the data set is adequate. DQA is the scientific and statistical
process that determines whether environmental data are of the right type, quality, and quantity to
support a specific decision. DQA consists of five steps that parallel the activities of a statistician
analyzing a data set; and include the use of statistical and graphical tools that nonstatisticians can
apply to data sets (see Figure 8-1).
DQA involves the application of statistical tools to determine whether the data are of
appropriate quality to support the decision with acceptable confidence. To conclude the
assessment phase, it is necessary to document all the relevant information collected over all phases
of the project's life cycle. The conclusion from a DQA must be presented in a fashion that
facilitates the comprehension of the important points. Care should be taken to explain statistical
nomenclature and avoid the use of statistical jargon whenever possible. For more information on
Data Quality Assessment, see EPA's guidance document, Guidance for Data Quality
Assessment: Practical Methods for Data Analysis (EPA QA/G-9), (U.S. EPA, 1997a) and the
associated software Data Quality Assessment Statistical Toolbox (DataQUEST) (EPA QA/G-
9D), (U.S. EPA, 1997b).
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EPA QA/G-4 8 - 3 August 2000
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1. Review the DQOs and Sampling Designs
Review DQO outputs; if DQOs have not been developed, define the statistical
hypothesis and specify tolerable limits on decision errors; and review the
sampling design and the data collection documentation for consistency.
1
r
2. Conduct Preliminary Data Review
Generate statistical quantities and graphical representations that describe the
data and use this information to learn about the structure of the data and to
identify any patterns or relationships.
1
r
3. Select the Statistical Test
Select the most appropriate procedure for summarizing and analyzing the
data based on the preliminary data review and identify the underlying
assumptions of the test.
1
I
4. Verify the Assumptions of the Statistical Test
Examine the underlying assumption of the statistical test in light of the
environmental data actually collected.
1
r
5. Draw Conclusions from the Data
Perform the calculations of the statistical hypothesis tests and document the
inferences drawn as a result of these calculations; and evaluate the
performance of the sampling design if the design is to be used again.
Figure 8-1. Data Quality Assessment Process
EPA QA/G-4
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APPENDIX A
DERIVATION OF SAMPLE SIZE FORMULA FOR TESTING MEAN
OF NORMAL DISTRIBUTION VERSUS AN ACTION LEVEL
This appendix presents a mathematical derivation of the sample size formula used in the DQO
Example 1.
Let Xb X2,...,Xn denote a random sample from a normal distribution with unknown mean (i and
known standard deviation o. The decision maker wishes to test the null hypothesis H0: (i = AL versus the
alternative HA: ^ > AL, where AL, the action level, is some prescribed constant; the false positive (Type I)
error rate is a (i.e., probability of rejecting H0 when (i = AL is a); and for some fixed constant U > AL
(where U is the other bound of the gray region), the false negative (Type II) error rate is (3 (i.e., probability
of rejecting H0 when (i = U is 1-p). Let X denote the sample mean of the Xs. It will have a normal
distribution with mean (i and variance o2/n. Hence the random variable Z, defined by
Z = v Hyv , (A - 1)
a
will have a standard normal distribution (mean 0, variance 1). Let zp denote the p* percentile of the
standard normal distribution (available in most statistics books). Recall that the symmetry of the standard
normal distribution implies that zp = -z^.
Case 1: Standard Deviation Known
The test of H0 versus HA is performed by calculating the test statistic.
T = (X-AL) Z]_o, the null hypothesis is rejected.
Note that
T = - - = Z+e(M) (A_3)
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where
e(n) = . (A . 4)
Thus T has a normal distribution with mean e((i) and variance 1, and, in particular, e(AL) = 0. Hence the
Type I error rate is
Projecting HQH0} = Pr[T>Zl_^=AL\ = Pr[Z^(AL)>Zl_^ = i (A - 5)
Achieving the desired power 1-p when ^ = U requires that
Pr[reject HQ\y.= U] = 1 - P.
