United States Solid Waste and EPA530-D-02-002
Environmental Protection Emergency Response August 2002
Agency (5305W) www.epa.gov/osw
Office of Solid Waste
vvEPA RCRA Waste Sampling
Draft Technical Guidance
Planning, Implementation,
and Assessment
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EPA530-D-02-002
August 2002
RCRA Waste Sampling
Draft Technical Guidance
Planning, Implementation,
and Assessment
Office of Solid Waste
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
The United States Environmental Protection Agency's Office of Solid Waste (EPA or the
Agency) has prepared this draft document to provide guidance to project planners, field
personnel, data users, and other interested parties regarding sampling for the evaluation of
solid waste under the Resource Conservation and Recovery Act (RCRA).
EPA does not make any warranty or representation, expressed or implied, with respect to the
accuracy, completeness, or usefulness of the information contained in this report. EPA does
not assume any liability with respect to the use of, or for damages resulting from the use of, any
information, apparatus, method, or process disclosed in this report. Reference to trade names
or specific commercial products, commodities, or services in this report does not represent or
constitute an endorsement, recommendation, or favoring by EPA of the specific commercial
product, commodity, or service. In addition, the policies set out in this document are not final
Agency action, but are intended solely as guidance. They are not intended, nor can they be
relied upon, to create any rights enforceable by any party in litigation with the United States.
EPA officials may decide to follow the guidance provided in this document, or to act at variance
with the guidance, based on an analysis of specific site or facility circumstances. The Agency
also reserves the right to change this guidance at any time without public notice.
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ACKNOWLEDGMENTS
Development of this document was funded, wholly or in part, by the United States
Environmental Protection Agency (U.S. EPA) under Contract No. 68-W6-0068 and 68-W-OO-
122. It has been reviewed by EPA and approved for publication. It was developed under the
direction of Mr. Oliver M. Fordham, Office of Solid Waste (OSW) and Kim Kirkland (OSW) in
collaboration with Dr. Brian A. Schumacher, Office of Research and Development (ORD). This
document was prepared by Mr. Robert B. Stewart, Science Applications International
Corporation (SAIC). Additional writers included Dr. Kirk Cameron (MacStat Consulting, Ltd.),
Dr. Larry P. Jackson (Environmental Quality Management), Dr. John Maney (Environmental
Measurements Assessment Co.), Ms. Jennifer Bramlett (SAIC), and Mr. Oliver M. Fordham
(U.S. EPA).
EPA gratefully acknowledges the contributions of the technical reviewers involved in this effort,
including the following:
U.S. EPA Program Offices
Deana Crumbling, TIO
Evan Englund, ORD
George Flatman, ORD
Joan Fisk, OERR
David Friedman, ORD
Chris Gaines, OW
Gail Hansen, OSW
Barnes Johnson, OSW
Dan Granz, Region I
Bill Cosgrove, Region IV
Mike Neill, Region IV
Judy Sophianopoulos, Region IV
Brian Freeman, Region V
Gene Keepper, Region VI
Gregory Lyssy, Region VI
Bill Gallagher, Region VI
Deanna Lacy, Region VI
Maria Martinez, Region VI
Joe Lowry, NEIC
John Nocerino, ORD
Brian A. Schumacher, ORD
Jim Thompson, OECA
Jeff Van Ee, ORD
Brad Venner, NEIC
John Warren, OEI
U.S. EPA Regions
Walt Helmick, Region VI
Charles Ritchey, Region VI
Terry Sykes, Region VI
Stephanie Doolan, Region VII
Dedriel Newsome, Region VII
Tina Diebold, Region VIII
Mike Gansecki, Region VIII
Roberta Hedeen, Region X
Mary Queitzsch, Region X
ASTM Subcommittee D-34
Brian M. Anderson, SCA Services
Eric Chai, Shell
Alan B. Crockett, IN EL
Jim Frampton, CA DTSC
Susan Gagner, LLNL
Alan Hewitt, CRREL
Larry Jackson, EQM
John Maney, EMA
Other Organizations
Jeffrey Farrar, U.S. Bureau of Reclamation
Jeff Myers, Westinghouse SMS
Rock Vitale, Environmental Standards
Ann Strahl, Texas NRCC
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CONTENTS
1 INTRODUCTION 1
1.1 What Will I Find in This Guidance Document? 1
1.2 Who Can Use This Guidance Document? 1
1.3 Does This Guidance Document Replace Other Guidance? 2
1.4 How Is This Document Organized? 3
2 SUMMARY OF RCRA REGULATORY DRIVERS FOR WASTE SAMPLING AND
ANALYSIS 6
2.1 Background 6
2.2 Sampling For Regulatory Compliance 8
2.2.1 Making a Hazardous Waste Determination 8
2.2.2 Land Disposal Restrictions (LDR) Program 9
2.2.3 Other RCRA Regulations and Programs That May Require Sampling
and Testing 10
2.2.4 Enforcement Sampling and Analysis 10
3 FUNDAMENTAL STATISTICAL CONCEPTS 13
3.1 Populations, Samples, and Distributions 14
3.1.1 Populations and Decision Units 14
3.1.2 Samples and Measurements 15
3.1.3 Distributions 17
3.2 Measures of Central Tendency, Variability, and Relative Standing 18
3.2.1 Measures of Central Tendency 18
3.2.2 Measures of Variability 19
3.2.3 Measures of Relative Standing 21
3.3 Precision and Bias 21
3.4 Using Sample Analysis Results to Classify a Waste or to Determine Its Status
Under RCRA 24
3.4.1 Using an Average To Determine Whether a Waste or Media Meets the
Applicable Standard 24
3.4.2 Using a Proportion or Percentile To Determine Whether a Waste or
Media Meets an Applicable Standard 26
3.4.2.1 Using a Confidence Limit on a Percentile to Classify a Waste
or Media 27
3.4.2.2 Using a Simple Exceedance Rule Method To Classify
a Waste 27
3.4.3 Comparing Two Populations 28
3.4.4 Estimating Spatial Patterns 29
4 PLANNING YOUR PROJECT USING THE DQO PROCESS 30
4.1 Step 1: State the Problem 32
4.1.1 Identify Members of the Planning Team 32
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4.1.2 Identify the Primary Decision Maker 32
4.1.3 Develop a Concise Description of the Problem 32
4.2 Step 2: Identify the Decision 33
4.2.1 Identify the Principal Study Question 33
4.2.2 Define the Alternative Actions That Could Result from Resolution of the
Principal Study Question 34
4.2.3 Develop a Decision Statement 34
4.2.4 Organize Multiple Decisions 34
4.3 Step 3: Identify Inputs to the Decision 34
4.3.1 Identify the Information Required 34
4.3.2 Determine the Sources of Information 35
4.3.3 Identify Information Needed To Establish the Action Level 35
4.3.4 Confirm That Sampling and Analytical Methods Exist That Can Provide
the Required Environmental Measurements 36
4.4 Step 4: Define the Study Boundaries 36
4.4.1 Define the Target Population of Interest 36
4.4.2 Define the Spatial Boundaries 37
4.4.3 Define the Temporal Boundary of the Problem 37
4.4.4 Identify Any Practical Constraints on Data Collection 38
4.4.5 Define the Scale of Decision Making 38
4.5 Step 5: Develop a Decision Rule 39
4.5.1 Specify the Parameter of Interest 39
4.5.2 Specify the Action Level for the Study 40
4.5.3 Develop a Decision Rule 41
4.6 Step 6: Specify Limits on Decision Errors 41
4.6.1 Determine the Possible Range on the Parameter of Interest 43
4.6.2 Identify the Decision Errors and Choose the Null Hypothesis 43
4.6.3 Specify a Range of Possible Parameter Values Where the
Consequences of a False Acceptance Decision Error are Relatively
Minor (Gray Region) 45
4.6.4 Specify an Acceptable Probability of Making a Decision Error 47
4.7 Outputs of the First Six Steps of the DQO Process 48
5 OPTIMIZING THE DESIGN FOR OBTAINING THE DATA 50
5.1 Review the Outputs of the First Six Steps of the DQO Process 50
5.2 Consider Data Collection Design Options 51
5.2.1 Simple Random Sampling 57
5.2.2 Stratified Random Sampling 57
5.2.3 Systematic Sampling 59
5.2.4 Ranked Set Sampling 60
5.2.5 Sequential Sampling 61
5.2.6 Authoritative Sampling 62
5.2.6.1 Judgmental Sampling 63
5.2.6.2 Biased Sampling 64
5.3 Composite Sampling 64
5.3.1 Advantages and Limitations of Composite Sampling 65
5.3.2 Basic Approach To Composite Sampling 66
5.3.3 Composite Sampling Designs 67
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5.3.3.1 Simple Random Composite Sampling 67
5.3.3.2 Systematic Composite Sampling 68
5.3.4 Practical Considerations for Composite Sampling 69
5.3.5 Using Composite Sampling To Obtain a More Precise Estimate of the
Mean 69
5.3.6 Using Composite Sampling To Locate Extreme Values
or "Hot Spots" 71
5.4 Determining the Appropriate Number of Samples Needed To Estimate the
Mean 73
5.4.1 Number of Samples to Estimate the Mean: Simple Random Sampling 75
5.4.2 Number of Samples to Estimate the Mean: Stratified Random
Sampling 77
5.4.2.1 Optimal Allocation 78
5.4.2.2 Proportional Allocation 78
5.4.3 Number of Samples to Estimate the Mean: Systematic Sampling .... 80
5.4.4 Number of Samples to Estimate the Mean: Composite Sampling .... 80
5.5 Determining the Appropriate Number of Samples to Estimate A Percentile or
Proportion 81
5.5.1 Number of Samples To Test a Proportion: Simple Random or
Systematic Sampling 81
5.5.2 Number of Samples When Using a Simple Exceedance Rule 83
5.6 Selecting the Most Resource-Effective Design 84
5.7 Preparing a QAPP or WAP 84
5.7.1 Project Management 85
5.7.2 Measurement/Data Acquisition 86
5.7.3 Assessment/Oversight 86
5.7.4 Data Validation and Usability 86
5.7.5 Data Assessment 87
6 CONTROLLING VARIABILITY AND BIAS IN SAMPLING 88
6.1 Sources of Random Variability and Bias in Sampling 88
6.2 Overview of Sampling Theory 90
6.2.1 Heterogeneity 90
6.2.2 Types of Sampling Error 91
6.2.2.1 Fundamental Error 92
6.2.2.2 Grouping and Segregation Error 93
6.2.2.3 Increment Delimitation Error 94
6.2.2.4 Increment Extraction Error 94
6.2.2.5 Preparation Error 94
6.2.3 The Concept of "Sample Support" 94
6.3 Practical Guidance for Reducing Sampling Error 95
6.3.1 Determining the Optimal Mass of a Sample 96
6.3.2 Obtaining the Correct Shape and Orientation of a Sample 98
6.3.2.1 Sampling of a Moving Stream of Material 98
6.3.2.2 Sampling of a Stationary Batch of Material 99
6.3.3 Selecting Sampling Devices That Minimize Sampling Errors 99
6.3.3.1 General Performance Goals for Sampling Tools and
Devices 99
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6.3.3.2 Use and Limitations of Common Devices 100
6.3.4 Special Considerations for Sampling Waste and Soils for Volatile
Organic Compounds 101
7 IMPLEMENTATION: SELECTING EQUIPMENT AND CONDUCTING
SAMPLING 102
7.1 Selecting Sampling Tools and Devices 102
7.1.1 Step 1: Identify the Waste Type or Medium to be Sampled 104
7.1.2 Step 2: Identify the Site or Point of Sample Collection 104
7.1.2.1 Drums and Sacks or Bags 104
7.1.2.2 Surface Impoundments 105
7.1.2.3 Tanks 105
7.1.2.4 Pipes, Point Source Discharges, or Sampling Ports 106
7.1.2.5 Storage Bins, Roll-Off Boxes, or Collection Hoppers .... 106
7.1.2.6 Waste Piles 106
7.1.2.7 Conveyors 106
7.1.2.8 Structures and Debris 107
7.1.2.9 Surface or Subsurface Soil 107
7.1.3 Step 3: Consider Device-Specific Factors 107
7.1.3.1 Sample Type 108
7.13.2 Sample Volume 108
7.1.3.3 Other Device-Specific Considerations 108
7.1.4 Step 4: Select the Sampling Device 108
7.2 Conducting Field Sampling Activities 122
7.2.1 Selecting Sample Containers 122
7.2.2 Sample Preservation and Holding Times 123
7.2.3 Documentation of Field Activities 124
7.2.4 Field Quality Control Samples 124
7.2.5 Sample Identification and Chain-of-Custody Procedures 125
7.2.6 Decontamination of Equipment and Personnel 128
7.2.7 Health and Safety Considerations 130
7.2.8 Sample Packaging and Shipping 131
7.2.8.1 Sample Packaging 131
7.2.8.2 Sample Shipping 133
7.3 Using Sample Homogenization, Splitting, and Subsampling Techniques . . . 134
7.3.1 Homogenization Techniques 134
7.3.2 Sample Splitting 135
7.3.3 Subsampling 135
7.3.3.1 Subsampling Liquids 136
7.3.3.2 Subsampling Mixtures of Liquids and Solids 136
7.3.3.3 Subsampling Soils and Solid Media 136
8 ASSESSMENT: ANALYZING AND INTERPRETING DATA 139
8.1 Data Verification and Validation 139
8.1.1 Sampling Assessment 139
8.1.1.1 Sampling Design 140
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8.112 Sampling Methods 141
8.1.1.3 Sample Handling and Custody Procedures 141
8.11.4 Documentation 141
8.115 Control Samples 142
8.1.2 Analytical Assessment 142
8.12.1 Analytical Data Verification 143
8.12.2 Analytical Data Validation (Evaluation) 144
8.2 Data Quality Assessment 145
8.2.1 Review the DQOs and the Sampling Design 145
8.2.2 Prepare Data for Statistical Analysis 145
8.2.3 Conduct Preliminary Review of the Data and Check Statistical
Assumptions 147
8.2.3.1 Statistical Quantities 147
8.2.3.2 Checking Data for Normality 147
8.2.3.3 How To Assess "Outliers" 148
8.2.4 Select and Perform Statistical Tests 149
8.2.4.1 Data Transformations in Statistical Tests 150
8.2.4.2 Treatment ofNondetects 154
8.2.5 Draw Conclusions and Report Results 154
Appendix A: Glossary of Terms 157
Appendix B: Summary of RCRA Regulatory Drivers for Conducting Waste Sampling
and Analysis 171
Appendix C: Strategies for Sampling Heterogeneous Wastes 191
Appendix D: A Quantitative Approach for Controlling Fundamental Error 197
Appendix E: Sampling Devices 201
Appendix F: Statistical Methods 241
Appendix G: Statistical Tables 263
Appendix H: Statistical Software 273
Appendix I: Examples of Planning, Implementation, and Assessment for RCRA
Waste Sampling 277
Appendix J: Summary of ASTM Standards 305
References 323
Index 337
VII
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LIST OF ACRONYMS
AL Action Level
ASTM American Society for Testing and Materials
BOAT Best Demonstrated Available Technology
BIF Boiler and Industrial Furnace
CERCLA Comprehensive, Environmental Response, Compensation & Liability Act
CFR Code of Federal Regulations
DOT Department of Transportation
DQA Data Quality Assessment
DQO Data Quality Objective
EA Exposure area
FR Federal Register
HWIR Hazardous Waste Identification Rule (waste)
I ATA International Air Transport Association
ICR Ignitability, Corrosivity, and Reactivity
IDW Investigation-derived waste
LCL Lower confidence limit
LDR Land Disposal Restrictions
ORD Office of Research and Development
OSHA Occupational Safety and Health Administration
OSW Office of Solid Waste
PBMS Performance-based measurement system
ppm Parts per million
QAD Quality Assurance Division
QAPP Quality Assurance Project Plan
QA/QC Quality Assurance/Quality Control
RCRA Resource Conservation and Recovery Act
RT Regulatory Threshold
SOP Standard operating procedure
SWMU Solid waste management unit
TC Toxicity Characteristic
TCLP Toxicity Characteristic Leaching Procedure
TSDF Treatment, storage, or disposal facility
UCL Upper confidence limit
USEPA U.S. Environmental Protection Agency (we, us, our, EPA, the Agency)
UTS Universal Treatment Standard
VOC Volatile organic compound
WAP Waste analysis plan
VIII
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RCRA WASTE SAMPLING
DRAFT TECHNICAL GUIDANCE
INTRODUCTION
1.1 What Will I Find in This Guidance Document?
You'll find recommended procedures for sampling solid waste under the Resource Conservation
and Recovery Act (RCRA). The regulated and regulatory communities can use this guidance to
develop sampling plans to determine if (1) a solid waste exhibits any of the characteristics of a
hazardous waste1, (2) a hazardous waste is prohibited from land disposal, and (3) a numeric
treatment standard has been met. You also can use information in this document along with
that found in other guidance documents to meet other sampling objectives such as site
characterization under the RCRA corrective action program.
This guidance document steps you through the
three phases of the sampling and analysis
process shown in Figure 1: planning,
implementation, and assessment. Planning
involves "asking the right questions." Using a
systematic planning process such as the Data
Quality Objectives (DQO) Process helps you
do so. DQOs are the specifications you need
to develop a plan for your project such as a
quality assurance project plan (QAPP) or a
waste analysis plan (WAP). Implementation
involves using the field sampling procedures
and analytical methods specified in the plan
and taking measures to control error that might
be introduced along the way. Assessment is
the final stage in which you evaluate the
results of the study in terms of the original
objectives and make decisions regarding
management or treatment of the waste.
1.2 Who Can Use This Guidance
Document?
Any person who generates, treats, stores, or
disposes of solid and hazardous waste and
conducts sampling and analysis under RCRA
can use the information in this guidance
document.
PLANNING
Data Quality Objectives Process,
Quality Assurance Project Plan
or Waste Analysis Plan
IMPLEMENTATION
Field Sample Collection, Sample Analysis, and
Associated Quality Assurance/Quality Control
Activities
ASSESSMENT
Data Verification & Validation,
Data Quality Assessment,
Conclusions Drawn from Data
Figure 1. QA Planning and the Data Life Cycle (after
USEPA1998a).
If a solid waste is not excluded from regulation under 40 CFR 261, then a generator must determine
whether the waste exhibits any of the characteristics of hazardous waste. A generator may determine if a waste
exhibits a characteristic either by testing the waste or applying knowledge of the waste, the raw materials, and the
processes used in its generation.
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For the development of a technically sound sampling and project plan, seek competent advice
during the initial stages of project design. This is particularly true in the early developmental
stages of a sampling plan when planners need to understand basic statistical concepts, how to
establish objectives, and how the results of the project will be evaluated.
This document is a practical guide, and many examples are included throughout the text to
demonstrate how to apply the guidance. In addition, we have included a comprehensive
glossary of terms in Appendix A to help you with any unfamiliar terminology. We encourage you
to review other documents referenced in the text, especially those related to the areas of
sampling theory and practice and the statistical analysis of environmental data.
1.3 Does This Guidance Document Replace Other Guidance?
EPA prepared this guidance document to update technical information contained in other
sources of EPA guidance such as Chapter Nine "Sampling Plan" found in Test Methods for
Evaluating Solid Waste, Physical/Chemical Methods, EPA publication SW-846 (1986a). This
draft guidance document does not replace SW-846 Chapter Nine, nor does it create, amend, or
otherwise alter any regulation. Since publication of SW-846 Chapter Nine, EPA has published a
substantial body of additional sampling and statistical guidance documents that support waste
and site characterization under both RCRA and the Comprehensive, Environmental Response,
Compensation & Liability Act (CERCLA) or "Superfund." Most of these guidance documents,
which focus on specific Agency regulations or program initiatives, should continue to be used,
as appropriate. Relevant EPA guidance documents, other references, and resources are
identified in Appendix B and throughout this document.
In addition to RCRA program-specific guidance documents issued by EPA's Office of Solid
Waste (OSW), EPA's Office of Environmental Information's Quality Staff has developed policy
for quality assurance, guidance documents and software tools, and provides training and
outreach. For example, the Quality Staff have issued guidance on the following key topic areas:
The data quality objectives process (USEPA 2000a, 2000b, and 2001 a)
Preparation of quality assurance project plans (USEPA 1998a and 2001 b) and
sampling plans (2000c)
Verification and validation of environmental data (USEPA 2001 c)
Data quality assessment (USEPA 2000d).
Information about EPA's Quality System and QA procedures and policies can be found on the
World Wide Web at http://www.epa.gov/quality/.
If you require additional information, you should review these documents and others cited in this
document. In the future, EPA may issue additional supplemental guidance supporting other
regulatory initiatives.
Finally, other organizations including EPA Regions, States, the American Society for Testing
and Materials (ASTM), the Department of Defense (e.g., the Air Force Center for Environmental
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Excellence), and the Department of Energy have developed a wide range of relevant guidance
and methods. Consult these resources for further assistance, as necessary.
1.4 How Is This Document Organized?
As previously indicated in Figure 1, this guidance document covers the three components of a
sampling and analysis program: planning, implementation, and assessment. Even though the
process is pictured in a linear format, in practice a sampling program should include feedback
between the various components. You should review and analyze data as collected so you can
determine whether the data satisfy the objectives of the study and if the approach or objectives
need to be revised or refined, and so you can make reasoned and intelligent decisions.
The remaining sections of this guidance document address specific topics pertaining to various
components of a sampling program. These sections include the following:
Section 2 - Summary of RCRA Regulatory Drivers for Waste Sampling and
Analysis - This section identifies and summarizes the major RCRA programs that
specify some sort of sampling and testing to determine if a waste is a hazardous waste,
to determine if a hazardous waste treatment standard is attained, and other
determinations.
Section 3 - Fundamental Statistical Concepts - This section provides an overview of
fundamental statistical concepts and how the sample analysis results can be used to
classify a waste or determine its status under RCRA. The section serves as a refresher
to those familiar with basic statistics. In those cases where you require more advanced
techniques, seek the assistance of a professional environmental statistician. Detailed
guidance on the selection and use of statistical methods is provided in Section 8 and
Appendix F.
Section 4 - Planning Your Project Using the DQO Process - The first phase of
sampling involves development of DQOs using the DQO Process or a similar structured
systematic planning process. The DQOs provide statements about the expectations and
requirements of the data user (such as the decision maker).
Section 5 - Optimizing the Design for Obtaining the Data - This section describes
how to link the results of the DQO Process with the development of the QAPP. You
optimize the sampling design to control sampling errors within acceptable limits and
minimize costs while continuing to meet the sampling objectives. You document the
output of the DQO Process in a QAPP, WAP, or similar planning document. Here is
where you translate the data requirements into measurement performance specifications
and QA/QC procedures.
Section 6 - Controlling Variability and Bias in Sampling - In this section, we
recognize that random variability and bias (collectively known as "error") in sampling
account for a significant portion of the total error in the sampling and analysis process -
far outweighing typical analytical error. To address this concern, the section describes
the sources of error in sampling and offers some strategies for minimizing those errors.
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Section 7 - Implementation: Selecting Equipment and Conducting Sampling - In
this section, we describe the steps for selecting sampling equipment based on the
physical and chemical characteristics of the media to be sampled and the type of RCRA
unit or location from which the samples will be obtained. The section provides guidance
on field sampling activities, such as documentation, chain-of-custody procedures,
decontamination, and sample packaging and shipping. Finally, guidance is provided on
sample homogenization (or mixing), splitting, and subsampling.
Section 8 - Assessment: Analyzing and Interpreting Data - Once you have obtained
the data in accordance with the elements of the QAPP or WAP, you should evaluate the
data to determine whether you have satisfied the DQOs. Section 8 describes the data
quality assessment (DQA) process and the statistical analysis of waste-sampling data.
Appendix A - Glossary of Terms - This appendix comprises a glossary of terms that
are used in this document.
Appendix B - Summary of RCRA Regulatory Drivers for Conducting Waste
Sampling and Analysis - An overview of the RCRA regulatory requirements and other
citations related to waste sampling and testing is provided in this appendix.
Appendix C - Strategies for Sampling Heterogeneous Wastes - The heterogeneity
of a waste or media plays an important role in how you collect and handle samples and
what type of sampling design you use. This appendix provides a supplemental
discussion of large-scale heterogeneity of waste and its impact on waste-sampling
strategies. Various types of large-scale heterogeneity are identified and techniques are
described for stratifying a waste stream based on heterogeneity. Stratified sampling can
be a cost-effective approach for sampling and analysis of heterogeneous wastes.