Therefore,
Pr[T40), the approximation is good. The particular
noncentral t distribution involved in the calculation depends on the sample size n. Thus, determining the
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exact minimum n that will satisfy the Type I and Type II error rate conditions requires an iterative
approach in which the noncentral t probabilities are calculated for various n values until the desired
properties are achieved. With the aid of a computer routine for calculating such probabilities, this is not
difficult; however, a simple and direct approach for approximating n is available. This approach, whose
derivation is described in the paragraphs below, leads to the following approximate but very accurate
formula for n:
(A-8)
In practice, since o is unknown, a prior estimate of it must be used in Equation A - 8.
The approach is based on the assumption that, for a given constant k, the statistic X - kS
is approximately normal with mean (i-ko and variance (o2/n)(l+k2/2) (Guenther, 1977 and 1981).
The classical t-test rejects H0 when, T = \(X - AL}IS\fn)\>D where the critical value D is
chosen to achieve the desired Type I error rate a. The inequality can be rearranged as
X - kS >AL, where k = D\fn. Subtracting the mean (assuming H0) and dividing by the standard
deviation of X - kS on both sides of the inequality leads to
X-kS-(AL-ko) ^ AL-(AL-ko) kJn
- - = (A - 9)
By the distributional assumption on X - kS , the left side of Equation A - 9 is approximately
standard normal when \a = AL, and the condition that the Type I error rate is a becomes
Pr[Z>kJn/}/\ + k2/2] = a, (A -10)
i.e., ZI_K = k^l + k2/2. (A -11)
One can show that Equation A - 1 1 is equivalent to
P/2] = \-zl_J2n. (A- 12)
The condition that the Type II error rate is (3 (or that power is 1-p) when (i = U means that the event of
incorrectly accepting H0 given X - kS < AL should have probability p. Subtracting the mean
(U - ko) and dividing by the standard deviation of X - kS on both sides of this inequality yields
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EPA QA/G-4 A - 3 August 2000
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X-kS-(U-ka) AL-(U-ka)
(A-13)
Again, the left side is approximately standard normal and the Type II error rate condition becomes
Pr \Z < [AL - (U - £o)]/[(oV«)A/l
which implies
(AL-U)+ko
(A-14)
Subtracting Equation A - 14 from Equation A - 11 yields
. _ _ (U-AL)_
P/2
(A-15)
or
(U-AL)
Substituting Equation A - 12 into the denominator on the right side of Equation A - 16 yields
(A-16)
(U-AL)
= Jn< \-z_J2n.
(A-17)
Squaring both sides of Equation A - 17 and solving for n yields Equation A - 8.
References
Guenther, William C. 1977. Sampling Inspection in Statistical Quality Control. Griffin's Statistical
Monographs and Courses, No. 37, London: Charles Griffin.
Guenther, William C. 1981. Sample size formulas for normal theory T test. The American
Statistician 35, 4.
EPA QA/G-4
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APPENDIX B
BIBLIOGRAPHY
ANSI/ASQC E4-1994, Specifications and Guidelines for Environmental Data Collection and
Environmental Technology Program. 1994. American Society for Quality, Milwaukee,
WI.
40 CFR 50, Code of Federal Regulations, "National Ambient Air Quality Standards for
Paniculate Matter; Availability of Supplemental Information and Request for Comments,
Final Rule." Washington, DC.
40 CFR 261, Code of Federal Regulations, Appendix II, "Identification and Listing of Hazardous
Waste; Method 1311 Toxicity Characteristic Leaching Procedure (TCLP)." Washington,
DC.
40 CFR 745, Code of Federal Regulations, "Lead; Identification of Dangerous Levels of Lead;
Proposed Rule." Washington, DC.
ASTM El 613-99, "Standard Test Method for Determination of Lead by Inductively Coupled
Plasma Atomic Emission Spectrometry (ICP-AES), Flame Atomic Absorption
Spectrometry (FAAS), or Graphite Furnace Atomic Absorption Spectrometry (GFAAS)
Techniques," American Society for Testing and Materials, 1999.
ASTM E1644-98, "Standard Practice for Hot Plate Digestion of Dust Wipe Samples for the
Determination of Lead," American Society for Testing and Materials, 1998.
ASTM El728-99, "Standard Practice for Field Collection of Settled Dust Samples Using Wipe
Sampling Methods for Lead Determination by Atomic Spectrometry Techniques,"
American Society for Testing and Materials, 1999.
Gilbert, R.O. Statistical Methods for Environmental Pollution Monitoring. 1987. John Wiley,
New York.
Grumley, Thomas. 1994. Institutionalizing the Data Quality Objectives Process for EM's
Environmental Data Collection Activities. Department of Energy, Washington, DC.
Harbinger Communications. 1996. The Harbinger File: A Directory of Citizen Groups,
Government Agencies, and Environmental Education Programs Concerned with
California Environmental Issues. Santa Cruz, CA.
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EPA QA/G-4 B -1 August 2000
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HUD (Department of Housing and Urban Development). 1995. Guidelines for the Evaluation
and Control of Lead-based Paint Hazards in Housing. Office of Lead-based Paint
Abatement and Poisoning Prevention, Washington, DC.
Myers, J.C. Geostatistical Error Management: Quantifying Uncertainty for Environmental
Sampling and Mapping. 1997. Van Nostrand Reinhold. New York.
Thompson, S.K. Sampling. 1992. John Wiley. New York.
U.S. EPA (Environmental Protection Agency). 1994a. Guidance for the Data Quality
Objectives Process (EPA QA/G-4). EPA/600/R-96/055. Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994b. Data Quality Objectives Decision Error
Feasibility Trials (DEFT) Software (EPA QA/G-4D). EPA/600/R-96/056. Washington,
DC.
U.S. EPA (Environmental Protection Agency). 1996. Soil Screening Guidance: User's Guide.
EPA/540/R-96/0180. Washington, DC.
U.S. EPA (Environmental Protection Agency). 1997a. Guidance for Data Quality Assessment:
Practical Methods for Data Analysis (EPA QA/G-9). EPA/600/R-96/084. Washington,
DC.
U.S. EPA (Environmental Protection Agency). 1997b. Data Quality Assessment Statistical
Toolbox (DataQUEST) (EPA QA/G-9D). EPA/600/R-96/085. Washington, DC.
U.S. EPA (Environmental Protection Agency). 1998. Guidance on Quality Assurance Project
Plans (EPA QA/G-5). EPA/600/R-98/018. Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999. Data Quality Objectives for Hazardous
Waste Site Investigations (EPA QA/G-4HW). Washington, DC.
U.S. EPA (Environmental Protection Agency). 2000a. EPA Order 5360.1 A2: Policy and
Program Requirements for the Mandatory Agency-wide Quality System.. Washington,
DC.
U.S. EPA (Environmental Protection Agency). 2000b. EPA Order 5360: The EPA Quality
Manual for Environmental Programs. Washington, DC.
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APPENDIX C
Data Quality Objectives; Household Dust Lead Hazard Assessment
This example concerns the use of the median in planning for environmental decision
making. The example is presented in a continuous format to show the seven-step DQO Process in
its entirety.
0. Background
The adverse health effects resulting from exposure to lead hazards (paint, dust, and soil)
have received increasing attention because chronic exposure to low levels of lead can cause
impairment of the central nervous system, mental retardation, and behavioral disorders. Young
children (below the age of six) are at a particularly high risk for these adverse effects. Concern
about the exposure to lead hazards in residential housing has led federal agencies, including the
EPA and Department of Housing and Urban Development, to develop programs to evaluate, and
ultimately control, lead hazards in housing.