Appendix D- A Quantitative Approach for Controlling Fundamental Error - The
mass of a sample can influence our ability to obtain reproducible analytical results. This
appendix provides an approach for determining the appropriate mass of a sample of
particulate material using information about the size and shape of the particles.
Appendix E - Sampling Devices - This appendix provides descriptions of
recommended sampling devices. For each type of sampling device, information is
provided in a uniform format that includes a brief description of the device and its use,
advantages and limitations of the device, and a figure to indicate the general design of
the device. Each summary also identifies sources of other guidance on each device,
particularly any relevant ASTM standards.
Appendix F - Statistical Methods - This appendix provides statistical guidance for the
analysis of data generated in support of a waste-testing program under RCRA.
Appendix G - Statistical Tables - A series of statistical tables needed to perform the
statistical tests used in this guidance document are presented here.
Appendix H - Statistical Software - A list of statistical software and "freeware" (no-
cost software) that you might find useful in implementing the statistical methods outlined
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in this guidance document is contained in this appendix, as are Internet addresses at
which you can download no-cost software.
Appendix I - Examples of Planning, Implementation, and Assessment for RCRA
Waste Sampling - Two hypothetical examples of how to apply the planning,
implementation, and assessment guidance provided in this guidance document are
provided here.
Appendix J - Summaries ofASTM Standards - This appendix provides summaries of
ASTM standards related to waste sampling and referenced in this document.
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2 SUMMARY OF RCRA REGULATORY DRIVERS FOR WASTE SAMPLING AND
ANALYSIS
2.1 Background
Through RCRA, Congress provided EPA with the framework to develop regulatory programs for
the management of solid and hazardous waste. The provisions of RCRA Subtitle C establish
the criteria for identifying hazardous waste and managing it from its point of generation to
ultimate disposal. EPA's regulations set out in 40 CFR Parts 260 to 279 are the primary source
for the requirements of the hazardous waste program. These regulations were developed over
a period of 25 years. While EPA's approach for developing individual regulations may have
evolved over this period, the current RCRA statute and codified regulations remain the standard
for determining compliance.
Many of the RCRA regulations either require the waste handler to conduct sampling and
analysis, or they include provisions under which sampling and analysis can be performed at the
discretion of the waste handler. If the regulations require sampling and analysis of a waste or
environmental media, then any regulatory requirements for conducting the sampling and
analysis and for evaluating the results must be followed. Regardless of whether there are
regulatory requirements to conduct sampling, some waste handlers may wish to conduct a
sampling program that allows them to quantify any uncertainties associated with their waste
classification decisions. The information in this document can be used to aid in the planning
and implementation of such a sampling program.
Some RCRA regulations do not specify sampling and analysis requirements and/or do not
specify how the sample analysis results should be evaluated. In many cases, this is because
EPA realized that the type, quantity, and quality of data needed should be specified on a site-
specific basis, such as in the waste analysis plan of a permitted facility. In those situations, you
can use the guidance in this document to help you plan and implement the sampling and
analysis program, evaluate the sample analysis results against the regulatory standards, and
quantify the level of uncertainty associated with the decisions.
This section identifies the major RCRA programs that specify some sort of sampling and testing
to determine if a waste is a hazardous waste, to determine if a hazardous waste treatment
standard is attained, or to meet other objectives such as site characterization. Table 1 provides
a listing of these major RCRA programs that may require waste sampling and testing as part of
their implementation. Appendix B provides a more detailed listing of the regulatory citations, the
applicable RCRA standards, requirements for demonstrating attainment or compliance with the
standards, and relevant USEPA guidance documents.
Prior to conducting a waste sampling and testing program to comply with RCRA, review the
specific regulations in detail. Consult the latest 40 CFR, related Federal Register notices, and
EPA's World Wide Web site (www.epa.gov) for new or revised regulations. In addition, because
some states have requirements that differ from EPA regulations and guidance, we recommend
that you consult with a representative from your State if your State is authorized to implement
the regulation.
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Table 1. Major RCRA Program Areas Involving Waste Sampling and Analysis 1
40 CFR Citation
Program Description
Hazardous Waste Identification
§261.3(a)(2)(v)
§261.3(c)(2)(ii)(C)
§261.21
§261.22
§261.23
§261.24
§261.38(c)(8)
Part 261, Appendix I
Mixed Hazardous Waste
Used oil rebuttable presumption (also Part 279, Subparts B, E, F and G standards
for the management of used oil)
Generic exclusion levels for K061, K062, and F006 nonwastewater HTMR residues
Characteristic of Ignitability
Characteristic of Corrosivity
Characteristic of Reactivity
Toxicity Characteristic
Exclusion of Comparable Fuels from the Definition of Solid and Hazardous Waste
Representative Sampling Methods
Joint EPA-NRC sampling guidance. See November 20, 1997 Federal Register (62
FR 62079)
Land Disposal Restriction Program
§ 268.6
§ 268.40
§ 268.44
§268.49(c)(1)
Petitions to Allow Land Disposal of a Waste Prohibited Under Subpart C of Part
268 (No-Migration Petition). Sampling and testing criteria are specified at §
268.6(b)(1)and(2).
Land Disposal Restriction (LDR) concentration-level standards
Land Disposal Restriction Treatability Variance
Alternative LDR Treatment Standards for Contaminated Soil
Other RCRA Programs and References
§260.10
Part 260, Subpart C
Part 262, Subpart A
Part 262, Subpart C
Part 264, Subpart A
Parts 264/265, Subpart B
Parts 264/265, Subpart F
Parts 264/265, Subpart G
Parts 264, Subpart I
Parts 264/265 - Subpart J
Definitions (for Representative Sample)
Rulemaking Petitions
Generator Standards - General (including § 262.1 1 Hazardous Waste
Determination)
Pre-Transport Requirements
Treatment, Storage, and Disposal Facility Standards - General
Treatment, Storage, and Disposal Facility Standards - General Facility Standards
Releases from Solid Waste Management Units (ground-water monitoring)
Closure and Post-Closure
Use and Management of Containers
Tank Systems
1. Expanded descriptions of the programs listed in Table 1 are given in Appendix B.
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Table 1. Major RCRA Program Areas Involving Waste Sampling and Analysis (continued)
40 CFR Citation
Program Description
Other RCRA Programs and References (continued)
Parts 264/265 - Subpart M
Part 264/265 - Subpart O
Part 264, Subpart S
Parts 264/265 - Subparts
AA/BB/CC
Part 266- Subpart H
Part 270 - Subpart B
Part 270- Subpart C
Part 270 - Subpart F
Part 273
Part 279
Land Treatment
Incinerators
Corrective Action for Solid Waste Management Units (including § 264.552
Corrective Action Management Units)
Air Emission Standards
Hazardous Waste Burned in Boiler and Industrial Furnaces (BIFs) (including
§ 266.1 12 Regulation of Residues)
Permit Application, Hazardous Waste Permitting
Conditions Applicable to All Permits
Special Forms of Permits
Standards for Universal Waste Management
Standards for the Management of Used Oil
2.2 Sampling For Regulatory Compliance
Many RCRA programs involve sampling and analysis of waste or environmental media by the
regulated community. Sampling and analysis often is employed to make a hazardous waste
determination (see Section 2.2.1), to determine if a waste is subject to treatment or, if so, has
been adequately treated under the Land Disposal Restrictions program (see Section 2.2.2), or
in responding to other RCRA programs that include routine monitoring, unit closure, or cleanup
(see Section 2.2.3).
2.2.1 Making a Hazardous Waste Determination
Under RCRA, a hazardous waste is defined as a solid waste, or a combination of solid wastes
which, because of its quantity, concentration, or physical, chemical, or infectious characteristics,
may cause, or significantly contribute to an increase in mortality or an increase in serious
irreversible or incapacitating reversible illness, or pose a substantial present or potential hazard
to human health or the environment when improperly treated, stored, transported, disposed, or
otherwise managed. The regulatory definition of a hazardous waste is found in 40 CFR § 261.3.
Solid wastes are defined by regulation as hazardous wastes in two ways. First, solid wastes
are hazardous wastes if EPA lists them as hazardous wastes. The lists of hazardous wastes
are found in 40 CFR Part 261, Subpart D. Second, EPA identifies the characteristics of a
hazardous waste based on criteria in 40 CFR § 261.10. Accordingly, solid wastes are
hazardous if they exhibit any of the following four characteristics of a hazardous waste:
ignitability, corrosivity, reactivity, or toxicity (based on the results of the Toxicity Characteristic
Leaching Procedure, or TCLP). Descriptions of the hazardous waste characteristics are found
in 40 CFR Part 261, Subpart C.
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Generators must conduct a hazardous waste determination according to the hierarchy specified
in 40 CFR § 262.11. Persons who generate a solid waste first must determine if the solid waste
is excluded from the definition of hazardous waste under the provisions of 40 CFR § 261.4.
Once the generator determines that a solid waste is not excluded, then he/she must determine if
the waste meets one or more of the hazardous waste listing descriptions and determine whether
the waste is mixed with a hazardous waste, is derived from a listed hazardous waste, or
contains a hazardous waste.
For purposes of compliance with 40 CFR Part 268, or if the solid waste is not a listed hazardous
waste, the generator must determine if the waste exhibits a characteristic of a hazardous waste.
This evaluation involves testing the waste or using knowledge of the process or materials used
to produce the waste.
When a waste handler conducts testing to determine if the waste exhibits any of the four
characteristics of a hazardous waste, he or she must obtain a representative sample (within the
meaning of a representative sample given at § 260.10) using the applicable sampling method
specified in Appendix I of Part 261 or alternative method (per § 261.20(c))1 and test the waste
for the hazardous waste characteristics of interest at § 261.21 through 261.24.
For the purposes of subpart 261, the identification of hazardous waste, the regulations state that
a sample obtained using any of the applicable sampling methods specified in Appendix I of Part
261 to be a representative sample within the meaning of the Part 260 definition of
representative sample. Since these sampling methods are not officially required, anyone
desiring to use a different sampling method may do so without demonstrating the equivalency of
that method under the procedures set forth in § 260.21. The user of an alternate sampling
method must use a method that yields samples that "meet the definition of representative
sample found in Part 260" (45 FR 33084 and 33108, May 18, 1990). Such methods should
enable one to obtain samples that are equally representative as those specified in Appendix I of
Part 261. The planning process and much of the information described in this guidance
document may be helpful to someone regulated under Part 261 wishing to use an alternate
sampling method. The guidance should be help full as well for purposes other than Part 261.
Certain states also may have requirements for identifying hazardous wastes in addition to those
requirements specified by Federal regulations. States authorized to implement the RCRA or
HSWA programs under Section 3006 of RCRA may promulgate regulations that are more
stringent or broader in scope than Federal regulations.
2.2.2 Land Disposal Restrictions (LDR) Program
The LDR program regulations found at 40 CFR Part 268 require that a hazardous waste
generator determine if the waste has to be treated before it can be land disposed. This is done
by determining if the hazardous waste meets the applicable treatment standards at § 268.40,
§ 268.45, or §268.49. EPA expresses treatment standards either as required treatment
technologies that must be applied to the waste or as contaminant concentration levels that must
1 Since the 40 CFR Part 261 Appendix I sampling methods are not formally adopted by the EPA
Administrator, a person who desires to employ an alternative sampling method is not required to demonstrate the
equivalency of his or her method under the procedures set forth in §§ 260.20 and 260.21 (see comment at
§261.20(c)).
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be met. (Alternative LDR treatments standards have been promulgated for contaminated soil,
debris, and lab packs.) Determining the need for waste treatment can be made by either of two
ways: testing the waste or using knowledge of the waste (see § 268.7(a)).
If a hazardous waste generator is managing and treating prohibited waste or contaminated soil
in tanks, containers, or containment buildings to meet the applicable treatment standard, then
the generator must develop and follow a written waste analysis plan (WAP) in accordance with
§ 268.7(a)(5).
A hazardous waste treater must test their waste according to the frequency specified in their
WAP as required by 40 CFR 264.13 (for permitted facilities) or 40 CFR 265.13 (for interim
status facilities). See § 268.7(b).
If testing is performed, no portion of the waste may exceed the applicable treatment standard,
otherwise, there is evidence that the standard is not met (see 63 FR 28567, March 26, 1998).
Statistical variability is "built in" to the standards (USEPA 1991c). Wastes that do not meet
treatment standards can not be land disposed unless EPA has granted a variance, extension, or
exclusion (or the waste is managed in a "no-migration unit"). In addition to the disposal
prohibition, there are prohibitions and limits in the LDR program regarding the dilution and
storage of wastes. The program also requires tracking and recordkeeping to ensure proper
management and safe land disposal of hazardous wastes.
General guidance on the LDR program can be found in Land Disposal Restrictions: Summary of
Requirements (USEPA 2001 d). Detailed guidance on preparing a waste analysis plan (WAP)
under the LDR program can be found in Waste Analysis at Facilities That Generate, Treat,
Store, and Dispose of Hazardous Wastes - A Guidance Manual (USEPA 1994a). Detailed
guidance on measuring compliance with the alternative LDR treatment standards for
contaminated soil can be found in Guidance on Demonstrating Compliance With the Land
Disposal Restrictions (LDR) Alternative Soil Treatment Standards (USEPA 2002a).
2.2.3 Other RCRA Regulations and Programs That May Require Sampling and Testing
In addition to the RCRA hazardous waste identification regulations and the LDR regulations,
EPA has promulgated other regulations and initiated other programs that may involve sampling
and testing of solid waste and environmental media (such as ground water or soil). Program-
specific EPA guidance should be consulted prior to implementing a sampling or monitoring
program to respond to the requirements of these regulations or programs. For example, EPA
has issued separate program-specific guidance on sampling to support preparation of a
delisting petition, ground-water and unsaturated zone monitoring at regulated units, unit closure,
corrective action for solid waste management units, and other programs. See also Appendix B
of this document.
2.2.4 Enforcement Sampling and Analysis
The sampling and analysis conducted by a waste handler during the normal course of operating
a waste management operation might be quite different than the sampling and analysis
conducted by an enforcement agency. The primary reason is that the data quality objectives
(DQOs) of the enforcement agency often may be legitimately different from those of a waste
handler. Consider an example to illustrate this potential difference in approach: Many of
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RCRA's standards were developed as concentrations that should not be exceeded (or equaled)
or as characteristics that should not be exhibited for the waste or environmental media to
comply with the standard. In the case of such a standard, the waste handler and enforcement
officials might have very different objectives. An enforcement official, when conducting a
compliance sampling inspection to evaluate a waste handler's compliance with a "do not
exceed" standard, take only one sample. Such a sample may be purposively selected based on
professional judgment. This is because all the enforcement official needs to observe - for
example to determine that a waste is hazardous - is a single exceedance of the standard.
A waste handler, however, in responding to the same regulatory standard may want to ensure,
with a specified level of confidence, that his or her waste concentrations are low enough so that
it would be unlikely, for example, that an additional sample drawn from the waste would exceed
the regulatory standard. In designing such an evaluation the waste handler could decide to take
a sufficient number of samples in a manner that would allow evaluation of the results statistically
to show, with the desired level of confidence, that there is a low probability that another
randomly selected sample would exceed the standard.
An important component of the enforcement official's DQO is to "prove the positive." In other
words, the enforcement official is trying to demonstrate whether the concentration of a specific
constituent in some portion of the waste exceeds the "do not exceed" regulatory level. The
"prove the positive" objective combined with the "do not exceed" standard only requires a single
observation above the regulatory level in order to draw a valid conclusion that at least some of
the waste exceeds the level of concern.
The Agency has made it clear that in "proving the positive," the enforcement agency's DQOs
may not require low detection limits, high analyte recoveries, or high degrees of precision:
"If a sample possesses the property of interest, or contains the constituent at a
high enough level relative to the regulatory threshold, then the population from
which the sample was drawn must also possess the property of interest or
contain that constituent. Depending on the degree to which the property of
interest is exceeded, testing of samples which represent all aspects of the waste
or other material may not be necessary to prove that the waste is subject to
regulation" (see 55 FR 4440, "Hazardous Waste Management System: Testing
and Monitoring Activities," February 8, 1990).
A waste handler may have a different objective when characterizing his or her waste. Instead,
the waste handler may wish to "prove the negative." While proving the negative in absolute
terms is not realistic, the waste handler may try to demonstrate with a desired level of
confidence that the vast majority of his or her waste is well below the standard such that
another sample or samples taken from the waste would not likely exceed the regulatory
standard. The Agency also has spoken to the need for sound sampling designs and proper
quality control when one is trying to "prove the negative:"
"The sampling strategy for these situations (proving the negative) should be
thorough enough to insure that one does not conclude a waste is nonhazardous
when, in fact, it is hazardous. For example, one needs to take enough samples
so that one does not miss areas of high concentration in an otherwise clean
material. Samples must be handled so that properties do not change and
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contaminants are not lost. The analytical methods must be quantitative, and
regulatory detection limits must be met and documented" (see 55 FR 4440,
"Hazardous Waste Management System: Testing and Monitoring Activities,"
Februarys, 1990).
"Proving the negative" can be a more demanding objective for the waste handler in terms of the
sampling strategy and resources than that faced by the enforcement official. To address this
objective the waste handler could use the advice in this or similar guidance documents. In
doing so, the waste handler should establish objectives using a systematic planning process,
design a sampling and analysis plan based on the objectives, collect and analyze the
appropriate number of samples, and use the information from the sample analysis results for
decision-making.
The distinction between a sampling strategy designed to "prove the negative" versus one
designed to "prove the positive" also has been supported in a recent judicial ruling. In United
States v. Allen Bias (9th Cir. 2001) the Government used a limited number of samples to prove
that hazardous waste was improperly managed and disposed. The court affirmed that
additional sampling by the Government was not necessary to "prove the positive."
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3 FUNDAMENTAL STATISTICAL CONCEPTS
Throughout the life cycle of a waste-testing program, the tools of statistics often are employed -
in planning, implementation, and assessment. For example, in the planning phase, you may
state certain project objectives quantitatively and use statistical terminology. Designing and
implementing a sampling plan requires an understanding of error and uncertainty. Statistical
techniques can be used to describe and evaluate the data and to support decisions regarding
the regulatory status of a waste or contaminated media, attainment of treatment or cleanup
goals, or whether there has been a release to the environment. Because statistical concepts
may be used throughout the sampling and analysis program, an understanding of basic
statistical concepts and terminology is important.
Do the RCRA regulations require statistical
sampling?
Some RCRA regulations require the use of statistical
tests (e.g., to determine if there has been a release to
ground water from a waste management unit under
40 CFR Subpart F), whereas, other RCRA regulations
do not require the use of statistical tests (such as
those for determining if a solid waste is or is not a
hazardous waste or determining compliance with LDR
treatment standards). Even where there is no
regulatory obligation to conduct sampling or apply
statistical tests to evaluate sampling results, statistical
methods can be useful in interpreting data and
managing uncertainty associated with waste
classification decisions.
While statistical methods can be valuable in
designing and implementing a scientifically
sound waste-sampling program, their use
should not be a substitute for knowledge of
the waste or as a substitute for common
sense. Not every problem can, or necessarily
must, be evaluated using probabilistic
techniques. Qualitative expressions of
decision confidence through the exercise of
professional judgment (such as a "weight of
evidence" approach) may well be sufficient,
and in some cases may be the only option
available (Crumbling 2001).
If the objective of the sampling program is to
make a hazardous waste determination, the
regulations allow that a single representative sample is sufficient to classify a waste as
hazardous. If a representative sample is found to have the properties set forth for the
corrosivity, ignitability, reactivity, or toxicity characteristics, then the waste is hazardous. The
regulations do not address directly what is a sufficient number of samples to classify a solid
waste as nonhazardous. However, for a petition to reclassify (delist) a listed hazardous waste,
which includes a determination that the listed hazardous waste is not a characteristic hazardous
waste (a "nonhazardous" classification), the regulations provide that at least four representative
samples sufficient to represent the variability or uniformity of the waste must be tested (40 CFR
260.22). This approach is not necessarily based on any statistical method but reflects concepts
of proving the negative and proving the positive (see also Section 2.2.4).
Even if you have no formal training in statistics, you probably are familiar with basic statistical
concepts and how samples are used to make inferences about the population from which the
samples were drawn. For example, the news media frequently cite the results of surveys that
make generalized conclusions about public opinion based on interviews with a relatively small
proportion of the population. These results, however, are only estimates because no matter
how carefully a survey is done, if repeated over and over in an identical manner, the answer will
be a little different each time. There always will be some random sampling variation because it
is not possible to survey every member of a population. There also will be measurement and
estimation errors because of mistakes made in how data are obtained and interpreted.
Responsible pollsters report this as their "margin of error" along with the findings of the survey
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(Edmondson 1996).
Similar to surveys of human populations, waste characterization studies can be designed in
such a way that a population can be identified, samples can be collected, and the uncertainty in
the results can be reported.
The following sections provide a brief overview of the statistical concepts used in this guidance.
Four general topics are described:
Populations, samples, and distributions (Section 3.1)
Measures of central tendency, variability, and relative standing (Section 3.2)
Precision and bias (Section 3.3)
Using sample analysis results to classify a waste or determine its status under
RCRA (Section 3.4).
Guidance on selecting and using statistical methods for evaluating data is given in Section 8.2
and Appendix F of this document. Statistical tables are given in Appendix G. Additional
statistical guidance can be found in Guidance for Data Quality Assessment, EPA QA/G-9
(USEPA 2000d) and other references cited.
3.1 Populations, Samples, and Distributions
A "population" consists of all the waste or media whose characteristics are to be studied and
estimated. A set of observations, known as a statistical sample, is a portion of the population
that is studied in order to learn about the whole population. Sampling is necessary when a
study of the entire population would be too expensive or physically impossible.
Inferences about the population are made from samples selected from the population. For
example, the sample mean (or average) is a consistent estimator of the population mean. In
general, estimates made from samples tend to more closely approximate the true population
parameter as the number of samples increases. The precision of these inferences depends on
the theoretical sampling distribution of the statistic that would occur if the sampling process
were repeated over and over using the same sampling design and number of samples.
3.1.1 Populations and Decision Units
A "population" is the entire selection of interest for study. Populations can have spatial
boundaries, which define the physical area to be studied, and temporal boundaries, which
describe the time interval the study will represent. The definition of the population can be
subjective, defined by regulation or permit condition, or based on risks to human health and the
environment. In all cases, however, the population needs to be finite and have well-defined,
unambiguous physical and/or temporal boundaries. The physical boundary defines the size,
shape, orientation, and location of the waste or media about which a decision will be made.
For a large population of waste or media, you may wish to subdivide the population into smaller
units about which decisions can be made, rather than attempt to characterize the entire
14
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population. These units are called "decision units," and they may represent a single type of
waste at the point of waste generation, a waste from a single batch operation, waste generated
over a specified time, or a volume of waste or contaminated media (such as soil) subject to
characterization, removal, and/or treatment. The concept of a decision unit is similar to an
"exposure unit" (Neptune, etal. 1990, Blacker and Goodman 1994a and 1994b, Myers 1997), or
"exposure area" (USEPA 1992a and 1996a) in EPA's Superfund program in which risk-based
decisions consider the mass or area of the waste or media. A decision unit also is analogous to
a "remediation unit" as described in EPA's Data Quality Objective Process for Superfund
(USEPA 1993a).
When using samples to determine whether a solid waste is a hazardous waste, that
determination must be made at the point of generation (i.e., when the waste becomes a solid
waste).
Hypothetical examples of populations or decision units that might be encountered in the context
of RCRA waste characterization follow:
Filter cake being placed in a 25-cubic-yard roll-off bin at the point of waste
generation
Waste water contained in a 55-gallon drum
Liquid waste flowing from the point of generation during a specified time interval
A block of soil (e.g., 10-feet-by-10-feet square, 6-inches deep) within a solid
waste management unit (SWMU).
In some situations, it will be appropriate to define two separate populations for comparison to
each other. For example, in monitoring a land-based waste management unit to determine if
there has been a release to the subsurface at statistically significant levels above background, it
is necessary to establish two populations: (1) a background population and (2) an exposed (or
downgradient) population in the soil, pore-water, or ground-water system.