A critical pathway for exposure to lead by a child is through the ingestion of household
dust because dust collects on hands, toys, and food and is easily transferred by hand-to-mouth
activities. As a result of the concern about the dust-to-mouth pathway, an important component
of risk assessment is dust sampling. Dust sampling offers a way of characterizing dust lead levels
at a property and determining if intervention is warranted. One of the preferred methods for
sampling residential dust is using baby wipes to wipe a specified surface area. A single area may
be sampled using an individual wipe; or multiple areas of a room may be sampled with individual
wipes, and the individual wipes combined, or composited, then submitted to the laboratory as a
single sample (40 CFR 745). The distribution of dust lead levels is such that normality cannot be
assumed and a 50th percentile (the median) is the appropriate risk assessment level. This example
demonstrates use of the median (i.e., 50th percentile) as the primary population parameter of
concern
1. State the Problem
How were the planning team members selected? The planning team included the
property owners, a certified risk assessor (to collect and handle dust samples and serve as a liaison
with the laboratory), a statistician, and a quality assurance specialist. The decision makers were
the property owners.
How was the problem described and a conceptual model of the potential hazard
developed? The problem was described as evaluating potential hazards associated with lead in
dust in a single-family residence because other residences in the neighborhood had shown levels of
lead in dust that might pose potential hazards.
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The conceptual model described a single-family residence in a neighborhood where
hazardous levels of lead had been detected in other residences. Interior sources of lead in dust
were identified as lead-based paint on doors, walls, and trim, which deteriorated to form, or attach
to, dust particles. Exterior sources included lead in exterior painted surfaces that had deteriorated
and leached into the dripline soil, or lead deposited from gasoline combustion fumes that
accumulated in soil. In these cases, soil could be tracked into the house, and collected as dust on
floors, window sills, toys, etc. Because this dust could be easily ingested through hand-to-mouth
activities, dust was considered to be a significant exposure route. Levels of lead in floor dust
were to be used as an indicator of the potential hazard.
What were the available resources and relevant deadlines? The property owners were
willing to commit up to $1,000 for the study. To minimize inconvenience to the family, all
sampling would be conducted during one calendar day.
2. Identify the Decision
What was the Decision Statement? The decision statement was determining if there
were significant levels of lead in floor dust at the residence.
What were the alternative actions? If there were significant levels of lead in floor dust
at the residence, the team planned follow-up testing to determine whether immediately dangerous
contamination exists and the location of the contamination in the property. If not, the team
decided there was not a potential lead hazard, and testing was discontinued.
3. Identify the Inputs to the Decision
Identify the kind of information. The assessment of a dust lead hazard was evaluated
by measuring dust lead loadings by individual dust wipe sampling.
Identify the source of information. The EPA proposed standard stated that if dust lead
levels were above 50 |ig/ft2 on bare floors, a lead health hazard was possible and follow-up testing
and/or intervention should be undertaken (40 CFR 745).
What sampling and analytical methods were appropriate? Wipe samples were
collected according to ASTM standard practice E1728. These samples were digested in
accordance with ASTM standard practice El644 and the sample extracts were chemically
analyzed by ASTM standard test method E1613. The results of these analyses provided
information on lead loading (i.e., jig of lead per square foot of wipe area) for each dust sample.
The detection limit was well below the Action Level.
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EPA QA/G-4 C- 2 August 2000
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4. Define the Boundaries of the Study
What population was sampled? Dust contained in 1 ft2 area of floors of the residence
was sampled and sent to a laboratory for analysis.
What were the spatial boundaries? The spatial boundaries of the study area were
defined as all floor areas within the dwelling that were reasonably accessible to young children
who lived at, or visited, the property.
What was an appropriate time frame for sampling? The test results were considered
to appropriately characterize the current and future hazards. It was possible that lead contained in
soil could be tracked into the residence and collect on surfaces, but no significant airborne sources
of lead deposition were known in the region. The dust was not expected to be transported away
from the property; therefore, provided the exterior paint was maintained in intact condition, lead
concentrations measured in the dust were not expected to change significantly over time.
What were the practical constraints for collecting data? Permission from the residents
was required before risk assessors could enter the residence to collect dust wipe samples.
Sampling was completed within 1 calendar day to minimize the inconvenience to the residents.
What was the scale of the decision making? The decision unit was the interior floor
surface (approximately 1,700 ft2) of the residence at the time of sampling and in the near future.
5. Develop a Decision Rule
What was the decision rule and Action Level? From 40 CFR 745, the median was
selected as the appropriate parameter to characterize the population under study. The median
dust lead loading was defined to be that level, measured in |ig/ft2, above and below which 50% of
all possible dust lead loadings at the property were expected to fall. If the true median dust
loading in the residence was greater than 50 |ig/ft2, then the planning team required followup
testing. Otherwise, they decided that a dust lead hazard was not present and discontinued testing.
6. Specify Tolerable Limits on Decision Errors
How was the baseline condition set? The baseline condition adopted by the property
owners was that the true median dust lead loading was above the EPA hazard level of 50 |ig/ft2
due to the seriousness of a potential hazard. The planning team decided that the most serious
decision error would be to decide that the true median dust lead loading was below the EPA
hazard level of 50 |ig/ft2, when in truth the median dust lead loading was above the hazard level.
This incorrect decision would result in significant exposure to dust lead and potential adverse
health effects.
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EPA QA/G-4 C- 3 August 2000
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How was the gray region specified? The edge of the gray region was designated by
considering that a false acceptance decision error would result in the unnecessary expenditure of
scarce resources for follow-up testing and/or intervention associated with a presumed hazard that
did not exist. The planning team decided that this decision error should be adequately controlled
for true dust lead loadings of 40 |ig/ft2 and below.
How were tolerable decision error limits set? Since human exposure to lead dust
hazards causes serious health effects, the planning team decided to limit the false rejection error
rate to 5%. This meant that if this dwelling's true median dust lead loading was greater than 50
|ig/ft2, the baseline condition would be correctly rejected 19 out of 20 times. The false
acceptance decision, which would result in unnecessary use of testing and intervention resources,
was allowed to occur more frequently (i.e., 20% of the time when the true dust-lead loading is 40
|ig/ft2 or less). These are shown in Figure C-l.
H £
0 o
e l
Q -c
»i
.tS £ 0.20
1 w
.o o.io H
Tolerable False
Acceptance Decision
Error I
Tolerable False
Rejection Decision
Error Rates
Gray Region
Relatively Large
Decision Error Rates are
Considered Tolerable
Action Level
True Value of the Parameter (Median Dust-lead loading, ug/ft )
Figure C-l. Decision Performance Goal Diagram for Dust Lead Loading
7. Optimize the Design for Obtaining Data
What was the selected sampling design? The planning team determined that the cost of
sending a certified risk assessor to the property for collecting and handling dust wipe samples was
about $400. Also, an NLLAP-recognized laboratory was selected to analyze the collected wipe
samples at a cost of $10 per sample. Thus, a maximum of 60 samples could be obtained within
the study's cost constraint of $1,000. From Step 6 the initial gray region lower bound for the
study was set at 40 |ig/ft2, but, the team found that this requirement could not be met given the
specified decision errors (i.e., false rejection rate of 5% and false acceptance rate of 20%),
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assumed standard deviation (of the natural logarithms), range, and cost constraints of the study
(i.e., a maximum of 60 samples). The planning team decided they were unwilling to relax the
decision error rate requirements and elected to expand the width of the gray region from the
original 40 to 50 |ig/ft2 to the less restrictive range of 35 to 50 |ig/ft2. Further, the planning team
decided that a standard deviation (of the natural logarithms) value of o=1.0 was probably more
realistic than the more conservative estimate of o=1.5.
The planning team used the upper variability bound to develop Table C-l which presented
statistical sample size requirements across various assumed dust lead loading standard deviations
(of the natural logarithms) and various lower bounds of the gray region. This table indicated that
sample size requirements increased rather dramatically as variability increased and/or as the gray
region was made more narrow.