In situations in which the boundaries of the waste or contamination are not obvious or cannot be
defined in advance (such as the case of contaminated soil in situ, as opposed to excavated soil
in a pile), the investigator is interested in the location of the contamination as well as the
concentration information. Such a sampling objective is best addressed by spatial analysis, for
example, by using geostatistical methods (See also Section 3.4.4).
3.1.2 Samples and Measurements
Samples are portions of the population. Using information from a set of samples (such as
measurements of chemical concentrations) and the tools of inductive statistics, inferences can
be made about the population. The validity of the inferences depends on how closely the
samples represent the physical and chemical properties of the population of interest.
In this document, we use the word "sample" in several different ways. To avoid confusion,
definitions of terms follow:
15
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Sample: A portion of material that is taken from a larger quantity for the purpose
of estimating properties or composition of the larger quantity (from ASTM D
6233-98).
Statistical sample: A set of samples or measurements selected by probabilistic
means (i.e., by using some form of randomness).
We sometimes refer to a "set of samples" to indicate more than one individual sample that may
or may not have been obtained by probabilistic means.
Outside the fields of waste management and environmental sciences, the concept of a sample
or "sampling unit" is fairly straightforward. For example, a pollster measures the opinions of
individual human beings, or the QC engineer measures the diameter of individual ball bearings.
It is easy to see that the measurement and the sampling unit correspond; however, in sampling
waste or environmental media, what is the appropriate "portion" that should be in a sampling
unit? The answer to this question requires consideration of the heterogeneities of the sample
media and the dimension of the sampling problem (in other words, are you sampling over time
or sampling over space?). The information can be used to define the appropriate size, shape,
and orientation of the sample. The size, shape, and orientation of a sample are known as the
sample support, and the sample support will affect the measurement value obtained from the
sample.
Population or
"Decision Unit"
\
Sample analysis \
results used to make
conclusions about the
waste /
Primary
Sample
(e.g., a core)
As shown in Figure 2, after a sample of a
certain size, shape, and orientation is
obtained in the field (as the primary
sample), it is handled, transported, and
prepared for analysis. At each stage,
changes can occur in the sample (such
as the gain or loss of constituents,
changes in the particle size distribution,
etc.). These changes accumulate as
errors throughout the sampling process
such that measurements made on
relatively small analytical samples (often
less than 1 gram) may no longer
"represent" the population of interest.
Because sampling and analysis results
may be relied upon to make decisions
about a waste or media, it is important to
understand the sources of the errors
introduced at each stage of sampling
and take steps to minimize or control those errors. In doing so, samples will be sufficiently
"representative" of the population from which they are obtained.
The RCRA solid waste regulations at 40 CFR §260.10 define a representative sample as:
"a sample of a universe or whole (e.g., waste pile, lagoon, ground water) which
can be expected to exhibit the average properties of the universe or whole."
Field
Sample
1 Gram
Subsample
1 Quart
Figure 2. Very small analytical samples are used to make
decisions about much larger volumes (modified after Myers
1997).
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RCRA implementors, at a minimum, must use this definition when a representative sample is
called for by the regulations. Various other definitions of a representative sample have been
developed by other organizations. For example, ASTM in their consensus standard D 6044-96
defines a representative sample as "a sample collected in such a manner that it reflects one or
more characteristics of interest (as defined by the project objectives) of a population from which
it was collected" (ASTM D 6044). A detailed discussion of representativeness also is given in
Guidance on Data Quality Indicators (USEPA 2001 e).
3.1.3 Distributions
Histogram
o
c
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Many of the tools used in statistics are based on the assumption that the data are normally
distributed, can be transformed to a normal scale, or can be treated as if they are approximately
normal. The assumption of a normal distribution often can be made without significantly
increasing the risk of making a "wrong" decision. Of course, the normal and lognormal
distributions are assumed models that only approximate the underlying population distribution.
Another distribution of interest is known as the binomial distribution. The binomial distribution
can be used when the sample analysis results are interpreted as either "fail" or "pass" (e.g., a
sample analysis result either exceeds a regulatory standard or does not exceed the standard).
In some cases, you may not be able to "fit" the data to any particular distributional model. In
these situations, we recommend you consider using a "distribution-free" or "nonparametric"
statistical method (see Section 8.2).
A simple but extremely useful graphical
test for normality is to graph the data as a
probability plot. In a probability plot, the
vertical axis has a probability scale and
the horizontal axis has a data scale. In
general, if the data plot as a straight line,
there is a qualitative indication of
normality. If the natural logarithms of the
data plot as a straight line, there is an
indication of lognormality.
Figure 5 provides an example of a normal
probability plot created from the same
data used to generate the histogram in
Figure 3. Guidance on constructing
probability plots can be found in EPA's
Guidance for Data Quality Assessment,
EPA QA/G-9 (USEPA 2000d).
Normal Probability Plot
o
Q_
Average: 9.21546
Std Dev: 4.7209
N of data: 11
Total Pb (mg/L)
Figure 5. Normal probability plot
Section 8 (Assessment: Analyzing and Interpreting Data) provides guidance on checking the
distribution of data sets and provides strategies for handling sample data exhibiting a non-
normal distribution.
3.2 Measures of Central Tendency, Variability, and Relative Standing
In addition to graphical techniques for summarizing and describing data sets, numerical
methods can be used. Numerical methods can be used to describe the central tendency of the
set of measurements, the variability or spread of the data, and the relative standing or relative
location of a measurement within a data set.
3.2.1 Measures of Central Tendency
The average or mean often is used as a measure of central tendency. The mean of a set of
quantitative data is equal to the sum of the measurements divided by the number of
measurements contained in the data set. Other measures of central tendency include the
18
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median (the midpoint of an ordered data set in which half the values are below the median and
half are above) and the mode (the value that occurs most often in the distribution). For
distributions that are not symmetrical, the median and the mean do not coincide. The mean for
a lognormal distribution, for instance, will exceed its median (see Figure 4(b)).
The true population mean, // ("mu"), is the average of the true measurements (e.g., of the
constituent concentration) made over all possible samples. The population mean is never
known because we cannot measure all the members of a population (or all possible samples).
We can, however, estimate the population mean by taking random samples from the population.
The average of measurements taken on random samples is called the sample mean. The
sample mean is denoted by the symbol x ("x-bar") and calculated by summing the value
obtained from each random sample ( xi ) and dividing by the number of samples ( n ):
i Equation 1
Box 1 provides an example calculation of the sample mean.
Box 1. Example Calculation of the Sample Mean
Using Equation 1 and the following four data points in parts per million (ppm): 86, 90, 98, and 104, the following is an
example of computing the sample mean.
86+90 + 98 + 104
f = - - - = 95ppm
Therefore, the sample mean is 95 ppm.
3.2.2 Measures of Variability
Random variation in the population is described by "dispersion" parameters - the population
variance (<72) and the population standard deviation (
-------
being employed for the n samples. If there were no sample handling or measurement error,
this sample variance (s2) would estimate the population variance (<7 ).
The population standard deviation (
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more certainty in estimates and decisions made from the data.
Because x is an estimate of a population parameter based on a statistical sample, we expect
its value to be different each time a new set of samples is drawn from the population. The
means calculated from repeated statistical samples also form a distribution. The estimate of the
standard deviation of the sampling distribution of means is called the standard error.
The standard error of the mean (s~) is estimated by:
s
s~ = —;= Equation 4
V«
The standard error is used in equations to calculate the appropriate number of samples to
estimate the mean with specified confidence (see Section 5.4), and it is used in statistical tests
to make inferences about x (see Appendix F).
3.2.3 Measures of Relative Standing
In addition to measures of central tendency and variability to describe data, we also may be
interested in describing the relative standing or location of a particular measurement within a
data set. One such measure of interest is the percentile ranking. A population percentile
represents the percentage of elements of a population having values less than a specified
value. Mathematically, for a set of n measurements the pth percentile (or quantile) is a
number such that p% of the measurements fall below the pin. percentile, and (100 — p}%
fall above it. For example, if a measurement is located at the 99th percentile in a data set, it
means that 99 percent of measurements are less than that measurement, and 1 percent are
above. In other words, almost the entire distribution lies below the value representing the 99th
percentile. Figure 6 depicts the relationship between the mean, the 50th percentile, and the 99th
percentile in a normal distribution.
Just like the mean and the median, a percentile is a population parameter that must be
estimated from the sample data. As indicated in Figure 6, for a normal distribution a "point
estimate" of a percentile (xp) can be obtained using the sample mean (x) and the sample
standard deviation (s) by:
xp=x+zps Equations
where zp is the pth quantile of the standard normal distribution. (Values of zp that
correspond to values of p can be obtained from the last row of Table G-1 in Appendix G). A
probability plot (see Figure 5) offers another method of estimating normal percentiles. See
EPA's Guidance for Data Quality Assessment, EPA QA/G-9 (USEPA 2000d) for guidance on
constructing probability plots and estimating percentiles.
21
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Precise
Precise
True
Concentration
= 100 ppm
Unbiased
Ave. = 100 = True Value
Biased
True
Value
Ave. = 170
Imprecise
Imprecise
True
Concentration
= 100 ppm
3.3 Precision and Bias
The representativeness of a statistical
sample (that is, a set of samples) can be
described in terms of precision and
bias. Precision is a measurement of the
closeness of agreement between
repeated measurements. Bias is the
systematic or consistent over- or
underestimation of the true value (Myers
1997, USEPA2000d).
The analogy of a target often is used to
illustrate the concepts of precision and
bias. In Figure 7, the center of each
target represents the true (but unknown)
average concentration in a batch of
waste. The "shots" in targets (a) through
(d) represent measurement results from
samples taken to estimate the true
concentration. The figure also can be
used to illustrate precision and bias
associated with measurement processes
within a laboratory in which the same
sample is analyzed multiple times (for
example, four times).
Figure 7(a) indicates high precision and
low bias in the sampling and analysis
results. Generally, high precision and
minimal bias are required when one or
more chemical constituents in a solid
waste are present at concentrations
close to the applicable regulatory
threshold or action level. Note that each
of the measurements in Figure 7(a) is in
close agreement with the true value.
These measurements can be described as having high accuracy.
If the sampling and measurement process is very precise but suffers from bias (such as use of
an incorrect sampling procedure or contamination of an analytical instrument), the situation
could be as pictured in Figure 7(b) in which the repeated measurements are close to one
another but not close to the true value. In fact, the data express a significant 70 percent bias
that might go undetected if the true value is not known.
The opposite situation is depicted in Figure 7(c), where the data show low precision (that is,
high dispersion around the mean) but are unbiased because the samples lack any systematic
error and the average of the measurements reflects the true average concentration. Precision
in sampling can be improved by increasing the number of samples, increasing the volume
Unbiased
Ave. = 100 = True Value
Biased
True
Value
Ave. = 150
Figure 7. Shots at a target illustrate precision and bias (modified
after Jessen 1978).
22
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(mass) of each sample, or by employing a composite sampling strategies. Note, however, that
relatively imprecise results can be tolerated if the contaminants of concern occur at levels either
far below or far above their applicable thresholds.
Figure 7(d) depicts the situation where the sampling and analytical process suffers from both
imprecision and bias. In both Figures 7(b) and (d), the bias will result in an incorrect estimate of
the true concentration, even if innumerable samples are collected and analyzed to control the
impact of imprecision (i.e., bias will not "cancel out" with increasing numbers of samples).
There are several types and causes of bias, including sampling bias, analytical bias, and
statistical bias:
Sampling Bias: There are three potential sources of sampling bias: (1) Bias can be
introduced in the field and the laboratory through the improper selection and use of
devices for sampling and subsampling. Bias related to sampling tools can be minimized
by ensuring all of the material of interest for the study is accessible by the sampling tool.
(2) Bias can be introduced through improper design of the sampling plan. Improper
sampling design can cause parts of the population of interest to be over- or under-
sampled, thereby causing the estimated values to be systematically shifted away from
the true values. Bias related to sampling design can be minimized by ensuring the
sampling protocol is impartial so there is an equal chance for each part of the waste to
be included in the sample over both the spatial and temporal boundaries defined for the
study. (3) Bias can be introduced in sampling due to the loss or addition of
contaminants during sampling and sample handling. This bias can be controlled using
sampling devices made of materials that do not sorb or leach constituents of concern,
and by use of careful decontamination and sample handling procedures. For example,
agitation or homogenization of samples can cause a loss of volatile constituents, thereby
indicating a concentration of volatiles lower than the true value. Proper decontamination
of sampling equipment between sample locations or the use of disposable devices, and
the use of appropriate sample containers and preservatives also can control bias in field
sampling.
Analytical Bias: Analytical (or measurement) bias is a systematic error caused by
instrument contamination, calibration drift, or by numerous other causes, such as
extraction inefficiency by the solvent, matrix effect, and losses during shipping and
handling.
Statistical Bias: After the sample data have been obtained, statistics are used to
estimate population parameters using the sample data. Statistical bias can occur in two
situations: (1) when the assumptions made about the sampling distribution are not
consistent with the underlying population distribution, or (2) when the statistical estimator
itself is biased.
Returning to Figure 7, note that each target has an associated frequency distribution curve.
Frequency curves are made by plotting a concentration value versus the frequency of
occurrence of that concentration. The curves show that as precision decreases (i.e., the
variance <72 increases), the curve flattens out and an increasing number of measurements are
found further away from the average (figures c and d). More precise measurements result in
steeper curves (figures a and b) with the majority of measurements relatively closer to the
23
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average value in normally distributed data. The greater the bias (figures b and d) the further the
average of the measurements is shifted away from the true value. The smaller the bias (figures
a and c) the closer the average of the samples is to the true average.
Representative samples are obtained by controlling (at acceptable levels) random variability
((72) and systematic error (or bias) in sampling and analysis. Quality control procedures and
samples are used to estimate the precision and bias of sampling and analytical results.
3.4 Using Sample Analysis Results to Classify a Waste or to Determine Its Status
Under RCRA
If samples are used to classify a waste or determine its regulatory status, then the sampling
approach (including the number and type of samples) must meet the requirements specified by
the regulations. Regardless of whether or not the regulations specify sampling requirements or
the use of a statistical test, the Agency encourages waste handlers to use a systematic planning
process such as the DQO Process to set objectives for the type, quantity, and quality of data
needed to ensure with some known level of assurance that the regulatory standards are
achieved.
After consideration of the objectives identified in the planning process, careful implementation of
the sampling plan, and review of the analytical results, you can use the sample analysis results
to classify a waste or make other decisions regarding the status of the waste under RCRA. The
approach you select to obtain and evaluate the results will be highly dependent on the
regulatory requirements (see Section 2 and Appendix B) and the data quality objectives (see
Section 4 and Section 5).
The following sections provide a conceptual overview of how you can use sample analysis
results to classify a waste or determine its status under RCRA. Guidance is provided on the
following topics:
Using an average to measure compliance with a fixed standard (Section 3.4.1)
Using the maximum sample analysis result or an upper percentile to measure
compliance with a fixed standard (Section 3.4.2)
There are other approaches you might use to evaluate sample analysis results, including tests
that compare two populations, such as "downgradient" to "background" (see Section 3.4.3), and
analysis of spatial patterns of contamination (see Section 3.4.4).
Detailed statistical guidance, including the necessary statistical equations, is provided in Section
8.2 and Appendix F.
3.4.1 Using an Average To Determine Whether a Waste or Media Meets the Applicable
Standard
The arithmetic average (or mean) is a common parameter used to determine whether the
concentration of a constituent in a waste or media is below a fixed standard. The mean often is
used in cases in which a long-term (chronic) exposure scenario is assumed (USEPA 1992c) or
where some average condition is of interest.
24
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Because of the uncertainty associated with estimating the true mean concentration, a
confidence interval on the mean is used to define the upper and lower limits that bracket the
true mean with a known level of confidence. If the upper confidence limit (UCL) on the mean
is less than the fixed standard, then we can conclude the true average is below the standard
with a known amount of confidence. As an alternative to using a statistical interval to draw
conclusions from the data, you could use hypothesis testing as described in EPA's Guidance for
the Data Quality Objectives Process, EPA QA/G-4 (USEPA 2000b) and Guidance for Data
Quality Assessment, EPA QA/G-9 (USEPA 2000d).
Confidence intervals are calculated using
the sample analysis results. Figure 8
shows what is expected to happen when
ten different sets of samples are drawn
from the same waste and a confidence
interval for the mean is calculated for each
set of samples. The true (but unknown)
mean (jU)- shown as a vertical line -
does not change, but the positions of the
sample means (x ) and confidence
intervals (shown as the horizontal lines)
do change. For most of the sampling
events, the confidence interval contains
the true mean, but sometimes it does not.
In this particular example, we expect 8 out
of 10 intervals to contain the true mean,
so we call this an "80-percent confidence
interval on the mean." In practice, you
only have one set of data from one
sampling event, not ten. Note that an
equal degree of uncertainty is associated
with the parameter of interest being
located outside each of the two interval
endpoints. Consequently, the confidence
interval employed in this example is, for all
practical purposes, a 90-percent interval.
We will refer to this as a "one-sided 90-
percent confidence limit on the mean." Of
course, other levels of confidence could
be used, such as a 95-percent confidence
limit.
The width of the confidence interval
(defined by the upper and lower
confidence limits) is an indicator of the
precision of the estimate of the parameter
of interest. Generally, one can improve
precision (i.e., reduce the standard error,
s I *Jn ) by taking more samples,
increasing the physical size of each
Confidence Interval
I 1
Sample Mean
Sample Set
1
2
3
4
5
6
7
8
9
10
Figure 8. 80-percent confidence intervals calculated from 10
equal-sized sets of samples drawn at random from the same
waste stream
A I
s
B I
!
Sample mean *, Specification Level
= true mean ^/^
^
7
^ ., 95% UCL
H Waste
inappropriately
judged a solid
\ waste
Concentration
95%
/
^x
UCLs^
Hi
1
^ Specification Level
k Waste
appropriately
judged to
achieve the
exclusion level
Concentration
Figure 9. Example of how sampling precision could impact a
waste exclusion demonstration under 40 CFR 261.38. Due to
imprecision (A), the waste is inappropriately judged a solid
waste. With more precise results (B), the entire confidence
interval lies below the specification level, and the waste is
appropriately judged eligible for the comparable fuels
exclusion.
25
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sample (i.e., increasing the sample support), and by minimizing random variability introduced in
the sampling and measurement processes.
For example, Figure 9 shows how sampling precision can affect the ability to claim an exclusion
from the definition of solid waste under the comparable fuels regulations at 40 CFR 261.38. In
Figure 9 "A," the sampling results are unbiased, but they are not sufficiently precise. In fact, the
imprecision causes the confidence intervals to "straddle" the specification level; thus, there is
not statistically significant evidence that the mean is below the standard. Imprecision can be
caused by the heterogeneity of the material sampled, by random errors in the field and
laboratory, and by too few samples. In Figure 9 "B," the results also are unbiased, but
significant improvement in precision is observed (e.g., because more or larger samples were
analyzed and errors were kept within acceptable limits), allowing us to conclude that the mean
is indeed below the specification level.
Detailed guidance on the calculation of confidence limits for the mean can be found in Appendix
F of this document.
3.4.2 Using a Proportion or Percentile To Determine Whether a Waste or Media Meets
an Applicable Standard
Under RCRA, some regulatory thresholds are defined as concentration values that cannot be
exceeded (e.g., the RCRA LDR program concentration-based treatment standards for
hazardous waste specified at § 268.40 and § 268.48), concentration values that cannot be
equaled or exceeded (e.g., the Toxicity Characteristic maximum concentration levels specified
at § 261.24), or waste properties that cannot be exhibited (e.g., ignitability per § 261.21,
corrosivity per § 261.22, or reactivity per § 261.23) for the waste to comply with the regulatory
standard.
To demonstrate compliance with such a standard using sampling, it is necessary to consider the
waste or site (whose boundaries are defined as a decision unit) as a population of discrete
sample units (of a defined size, shape, and orientation). Ideally, none of these sample units
may exceed the standard or exhibit the properties of concern for the waste or site to be in
compliance with the standard. However, since it is not possible to know the status of all
portions of a waste or site, samples must be used to infer - using statistical methods - what
proportion or percentage of the waste complies, or does not comply, with the standard.
Generally, few if any samples drawn from the population of interest may exceed the regulatory
standard or exhibit the property of concern to demonstrate with reasonable confidence that a
high proportion or percentage of the population complies with the standard.
Two simple methods for measuring whether a specified proportion or percentile of a waste or
media meets an applicable standard are described in the following sections:
Using an upper confidence limit on a percentile to classify a waste or media
(Section 3.4.2.1), and
Using a simple exceedance rule method to classify a waste or media (Section
3.4.2.2).
26
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3.4.2.1
Using a Confidence Limit on a Percentile to Classify a Waste or Media
Sample
Mean
UCL on Upper
Percentile or
"Tolerance Limit"
Concentration
A percentile is a population parameter.
We cannot know the true value of that
parameter, but we can estimate it from a
statistical sample drawn from the
population by using a confidence interval
for a percentile. If the upper confidence
limit (UCL) on the upper percentile is
below the fixed standard, then there is
statistically significant evidence that the
specified proportion of the waste or media
attains the standard (see Figure 10). If
the UCL on the upper percentile exceeds
the standard (but all sample analysis
results are below the standard), then the
waste or media still could be judged in
compliance with the standard; however,
you would not have the specified degree
of confidence that the specified proportion
of the waste or media complies with the
standard (see also the exceedance rule method, Section 3.4.2.2).
Detailed guidance on the calculation of confidence limits for percentiles can be found in Section
8.2 and Appendix F of this document. Methods also are given in Conover (1999), Gilbert (1987,
page 136), Hahn and Meeker (1991), and USEPA (1989a). A possible alternative to using a
confidence limit on a percentile is the use of the "one-sample test for proportions" (see Section
3.2.2.1 of USEPA 2000d).
Confidence Interval on
99th Percentile
"Point estimate" of
99th percentile
Figure 10. For a high percentile (e.g., the 99th percentile) to be
less than an applicable standard, the mean concentration must
be well below the standard.
3.4.2.2
Using a Simple Exceedance Rule Method To Classify a Waste
One of the most straightforward methods for determining whether a given proportion or
percentage of a waste (that is, all possible samples of a given sample support) complies with an
applicable standard is to use a simple exceedance rule. To apply the method, simply obtain a
number of samples and require that zero or few sample analysis results be allowed to exceed
the applicable standard or possess the property (or "attribute") of interest. The method (also
known as "inspection by attributes") is from a class of methods known as acceptance sampling
plans (Schilling 1982, ASQ 1988 and 1993, and DoD 1996). One simple form of the
exceedance rule, sometimes used by regulatory enforcement agencies, specifies zero
exceedances in a set of samples. This method can be used to classify a waste (i.e., determine
if it exhibits the characteristics of ignitability, corrosivity, reactivity1, or toxicity) or to determine its
status under RCRA (that is, to determine if the waste is prohibited from land disposal or if it
attains an LDR treatment standard).
The method is attractive because it is simple (e.g., because sample analysis results are
EPA uses a narrative criteria to define most reactive wastes, and waste handlers should use their
knowledge to determine if a waste is sufficiently reactive to be regulated.
27
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recorded as either "pass" or "fail" and statistical tables can be used instead of equations), it
does not require an assumption about the form of the underlying distribution, and it can be used
when a large proportion of the data are reported as less than a quantitation limit. Furthermore,
the method has statistical properties that allow the waste handler to have a known level of
confidence that at least a given proportion of the waste complies with the standard. One
potential drawback of using an exceedance rule is that with a small number of samples, you
might not be able to conclude with high confidence that a high proportion of the waste complies
with the applicable standard (unless you have sufficient knowledge of the waste indicating there
is little variability in concentrations or properties). That is, with a small number of samples,
there is little statistical power: an unacceptably large proportion of the waste or site could
exceed the standard or exhibit the property even though no such exceedances or properties
were observed in the samples. Increasing the number of samples will improve the statistical
performance.
As a practical matter, it is suggested that you scale the statistical performance and acceptance
requirements (and thus, the number of samples) to the size of the lot or batch of waste of
interest. For example, when large and/or very heterogeneous volumes of waste are the subject
of the study, decision-makers may require high confidence that a high proportion of the waste
meets the applicable standard. A relatively large number of samples will be required to satisfy
these criteria if the exceedance rule is used. On the other hand, decision-makers may choose
to relax the statistical performance criteria when characterizing a small volume of waste (or a
very homogeneous waste) and thus fewer samples would be needed.