Therefore, based on Table C-l, the planning team decided that a total of 50 samples
should be collected by a certified risk assessor (all within 1 calendar day) using simple random
sampling throughout the residence. Samples were sent to the selected NLLAP-recognized
laboratory for analysis. The total study cost was approximately $900 to the property owners.
What were the key assumptions supporting the selected design? The dust lead
loading data was assumed to be log-normally distributed. The geometric mean was computed
using the data because the true median and true geometric mean are the same when log-normality
is assumed. The true variability in dust lead loadings was not known, but past data was used to
estimate a reasonable upper bound on variability.
Table C-l. Number of Samples Required for Determining
If the True Median Dust Lead Loading is Above the Standard
Gray Region
(jig/ft2)
20-50
25-50
30-50
35-50
40-50
45-50
Standard Deviation of Natural Logarithms
o=0.5
6
8
14
26
64
280
0=1.0
9
15
26
50
126
559
o=1.5
13
21
37
75
188
837
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APPENDIX D
GLOSSARY OF TERMS
acceptance criteria - specific limits placed on characteristics of an item, process, or service
defined in requirements documents.
action level - the numerical value that causes a decision maker to choose one of the alternative
actions (e.g., compliance or noncompliance). It may be a regulatory threshold standard, such as a
maximum contaminant level for drinking water; a risk-based concentration level; a technology
limitation; or a reference-based standard. Note that the action level defined here is specified
during the planning phase of a data collection activity; it is not calculated from the sampling data.
alternative condition - a tentative assumption to be proven either rue or false. When hypothesis
testing is applied to site assessment decisions, the data are used to choose between a presumed
baseline condition of the environment and an alternative condition. The alternative condition is
accepted only when there is overwhelming proof that the baseline condition is false. This is often
called the alternative hypothesis in statistical tests.
alternative hypothesis - see alternative condition.
baseline condition - a tentative assumption to be proven either true or false. When hypothesis
testing is applied to site assessment decision, the data are used to choose between a presumed
baseline condition of the environment and an alternative condition. The baseline condition is
retained until overwhelming evidence indicates that the baseline condition is false. This is often
called the null hypothesis in statistical tests.
bias - the systematic or persistent distortion of a measurement process that causes errors in one
direction (i.e., the expected sample measurement is different from the sample's true value.
boundaries - the spatial and temporal conditions and practical constraints under which
environmental data are collected. Boundaries specify the area of volume (spatial boundary) and
the time period (temporal boundary) to which a decision will apply.
confidence interval - the numerical interval constructed around a point estimate of a population
parameter, combined with a probability statement (the confidence coefficient) linking to the
population's true parameter value. If the same confidence interval construction technique and
assumptions are used to calculate future intervals, they will include the unknown population
parameter with the same specified probability.
data collection design - see sampling design
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data quality assessment (DQA) - a statistical and scientific evaluation of the data set to
determine the validity and performance of the data collection design and statistical test, and to
determine the adequacy of the data set for its intended use.
data quality objectives (DQOs) - qualitative and quantitative statements derived from the DQO
Process that clarify study objectives, define the appropriate type of data, and specify tolerable
levels of potential decision errors that will be used as the basis for establishing the quality and
quantity of data needed to support decisions.
data quality objectives process - a systematic planning tool to facilitate the planning of
environmental data collection activities. Data quality objectives are the qualitative and
quantitative outputs from the DQO Process.
decision error - the error that occurs when the dat mislead the site manager into choosing the
wrong response action, in the sense that a different response action wold have been chosen if the
site manager had access to unlimited "perfect data" or absolute truth. In statistical test, decision
errors are labeled as false rejection or false acceptance depending on the concerns of the decision
maker and the baseline condition chosen.
decision performance curve - a graphical representation of the quality of a decision process. In
statistical terms it is know as a power curve (or a reverse power curve depending on the
hypotheses being tested).
decision performance goal diagram (DPGD) - a graphical representation of the tolerable risks
of decision errors. It is used in conjunction with the decision performance curve.
defensible - the ability to withstand any reasonable challenge related to the veracity or integrity of
project and laboratory documents and derived data.
detection limit (DL) - a measure of the capability of an analytical method of distinguish samples
that do not contain a specific analyte from sample that contain low concentrations of the analyte;
the lower concentration or among of the target analyte that can be determine to be different from
zero by a single measurement at a stated level of probability. DLs are analyte- and matrix-specific
and may be laboratory dependent.
distribution - (1) the appointment of an environmental contaminant at a point over time, over an
area, or within a volume; (2) a probability function (density function, mass function, or
distribution function) used to describe a set of observations (statistical sample) or a population
from which the observations are generated.
environmental conditions - the description of a physical medium (e.g., air, water, soil, sediment)
or a biological system expressed in terms of its physical, chemical, radiological, or biological
characteristics.
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environmental data -any measurements or information that describe environmental processes,
location, or conditions; ecological or health effects and consequences; or the performance of
environmental technology. For EPA, environmental data include information collected directly
from measurements, produced from models, and compiled from other sources such as data bases
or the literature.
environmental processes - manufactured or natural processes that produce discharges to, or that
impact, the ambient environment.
environmental programs - work or activities involving the environment, including but not
limited to: characterization of environmental processes and conditions; environmental monitoring;
environmental research and development; and the design, construction, and operation of
environmental technologies; and laboratory operations on environmental samples.
environmental technology - an all-inclusive term used to describe pollution control devices and
systems, waste treatment processes and storage facilities, and site remediation technologies and
their components that may be utilized to remove pollutants or contaminants from, or to prevent
them from entering, the environment. Examples include wet scrubbers (air), soil washing (soils),
granulated activated carbon unit (water), and filtration (air, water). Usually, this term applies to
hardware-based systems; however, it can also apply to methods or techniques used for pollution
prevention, pollutant reduction, or containment of contamination to prevent further movement of
the contaminants, such as capping, solidification or vitrification, and biological treatment.
estimate - a characteristic from the sample from which inferences on parameters can be made.
false acceptance decision error - the error that occurs when a decision maker accepts the
baseline condition when it is actually false. Statisticians usually refer to the limit on the possibility
of a false acceptance decision error as beta (P) and it is related to the power of the statistical test
used in decision making. An alternative name is false negative decision error.
false negative decision error - see false acceptance decision error.
false positive decision error - see false rejection decision error.
false rejection decision error - the error that occurs when a decision maker rejects the baseline
condition (null hypothesis) when it actually is true. Statisticians usually refer to the limit on the
possibility of a false rejection decision error as alpha (a), the level of significance, or the size of
the critical region, and it is expressed numerically as a probability. An alternative name is false
positive decision error.
field variability - see sampling design error.
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gray region - the range of possible parameter values near the action level where the cost of
determining that the alterative condition is true outweighs the expected consequences of a
decision error. It is an area where it will not be feasible to control the false acceptance decision
error limits to low levels because the high costs of sampling and analysis outweigh the potential
consequences of choosing the wrong course of action. It is sometimes referred to as the region
where it is "too close to call."
limits on decision errors - the acceptable decision error rates established by a decision maker.
Economic, health, ecological, political, and social consequences should be considered when
setting limits on decision errors.
mean - a measure of central tendency. A population mean is the expected value ("average"
value) from a population. A sample mean is the sum of all the values of a set of measurements
divided by the number of values in the set.
measurement error - the difference between the true or actual state and that which is reported
from measurements. Also known as measurement variability.
median - a measure of central tendency, it is also the 50th percentile. The sample median is the
middle value for an ordered set of n values; represented by the central value when n is odd or by
the average of the two most central values when n is even.
medium - a substance (e.g., air, water, soil) that serves as a carrier of the analytes of interest.