Detailed guidance on the use of an exceedance rule is provided in Section 5.5.2 and in
Appendix F, Section F.3.2, of this document. The exceedance rule method also is described in
Methods for Evaluating the Attainment of Cleanup Standards. Volume 1: Soils and Solid Media
(USEPA 1989a, Section 7.4).
3.4.3 Comparing Two Populations
Some environmental studies do not involve testing compliance against a fixed standard but
require comparison of two separate data. This type of analysis is common for detecting
releases to ground water at waste management units such as landfills and surface
impoundments, detecting releases to soil and the unsaturated zone at land treatment units, or
determining if site contamination is distinguishable from natural background concentrations. In
these situations, the operator must compare "on site" or "downgradient" concentrations to
"background."
For example, at a new land-based waste management unit (such as a new landfill), we expect
the concentrations in a set of samples from downgradient locations to be similar to a set of
samples from background locations. If a statistically significant change in downgradient
conditions is detected, then there may be evidence of a release to the environment. Statistical
methods called two-sample tests can be used to make such comparisons (they are called two-
sample tests because two sets of samples are used). A two-sample test also could be used to
measure changes in constituent concentrations in a waste or soil "before" treatment and "after"
treatment to assess the effectiveness of the treatment process (see USEPA 2002a).
For detailed guidance on the use of two-sample tests, see EPA's G-9 guidance (USEPA 2000d)
and EPA's guidance on the statistical analysis of ground-water monitoring data (USEPA 1989b
28
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and 1992b).
Note that detecting a release to the environment may not necessarily involve use of a statistical
test and may not even involve sampling. For example, observation of a broken dike at a surface
impoundment may indicate that a release has occurred.
3.4.4 Estimating Spatial Patterns
Under some circumstances, a site investigator may wish to determine the location of a
contaminant in the environment as well as its concentration. Knowledge of spatial trends or
patterns may be of particular value when conducting risk assessments or locating areas for
clean-up or removal under the RCRA Corrective Action program. Estimation of spatial patterns
is best addressed by geostatistics or other spatial data analysis methods.
Geostatistical models are based on the notion that elements of the population that are close
together in space and/or time exhibit an identifiable relationship or positive correlation with one
another. Geostatistical techniques attempt to recognize and describe the pattern of spatial
dependence and then account for this pattern when generating statistical estimates. On the
other hand, "classical" methods assume that members of a population are not correlated
(USEPA 1997a).
While a full treatment of spatial analysis and geostatistics is beyond the scope of this guidance,
certain techniques recommended in the guidance require consideration of spatial differences.
For example, you may need to consider whether there are any spatial correlations in a waste or
site when selecting a sampling design. There are some relatively simple graphical techniques
that can be used to explore possible spatial patterns or relationships in data. For example,
posting plots or spatial contour maps can be generated manually or via software (e.g., see
EPA's Geo-EAS software described in Appendix H). Interested readers can find a more
comprehensive explanation of spatial statistics in texts such as Myers (1997), Isaaks and
Srivastava (1989), Journel (1988), USEPA (1991a, 1997a), or consult a professional
environmental statistician or geostatistician.
29
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PLANNING YOUR PROJECT USING THE DQO PROCESS
To be successful, a waste-testing program must yield data of the type and quality necessary to
achieve the particular purpose of the program. This is accomplished through correct, focused,
and well-documented sampling, testing, and data evaluation activities. In each case, a clear
understanding of the program objectives and thorough planning of the effort are essential for a
successful, cost-effective waste-testing program.
Each program design is unique because of the many possible variables in waste sampling and
analysis such as regulatory requirements, waste and facility-specific characteristics, and
objectives for the type and quantity of data to be provided. Nonetheless, a systematic planning
process such as the Data Quality Objectives (DQO) Process, which takes these variables into
account, can be used to guide planning efforts. EPA recommends using the DQO Process
when data are being used to select between two opposing conditions, such as determining
compliance with a standard.
The DQO Process yields qualitative and quantitative statements that:
Clarify the study objectives
Define the type, quantity, and quality of required data
Determine the most appropriate conditions from which to collect the samples
Specify the amount of uncertainty you are willing to accept in the results
Specify how the data will be
used to test a decision rule.
The outputs of the DQO Process are used to
define the quality control requirements for
sampling, analysis, and data assessment.
These requirements are then incorporated into
a QAPP, WAP, or other similar planning
document.
The DQO Process comprises seven planning
steps depicted in Figure 11. The figure shows
one of the most important features of the
process: its iterative nature. You don't have to
"get it right the first time." You can use existing
information to establish DQOs. If the initial
design is not feasible, then you can iterate
through one or more of the earlier planning
steps to identify a sampling design that will
meet the budget and generate data that are
adequate for the decision. This way, you can
evaluate sampling designs and related costs in
advance before significant time and resources
are expended to collect and analyze samples.
In a practical sense, the DQO Process offers a
structured approach to "begin with the end in
State the Problem
A
Identify the Decision
1
Identify Inputs to the Decision
i
Define the Study Boundaries
1
Develop a Decision Rule
i
Specify Limits on Decision Errors
Optimize the Design for Obtaining Data
Figure 11. The seven steps of the DQO Process (from
USEPA2000b)
30
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Systematic Planning and the DQO Process:
EPA References and Software
Guidance for the Data Quality Objectives Process, EPA
QA/G-4, August 2000, EPA/600/R-96/055. Provides
guidance on how to perform the DQO Process.
Data Quality Objectives Decision Error Feasibility Trials
Software (DEFT) - User's Guide, EPA QA/G-4D,
September 2001, EPA/240/B-01/007 (User's Guide and
Software). PC-based software for determining the
feasibility of data quality objectives defined using the
DQO Process.
Guidance for the Data Quality Objectives Process for
Hazardous Waste Sites, EPA QA/G-4HW, January
2000, EPA/600/R-00/007. Provides guidance on
applying the DQO Process to hazardous waste site
investigations.
mind." It is a framework for asking the right
questions and using the answers to develop
and implement a cost-effective plan for data
collection. The DQO Process does not
necessarily proceed in a linear fashion or
involve rigid procedures; rather, it is a thought
process to enable you to get useful information
in a cost-effective manner.
Failure to establish DQOs before implementing
field and laboratory activities can cause
difficulties in the form of inefficiencies,
increased or unnecessary costs, or the
generation of unusable data. For example, if
the limit of quantitation for sample analysis is
greater than the Action Level, then the data will
not be useable for its intended purpose; or, if
you do not collect enough samples, then you
may not be able to draw conclusions with the desired level of confidence.
When properly used, the DQO Process:
Provides a good way to document the key activities and decisions necessary to
address the problem and to communicate the approach to others.
Involves key decision makers, other data users, and technical experts in the
planning process before data collection begins which helps lead to a consensus
prior to beginning the project and makes it easier to change plans when
circumstances warrant because involved parties share common understandings,
goals, and objectives.
Develops a consensus approach to limiting decision errors that strikes a balance
between the cost of an incorrect decision and the cost of reducing or eliminating
the possible mistake.
Saves money by greatly reducing the tendency to collect unneeded data by
encouraging the decision makers to focus on data that support only the
decision(s) necessary to solve the problem(s). When used with a broader
perspective in mind, however, the DQO Process may help identify opportunities
to consolidate multiple tasks and improve the efficiency of the data collection
effort.1
In some cases, it might be appropriate and cost-effective to collect data beyond that required to support a
near-term decision. For example, if a drill rig is mobilized to collect deep soil samples to determine the need for
remediation, it would be cost-effective to also collect relatively low-cost data (such as geotechnical parameters, total
organic carbon, moisture content, etc.) needed by engineers to design the remedy. Otherwise, unnecessary costs
might be incurred to remobilize a drill rig to obtain data that could have been obtained in the initial effort.
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The remainder of this section addresses how the DQO Process can be applied to RCRA waste-
characterization studies. While the discussion is based on EPA's G-4 guidance (USEPA
2000b), some steps have been modified or simplified to allow for flexibility in their use. Keep in
mind that not all projects or decisions (such as a hazardous waste determination) will require
the full level of activities described in this section, but the logic applies nonetheless. In fact,
EPA encourages use of a "graded approach" to quality assurance. A graded approach bases
the level of management and QA/QC activities on the intended use of the results and the
degree of confidence needed in their quality (USEPA 2001f).
4.1 Stepl: State the Problem
Before developing a data gathering
program, the first step is to state the
problem or determine what question or
questions are to be answered by the
study. For many waste characterization or
monitoring programs the questions are
spelled out in the applicable regulations;
however, in some cases, determining the
actual problem or question to be
answered may be more complex. As part
of this step, perform the four activities
described in the following sections.
DQO Step 1: State the Problem
Purpose
To define the problem so that the focus of the study will
be unambiguous.
Activities
• Identify members of the planning team.
Identify the primary decision maker(s).
Develop a concise description of the problem.
• Determine resources - budget, personnel, and
schedule.
4.1.1 Identify Members of the Planning Team
The planning team comprises personnel representing all phases of the project and may include
stakeholders, decision makers, technical project managers, samplers, chemists, process
engineers, QA/QC managers, statisticians, risk assessors, community leaders, grass roots
organizations, and other data users.
4.1.2 Identify the Primary Decision Maker
Identify the primary decision maker(s) or state the process by which the decision will be made
(for example, by consensus).
4.1.3 Develop a Concise Description of the Problem
Develop a problem description to provide background information on the fundamental issue to
be addressed by the study. For RCRA waste-related studies, the "problem" could involve
determining one of the following: (1) if a solid waste should be classified as a hazardous waste,
(2) if a hazardous waste is prohibited from land disposal, (3) if a treated hazardous waste
attains the applicable treatment standard, (4) if a cleanup goal has been attained, or (5) if
hazardous constituents have migrated from a waste management unit.
Summarize existing information into a "conceptual model" or conceptual site model (CSM)
including previous sampling information, preliminary estimates of summary statistics such as the
mean and standard deviation, process descriptions and materials used, and any spatial and
temporal boundaries of the waste or study area that can be defined. A CSM is a
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three-dimensional "picture" of site conditions at a discrete point in time (a snapshot) that
conveys what is known or suspected about the facility, releases, release mechanisms,
contaminant fate and transport, exposure pathways, potential receptors, and risks. The CSM
does not have to be based on a mathematical or computer model, although these tools often
help to visualize current information and predict future conditions. The CSM should be
documented by written descriptions of site conditions and supported by maps, cross sections,
analytical data, site diagrams that illustrate actual or potential receptors, and any other
descriptive, graphical, or tabular illustrations necessary to present site conditions.
4.1.4 Specify Available Resources and Relevant Deadlines
Identify available financial and human resources, identify deadlines established by permits or
regulations, and establish a schedule. Allow time for developing acceptance and performance
criteria, preparing planning documents (such as a QAPP, sampling plan, and/or WAP),
collecting and analyzing samples, and interpreting and reporting data.
4.2 Step 2: Identify the Decision
The goal of this step is to define the
questions that the study will attempt to
answer and identify what actions may be
taken based on the outcome of the study.
As part of this step, perform the four
activities described in the following
sections.
4.2.1 Identify the Principal Study
Question
DQO Step 2: Identify the Decision
Purpose
To define what specific decisions need to be made or
what questions need to be answered.
Activities
Identify the principal study question.
• Define the alternative actions that could result from
resolution of the principal study question.
Develop a decision statement.
• Organize multiple decisions.
Based on the problem identified in Step
1, identify the study question and state it
as specifically as possible. This is an
important step because the manner in which you frame the study question can influence
whether sampling is even appropriate, and if so, how you will evaluate the results. Here are
some examples of study questions that might be posed in a RCRA-related waste study:
Does the filter cake from the filter press exhibit the TC at its point of generation?
Does the treated waste meet the universal treatment standard (UTS) for land
disposal under 40 CFR 268?
Has the soil remediation at the SWMU attained the cleanup goal for benzene?
Have hazardous constituents migrated from the land treatment unit to the
underlying soil at concentrations significantly greater than background
concentrations?
Are radioactive and hazardous wastes colocated, producing a mixed waste
management scenario?
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Before conducting a waste-sampling and testing program to comply with RCRA, you should
review the specific regulatory requirements in 40 CFR in detail and consult with staff from your
EPA region or the representative from your State (if your State is authorized to implement the
regulation).
4.2.2 Define the Alternative Actions That Could Result from Resolution of the Principal
Study Question
Generally, two courses of action will result from the outcome of the study. One that involves
action, such as deciding to classify a solid waste as a hazardous waste, and one that requires
an alternative action, such as deciding to classify a solid waste as a nonhazardous solid waste.2
4.2.3 Develop a Decision Statement
In performing this activity, simply combine the principal study question and the alternative
actions into a "decision statement." For example, you may wish to determine whether a waste
exhibits a hazardous waste characteristic. The decision statement should be in writing (for
example, in the QAPP) and agreed upon by the planning team. This approach will help avoid
misunderstandings later in the process.
4.2.4 Organize Multiple Decisions
If several separate decisions statements must be defined to address the problem, then you
should list them and identify the sequence in which they should be resolved. For example, if
you classify a solid waste as a nonhazardous waste, then you will need to make a waste
management decision. Options might include land disposal (e.g., in an industrial landfill or a
municipal solid waste landfill), recycling, or some other use. You might find it helpful to
document the decision resolution sequence and relationships in a diagram or flowchart.
4.3 Step 3: Identify Inputs to the
Decision
In most cases, it will be necessary to
collect data or new information to resolve
the decision statement. To identify the
type and source of this information,
perform the activities outlined in the
following four sections.
4.3.1 Identify the Information
Required
For RCRA-related waste studies,
information requirements typically will
DQO Step 3: Identify Inputs to the Decision
Purpose
To identify data or other information required to resolve
the decision statement.
Activities
Identify the information required to resolve the
decision statement.
Determine the sources of information.
• Identify information needed to establish the Action
Level.
Identify sampling and analysis methods that can
meet the data requirements.
Testing alone might not be sufficient to determine if a solid waste is hazardous waste. You also should
apply knowledge of the waste generation process to determine if the solid waste is a hazardous waste under 40 CFR
261.
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include samples to be collected, variables to be measured (such as total concentrations, TCLP
results, or results of tests for other characteristics, such as reactivity, ignitability, and
corrosivity), the units of measure (such as mg/L), the form of the data (such as on a dry weight
basis), and waste generation or process knowledge.
4.3.2 Determine the Sources of Information
Identify and list the sources of information needed and qualitatively evaluate the usefulness of
the data. Existing information, such as analytical data, can be very valuable. It can help you
calculate the appropriate number of new samples needed (if any) and reduce the need to collect
new data (see also Section 5.4).
4.3.3 Identify Information Needed To Establish the Action Level
The Action Level is the threshold value that provides the criterion for choosing between
alternative actions. Under RCRA, there are several types of Action Levels.
The first type of Action Level is a fixed standard or regulatory threshold (RT) usually specified as
a concentration of a hazardous constituent (e.g., in mg/L). Examples of regulatory thresholds
that are Action Levels in the RCRA regulations include the TC Regulatory Levels at 40 CFR
261.24 and the Land Disposal Restrictions (LDR) numeric treatment standards at 40 CFR
268.40.
Another criterion for choosing between alternative actions is defined by the property of a waste.
Three such properties are defined in the RCRA regulations: ignitability (§ 261.21), corrosivity
(§ 261.22), and reactivity (§ 261.23). The results of test methods used to determine if a waste is
ignitable, corrosive, or reactive are interpreted as either "pass" or "fail" -- i.e., the waste either
has the property or it does not. Note that a concentration measurement, such as a TCLP
sample analysis result, also can be interpreted as either "pass" or "fail" based on whether the
value is less than or greater than a specified threshold.
A third criterion for choosing between alternative actions involves making a comparison
between constituent concentrations at different times or locations to determine if there has been
a change in process or environmental conditions over time. In these situations, you need to
determine if the two sets of data are different relative to each other rather than checking for
compliance with a fixed standard.
Finally, an Action Level can represent a proportion of the population having (or not having)
some characteristic. For example, while it might be desirable to have all portions of a waste or
site comply with a standard, it would be more practical to test whether some high proportion
(e.g., 0.95) of units of a given size, shape, and orientation comply with the standard. In such a
case, the Action Level could be set at 0.95.
For more information on identifying the Action Level, see Section 2 (RCRA regulatory drivers for
waste sampling and testing), the RCRA regulations in 40 CFR, ASTM Standard D 6250
(Standard Practice for Derivation of Decision Point and Confidence Limit for Statistical Testing
of Mean Concentration in Waste Management Decisions), or consult with your State or EPA
Regional staff.
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4.3.4 Confirm That Sampling and Analytical Methods Exist That Can Provide the
Required Environmental Measurements
Identify and evaluate candidate sampling and analytical methods capable of yielding the
required environmental measurements. You will need to revisit this step during Step 7 of the
DQO Process ("Optimize the Design for Obtaining the Data") after the quantity and quality of the
necessary data are fully defined. In evaluating sampling methods, consider the medium to be
sampled and analyzed, the location of the sampling points, and the size, shape and orientation
of each sample (see also Section 6, "Controlling Variability and Bias in Sampling" and Section
7, "Implementation: Selecting Equipment and Conducting Sampling").
In evaluating analytical methods, choose the appropriate candidate methods for sample
analyses based on the sample matrix and the analytes to be determined.
Guidance on the selection of analytical methods can be found in Chapter Two of SW-846
("Choosing the Correct Procedure"). Up-to-date information on analytical methods can be found
at SW-846 "On Line" at http://www.epa.gov/epaoswer/hazwaste/test/main.htm.
4.4 Step 4: Define the Study Boundaries
In this step 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.
To define the study boundaries, perform
the activities described in the following
five sections.
4.4.1 Define the Target Population of
Interest
It is important for you to clearly define the
target population to be sampled. Ideally,
the target population coincides with the
population to be sampled (Cochran 1977)
- that is, the target population should represent the total collection of all possible sampling units
that could be drawn. Note that the "units" that make up the population are defined operationally
based on their size, shape, orientation, and handling (i.e., the "sample support").3 The sampling
unit definition must be considered when defining the target population because any changes in
the definition can affect the population characteristics. See Section 6.3.1 for guidance on
establishing the appropriate size (mass) of a sample, and see Section 6.3.2 for guidance on
DQO Step 4: Define the Study Boundaries
Purpose
To define the spatial and temporal boundaries that are
covered by the decision statement.
Activities
Define the target population of interest.
Define the "sample support"
• Define the spatial boundaries that clarify what the
data must represent.
• Define the time frame for collecting data and making
the decision.
Identify any practical constraints on data collection.
• Determine the smallest subpopulation, area, volume,
or time for which separate decisions must be made.
The physical size (expressed as mass or volume), shape, and orientation of a sample is known as the
sample support. Sample support plays an important role in characterizing waste or environmental media and in
minimizing variability caused by the sampling process. The concept of support is discussed in greater detail in
Section 6.2.3.
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establishing the appropriate shape and orientation of sample.
Define the target population in terms of sampling units, the decision-making volume, and the
location of that volume.
Sampling at the point of generation is required by regulation when determining the regulatory
status of a waste. See 55 FR 11804, March 29, 1990, and 55 FR 22652, June 1, 1990.
J
4.4.2 Define the Spatial Boundaries
If sampling at the point of waste generation (i.e., before the waste is placed in a container or
transport unit), then the sampling problem could involve collecting samples of a moving stream
of material, such as from a conveyor, discharge pipe, or as poured into a container or tank. If
so, then physical features such as the width of the flow or discharge and the rate of flow or
discharge will be of interest for defining the spatial boundary of the problem.
If the sampling problem involves collecting samples from a waste storage unit or transport
container, then the spatial boundaries can be defined by some physical feature, such as
volume, length, width, height, etc. The spatial boundaries of most waste storage units or
containers can be defined easily. Examples of these units follow:
Container such as a drum or a roll-off box
Tank
Surface Impoundment
Staging Pile
Waste Pile
Containment Building.
In other cases, the spatial boundary could be one or more geographic areas, such as areas
representing "background" and "downgradient" conditions at a land treatment unit. Another
example is a SWMU area that has been subject to remediation where the objective is verify that
the cleanup goal has been achieved over a specified area or volume at the SWMU. If the study
requires characterization of subsurface soils and ground water, then consult other guidance (for
example, see USEPA 1989a, 1989b, 1991d, 1992a, 1993c, and 1996b).
To help the planning team visualize the boundary, it may be helpful to prepare a drawing, map,
or other graphical image of the spatial boundaries, including a scale and orientation (e.g., a
north arrow). If appropriate and consistent with the intended use of the information, maps also
should identify relevant surface features (such as buildings, structures, surface water bodies,
topography, etc.) and known subsurface features (pipes, utilities, wells, etc.).
If samples of waste will be taken at the point of generation (e.g., when the waste becomes a
solid waste), the location of that point should be defined in this step of the DQO Process.
4.4.3 Define the Temporal Boundary of the Problem
A temporal boundary could be defined by a permit or regulation (such as the waste generated
per day) or operationally (such as the waste generated per "batch" or truck load). You should
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determine the time frame to which the decision applies and when to collect the data. In some
cases, different time intervals might be established to represent different populations (e.g., in
the case where there is a process change over time that affects the character of the waste).
Waste characteristics or chemistry, such as the presence of volatile constituents, also could
influence the time frame within which samples are collected. For example, volatilization could
occur over time.
4.4.4 Identify Any Practical Constraints on Data Collection
Identify any constraints or obstacles that could potentially interfere with the full implementation
of the data collection design. Examples of practical constraints include physical access to a
sampling location, unfavorable weather conditions, worker health and safety concerns,
limitations of available sampling devices, and availability of the waste (e.g., as might be the
case for wastes generated from batch processes) that could affect the schedule or timing of
sample collection.
4.4.5 Define the Scale of Decision Making
Define the smallest, most appropriate subsets of the population (sub-populations), waste, or
media to be characterized based on spatial or temporal boundaries. The boundaries will define
the unit of waste or media about which a decision will be made. The unit is known as the
decision unit.
When defining the decision unit, the consequences of making a decision error should be
carefully considered. The consequences of making incorrect decisions (Step 6) are associated
with the size, location, and shape of the decision unit. For example, if a decision, based on the
data collected, results in a large volume of waste being classified as nonhazardous, when in
fact a portion of the waste exhibits a hazardous waste characteristic (e.g., due to the presence
of a "hot spot"), then the waste generator could potentially be found in violation of RCRA . To
limit risk of managing hazardous waste with nonhazardous waste, the waste handler should
consider dividing the waste stream into smaller decision units - such as the volume of waste
that would be placed into an individual container to be shipped for disposal - and make a
separate waste classification decision regarding each decision unit.
The planning team may establish decision units based on several considerations:
• Risk - The scale of the decision making could be defined based on an exposure
scenario. For example, if the objective is to evaluate exposures via direct contact
with surface soil, each decision unit could be defined based on the geographic
area over which an individual is assumed to move randomly across over time. In
EPA's Superfund program, such a unit is known as an "exposure area" or EA
(USEPA 1992c and 1996f). An example of an EA from EPA's So/7 Screening
Guidance: User's Guide (USEPA 1996f) is the top 2 centimeters of soil across a
0.5-acre area. In this example, the EA is the size of a suburban residential lot
and the depth represents soil of the greatest concern for incidental ingestion of
soil, dermal contact, and inhalation of fugitive dust.
If evaluation of a decision unit or EA for the purpose of making a cleanup
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decision finds that cleanup is needed, then the same decision unit or EA should
be used when evaluating whether the cleanup standard has been attained.
Furthermore, the size, shape, and orientation (the "sample support") of the
samples used to determine that cleanup was necessary should be the same for
samples used to determine whether the cleanup standard is met (though this last
condition is not strictly necessary when the parameter of interest is the mean).
• Operational Considerations - The scale of the decision unit could be defined
based on operational considerations, such as the need to characterize each
"batch" of waste after it has been treated or the need to characterize each drum
as it is being filled at the point of waste generation. As a practical matter, the
scale for the decision making often is defined by the spatial boundaries - for
example as defined by a container such as a drum, roll-off box, truck load, etc. or
the time required to fill the container.
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
facility. 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.
Additional information and considerations on defining the scale of the decision making can be
found in Guidance for the Data Quality Objectives Process for Hazardous Waste Site
Operations EPA QA/G-4HW (USEPA 2000a) and Guidance for the Data Quality Objectives
Process EPA QA/G-4 (USEPA 2000b).
4.5 Step 5: Develop a Decision Rule
A statement must be developed that combines the parameter of interest and the Action Levels
with the DQO outputs already developed. The combination of these three elements forms the
decision rule and summarizes what attributes the decision maker wants to study and how the
information will assist in solving the central problem. To develop the decision rule, perform the
activities described in the following three sections:
4.5.1 Specify the Parameter of Interest
A statistical "parameter" is a descriptive
measure of a population such as the
population mean, median, or a percentile
(see also Section 3.2). See Table 2.
Some of the RCRA regulations specify the
parameter of interest. For example, the
comparable fuels sampling and analysis
requirements at 40 CFR 261.38(c)(8)(iii)(A)
specify the mean as the parameter of
interest, and the ground-water monitoring
requirements at 40 CFR 264.97 specify the
parameter of interest for each statistical
DQO Step 5: Develop a Decision Rule
Purpose
To define the parameter of interest, specify the Action
Level and integrate previous DQO outputs into a single
statement that describes a logical basis for choosing
among alternative actions; i.e., define how the data will
be used to make a decision.
Activities
Specify the parameter of interest (mean, median,
percentile).
Specify the Action Level for the study.
• Develop a decision rule.
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test. Other RCRA regulations do not specify the parameter of interest, however, you can select
a parameter based on what the Action Level is intended to represent. In general, if an Action
Level is based on long-term average health effects, the parameter of interest could be the
population mean (USEPA 1992a). If the Action Level represents a value that should never (or
rarely) be exceeded, then the parameter of interest could be an upper population percentile,
which can serve as a reasonable approximation of the maximum value.
If the objective of the study does not involve estimation of a parameter or testing a hypothesis,
then specification of a parameter is not necessary.
Table 2. Population Parameters and Their Applicability to a Decision Rule
Parameter
Definition
Appropriate Conditions for Use
Mean
Median
Average
Estimate central tendency: Comparison of middle part of
population to an Action Level.
Middle observation of the
distribution; 50th percentile;
half of data are above and
below
May be preferred to estimate central tendency if the population
contains many values that are less than the limit of quantitation.
The median is not a good choice if more than 50% of the
population is less than the limit of quantitation because a true
median does not exist in this case. The median is not
influenced by the extremes of the contaminant distribution.
Percentile Specified percent of sample
that is equal to or below the
given value
For cases where it is necessary to demonstrate that, at most,
only a small portion of a population could 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.
4.5.2 Specify the Action Level for the Study
You should specify an Action Level or concentration limit that would cause the decision maker
to choose between alternative actions. Examples of Action Levels follow:
Comparable/syngas fuel constituent specification levels specified at § 261.38
Land disposal restrictions concentration level treatment standards at § 268.40
and § 268.48
Risk-based cleanup levels specified in a permit as part of a corrective action
"Pass" or "fail" thresholds for tests for ignitability, corrosivity, reactivity4, and
toxicity.
Also, be sure the detection or quantitation limits for the analytical methods identified in DQO
Step 3 (Section 4.3) are below the Action Level, if possible.
EPA uses a narrative criteria to define most reactive wastes, and waste handlers should use their
knowledge to determine if a waste is sufficiently reactive to be regulated.
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If your objective is to compare "onsite" to "background" to determine if there is a statistically
significant increase above background (as would be the case for monitoring releases from a
land treatment unit under § 264.278), you will not need to specify an Action Level; rather, the
Action Level is implicitly defined by the background concentration levels and the variability in the
data. A summary of methods for determining background concentrations in soil can be found in
USEPA 1995a. Methods for determining background concentrations in ground water can be
found in USEPA 1989b and 1992b.
Finally, note that some studies will not require specification of a regulatory or risk-based Action
Level. For example, if the objective may be to identify the existence of a release, samples could
be obtained to verify the presence or absence of a spill, leak, or other discharge to the
environment. Identifying a potential release also could include observation of abandoned or
discarded barrels, containers, and other closed receptacles containing hazardous wastes or
constituents (see 61 FR No. 85, page 19442).
4.5.3 Develop a Decision Rule
After you have completed the above activities, you can construct a decision rule by combining
the selected population parameter and the Action Level with the scale of the decision making
(from DQO Process Step 4) and the alternative action (from DQO Step 2). Decision rules are
expressed as "if (criterion)..., then (action)...." A hypothetical example follows:
"If the true 95th percentile of all possible 100-gram samples of the waste being
placed in the 20-cubic yard container is less than 5.0 mg/L TCLP lead, then the
solid waste will be classified as nonhazardous waste. Otherwise, the solid waste
will be classified as a RCRA hazardous waste."
Note that this is a functional decision rule based on an ideal condition (i.e., knowledge of the
true concentration that equals the 95th percentile of all possible sample analysis results). It also
identifies the boundary of the study by specifying the sample unit (100-gram samples in
accordance with the TCLP) and the size of the decision unit. It does not, however, specify the
amount of uncertainty the decision maker is willing to accept in the estimate. You specify that in
the next step.
4.6 Step 6: Specify Limits on
Decision Errors
Because samples represent only a portion
of the population, the information available
to make decisions will be incomplete;
hence, decision errors sometimes will be
made. Decision errors occur because
decisions are made using estimates of the
parameter of interest, rather than the true
(and unknown) value. In fact, if you
repeatedly sampled and analyzed a waste
over and over in an identical manner the
results would be a little different each time
(see Figure 8 in Section 3). This variability
Step 6: Specify Limits on Decision Errors
Purpose
To specify the decision maker's tolerable limits on
decision error.
Activities
• Identify potential sources of variability and bias in the
sampling and measurement processes (see Section 6)
• Determine the possible range on the parameter of
interest.
Choose the null hypothesis.
• Consider the consequences of making an incorrect
decision.
Specify a range of values where the consequences
are minor (the "gray region")
Specify an acceptable probability of making a decision
error.
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in the results is caused by the non-homogeneity of the waste or media, slight differences in how
the samples of the waste were collected and handled, variability in the analysis process, and
the fact that only a small portion of the waste is usually ever sampled and tested. (See Section
6.1 fora more detailed discussion of sources of variability and bias in sampling). For example,
if you conduct sampling and analysis of a solid waste and classify it as "nonhazardous" based
on the results, when in fact it is a hazardous waste, you will have made a wrong decision or
decision error. Alternatively, if you classify a solid waste as hazardous, when in fact it is
nonhazardous, you also will have made a decision error.
There are two types of decision error. A "Type I" or "false rejection" decision error occurs if you
reject the null hypothesis when it is true. (The "null hypothesis" is simply the situation presumed
to be true or the "working assumption".) A "Type II" or "false acceptance" decision error occurs
if you accept the null hypothesis when it is false.5
Table 3 summarizes the four possible situations that might arise when a hypothesis is tested.
The two possible true conditions correspond to the two columns of the table: the null
hypothesis or "baseline assumption" is either true or the alternative is true. The two kinds of
decisions are shown in the body of the table. Either you decide the baseline is true, or you
decide the alternative is true. Associated with these two decisions are the two types of risk -
the risk of making a Type I (false rejection) error (denoted by a) and the risk of making a Type
II (false acceptance) error (denoted by /?). You can improve your chances of making correct
decisions by reducing a and /3 (which often requires more samples or a different sampling
design) and by using field sampling techniques that minimize errors related to sampling
collection and handling (see also Sections 6 and 7).
Table 3. Conclusions and Consequences for a Test of Hypotheses
True Condition
Baseline is True Alternative is True
Decision
Based on
Sample Data
Baseline is True
Alternative is True
Correct Decision
Type I (false rejection) error
(probability a )
Type II (false acceptance) error
(probability ft]
Correct Decision
For many sampling situations under RCRA, the most conservative (i.e., protective of the
environment) approach is to presume that the constituent concentration in the waste or media
exceeds the standard in the absence of strong evidence to the contrary.6 For example, in
5 Statisticians sometimes refer to a Type I error as a "false positive," and a Type II error as a "false
negative." The terms refer to decision errors made relative to a null hypothesis, and the terms may not necessarily
have the same meaning as those used by chemists to describe analytical detection of a constituent when it is not
really present ("false positive") or failure to detect a constituent when it really is present ("false negative").
6 An exception to this assumption is found in "detection monitoring" and "compliance monitoring" in which
underlying media (such as soil, pore water, or ground water) at a new waste management unit are presumed "clean"
until a statistically significant increase above background is demonstrated (in the case of detection monitoring) or a
statistically significant increase over a fixed standard is demonstrated (in the case of compliance or assessment
monitoring).
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testing a solid waste to determine if it exhibits the TC, the null hypothesis can be stated as
follows: "the concentration is equal to or greater than the TC regulatory level." The alternative
hypothesis is "the concentration is less than the TC regulatory level." After completion of the
sampling and analysis phase, you conduct an assessment of the data. If your estimate of the
parameter of interest is less than the threshold when the true value of the parameter exceeds
the threshold, you will make a decision error (a Type I error). If the estimate of the parameter of
interest is greater than the threshold when the true value is less than the threshold, you also will
make an error (a Type II error) - but one that has little potential adverse impacts to human
health and the environment.
Note that during the planning phase and during sampling you will not know which kind of error
you might make. Later, after a decision has been made, if you rejected the null hypothesis then
you either made a Type I (false rejection) decision error or not; you could not have made a Type
II (false acceptance) decision error. On the other hand, if you did not reject the null hypothesis,
then you either made a Type II (false acceptance) error or not; you could not have made a Type
I (false rejection) error. In either case, you will know which type of error you might have made
and you will know the probability that the error was made.
In the RCRA program, EPA is concerned primarily with controlling errors having the most
adverse consequences for human health and the environment. In the interest of protecting the
environment and maintaining compliance with the regulations, there is an incentive on the part
of the regulated entity to minimize the chance of a Type I decision error. The statistical
methods recommended in this document emphasize controlling the Type I (false rejection) error
rate and do not necessarily require specification of a Type II (false acceptance) error rate.
The question for the decision maker then becomes, what is the acceptable probability (or
chance) of making a decision error? To answer this question, four activities are suggested.
These activities are based on guidance found in Guidance for the Data Quality Objectives
Process QA/G-4 (USEPA 2000b) but have been tailored for more direct application to RCRA
waste-related studies. The Guidance for the Data Quality Objectives Process EPA QA/G-4
also provides detailed guidance on the use of a graphical construct called a Decision
Performance Curve to represent the quality of a decision process.
4.6.1 Determine the Possible Range on the Parameter of Interest
Establish the possible range (maximum and minimum values) of the parameter of interest using
data from a pilot study, existing data for a similar waste stream, or process knowledge (e.g.,
using a materials-balance approach). It is desirable, but not required, to have an estimate of
the standard deviation as well.
4.6.2 Identify the Decision Errors and Choose the Null Hypothesis
Table 4 presents four examples of decision errors that could be made in a RCRA waste study.
In the first three examples, the consequences of making a Type I error could include increased
risk to human health and the environment or a potential enforcement action by a regulatory
authority. The consequences of making a Type II error could include unnecessary financial and
administrative resources required to manage the waste as hazardous (when, in fact, it is not) or
continuing site cleanup activities when, in fact, the site is "clean."
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Table 4. Examples of Possible Decision Errors in RCRA Waste Studies
Regulatory Requirement
"Null Hypothesis"
(baseline condition)
Possible Decision Errors
Type I Error ( (X )
"False Rejection"
Type II Error (j3)
"False Acceptance"
Example 1: Under 40 CFR
261.11, conduct sampling to
determine if a solid waste is a
hazardous waste by the TC.
The solid waste contains TC
constituents at
concentrations equal to or
greater than their applicable
regulatory levels (i.e., the
solid waste is a hazardous
waste).
Concluding the waste
is not hazardous
when, in fact, it is.
Deciding the waste is
hazardous when, in
fact, it is not.
Example 2: Under 40 CFR
268.7, conduct sampling and
testing to certify that a
hazardous waste has been
treated so that concentrations
of hazardous constituents
meet the applicable LDR
treatment standards.
The concentration of the
hazardous constituents
exceeds the treatment
standard (i.e., the treatment
standard has not been
attained).
Concluding the
treatment standard
has been met when, in
fact, it has not.
Concluding the
treatment standard
has not been met
when, in fact, it has.
Examples: Under 40 CFR
264.101 (and proposed
Subpart S - Corrective Action
at SWMUs), a permittee
conducts testing to determine
if a remediation at a SWMU
has attained the risk-based
cleanup standard specified in
the permit*
The mean concentration in
the SWMU is greater than the
risk-based cleanup standard
(i.e., the site is
contaminated).f
Concluding the site is
"clean" when, in fact, it
is contaminated.
Concluding the site is
still contaminated
when, in fact, it is
"clean."
Example 4: Under 40 CFR
264.98(f), detection
monitoring, monitor ground
water at a regulated unit to
determine if there is a
statistically significant
increase of contamination
above background.
The level of contamination in
each point of compliance well
does not exceed background.
Concluding the
contaminant
concentration in a
compliance well
exceeds background
when, in fact, it does
not.
Concluding the
contaminant
concentration in a
compliance well is
similar to background
when, in fact, it is
higher.
* If the cleanup standard is based on "background" rather than a risk-based cleanup standard, then the
hypotheses would be framed in reverse where the mean background and on-site concentrations are presumed
equal unless there is strong evidence that the site concentrations are greater than background.
f A parameter other than the mean may be used to evaluate attainment of a cleanup standard (e.g., see USEPA
1989a).
In Example 4, however, the null hypothesis is framed in reverse of Examples 1 through 3.
When conducting subsurface monitoring to detect contamination at a new unit (such as in
detection monitoring in the RCRA ground-water monitoring program), the natural subsurface
environment is presumed uncontaminated until statistically significant increases over the
background concentrations are detected. Accordingly, the null hypothesis is framed such that
the downgradient conditions are consistent with the background. In this case, EPA's emphasis
on the protection of human health and the environment calls for minimizing the Type II error -
the mistake of judging downgradient concentrations the same as the background when, in fact,
44
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they are higher. Detailed guidance on detection and compliance monitoring can be found in
RCRA Ground-Water Monitoring: Draft Technical Guidance (USEPA 1992c) and EPA's
guidance on the statistical analysis of ground-water monitoring data at RCRA facilities (USEPA
1989band 1992b).
4.6.3 Specify a Range of Possible Parameter Values Where the Consequences of a
False Acceptance Decision Error are Relatively Minor (Gray Region)
The "gray region" is one component of the quantitative decision performance criteria the
planning team establishes during the DQO Process to limit impractical and infeasible sample
sizes. The gray region is a range of possible parameter values near the action level where it is
"too close to call." This gray area is where the sample data tend toward rejecting the baseline
condition, but the evidence (data statistics) is not sufficient to be overwhelming. In essence, the
gray region 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.
In statistical language, 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 the calculations for
determining the number of samples that need to be collected so that the decision maker may
have confidence in the decision made based on the data collected.
The first boundary of the gray region is the Action Level. The other boundary of the gray region
is established by evaluating the consequences of a false 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 limit on the probability of this error occurring. The gray
region (or "area of uncertainty") establishes the minimum distance from the Action Level where
the decision maker would like to begin to control false acceptance decision errors.
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 quality of the decision process, including the boundaries of the gray region, can be depicted
graphically using a Decision Performance Goal Diagram (DPGD). Detailed guidance on the
construction and use of DPGDs is given in EPA DQO guidance documents (e.g., USEPA 2000a
and 2000b) and in Data Quality Objectives Decision Error Feasibility Trials Software (DEFT) -
User's Guide (USEPA 2001a). Figure 12(a) and Figure 12(b) show how some of the key
outputs of Step 6 of the DQO Process are depicted in a DPGD when the parameter of interest is
the mean (Figure 12(a)) and a percentile (Figure 12(b) .
The DPGD given in Figure 12(a) shows how the boundaries of the gray region are set when the
null hypothesis is established as "the true mean concentration exceeds the standard." Notice
that the planning team has set the action level at 5 ppm and the other boundary of the gray
region at 4 ppm. This implies that when the mean calculated from the sample data is less than
4 ppm (and the planning assumptions regarding variability hold true), then the data will be
considered to provide "overwhelming evidence" that the true mean (unknown, of course) is
below the action level.
45
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ra <
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0.9 -
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t_
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given support that have concentrations less
than the applicable standard)
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Figure 12(b). Decision Performance Goal Diagram where a percentile is the parameter of
interest. Null hypothesis (baseline condition): true proportion - of all possible samples of
a given support that are less than the applicable standard — is less than 0.90.
46
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Now consider the DPGD given in Figure 12(b). The figure shows how the gray region is set
when the null hypothesis is established as "the true proportion of samples below the
concentration standard is less than 0.90." Notice in this example the planning team has set the
action level at 0.90 and the other boundary of the gray region at 0.95. This implies that when
the proportion of samples that comply with the standard is greater than 0.95, then the data will
be considered to provide "overwhelming evidence" that the true proportion (unknown, of course)
is greater than the action level of 0.90.
The term "samples" refers to all possible samples of a specified size, shape, and orientation (or
sample support) drawn from the DQO decision unit. Sampling procedures and sample
support can affect the measurement value obtained on individual samples and have a profound
effect on the shape of the sampling distribution. Thus, the outcome of statistical procedures
that examine characteristics of the upper tail of the distribution can be influenced by the sample
support - more so than when the mean is the parameter of interest. Accordingly, when testing
for a proportion, a complete statement of the null hypothesis should include specification of the
sample support. See Sections 6.3.1 and 6.3.2 for guidance on establishing the appropriate
sample support as part of the DQO Process.
4.6.4 Specify an Acceptable Probability of Making a Decision Error
You can never completely eliminate decision errors or even know when they have occurred, but
you can quantify the probability of making such errors. In this activity, you establish the
acceptable probability of making a decision error.
The Type I error rate ((X ) is a measure of the amount of "mistrust" you have in the conclusion
(Myers 1997) and is also known as the significance level for a test. The flip side of this is the
amount of faith or confidence you have in the conclusion. The confidence level is denoted
mathematically as 1 — a . As stated previously, the Type I error (the error of falsely rejecting
the null hypothesis) is of greatest concern from the standpoint of environmental protection and
regulatory compliance.
The probability of making a Type II error (the error of falsely accepting the null hypothesis) also
can be specified. For example, if the sample data lead you to conclude that a waste does not
qualify for the comparable fuels exclusion (40 CFR 261.38), when the true mean concentration
in the waste is in fact below the applicable standard, then a Type II (false acceptance error) has
been made. (Note that some of the statistical methods given in this document do not require
specification of a Type II error rate).
As a general rule, the lower you set the probability of making a decision error, the greater the
cost in terms of the number of samples required, time and personnel required for sampling and
analysis, and financial resources required.
An acceptable probability level for making a decision error should be established by the
planning team after consideration of the RCRA regulatory requirements, guidance from EPA or
the implementing agency, the size (volume or weight) of the decision unit, and the
consequences of making a decision error. In some cases, the RCRA regulations specify the
Type I or Type II (or both) error rates that should be used. For example, when testing a waste
to determine whether it qualifies for the comparable/syngas fuel exclusion under 40 CFR
261.38, the regulations require that the determination be made with a Type I error rate set at 5
47
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percent (i.e., « = 0.05).7
In other cases, the regulations do not specify any decision error limits. The planning team must
specify the decision error limits based on their knowledge of the waste; impacts on costs,
human health, and ecological conditions; and the potential consequences of making a decision
error. For example, if the quantity of waste (that comprises a decision unit) is large and/or
heterogeneous, then a waste handler may require high confidence (e.g., 95 or 99 percent) that
a high proportion of the waste or media complies with the applicable standard. On the other
hand, if the waste quantity is a relatively small (e.g., a drum) and sampling and measurement
error can be minimized, then the waste handler may be willing to relax the confidence level
required or simply use a nonstatistical (e.g., judgmental) sampling design and reduce the
number of samples to be taken.
For additional guidance on controlling errors Section 6 and EPA's DQO guidance (USEPA
2000a and 2000b).
4.7 Outputs of the First Six Steps of the DQO Process
Table 5 provides a summary of the outputs of the first six steps of the DQO Process. Typically,
this information will be incorporated into a QAPP, WAP, or other similar planning document (as
described in Section 5.7). The DQOs can be simple and straight forward for simple projects and
can be documented in just a few pages with little or no supporting data. For more complex
projects, the DQOs can be more lengthy, and the supporting data may take up volumes. The
team that will be optimizing the sample design(s) will need the information to support their plan
development. The project manager and the individuals who assess the overall outcome of the
project also will need the information to determine if the DQOs were achieved.
Keep in mind that the DQO Process is an iterative one; it might be necessary to return to earlier
steps to modify inputs when new data become available or to change assumptions if achieving
the original DQOs is not realistic or practicable.
The last step (Step 7) in the DQO Process is described in detail in the next section of this
document. Example applications of the full DQO Process are presented in Appendix "I."
7 Under §261.38(c)(8)(iii)(A), a generator must demonstrate that "each constituent of concern is not present
in the waste above the specification level at the 95% upper confidence limit around the mean."
48
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DQO Step
Table 5. Summary of Outputs of the First Six Steps of the DQO Process
Expected Outputs
1. State the Problem
List of members of the planning/scoping team and their role/expertise in
the project. Identify individuals or organizations participating in the
project (e.g. facility name) and discuss their roles, responsibilities, and
organization.
A concise description of the problem.
Summary of available resources and relevant deadlines.
2. Identify the Decision
A decision statement that links the principal study question to possible
actions that will solve the problem or answer the question.
3. Identify Inputs to the Decision
A list of informational inputs needed to resolve the decision statement,
how the information will be used, sources of that information, and an
indication of whether the information is available for will need to be
obtained.
A list of environmental variables or characteristics that will be measured.
4. Define the Boundaries
A detailed description of the spatial and temporal boundaries of the
problem (i.e., define the population, each decision unit, and the sample
support).
Options for stratifying the population under study.
Any practical constraints that may interfere with the study.
5. Develop a Decision Rule
The parameter of interest that characterizes the population.
The Action Level or other method for testing the decision rule.
An "if ...then..." statement that defines the conditions that would cause
the decision maker to choose among alternative actions.
6. Specify Limits on Decision
Errors
Potential variability and bias in the candidate sampling and
measurement methods
The baseline condition (null hypothesis)
The boundaries of the gray region
The decision maker's tolerable decision error rates based on a
consideration of consequences of making an incorrect decision.
49
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OPTIMIZING THE DESIGN FOR OBTAINING THE DATA
Step 7: Optimize the Design for Collecting the Data
Purpose
To identify a resource-effective data collection design for
generating data that are expected to satisfy the DQOs.
Activities
• Review the outputs of the first six steps of the DQO
Process (see Section 5.1).
Consider various data collection design options,
including sampling and analytical design alternatives
(see Section 5.2), and composite sampling options
(see Section 5.3).
• For each data collection design alternative,
determine the appropriate number of samples (see
Section 5.4 or 5.5).
Select the most resource-effective design that
satisfies all of the data needs for the least costs (see
Section 5.6).
Prepare a QAPP, WAP, or similar planning document
as needed to satisfy the project and regulatory
requirement (see Section 5.7).
This section describes DQO Process Step
7, the last step in the DQO Process. The
purpose of this step is to identify an
optimal design for obtaining the data. An
optimal sampling design is one that
obtains the requisite information from the
samples for the lowest cost and still
satisfies the DQOs.
You can optimize the sampling design by
performing five activities that are
described in detail in this section. These
activities are based on those described in
Guidance for the Data Quality Objectives
Process EPA QA/G-4 (USEPA 2000b),
but they have been modified to more
specifically address RCRA waste-related
studies.
In this final planning step, combine the
data collection design information with the
other outputs of the DQO Process and
document the approach in a planning document such as a QAPP, WAP, or similar planning
document. As part of this step, it may be necessary to work through Step 7 more than once
after revisiting the first six steps of the DQO Process.
5.1 Review the Outputs of the First Six Steps of the DQO Process
Each of the steps in the DQO Process has a series of outputs that include qualitative and
quantitative information about the study. The outputs of the first six steps of the DQO Process,
as described in Section 4, serve as inputs to DQO Step 7.