natural variability - the variability that is inherent or natural to the media, objects, or people
being studied.
null hypothesis - see baseline condition.
parameter - a description measure of a characteristic of a population. For example, the mean of
a population (ji).
percentile - a value on a scale of 100 that indicates the percentage of a distribution that is equal
to or below it. For example, if 100 ppm is the 25th percentile of a sample, then 25 percent of the
dat are less than or equal to 10 ppm and 75 percent of the dat are greater than 10 ppm.
planning team - the group of people who perform the DQO Process. Members include the
decision maker (senior manager), site manager, representatives of other data users, senior
program and technical staff, someone with statistical expertise, and a quality assurance and quality
control advisor (such as a QA Manager).
population - the total collection of objects or people to be studied and from which a sample is to
be drawn.
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precision - a measure of mutual agreement among individual measurements of the same property,
usually under prescribed similar conditions expressed generally in terms of the standard deviation.
quality assurance (QA) - an integrated system of management activities involving planning,
implementation, documentation, assessment, reporting, and quality improvement to ensure that a
process, item, or service is of the type and quality needed and expected by the customer.
QA Project Plan - a document describing in comprehensive detail the necessary quality
assurance, quality control, and other technical activities that should be implemented to ensure that
the results of the work performed will satisfy the stated performance criteria.
quality control (QC) - the overall system of technical activities that measure the attributes and
performance of a process, item, or service against defined standards to verify that they meet the
stated requirements established by the customer; operational techniques and activities that are
used to fulfill requirements for quality.
quality system - a structured and documented system describing the policies, objectives,
principles, organizational authority, responsibilities, accountability, and implementation plan of an
organization or ensuring quality in its work processes, products (items), and services. The quality
system provides the framework for planning, implementing, documenting, and assessing work
performed by the organization and for carrying out required quality assurance and quality control.
range - the numerical difference between the minimum and maximum of a set of values.
sample - (a) a single item or specimen from a larger whole or group, such as any single sample of
any medium (e.g., air, water, soil); or (b) a group of samples from a statistical population whose
properties are studies to gain information about the whole. The definition is decided by context of
usage.
sampling - the process of obtaining a subset of measurements from a population.
sampling design - a design that specifies the final configuration of the environmental monitoring
effort to satisfy the DQOs. It includes what types of samples or monitoring information should be
collected; where, when, and under what conditions they should be collected; what variables are to
be measured; and what quality assurance and quality control components will ensure acceptable
sampling error and measurement error to meet the decision error rates specified in the DQOs.
The sampling design is the principal part of the QA Project Plan.
sampling design error - the error due to observing only a limited number of the total possible
values that make up the population being studied. Sampling errors are distinct from those due to
imperfect selection; bias in response; and mistakes in observation, measurement, or recording.
Also known as field variability.
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stakeholder - a person or organization having an interest in the development of the project.
standard deviation - a measure of the dispersion or imprecision of a sample or population
distribution expressed as the positive square root of the variance and has the same unit of
measurement as the mean.
statistic - a function of the sample measurements (e.g., the sample mean or sample variance).
standard operating procedure - a written document that details the method for an operation,
analysis, or action with thoroughly prescribed techniques and steps and that is officially approved
as the method for performing certain routine or repetitive tasks.
total study error - the sum of all the errors incurred during the process of sample design through
data reporting. This is usually conceived asa sum of individual variances at different stages of
sample collection and analysis. Also known as total variability.
total variability - see total study error.
type I error - the statistical term for false rejection decision error.
type II error - the statistical term for false acceptance decision error.
variability - refers to observed difference attributable to heterogeneity or diversity in a
population. Sources of variability are the results of natural random processes and stem from
environmental differences among the elements of the population. Variability is not usually
reducible by further measurement but can be better estimated by increased sampling.
variance - a measure of a the dispersion of a set of values. Small variance indicating a compact
set of values; larger variance indicates a set of values that is far more spread out and variable.
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