Review the existing information and DQO outputs (see Table 5). Determine if any data gaps
exist and determine whether filling those gaps is critical to completion of the project. Data gaps
can be filled by means of a "preliminary study" or "pilot study." A preliminary study or pilot can
include collection of samples to obtain preliminary estimates of the mean and standard
deviation. In addition, a preliminary study can help you verify waste or site conditions, identify
unexpected conditions or materials present, gain familiarization with the waste and facility
operations, identify how the waste can be accessed, check and document the physical state of
the material to be sampled, and identify potential health and safety hazards that may be
present.
Review the potential sources of variability and bias ("error") that might be introduced in the
sampling design and measurement processes. See Section 6 for a discussion of sources of
error in sampling and analysis.
50
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5.2 Consider Data Collection Design Options
Data collection design incorporates two interdependent activities - the sample collection design
and analytical design.
Sampling Design: In developing a sampling design, you consider various strategies for
selecting the locations, times, and components for sampling, and you define appropriate
sample support. Examples of sampling designs include simple random, stratified
random, systematic, and judgmental sampling. In addition to sampling designs, make
sure your organization has documented standard operation procedures (SOPs) that
describe the steps to be followed when implementing a sampling activity (e.g.,
equipment preparation, sample collection, decontamination). For guidance on
suggested content and format for SOPs, refer to Guidance for the Preparing Standard
Operating Procedures (SOPs) EPA QA/G-6 (USEPA 2001 c). Sampling QA/QC activities
also should be part of sampling design. Activities used to document, measure, and
control data quality include project-specific quality controls (e.g., duplicate samples,
equipment blanks, field blanks, and trip blanks) and the associated quality assessments
(e.g., audits, reviews) and assurances (e.g., corrective actions, reports to management).
These activities typically are documented in the QAPP (see Section 5.7 and USEPA
1998a).
Analytical Design: In DQO Steps 3 and 5, an Action Level and candidate analytical
methods were identified. The information should be used to develop analytical options
in terms of cost, method performance, available turnaround times, and QA/QC
requirements. The analytical options can be used as the basis for designing a
performance-based cost-effective analytical plan (e.g., deciding between lower-cost field
analytical methods and/or higher cost laboratory methods). Candidate laboratories
should have adequate SOPs that describe the steps to be followed when implementing
an analytical activity (e.g., sample receipt procedures, subsampling, sample preparation,
cleanup, instrumental analysis, data generation and handling). If field analytical
techniques are used, hard copies of the analytical methods or SOPs should be available
in the field. Refer to Chapter Two of SW-846 for guidance on the selection of analytical
methods.
The goal of this step is to find cost-effective design alternatives that balance the number of
samples and the measurement performance, given the feasible choices for sample designs and
measurement methods.
Sampling design is the "where, when, and how" component of the planning process. In the
context of waste sampling under RCRA, there are two categories of sampling designs: (1)
probability sampling and (2) authoritative (nonprobability) sampling. The choice of a sampling
design should be made after consideration of the DQOs and the regulatory requirements.
Probability sampling refers to sampling designs in which all parts of the waste or media under
study have a known probability of being included in the sample. In cases in which all parts of
the waste or media are not accessible for sampling, the situation should be documented so its
potential impacts can be addressed in the assessment phase. Probability samples can be of
various types, but in some way, they all make use of randomization, which allows probability
statements to be made about the quality of estimates derived from the resultant data.
51
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Probability sampling designs provide the
ability to reliably estimate variability, the
reproducibility of the study (within limits),
and the ability to make valid statistical
inferences. Five types of probability
sampling designs are described in Sections
5.2.1 through 5.2.5:
Simple random sampling
Stratified random sampling
Systematic sampling
Ranked set sampling
Sequential sampling.
A strategy that can be used to improve the
precision (reproducibility) of most sampling
designs is composite sampling.
Composite sampling is not a sampling
design in and of itself, rather composite
sampling is a strategy used as part of a
probability sampling design or an
authoritative sampling design. Composite
sampling is discussed in Section 5.3.
Sampling Over Time or Space?
An important feature of probability sampling designs is
that they can be applied along a line of time or in space
(see Figure 13) or both (Gilbert 1987):
Time
Sampling designs applied over time can be described by a
one-dimensional model that corresponds to flowing
streams such as the following:
• Solid materials on a conveyor belt
A liquid stream, pulp, or slurry moving in a pipe or from
a discharge point (e.g., from the point of waste
generation)
Continuous elongated piles (Pitard 1993).
Space
For practical reasons, sampling of material over a three-
dimensional space is best addressed as though the
material consists of a series of overlapping two-
dimensional planes of more-or-less uniform thickness
(Pitard 1993, Gy 1998). This is the case for obtaining
samples from units such as the following:
Drums, tanks, or impoundments containing single or
multi-phasic liquid wastes
• Roll-off bins, relatively flat piles, or other storage units
Landfills, soil at a land treatment unit, or a SWMU.
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 help design the study. Information obtained during DQO
Step 3 ("Identify Inputs to the Decision") and DQO Step 4 ("Define the Study Boundaries")
should prove useful at this stage. One of the activities suggested in DQO Step 4 is to segregate
the waste stream or media into less heterogeneous subpopulations as a means of segregating
variability. To determine if this activity is appropriate, it is critical to have an understanding of
the various kinds of heterogeneity the constituent of concern exhibits within the waste or media
(Pitard 1993). Making assumptions that a waste stream is homogeneous can result in serious
sampling errors. In fact, some authors suggest the word "homogeneous" be removed from our
sampling vocabulary (Pitard 1993, Myers 1997).
Table 6 provides a summary of sampling designs discussed in this guidance along with
conditions for their use, their advantages, and their disadvantages. Figure 13 provides a
graphical representation of the probability sampling designs described in this guidance. A
number of other sampling designs are available that might perform better for your particular
situation. Examples include cluster sampling and double sampling. If an alternative sampling
design is required, review other publications such as Cochran (1977), Gilbert (1987), USEPA
(2000c) and consult a professional statistician.
52
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Table 6. Guidance for Selection of Sampling Designs
Sampling Design
Appropriate Conditions for Use
Advantages
Limitations
Probability Sampling
Simple Random Sampling
(Section 5.2.1)
Useful when the population of
interest is relatively homogeneous
(i.e., there are no major patterns or
"hot spots" expected).
Provides statistically unbiased
estimates of the mean,
proportions, and the variability.
Easy to understand and
implement.
Least preferred if patterns or
trends are known to exist and are
identifiable.
Localized clustering of sample
points can occur by random
chance.
Stratified Random Sampling
(Section 5.2.2)
Most useful for estimating a
parameter (e.g., the mean) of wastes
exhibiting high heterogeneity (e.g.,
there are distinct portions or
components of the waste with high
and low constituent concentrations or
characteristics).
Ensures more uniform coverage
of the entire target population.
Potential for achieving greater
precision in estimates of the
mean and variance.
May reduce costs over simple
random and systematic sampling
designs because fewer samples
may be required.
Enables computation of reliable
estimates for population
subgroups of special interest.
Requires some prior knowledge
of the waste or media to define
strata and to obtain a more
precise estimate of the mean.
Statistical procedures for
calculating the number of
samples, the mean, and the
variance are more complicated
than for simple random sampling.
Systematic Sampling
(Section 5.2.3)
Useful for estimating spatial patterns
or trends overtime.
Preferred over simple random
when sample locations are
random within each systematic
block or interval.
Practical and easy method for
designating sample locations.
Ensures uniform coverage of site,
unit, or process.
May be lower cost than simple
random sampling because it is
easier to implement.
May be misleading if the sampling
interval is aligned with the pattern
of contamination, which could
happen inadvertently if there is
inadequate prior knowledge of the
pattern of contamination.
Not truly random, but can be
modified through use of the
"random within blocks" design.
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Table 6. Guidance for Selection of Sampling Designs (Continued)
Sampling Design
Appropriate Conditions for Use
Advantages
Limitations
Probability Sampling (continued)
Ranked Set Sampling
(Section 5.2.4)
Useful for reducing the number of
samples required.
Useful when the cost of analysis
is much greater than the cost of
collecting samples.
Inexpensive auxiliary variable
(based on expert knowledge or
measurement) is needed and can
be used to rank randomly
selected population units with
respect to the variable of interest.
Useful if the ranking method has
a strong relationship with
accurate measurements.
Can reduce analytical costs.
Requires expert knowledge of
waste or process or use of
auxiliary quantitative
measurements to rank population
units.
Sequential Sampling
(Section 5.2.5)
Applicable when sampling and/or
analysis are quite expensive,
when information concerning
sampling and/or measurement
variability is lacking, when the
waste and site 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 specific
hypothesis.
May not be especially useful if
multiple waste characteristics are
of interest or if rapid decision
making is necessary.
Can reduce the number of
samples required to make a
decision.
Allows a decision to be made
with less sampling if there is a
large difference between the two
populations or between the true
value of the parameter of interest
and the standard.
If the concentration of the
constituent of concern is only
marginally different from the
action level, sequential
procedures will require an
increasing number of samples
approaching that required for
other designs such as simple
random or systematic sampling.
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Table 6. Guidance for Selection of Sampling Designs (Continued)
Sampling Design
Appropriate Conditions for Use
Advantages
Limitations
Authoritative Sampling
Judgmental
(Section 5.2.6.1)
Biased
(Section 5.2.6.2)
Useful for generating rough
estimates of the average
concentration or typical property.
To obtain preliminary information
about a waste stream or site to
facilitate planning or to gain
familiarity with the waste matrix
for analytical purposes.
To assess the usefulness of
samples drawn from a small
portion of the waste or site.
To screen samples in the field to
identify "hot" samples for
subsequent analysis in a
laboratory.
Useful to estimate "worst-case" or
"best-case" conditions (e.g., to
identify the composition of a leak,
spill, or waste of unknown
composition).
Can be very efficient with
sufficient knowledge of the site or
waste generation process.
Easy to do and explain.
The utility of the sampling design
is highly dependent on expert
knowledge of waste.
Nonprobability-based so
inference to the general
population is difficult.
Cannot determine reliable
estimates of variability.
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Sampling Over Space (two-dimensional plan view)
Sampling Over Time or Along a Transect (one-
dimensional)
Simple Random Sampling
Simple Random Sampling
(b)
(a)
Stratified Random Sampling
Stratified Random Sampling
Strata
Strata —-"^
A high • medium • low
high • medium • low
(C)
(d)
Systematic Grid Sampling
t
t
Systematic Sampling
(0
(e)
Random Sampling Within Blocks
Random Sampling Within Segments
(h)
(g)
Figure 13. Probability sampling designs over space or along an interval (modified after Cochran 1977 and Gilbert
1987)
56
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5.2.1 Simple Random Sampling
The simplest type of probability sampling
is simple random sampling (without
replacement), in which 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 space
(Figure 13(a)) or in time (Figure 13(b)) and
are often appropriate at an early stage of
an investigation in which little is known
about nonrandom variation within the
waste generation process or the site. All
of the sampling units should have equal
volume or mass, and ideally be of the
same shape and orientation if applicable
(i.e., they should have the same "sample
support").
2.
3.
4.
Box 3. Simple Random Sampling: Procedure
Divide the area of the study into N equal-size grids,
intervals (if sampling over time), or other units. The
spacing between adjacent sampling locations should
be established in the DQOs, but the length should be
measurable in the field with reasonable accuracy. The
total number of possible sampling locations (N) should
be much larger than n (the number of samples to be
collected).*
Assign a series of consecutive numbers to each
location between 1 and N.
Draww integers between 1 and N from a random
number table or use the random number function on a
hand-held calculator (i.e., generate a random number
between 0 and 1 and multiply the number by N).
Collect samples at each of the n locations or intervals.
* For additional guidance on calculating spacing between
sampling locations, see Methods for Evaluating the
Attainment of Cleanup Standards, Volume I: Soil and Solid
Med/a(USEPA1989a).
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 (found in many statistical text books), a random number function
on a hand-held calculator, or by a computer.
One possible disadvantage of pure random sampling is that localized clustering of sample
points can occur. If this occurs, one option is to select a new random time or location for the
sample. Spatial or temporal biases could result if unknown trends, patterns, or correlations are
present. In such situations, stratified random sampling or systematic sampling are better
options.
5.2.2 Stratified Random Sampling
In stratified random sampling, a heterogeneous unit, site, or process is divided into
nonoverlapping groups called strata. Each stratum should be defined so that internally it is
relatively homogeneous (that is, the variability within each stratum is less than the variability
observed over the entire population) (Gilbert 1987). After each stratum is defined, then simple
random sampling is used within each stratum (see Figure 13(c) and 15(d)). For very
heterogeneous wastes, stratified random sampling can be used to obtain a more efficient
estimate of the parameter of interest (such as the mean) than can be obtained from simple
random sampling.
It is important to note that stratified random sampling, as described in this guidance, can be
used when the objective is to make a decision about the whole population or decision unit. If
the objective is to determine of a solid waste is a hazardous waste or to measure attainment of
a treatment standard for a hazardous waste, then any obvious "hot spots" or high concentration
wastes should be characterized separately from low concentration wastes to minimize mixing of
57
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hazardous waste with nonhazardous _ . _. .... . _ .
,. . . ., , Box 4. Stratified Random Sampling: Procedure
wastes and to prevent impermissible
Use prior knowledge of the waste stream or site to
divide the target population intoi nonoverlapping strata
such that the variability within stratum is less than the
dilution (see also Appendix C). If the
objective of the sampling effort is to identify
nonrandom spatial patterns (for example,
to create a map of contamination in shallow variability of the entire population
... , Figure 13c and Figure 13d). The
soils), then consider the use of a area vdume mass or time intervais.
geostatistical technique to evaluate the „ . . . ,. TT/ . , /^i. . . _.
a ^ 2. Assign a weight yyh to each Mil stratum. The value
ol LC.
of each W, should be determined based on its relative
importance to the data user, or it can be the proportion
of the volume, mass, or area of the waste that is in
stratum h .
Conduct random sampling within each stratum.
In stratified random sampling it is usually
necessary to incorporate prior knowledge
and professional judgment into a
probabilistic sampling design. Generally,
wastes or 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 be employed. For example, you
could stratify a waste based on particle size (such that relatively large pieces of contaminated
debris are assigned to one stratum and unconsolidated fines assigned to a separate stratum).
This is called stratification by component. See Appendix C of this guidance for additional
information on stratification, especially as a strategy for sampling heterogeneous wastes, such
as debris.
In stratified random sampling a decision must be made regarding the allocation of samples
among strata. When chemical variation within each stratum is known, samples can be allocated
among strata using optimum allocation in which more samples are allocated to strata that are
large, more variable internally, or cheaper to sample (Cochran 1977, Gilbert 1987). An
alternative is to use proportional allocation. In proportional allocation, the sampling effort in
each stratum is directly proportional to the size (for example, the mass) of the stratum. See
Section 5.4.2 for guidance on determining optimum and proportional allocation of samples to
strata.
There are several advantages to stratified random sampling. Stratified random sampling:
Ensures more uniform coverage of the entire target population
Ensures that subareas that contribute to overall variability are included in the
sample
Achieves greater precision in certain estimation problems
Generally will be more cost-effective than simple random sampling even when
imperfect information is used to form the strata.
There are also some disadvantages to stratified random sampling. Stratified random sampling
is slightly more difficult to implement in the field and statistical calculations for stratified sampling
are more complex than for simple random sampling (e.g., due to the use of weighting factors
and more complex equations for the appropriate number of samples).
58
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Box 5: Systematic Sampling: Procedure
Sampling Over Space
1. Determine the size of the area to be sampled.
2. Denote the surface area of the sample area by A .
3. Assuming a square grid is used, calculate the length
of spacing between grid nodes (L)
5.2.3 Systematic Sampling
Systematic sampling entails taking
samples at a preset interval of time or in
space and using a randomly selected time
or location as the first sampling point
(Gilbert 1987).
Systematic sampling over space involves
establishing a two-dimensional grid of the
unit or waste under investigation (Figure
13(e)). 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. As shown in
Figure 13(f), systematic sampling also can
be conducted along a transect (every five
feet, for example), along time intervals
(every hour, for example), or by flow or
batches (every 10,000 gallons, for
example) (King 1993).
The systematic sampling approach is
attractive because it can be easily
implemented in the field, but it has some
limitations such as not being truly random.
You can improve on this sampling design
by using random sampling within each grid
block (Figure 13(g)) or within each time
interval (Figure 13(h)). This approach
maintains the condition of equiprobability during the sampling event (Myers 1997) and can be
considered a form of stratified random sampling in which each of the boundaries of the strata
are arbitrarily defined (rather than using prior information) and only one random sample is taken
per stratum (Gilbert 1987). This approach is advantageous because it avoids potential
problems caused by cycles or trends.
Systematic sampling also is preferred when one of the objectives is to locate "hot spots" within a
site or otherwise map the pattern of concentrations over an area (e.g., using geostatistical
techniques). Even without using geostatistical methods, "hot spots" or other patterns could be
identified by using a systematic design (see "ELIPGRID" software in Appendix H and Gilbert
1987, page 119). On the other hand, the systematic sampling design should be used with
caution whenever there is a possibility of some type of cyclical pattern in the waste unit or
where n is the number of samples. The distance L
should be rounded to the nearest unit that can be
easily measured in the field.
4. To determine the sampling locations, randomly select
an initial sampling point within the area to be
sampled. Using this location as one intersection of
two gridlines, construct gridlines parallel to the
original grid and separated by distance L.
5. Collect samples at each grid node (line intersection)
(see Figure 13e). Alternatively, randomly select a
sampling point within each grid block (see Figure
I3g).
Sampling Along a Line (e.g., Over Time)
1. Determine the start time and point and the total length
of time (N) over which the samples will be collected.
2. Decide how many samples (n) will be collected over
the sampling period.
i N
Calculate a sampling interval where k = — .
n
Randomly select a start time and collect a sample
every £th interval until n samples have been obtained
(see Figure 13f). Alternatively, randomly select a
sampling point within each interval (Figure 13h).
3.
59
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process that might match the sampling frequency, especially processes being measured over
time (such as discharges from a pipe or material on a conveyor).
*B
*B
- Mean Concentration
Time
Figure 14. Potential pitfall of systematic sampling over time: cyclic
trend combined with a systematic sampling design (after Cochran 1977
and Gilbert 1987)
Figure 14 illustrates the potential
disadvantage of using systematic
sampling when cyclic trends are
present. When there is a cyclic
trend in a waste generation
process, using a uniform pattern of
sampling points can result in
samples with very unusual
properties. The sets of points
labeled "A" and "B" are systematic
samples for which the sampling
intervals are one period and one-
half period, respectively. The
points labeled "A" would result in a
biased estimate of the mean but a sampling variance of zero. The points labeled "B" would
result in an unbiased estimate of the mean with very small variance, even a zero variance if the
starting point happened to be aligned exactly with the mean.
5.2.4 Ranked Set Sampling
Ranked set sampling (RSS) (Mclntyre 1952) can create a set of samples that at a minimum is
equivalent to a simple random sample, but can be as much as two to three times more efficient
than simple random sampling. This is because RSS uses the availability of expert knowledge or
an inexpensive surrogate measurement or auxiliary variable that is correlated with the more
expensive measurement of interest. The auxiliary variable can be a qualitative measure, such
as visual inspection for color or an inexpensive quantitative (or semi-quantitative) measure that
can be obtained from a field instrument such as a photoionization detector for volatile organics
or an X-ray fluorescence analyzer for elemental analysis. RSS exploits this correlation to obtain
a sample that is more representative of the population than would be obtained by random
sampling, thereby leading to more precise estimates of the population parameters than random
sampling. RSS is similar to other probabilistic sampling designs such as simple random
sampling in that sampling points are identified and samples are collected. In RSS, however,
only a subset of the samples are selected for analysis.
RSS consists of creating m groups, each of size m (for a total of "m x m" 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 m samples are analyzed for the
more expensive measurement of interest (see Box 6 and Figure 15).
The true mean concentration of the characteristic of interest is estimated by the arithmetic
sample mean of the measured samples (e.g., by Equation 1). The population variance and
standard deviation also are estimated by the traditional equations (e.g., by Equations 2 and 3).
For additional information on RSS, see USEPA 1995b, USEPA 2000c, and ASTM D 6582
Standard Guide for Ranked Set Sampling: Efficient Estimation of a Mean Concentration in
Environmental Sampling.
60
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Box 6. Ranked Set Sampling:
Procedure
Identify some auxiliary characteristic by
which samples can be ranked in order
from lowest to highest (e.g., by use of a
low-cost field screening method).
Randomly select mXm samples
from the population (e.g., by using
simple random sampling).
Arrange these samples into in sets of
size m .
Within each set, rank the samples by
using only the auxiliary information on
the samples.
Select the samples to be analyzed as
follows (see Figure 17):
• In Set 1, select the sample with
rank 1
• In Set 2, select the sample with
rank 2, etc ...
• In Set m , select the unit with rank
m.
m = 4
Sell
Set 2
Set3
Set 4
1
O
0
0
O
U Sample sent for analysis
O Sample ignored
Rank
2 3
O
0
0
O
O
0
0
O
4
O
0
0
O
For example, if 12 samples are
needed, the process is repeated 2
more times using fresh samples.
Figure 15. Ranked set sampling. After the samples are
ranked in order from lowest to highest, a sample is selected for
analysis from Set 1 with Rank 1, from Set 2 with Rank 2, etc.
6. Repeat Steps 1 through 5 for T cycles to obtain a total of n = m • T samples for analysis.
5.2.5 Sequential Sampling
In sequential testing procedures (Wald 1973), sampling is performed by analyzing one (or more)
sample(s) at a time until enough data have been collected to meet the statistical confidence
level that the material does not exceed the critical level. The expected sample size, using this
sequential procedure, can be approximately 30- to 60-percent lower than a corresponding fixed
sample size test with the same power. The sequential procedure is especially helpful in
situations in which the contamination is very high or very low relative to the action level. In
these situations, the sequential procedure will quickly accumulate enough evidence to conclude
that the waste or site either meets or fails to meet the standard.
Figure 16 shows how the procedure operates in a simple example for determining the mean
concentration of a constituent of concern in soil. This particular example involves clean closure
of a waste management unit, however, the approach could be used for other situations in which
the mean is the parameter of interest. The procedure consists of analyzing groups of samples
and calculating the mean and 80-percent confidence interval (or upper 90-percent confidence
limit) for the mean after analysis of each group of samples. The horizontal axis represents the
number of sample units evaluated. The vertical axis represents the concentration of the
contaminant; plotted are the mean and 80-percent confidence interval after analysis of n
samples. The AL, against which the sample is to be judged, is shown as a horizontal line.
The sampled units are analyzed first in a small lot (e.g., five samples). After each evaluation the
mean and confidence interval on the mean are determined (point "a"). If the 90-percent UCL on
the mean value stays above the critical value, AL, after successive increments are analyzed,
the soil in the unit cannot be judged to attain the action level (point "b"). If the UCL goes below
61
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AL
Soil does not attain AL
Soil attains AL
I
5 10 20 40
Cumulative number of samples (ra)
• Mean calculated from n samples
I Confidence Interval
AL - Risk-based action level
Figure 16. Example of sequential testing for determining if
concentrations of a constituent of concern in soil at a closed
waste management unit are below a risk-based action level
(AL).
the critical value line, it may be concluded
that the soil attains the standard. In the
figure, the total number of samples is
successively increased until the 90-
percent UCL falls below the critical level
(points "c" and "d").
A sequential sampling approach also can
be used to test a percentile against a
standard. A detailed description of this
method is given in Chapter 8 of Methods
for Evaluating the Attainment of Cleanup
Standards Volume 1: Soil and Solid Media
(USEPA 1989a).
In sequential sampling, the number of
samples is not fixed a priori; rather, a
statistical test is performed after each
analysis to arrive at one of three possible
decisions: reject the hypothesis, accept
the hypothesis, or perform another analysis. This strategy is applicable when sampling and/or
analyses are quite expensive, when information concerning sampling and/or measurement
variability is lacking, when the waste and site 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 specific
hypothesis. It may not be especially useful if multiple waste characteristics are of interest or if
rapid decision making is necessary.
In planning for a sequential sampling program, the following considerations are important:
Pre-planning the effort between the field and laboratory, including developing a
system of pre-planned paperwork and sample containers
Arranging for a system of rapid delivery of samples to the laboratory
Providing rapid turnaround in the laboratory
Rapidly returning data to the planners, supervisors, and others responsible for
decision making.
If the sequential sampling program is carried out using field methods (e.g., portable detectors),
much of the inconvenience involved with shipping and return of results can be avoided.
5.2.6 Authoritative Sampling
Authoritative sampling is a nonstatistical sampling design because it does not assign an equal
probability of being sampled to all portions of the population. This type of sampling should be
considered only when the objectives of the investigation do not include the estimation of a
population parameter. For example, authoritative sampling might be appropriate when the
objective of a study is to identify specific locations of leaks, or when the study is focused solely
62
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on the sampling locations themselves. The validity of the data gathered with authoritative
sampling is dependent on the knowledge of the sampler and, although valid data sometimes
can be obtained, it is not recommended for the chemical characterization of wastes when the
parameter of interest (such as the mean) is near the action level.
Authoritative sampling (also known as judgmental sampling, biased sampling, nonprobability
sampling, nonstatistical sampling, purposive sampling, or subjective sampling) may be
appropriate under circumstances such as the following:
You need preliminary information about a waste stream or site to facilitate
planning or to gain familiarity with the waste matrix for analytical purposes.
You are conducting sampling for a RCRA Facility Assessment (RFA) to identify a
potential or actual release to the environment.
You have encountered a spill of an unknown chemical and need to determine the
chemical makeup of the spilled material.
You have access to only small portions of the population and judgment is applied
to assess the usefulness of samples drawn from the small portion.
You are screening samples in the field, using an appropriate field method, to
identify "hot" samples for subsequent analysis in a laboratory.
You are sampling to support case development for an enforcement agency or to
"prove the positive" (see also Section 2.2.4).
With authoritative sampling, it is not possible to accurately estimate the population variance.
Also, due to its subjective nature, the use of authoritative sampling by the regulated community
to demonstrate compliance with regulatory standards generally is not advisable except in those
cases in which a small volume of waste is in question or where the concentration is either well
above or well below the regulatory threshold.
The ASTM recognizes two types of authoritative sampling: judgmental sampling and biased
sampling (ASTM D 6311).
5.2.6.1 Judgmental Sampling
Judgmental sampling is a type of authoritative sampling. The goal of judgmental sampling is to
use process or site knowledge to choose one or more sampling locations to represent the
"average" concentration or "typical" property.
Judgmental sampling designs can be cost-effective //the people choosing the sampling
locations have sufficient knowledge of the waste. If the people choosing the sampling locations
intentionally distort the sampling by a prejudiced selection, or if their knowledge is wanting,
judgmental sampling can lead to incorrect and sometimes very costly decisions. Accurate and
useful data can be generated from judgmental sampling more easily if the population is
relatively homogeneous and the existence of any strata and their boundaries is known.
The disadvantages of judgmental sampling designs follow:
63
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It can be difficult to demonstrate that prejudice was not employed in sampling
location selection
Variances calculated from judgmental samples may be poor estimates of the
actual population variance
Population statistics cannot be generated from the data due to the lack of
randomness.
An example application of judgement sampling is given in Appendix C of Guidance for the Data
Quality Objectives Process for Hazardous Waste Site Operations (USEPA 2000a).
5.2.6.2 Biased Sampling
Biased sampling is the type of authoritative sampling that intends not to estimate average
concentrations or typical properties, but to estimate "worst" or "best" cases (ASTM D 6051-96).
The term "biased," as used here, refers to the collection of samples with expected very high or
very low concentrations. For example, a sample taken at the source of a release could serve as
an estimate of the "worst-case" concentration found in the affected media. This information
would be useful in identifying the constituent of concern and estimating the maximum level of
contamination likely to be encountered during a cleanup.
At times, it may be helpful to employ a "best case" or both a "best-case" and "worst-case"
biased sampling approach. For example, if there is a range of wastes and process knowledge
can be used to identify the wastes likely to have the lowest and highest contamination levels,
then these two extremes could be sampled to help define the extent of the problem.
Biased sampling, while having the ability to cost-effectively generate information, has similar
disadvantages to that of judgmental sampling.
5.3 Composite Sampling
Composite sampling is a strategy in which multiple individual or "grab" samples (from different
locations or times) are physically combined and mixed into a single sample so that a physical,
rather than a mathematical, averaging takes place.1 Figure 17 illustrates the concept of
composite samples. For a well-formed composite, a single measured value should be similar to
the mean of measurements of the individual components of the composite (Fabrizio, et al.
1995). Collection of multiple composite samples can provide improved sampling precision and
reduce the total number of analyses required compared to noncomposite sampling. This
strategy is sometimes employed to reduce analysis costs when analysis costs are large relative
to sampling costs. The appropriateness of using composite sampling will be highly dependent
on the DQOs (Myers 1997), the constituent of concern, and the regulatory requirements. To
realize the full benefits of composite sampling, field and laboratory personnel must carefully
1 Some authors use the term "discrete sample" to refer to an individual sample that is used to form a
composite sample. The RCRA regulations often use the term "grab sample." For the purpose of this guidance, the
terms "discrete," "grab," and "individual" sample have the same meaning.
64
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Individual Field Samples
Figure 17. Forming composite samples from individual
samples (from USEPA 1995c).
follow correct procedures for sample
collection, mixing, and subsampling (see
Sections 6 and 7).
5.3.1 Advantages and Limitations of
Composite Sampling
A detailed discussion of the advantages
and limitations of composite sampling is
presented in the Standard Guide for
Composite Sampling and Field
Subsampling for Environmental Waste
Management Activities (ASTM D 6051-96)
and EPA's Guidance for Choosing a
Sampling Design for Environmental Data Collection, EPA QA/G-5S (USEPA 2000c). Additional
information on composite sampling can be found in Edland and van Belle (1994), Gilbert (1987),
Garner, et al. (1988 and 1989), Jenkins, et al. (1996 and 1997), Myers (1997), and USEPA
(1995c).
Advantages
Three principal advantages to using composite sampling (see ASTM D 6051-96) follow:
It can improve the precision (i.e., reduce between-sample variance) of the
estimate of the mean concentration of a constituent in a waste or media (see
Section 5.3.5)
It can reduce the cost of estimating a mean concentration, especially in cases in
which analytical costs greatly exceed sampling costs or in which analytical
capacity is limited
A "local" composite sample, formed from several increments obtained from a
localized area, is an effective way to increase the sample support, which reduces
grouping and segregation errors (see also Section 6.2.2.2)
It can be used to determine whether the concentration of a constituent in one or
more individual samples used to form a composite might exceed a fixed standard
(i.e., is there a "hot spot"?) (see Section 5.3.6).
Limitations
Composite sampling should not be used if the integrity of the individual sample values changes
because of the physical mixing of samples (USEPA 1995c). The integrity of individual sample
values could be affected by chemical precipitation, exsolvation, or volatilization during the
pooling and mixing of samples. For example, volatile constituents can be lost upon mixing of
samples or interactions can occur among sample constituents. In the case of volatile
constituents, compositing of individual sample extracts within a laboratory environment may be
a reasonable alternative to mixing individual samples as they are collected.
65
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Listed below are some additional conditions under which compositing usually is not
advantageous:
When regulations require the use of discrete or grab samples. For example,
compliance with the LDR numeric treatment standards for non-wastewaters
typically is to be determined using "grab" samples rather than composite
samples. Grab samples processed, analyzed, and evaluated individually
normally reflect maximum process variability, and thus reasonably characterize
the range of treatment system performance. Typically, grab samples are used to
evaluate LDR non-wastewaters and composite samples are used to evaluate
LDR wastewaters, except when evaluating wastewaters for metals (D004
through D011) for which grab samples are required [40 CFR 268.40(b)].
When data users require specific data points to generate high-end estimates or
to calculate upper percentiles
When sampling costs are much greater than analytical costs
When analytical imprecision outweighs sampling imprecision and population
heterogeneity
When individual samples are incompatible and may react when mixed
When properties of discrete samples, such as pH or flash point, may change
qualitatively upon mixing. (Compositing of individual samples from different
locations to be tested for hazardous waste characteristic properties, such as
corrosivity, reactivity, ignitability, and toxicity, is not recommended)
When analytical holding times are too short to allow for analysis of individual
samples, //testing of individual samples is required later (for example, to identify
a "hot" sample) (see Section 5.3.6)
When the sample matrix impedes correct homogenization and/or subsampling
When there is a need to evaluate whether the concentrations of different
contaminants are correlated in time or space.
5.3.2 Basic Approach To Composite Sampling
The basic approach to composite sampling involves the following steps:
Identify the boundaries of the waste or unit. The boundaries may be spatial,
temporal, or based on different components or strata in the waste (such as
battery casings and soil)
Conduct sampling in accordance with the selected sampling design and collect a
set of« x g individual samples where g is the number of individual samples used
to form each composite and « is the number of such composites
66
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Group either randomly or systematically the set of n x g individual samples into n
composite samples and thoroughly mix and homogenize each composite sample
Take one or more subsamples from each composite
Analyze each subsample for the constituent(s) of concern.
The n composite samples can then be used to estimate the mean and variance (see Section
5.3.5) or identify "hot spots" in the waste (see Section 5.3.6).
5.3.3 Composite Sampling Designs
Composite sampling can be implemented as part of a statistical sampling design, such as
simple random sampling and systematic sampling. The choice of a sampling design to use with
compositing will depend upon the study objectives.
5.3.3.1
Simple Random Composite Sampling
n.g= 9
individual field
samples
Figure 18 shows how composite sampling
can be integrated into a simple random
sampling design. In this figure, the
decision unit could represent any waste or
media about which a decision must be
made (such as a block of contaminated soil
ataSWMU). Randomly positioned field
samples are randomly grouped together
into composite samples. The set of
composite samples can then be used to
estimate the mean and the variance.
Because the compositing process is a
mechanical way of averaging out
variabilities in concentrations from location
to location over a unit, the resulting
concentration data should tend to be more
normally distributed than individual
samples (Exner, et al. 1985). This is
especially advantageous because the
assumption of many statistical tests is that
the underlying data exhibit an approximately normal distribution.2
Decision Unit Boundary
BJ (C]
composite
samples
Subsamples analyzed
Figure 18. A basic approach to composite sampling. The
figure shows how composite sampling can be integrated into a
simple random sampling design. Random samples with the
same letter are randomly grouped into composite samples to
obtain an estimate of the unit-wide mean.
By the Central Limit Theorem (CLT), we expect composite samples to generate normally distributed data.
The CLT states that if a population is repeatedly sampled, the means of all the sampling events will tend to form a
normal distribution, regardless of the shape of the underlying distribution.
67
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5.3.3.2
Systematic Composite Sampling
Decision Unit Boundary
®
©
©
A B
A B
A systematic composite sampling design
is shown in Figure 19. The design can be
used to estimate the mean concentration
because each composite sample is
formed from field samples obtained across
the entire unit. For example, each field
sample collected at the "A" locations is
pooled and mixed into one composite
sample. The process is then repeated for
the "B," "C," and "D" locations. The
relative location of each individual field
sample (such as "A") should be the same
within each block.
This design is particularly advantageous
because it is easy to implement and
explain and it provides even coverage of
the unit. Exner, et al. (1985)
demonstrated how this design was used to make cleanup decisions for blocks of soil
contaminated with tetrachlorodibenzo-p-dioxin.
A second type of systematic composite involves collecting and pooling samples from within grid
blocks, time intervals, or batches of waste grouped together (see Figure 20).
®
®
©
Figure 19. Systematic composite sampling across a unit or
site. Samples with the same letter are pooled into composites.
If there is spatial correlation between the
grid blocks, compositing within grids can be
used to estimate block-to-block variability
(Myers 1997) or improve the estimate of
the mean within a block or interval (if
multiple composite samples are collected
within each block). In fact, compositing
samples collected from localized areas is
an effective means to control "short-range"
(small-scale) heterogeneity (Pitard 1993).
When this type of compositing is used on
localized areas in lieu of "grab" sampling, it
is an attractive option to improve
representativeness of individual samples
(Jenkins, etal. 1996).
Decision Unit Boundary
A) (A,
A (A,
© ©
© ©
B B
B B
C) (C
Figure 20. Systematic sampling within grid blocks or intervals.
Samples with the same letter are pooled into a composite
sample.
Systematic sampling within time intervals
could be used in cases in which
compositing occurs as part of sample
collection (such as sampling of liquid effluent with an autosampling device into a single sample
container over a specified time period).
68
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If the individual field sample locations are independent (that is, they have no temporal or spatial
correlation), then compositing within blocks can be an efficient strategy for estimating the
population mean. If the assumption of sample independence cannot be supported, then an
alternative design should be selected if the objective is to estimate the mean.
5.3.4 Practical Considerations for Composite Sampling
In creating composite samples from individual field samples, it is possible that a relatively large
volume of material will need to be physically mixed at some point - either in the field or in the
laboratory. Thorough mixing is especially important when the individual samples exhibit a high
degree of heterogeneity.
Once the individual samples are mixed, one or more subsamples must be taken because the
entire composite sample usually cannot be analyzed directly. A decision must be made as to
where the individual samples will be combined into the composite samples. Because large
samples (e.g., several kilograms or more) may pose increased difficulties to the field team for
containerization and shipping and pose storage problems for the laboratory due to limited
storage space, there may be a distinct advantage to performing mixing or homogenization in the
field. There are, however, some disadvantages to forming the composite samples in the field.
As pointed out by Mason (1992), the benefits of homogenization may be temporary because
gravity induced segregation can occur during shipment of the samples. Unless homogenization
(mixing), particle size reduction, and subsampling are carried out immediately prior to analysis,
the benefits of these actions may be lost. Therefore, if practical, it may be best to leave the
mixing and subsampling operations to laboratory personnel.
See Section 7.3 of this document and ASTM standards D 6051 and D 6323 for guidance on
homogenization, particle size reduction, and subsampling.
5.3.5 Using Composite Sampling To Obtain a More Precise Estimate of the Mean
When analytical error is minor compared to sampling error, then composite sampling can be a
resource-efficient mechanism for increasing the precision of estimates of the population mean.
If composite sampling is to be used to estimate the mean with a specified level of confidence,
then multiple composite samples can be used to estimate the mean and variance.
Alternately, confidence limits can be constructed around the sample analysis result for a single
composite sample if an estimate of the variance of the fundamental error is available (see Gy
1998, page 73).3 See Section 6.2.2.1 for a discussion of fundamental error.
The population mean (ju) can be estimated from the analysis of n composite samples (each
made from g individual samples). The population mean (ju) is estimated by the sample mean
Equation 6
ASTM D 6051, Standard Guide for Composite Sampling and Field Subsampling for Environmental Waste
Management Activities, also provides a procedure for estimating the precision of a single composite sample.
69
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The sample variance (s2) can then be calculated by
2 1 % i , , 2
s = > (xi — x) Equation 7
n — \~l
Note that Equations 6 and 7 are the same as Equations 1 and 2, respectively, for the mean and
variance. When the equations are used for composite sampling, xi is the measurement value
from a subsample taken from each n composite sample rather than each individual sample.
Use of these equations assumes equal numbers of individual field samples (g) are used to
form each composite, and equal numbers of subsamples are taken from each composite
sample and analyzed. If these assumptions are not correct, an alternative approach described
in Gilbert (1987, page 79) can be used.
By increasing the number of individual field samples (g) per composite sample, there will be a
corresponding decrease in the standard error (s~), thus improving the precision of the estimate
of the mean. Edland and van Belle (1994) show that by doubling the number of individual
samples per composite (or laboratory) sample, the expected size of the confidence interval
around the mean decreases by a factor of l/V?, which is a 29-percent decrease in the
expected width of the confidence interval. One of the key assumptions underlying the above
discussion is that variances between the samples greatly exceed the random error variance of
the analytical method (Garner, et al. 1988).
Wlliams, et al. (1989) demonstrated the benefits of using composite sampling to obtain a more
precise estimate of the mean. One of their objectives was to study the efficiency of using
composite sampling as compared to collecting individual samples for the purpose of estimating
the mean concentration at a site. Five sites known to have radium contamination in shallow
soils were extensively sampled. At each site, shallow soil samples were collected at
approximately uniformly spaced points over the entire site. Three types of samples were taken:
(1) individual 500-gram samples, (2) composite samples consisting often 50-gram aliquots
uniformly spaced over the site, and (3) composite samples consisting of twenty 25-gram
aliquots uniformly spaced over the site. The samples were measured for 226Ra. The results
indicated the individual samples yielded the least precision, even when more than twice as
many individual samples were collected. Sixty-six individual samples produced a standard error
of 1.35, while the thirty 10-aliquot composites and the thirty 20-aliquot composite samples
produced standard errors of 0.76 and 0.51 respectively. The results demonstrate that
composite sampling can produce more precise estimates of the mean with fewer analytical
samples.
Box 7 provides an example of how a mean and variance can be estimated using composite
sampling combined with systematic sampling.
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Box 7. Example of How To Estimate the Mean and Variance Using Systematic Composite Sampling
(Assume Samples Are Independent)
Under 40 CFR 261.38, a generator of hazardous waste-derived fuel is seeking an exclusion from the definition
of solid and hazardous-waste. To prepare the one-time notice under 40 CFR 261.38(c), the generator requires
information on the mean and variance of the concentrations of constituents of concern in the waste as
generated. The generator elects to use composite samples to estimate the mean and variance of the
nonvolatile constituents of concern.
Using a systematic sampling design, a
composite sample is prepared by taking an
individual (grab) sample at regular time
intervals t, through t4. The set of four grab
samples are thoroughly mixed to form a
composite, and one subsample is taken from
each composite for analysis. The process is
repeated until five composite samples are
formed (see Figure 21). (Note: If the
assumption of independent samples cannot
be supported, then a simple random design
should be used in which the 20 grab samples
are randomly grouped to form the five
composites).
The analytical results for one of the
constituents of concern, in ppm, are
summarized as follows for the composite
samples (n1 through ns):
2.75, 3.71, 3.28, 1.95, and 5.10.
/
Waste
Preparation
Process
n • g = 20
9 = 4
n = 5
(composites)
X Sampling
Point
w
c^3__ Z^i
Fuel
Storage
^ Tank J
4 *2 4 L4 L5 L6 L7 L8 L17 L18 L19 L20
1111 1111 1111
0 a 0
j * *
O O 0
One measurement taken on each composite sample
Figure 21. Example of systematic composite sampling
Using Equations 6 and 7 for the mean and variance of composite samples, the following results are obtained:
X
1^, 16.79
= - 2, *,• = —T~ = 3.36/7/wi
s =
1 " 1
V (jc. - J)2 = -[0.3721 + 0.1225 + 0.0064 + 1.99 + 3.03] = 1.38
n-l i=1 ' 4
The standard error is obtained as follows:
S 111
s^ = —i= = —— = 0.52ppm
5.3.6 Using Composite Sampling To Locate Extreme Values or "Hot Spots"
One disadvantage of composite sampling is the possibility that one or more of the individual
samples making up the composite could be "hot" (exceed a fixed standard), but remain
undetected due to dilution that results from the pooling process. If the sampling objective is to
determine if any one or more individual samples is "hot," composite sampling can still be used.
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A procedure for detecting hot spots using composite sampling is given below. The approach
assumes the underlying distribution is normal and the composite samples were formed from
equal-sized individual samples.
Let AL be some "action level" or regulatory threshold that cannot be exceeded in an individual
sample. Note that AL must be large relative to the quantitation limit for the constituent of
concern. For a measurement xi from a composite sample formed from g individual samples,
the following rules apply, assuming analytical and sampling error are negligible:
AL
\ix. < , then no single individual sample can be > AL
g
If xt > AL , then at least one must, and as many as all individual samples may,
be > AL
AL
If xt > , then at least one of the g individual samples must be > AL .
g-x,
As a general rule, we can say that no more than individual samples can be > AL .
AL
If one or more of the composites are "hot" (i.e., > AL ), then it might be desirable to go back
and analyze the individual samples used to form the composite. Consider saving splits of each
individual field sampling so individual samples can be analyzed later, if needed.
If compositing is used to identify a hot spot, then the number of samples that make up the
composite should be limited to avoid overall dilution below the analytical limit. It is possible for
a composite sample to be diluted to a concentration below the quantitation limit if many of the
individual samples have concentrations near zero and a single individual sample has a
concentration just above the action level. Mason (1992) and Skalski and Thomas (1984)
suggest the maximum number of identically sized individual samples (g) that can be used to
form such a composite should not exceed the action level (AL) divided by the quantitation limit
(QL). But the relationship of g < AL IQL indicates that the theoretical maximum number of
samples to form a composite can be quite high, especially given a very low quantitation limit.
As a practical matter, the number of individual samples used to form a composite should be
kept to a minimum (usually between 2 and 10).
An example of the above procedure, provided in Box 8, demonstrates how a "hot" drum can be
identified through the analysis of just nine samples (five composites plus four individual
analyses), resulting in considerable savings in analytical costs over analysis of individual
samples from each of the 20 drums.
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Box 8. How To Locate a "Hot Spot" Using Composite Sampling - Hypothetical Example
A secondary lead smelter produces a slag that under some operating conditions exhibits the Toxicity
Characteristic (TC) for lead. At the point of generation, a grab sample of the slag is taken as the slag is placed
in each drum. A composite sample is formed from the four grab samples representing a set of four drums per
pallet. The process is repeated until five composite samples representing five sets of four drums (20 drums
total) have been prepared (see Figure 22).
The generator needs to know if the waste
in any single drum in a given set of four
drums contains lead at a total
concentration exceeding 100 ppm. If the
waste in any single drum exceeds 100
ppm, then its maximum theoretical TCLP
leachate concentration could exceed the
regulatory limit of 5 mg/L. Waste in drums
exceeding 100 ppm total lead will be tested
using the TCLP to determine if the total
leachable lead equals or exceeds the TC
regulatory limit.
Composite
Samples
Q
I
The sample analysis results for total lead
are measured as follows (in ppm) in
composite samples n., through n5:
6, 9, 18, 20, and 45.
One measurement taken on each composite sample
Using the approach for locating a "hot spot"
in a composite sample, we observe that all
of the composite samples except for n5 are
less than AL I g or 100 ppm/4 (i.e., 25
ppm). The result for n5 (45 ppm) is greater than 25 ppm, indicating a potential exceedance of the TC regulatory
level. A decision about the set of drums represented by n5 can be made as follows:
= 1.8 or 1 (round
Figure 22. Composite sampling strategy for locating a "hot"
drum
No more than individual samples can be > AL , or no more than
AL WO ppm
down) individual sample exceeds 100 ppm total lead.
We now know that it is possible that one of the four drums on the fifth palette exceeds 100 ppm, but we do not
know which one. As a practical matter, analysis of all four of the individual samples should reveal the identity of
the "hot" drum (if, indeed, one exists); however, the above process of elimination could be repeated on two new
composite samples formed from samples taken from just the four drums in question.
5.4 Determining the Appropriate Number of Samples Needed To Estimate the Mean
This section provides guidance for determining the appropriate number of samples (n) needed
to estimate the mean. The procedures can be used when the objective is to calculate a
confidence limit on the mean. If the objective is to estimate a percentile, see Section 5.5.
To calculate the appropriate number of samples, it is necessary to assemble existing data
identified in DQO Step 3 ("Identify Inputs to the Decision") and Step 6 ("Specify Limits on
Decision Errors"). If the parameter of interest is the mean, you can calculate n using equations
presented in the following sections or by using EPA's DEFT software (USEPA 2001 a).
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Alternative equations can be found in the statistical literature and guidance, including ASTM
(Standard D 6311), Cochran (1977), Gilbert (1987), and USEPA (2000a, 2000b, and 2000d).
The equations presented here should yield the approximate minimum number of samples
needed to estimate the mean within the precision and confidence levels established in the DQO
Process; however, it is prudent to collect a somewhat greater number of samples than indicated
by the equations4 This is recommended to protect against poor preliminary estimates of the
mean and standard deviation, which could result in an underestimate of the appropriate number
of samples to collect. For analytes with long holding times (e.g., 6 months), it may be possible
to process and store extra samples appropriately until analysis of the initially identified samples
is completed and it can be determined if analysis of the additional samples is warranted.
It is important to note that the sample size equations do not account for the number or type of
control samples (or quality assessment samples) required to support the QC program
associated with your project. Control samples may include blanks (e.g., trip, equipment, and
laboratory), field duplicates, spikes, and other samples used throughout the data collection
process. Refer to Chapter One of SW-846 for recommendations on the type and number of
control samples needed to support your project. It is best to first determine how each type of
control sample is to be used, then to determine the number of that type based on their use (van
Ee, etal. 1990).
A key assumption for use of the sample size equations is that you have some prior estimate of
the total study error, measured as the sample standard deviation (s) or sample variance (s2).
Since total study error includes variability associated with the sampling and measurement
methods (see Section 6), it is important to understand the relative contributions that sampling
and analysis activities make to the overall estimate of variability. Lack of prior information
regarding population and measurement variability is one of the most frequently encountered
difficulties in sampling. It quickly resembles a "chicken-and-the-egg" question for investigators -
you need an estimate of the standard deviation to calculate how many samples you need, yet
you cannot derive that estimate without any samples. To resolve this seemingly paradoxical
question, two options are available:
Option 1. Conduct a pilot study. A pilot study (sometimes called an exploratory or
preliminary study) is the preferred method for obtaining estimates of the mean
and standard deviation, as well as other relevant information. The pilot study is
simply phase one of a multi-phase sampling effort (Earth, et al. 1989). For some
pilot studies, a relatively small number of samples (e.g., four or five or more) may
provide a suitable preliminary estimate of the standard deviation.
Option 2. Use data from a study of a similar site or waste stream. I n some cases, you
might be able to use sampling and analysis data from another facility or similar
operation that generates the same waste stream and uses the same process.
If neither of the above options can provide a suitable estimate of the standard deviation (s), a
crude approximation of s still can be obtained using the following approach adopted from
4 One exception is when sequential sampling is used in which the number of samples is not fixed a priori',
rather, the statistical test is performed after each round of sampling and analysis (see Section 5.2.5).
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USEPA 1989a (page 6-6). The approximation is based on the judgment of a person
knowledgeable of the waste and his or her estimate of the range within which constituent
concentrations are likely to fall. Given a range of constituent concentrations in a waste, but
lacking the individual data points, an approximate value for s may be computed by dividing the
range (the estimated maximum concentration minus the minimum concentration) by 6, or
s ~ Range 16. This approximation method should be used only if no other alternative is
available. The approach is based on the assumption that more than 99 percent of all normally
distributed measurements will fall within three standard deviations of the mean; therefore, the
length of this interval is 6s.
5.4.1 Number of Samples to Estimate the Mean: Simple Random Sampling
In Step 6 of the DQO Process ("Specify Limits on Decision Errors"), you established the width of
the gray region (A) and acceptable probabilities for making a decision error (a and /3).
Using this information, along with an estimate of the standard deviation (s), calculate the
appropriate number of samples (n ) for simple random sampling using
n=(z1_a+z1_pfs2 +z^ Equations
A2 2
where
z1_a = the pth quantile of the standard normal distribution (from the last row of
Table G-1, Appendix G), where a is the probability of making a Type I
set in DQO Step 6 (Section 4.6.4).
ZI_P = the pth quantile of the standard normal distribution (from the last row of
Table G-1, Appendix G), where j3 is the probability of making a Type II
error set in DQO Step 6 (Section 4.6.4).
s = an estimate of the standard deviation.
A = the width of the gray region from DQO Step 6.
An example application of Equation 8 is presented in Box 9.
Two assumptions underlie the use of Equation 8. First, it is assumed that data are drawn from
an approximately normal distribution. Second, it is assumed the data are uncorrelated. In
correlated data, two or more samples taken close to each other (in time or in space) will have
similar concentrations (Gilbert 1987). In situations in which spatial or temporal correlation is
expected, some form of systematic sampling is preferred.
If the underlying population appears to exhibit a lognormal distribution, normal theory sample
size equations (such as Equation 8) still can be used though they will tend to underestimate the
minimum number of samples when the geometric standard deviation (exp(sy)) is low (e.g.,
<2). If the underlying distribution is known to be lognormal, the method given by Land (1971,
1975) and Gilbert (1987) for calculating confidence limits for a lognormal mean can be solved
"in reverse" to obtain n. (A software tool for performing the calculation, MTCAStef 3.0, is
published by the Washington Department of Ecology. See Appendix H). Also, techniques
described by Perez and Lefante (1996 and 1997) can be used to estimate the sample sizes
needed to estimate the mean of a lognormal distribution. Otherwise, consult a professional
statistician for assistance.
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Box 9. Number of Samples Required to Estimate the Mean Using Simple Random Sampling:
Hypothetical Example
Under 40 CFR 261 .38, a generator of hazardous waste-derived fuel is seeking an exclusion from the definition of solid
and hazardous-waste. To prepare the one-time notice under 40 CFR 261 .38(c), the generator plans to conduct waste
sampling and analysis to support the exclusion. The output of the first six steps of the DQO Process are summarized
below:
Step 1: State the Problem: The planning team reviewed the applicable regulations, historical analyses, and process
chemistry information. The problem is to determine whether Appendix VIII constituents present in the waste are at
concentration levels less than those specified in Table 1 of §261 .38.
Step 2: Identify the Decision: If the waste attains the specification levels, then it will be judged eligible for the
exclusion from the definition of hazardous and solid waste.
Step 3: Identify Inputs to the Decision: Sample analysis results are required for a large number of constituents
present in the waste, however, most constituents are believed to be present at concentrations well below the
specification levels. Historically, benzene concentrations have been most variable, therefore, the planning team will
estimate the number of samples required to determine if the specification level for benzene is attained.
Step 4: Define the Boundaries: The DQO decision unit is defined as the batch of waste generated over a one-week
period. Samples will be taken as the waste exits the preparation process and prior to storage in a fuel tank (i.e., at
the point of generation).
Step 5: Develop a Decision Rule: The RCRA regulations at 40 CFR 261 .38(c)(8)(iii)(A) specify the mean as the
parameter of interest. The "Action Level" for benzene is specified in Table 1 of §268.38 as 4,100 ppm. If the mean
concentration of benzene within the DQO decision unit is less than or equal to 4,100 ppm, then the waste will be
considered eligible for the exclusion (for benzene). Otherwise, the waste will not be eligible for the exclusion for
benzene. (Note that the demonstration must be made for all Appendix VIII constituents known to be present in the
waste).
Step 6: Specify Limits on Decision Errors: In the interest of being protective of the environment, the null
hypothesis was established as "the mean concentration of benzene within the decision unit boundary exceeds 4,100
ppm," or H0: mean (benzene) > 4,100 ppm. The boundaries of the gray region were set at the Action Level (4,100
ppm) and at a value less than the Action Level at 3000 ppm. The regulations at §261.38(c)(8)(iii)(A) specify a Type I
(false rejection) error rate ( CK ) of 0.05. The regulations do not specify a Type II (false acceptance) error rate (p ),
but the planning team deemed a false acceptance as of lesser concern than a false rejection, and set the false
acceptance rate at 0.25. Sample analysis results from previous sampling and analyses indicate the standard
deviation ( s ) of benzene concentrations is about 1 ,200 ppm.
What is the appropriate number of samples to collect and analyze for a simple random sampling design?
Solution: Using Equation 8 and the outputs of the first six steps of the DQO Process, the number of samples is
determined as:
,
A2
(1.645 + 0.674)2(1200)2 (1.645)2 nnc 0/ J ,
= - - , + ~ - — = 7.75 « 8 (round up)
(4100-3000)2 2
where the values for Zl_a and Zj_ a are obtained from the last row of Table G-1 in Appendix G.
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5.4.2 Number of Samples to Estimate the Mean: Stratified Random Sampling
An important aspect of a stratified random sampling plan is deciding how many samples to
collect within each of the strata (Gilbert 1987). There are many ways to design a stratified
random sampling plan; the development here makes the following assumptions (refer to Section
5.2.2 for a description of terms and symbols used below):
Weights for each stratum (Wh) are known in advance. One possible way to
assign weights to each stratum is to calculate the ratio between the waste
volume classified as the /7th stratum and the total waste volume.
The number of possible sample units (i.e., physical samples) of a certain physical
size is much larger than the number of sample units that will be collected and
analyzed. As a general guide, this assumption should be reasonable as long as
the ratio between the stratum waste volume and the volume of the individual
samples is at least 100. Otherwise, you may need to consider formulas that
include the finite population correction (see Cochran 1977, page 24).
The number of sample units to be collected and analyzed in each stratum, due to
analytical costs and other considerations, generally will be fairly small.
A preliminary estimate of variability (s%) is available for each stratum. If this is
not the case, one can use an estimate of the overall variability (s2) as a
substitute for the separate stratum estimates. By ignoring possible differences in
the variance characteristics of separate strata, the sample size formulas given
below may tend to underestimate the necessary number of samples for each
strata (nh).
Given a set of stratum weights and sample measurements in each stratum, the overall mean
(xst) and overall standard error of the mean (s~ ) (i.e., for the entire waste under study) are
computed as follows for a stratified random sample:
h h Equation 9
n=i
and
Equation 10
Note that xh and s^ in these formulas represent the arithmetic mean and sample variance for
the measurements taken within each stratum.
In general, there are two approaches for determining the number of samples to take when
stratified random sampling is used: optimal allocation and proportional allocation.
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5.4.2. 1 Optimal Allocation
In optimal allocation, the number of samples assigned to a stratum (nh)\s proportional to the
relative variability within each stratum and the relative cost of obtaining samples from each
stratum. The number of samples can be determined to minimize the variance for a fixed cost or
to minimize the cost for a prespecified variance.
Optimal allocation requires considerable advance knowledge about the relative variability within
each stratum and the costs associated with obtaining samples from each stratum; therefore, we
recommend the use of proportional allocation (see below) as an alternative. For more complex
situations in which optimal allocation is preferred, consult a statistician or see Cochran (1977,
page 96), Gilbert (1987, page 50), or USEPA (1989a (page 6-13)).
5.4.2.2 Proportional Allocation
In proportional allocation, the number of samples assigned to a stratum ( nh ) is proportional to
the stratum size, that is, nh =nWh. To determine the total number of samples (n ) so that a
true difference ( A ) between the mean waste concentration and the Action Level can be
detected with Type I error rate (X and Type II error rate fi , use the following equation:
Equation 1 1
To use this formula correctly, the degrees of freedom (df ) connected with each t -quantile
(from Table G-1, Appendix G) in the above equation must be computed as follows:
Equation^
Because the degrees of freedom also depend on n, the final number of samples must be
computed iteratively. Then, once the final total number of samples is computed, the number of
samples for each stratum is determined by multiplying the total number of samples by the
stratum weight. An example of this approach is presented in Box 10.
If only an overall estimate of s2 is available in the preliminary data, Equation 11 reduces to:
[ti-g,df+t^-ft,df\ s Equation 13
A2
and Equation 12 reduces to
^ J^ W?
Equation 14
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Box 10. Number of Samples Required to Estimate the Mean Using Stratified Random Sampling -
Proportional Allocation: Hypothetical Example
Under the RCRA Corrective Action program, a facility owner has conducted a cleanup of a solid waste management
unit (SWMU) in which the contaminant of concern is benzene. The cleanup involved removal of all waste residues,
contaminated subsoils, and structures. The facility owner needs to conduct sampling and analysis to confirm that the
remaining soils comply with the cleanup standard.
Step 1: State the Problem: The planning team needs to confirm that soils remaining in place contain benzene at
concentrations below the risk-based levels established by the authorized state as part of the cleanup.
Step 2: Identify the Decision: If the soils attain the cleanup standard, then the land will be used for industrial
purposes. Otherwise, additional soil removal will be required.
Step 3: Identify Inputs to the Decision: A sampling program will be conducted, and sample analysis results for
benzene will be used to make the cleanup attainment determination.
Step 4: Define the Boundaries: The DQO decision unit is the top 6 inches of soil within the boundary of the SWMU.
Based on prior sample analysis results and field observations, two strata are identified: fine-grained soils in 20
percent of the unit ("Stratum 1"), and coarse-grained soils comprising the other 80 percent of the unit ("Stratum 2").
Based on the relative mass of the two strata, a weighting factor W, is assigned to each Mh stratum such that
Wl = 0.2 and W2 = 0.8.
Step 5: Develop a Decision Rule: The parameter of interest is established as the mean, and the Action Level for
benzene is set at 1.5 mg/kg. If the mean concentration of benzene within the DQO decision unit is less than or equal
to 1.5 mg/kg, then the unit will be considered "clean." Otherwise, another layer of soil will be removed.
Step 6: Specify Limits on Decision Errors: In the interest of being protective of the environment, the null
hypothesis is established as "the mean concentration of benzene within the decision unit boundary exceeds 1.5
mg/kg," or Ho: mean (benzene) > 1.5 mg/kg. The boundaries of the gray region are set at the Action Level (1.5
mg/kg) and at a value less than the Action Level at 1.0 mg/kg. The Type I error rate ( Ct) is set at 0.10 and the Type
II error rate (j3) is set at 0.25. Sample analysis results from n = 8 initial non-composite samples provided an
estimate of the overall standard deviation of s = 1.83 , and the standard deviations (Sh) within each /zth stratum of
s1 = 2.5 ands2 = 1.3 (and if = 6.25 and S2 = 1.69).
What is the appropriate number of samples to collect and analyze for a stratified random sampling design?
Solution: Using Equation 12 for the degrees of freedom under proportional allocation:
(0.2X6.25)2 (0.8X1.69)2
df, = ((0.2 X 6.25) + (0.8 X 1.69))2
8(0.2) - 1 8(0.8) - 1
= 2.3 » 2
Then, looking up the f-quantiles (from Table G-1, Appendix G) with 2 degree of freedom and taking A = 0.5 (i.e.,
1.5 ppm -1.0 ppm), the total sample size (using Equation 12) works out to
[1.886 + 0.816]2 .
77, = r——((0.2 x 6.25) + (0.8 x 1.69)) = 76
(0.5)2
Since the equations must be solved iteratively, recompute the formulas using n = 76. The same calculations give
d/2 = 48 and n2 = 41. After two more iterations, the sample size stabilizes at 77 = 42 . Using the proportional
allocation with 77 = 42 one should take 42(0.2) = 8.4 (round up to 9) measurements from the first stratum and
42(0.8) = 33.6 (round up to 34) measurements from the second stratum. Since four samples already were collected
from each stratum, at least five additional random samples should be obtained from the first stratum and at least thirty
additional random samples should be collected from the second stratum.
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In the example in Box 10, stratified random sampling provides a more efficient and cost-
effective design compared to simple random sampling of the same unit. If simple random
sampling were used, a total of 52 samples would be required. With stratified random sampling,
only 42 samples are required, thereby reducing sampling and analytical costs.
5.4.3 Number of Samples to Estimate the Mean: Systematic Sampling
Despite the attractiveness and ease of implementation of systematic sampling plans, whether
via a fixed square, rectangular, or triangular grid, or through the use of systematic random
sampling, methods for estimating the standard error of the mean are beyond the scope of this
guidance (for example, see Cochran 1977) and often involve more advanced geostatistical
techniques (for example, see Myers 1997). An alternate approach is to treat the set of
systematic samples as though they were obtained using simple random sampling. Such an
approach should provide reasonable results as long as there are no strong cyclical patterns,
periodicities, or significant spatial correlations between adjacent sample locations. If such
features are present or suspected to be present, consultation with a professional statistician is
recommended.
By regarding the systematic sample as a simple random sample, one can simply use the
algorithm and formulas for simple random sampling described in Section 5.4.1 (Equation 8) to
estimate the necessary sample size. As with all the sampling designs described in this section,
you should have a preliminary estimate of the sample variance before using the sample size
equation.
5.4.4 Number of Samples to Estimate the Mean: Composite Sampling
In comparison to noncomposite sampling, composite sampling may have the effect of
minimizing between-sample variation, thereby reducing somewhat the total number of
composite samples that must be submitted for analysis.
The appropriate number of composite samples to be collected from a waste or media can be
estimated by Equation 8 for simple random and systematic composite sampling. Equation 11
can be used when composite sampling will be implemented with a stratified random sampling
design (using proportional allocation). Any preliminary or pilot study conducted to estimate the
appropriate number of composite samples should be generated using the same compositing
scheme planned for the confirmatory study. If the preliminary or pilot study data were generated
using random "grab" samples rather than composites, then the sample variance (s2) in the
sample size equations should be replaced with s2/g where g is the number of individual or
grab samples used to form each composite (Edland and Van Belle 1994, page 45).
Additional guidance on the optimal number of samples required for composite sampling and the
number of subsample aliquots required to achieve maximum precision for a fixed cost can be
found in Edland and van Belle (1994, page 36 and page 44), Exner, et al. (1985, page 512), and
Gilbert (1987, page 78).
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5.5 Determining the Appropriate Number of Samples to Estimate A Percentile or
Proportion
This section provides guidance for determining the appropriate number of samples (n) needed
to estimate an upper percentile or proportion with a prespecified level of confidence. The
approaches can be used when the objective is to determine whether the upper percentile is less
than a concentration standard or whether a given proportion of the population or decision unit is
less than a specified value.
Two methods for determining the appropriate number of samples are given below: (1) Section
5.5.1 provides a method based on the assumption that the population is large and the samples
are drawn at random from the population, and (2) Section 5.5.2 provides a method with similar
assumptions but only requires specification of the level of confidence required and the number
of exceedances allowed (usually zero). For both methods, it is assumed that the measurements
can be expressed as a binary variable - that is, that the sample analysis results can be
interpreted as either in compliance with the applicable standard ("pass") or not in compliance
with the applicable standard ("fail").
5.5.1 Number of Samples To Test a Proportion: Simple Random or Systematic Sampling
This section provides a method for determining the appropriate number of samples when the
objective is to test whether a proportion or percentile of a population complies with an applicable
standard. 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 value.
The number of samples needed to test a proportion can be calculated using
zl_/3^/GR(\-GR)+zl_a^/
\L(\-AL)
A
n= —^- Equation 15
where
a = false rejection error rate
J3 = false acceptance error rate
z = the /7th percentile of the standard normal distribution (from the last row of
Table G-1 in Appendix G)
AL = the Action Level (e.g., the proportion of all possible samples of a given
support that must comply with the standard)
GR = other bound of the gray region,
A = width of the gray region (GR - AL), and
n = the number of samples.
An example calculation of n using the approach described here is presented in Box 11.
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Box 11. Example Calculation of the Appropriate Number of Samples Needed To Test a Proportion - Simple
Random or Systematic Sampling
A facility is conducting a cleanup of soil contaminated with pentachlorophenol (PCP). Based on the results of a field
test method, soil exceeding the risk-based cleanup level of 10 mg/kg total PCP will be excavated, classified as a solid
or hazardous waste, and placed into roll-off boxes for subsequent disposal, or treatment (if needed) and disposal.
The outputs of the first six steps of the DQO Process are summarized below.
Step 1: State the Problem: The project team needs to decide whether the soil being placed in each roll-off box is a
RCRA hazardous or nonhazardous waste.
Step 2: Identify the Decision: If the excavated soil is hazardous, it will be treated to comply with the applicable LDR
treatment standard and disposed as hazardous waste. If it is nonhazardous, then it will be disposed as solid waste in
a permitted industrial waste landfill (as long as it is not mixed with a listed hazardous waste).
Step 3: Identify Inputs to the Decision: The team requires sample analysis results for TCLP PCP to determine
compliance with the RCRA TC regulatory threshold of 100 mg/L.
Step 4: Define the Boundaries: The DQO "decision unit" for each hazardous waste determination is defined as a
roll-off box of contaminated soil. The "support" of each sample is in part defined by SW-846 Method 1311 (TCLP) as
a minimum mass of 100-grams with a maximum particle size of 9.5 mm. Samples will be obtained as the soil is
excavated and placed in the roll-off box (i.e., at the point of generation).
Step 5: Develop a Decision Rule: The project team wants to ensure with reasonable confidence that little or no
portions of the soil in the roll-off box are hazardous waste. The parameter of interest is then defined as the 90th
percentile. If the 90th percentile concentration of PCP is less than 100 mg/L TCLP, then the waste will be classified as
nonhazardous. Otherwise, it will be considered hazardous.
Step 6: Specify Limits on Decision Errors: The team establishes the null hypothesis (H0) as the "true proportion (P)
of the waste that complies with the standard is less than 0.90," or H0: P < 0.90. The false rejection error rate ( Of) is
set at 0.10. The false acceptance error rate (j3) is set at 0.30. The Action Level ( AL ) is 0.90, and the other
boundary of the gray region (GR ) is set at 0.99.
How many samples are required?
Solution: Using Equation 15 and the outputs of the first six steps of the DQO Process, the number of samples (n )
is determined as:
0.524^/0.99(1 - 0.99) + 1.282^/0.90(1-0.90)
= 23.5 - 24
0.99 - 0.90
where the values for Zl_a and Z^_g are obtained from the last row of Table G-1 in Appendix G.
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5.5.2 Number of Samples When Using a Simple Exceedance Rule
If a simple exceedance rule is used (see Section 3.4.2.2), then it is possible to estimate the
number of samples required to achieve a prespecified level of confidence that a given fraction of
the waste or site has a constituent concentration less than the standard or does not exhibit a
characteristic or property of concern. The approach is based on the minimum sample size
required to determine a nonparametric (distribution-free) one-sided confidence bound on a
percentile (Hahn and Meeker 1991 and USEPA 1989a).
If the exceedance rule specifies no exceedance of the standard in any sample, then the number
of samples that must achieve the standard can be obtained from Table G-3a in Appendix G.
The table is based on the expression:
n = log(a)/log(/0 Equation 16
where alpha (a ) is the probability of a Type I error and p is the proportion of the waste or site
that must comply with the standard. Alternatively, the equation can be rearranged so that
statistical performance (1 — a ) can determined for a fixed number of samples:
(\-a) = \-pn Equation 17
Notice that the method does not require specification of the other bound of the gray region, nor
does it require specification of a Type II (false acceptance) error rate (j3).
If the decision rule allows one exceedance of the standard in a set of samples, then the number
of samples required can be obtained from Table G-3b in Appendix G.
An example application of the above equations is presented in Box 12. See also Appendix F,
Section F.3.2.
Box 12. Example Calculation of Number of Samples Needed When a Simple Exceedance Rule Is Used -
Simple Random or Systematic Sampling
What is the minimum number of samples required (with no exceedance of the standard in any of the samples) to
determine with at least 90-percent confidence ( 1 - a = 0.90 ) that at least 90 percent of all possible samples from
the waste (as defined by the DQO decision unit) are less than the applicable standard?
From Table G-3a, we find that for 1- a = 0.90 and p = 0.90 that 22 samples are required. Alternately, using
Equation 16, we find
log(O.iQ) -1
log(0.90) -0.0457
If only 1 1 samples were analyzed (with no exceedance of the standard in any of the samples), what level of
confidence can we have that at least 90 percent of all possible samples are less than the standard? Using Equation
17, we find
(\-a) = \-pn =l-0.90n =1-0.3138 = 0.6862
Rounding down, we can say with at least 68 percent confidence that at least 90 percent of all possible samples would
be less than the applicable standard. _
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Standard Guide for Generation of Environmental Data
Related to Waste Management Activities: Selection and
Optimization of Sampling Design.
5.6 Selecting the Most Resource-Effective Design
If more than one sampling design option is
under consideration evaluate the various For addltlonal guidance on selecting the most resource-
under consideration, evaluate tne various efficjent desj see ASTM standard D 631 ^
designs based on their cost and the ability
to achieve the data quality and regulatory
objectives. Choose the design that
provides the best balance between the
expected cost and the ability to meet the
objectives. To improve the balance between meeting your cost objectives and achieving the
DQOs, it might be necessary to modify either the budget or the DQOs. As can be seen from the
sample size equations in Section 5.4 and 5.5, there is an interrelationship between the
appropriate number of samples and the desired level of confidence, expected variability (both
population and measurement variability), and the width of the gray region. To reduce costs (i.e.,
decrease the number of samples required), several options are available:
Decrease the confidence level for the test
Increase the width of the "gray region" (not recommended if the parameter of
interest is near the Action Level)
Divide the population into smaller less heterogeneous decision units, or use a
stratified sampling design in which the population is broken down into parts that
are internally less heterogeneous
Employ composite sampling (if non-volatile constituents are of interest and if
allowed by the regulations).
Note that seemingly minor modifications to the sampling design using one or more of the above
strategies may result in major increases or decreases in the number of samples needed.
When estimating costs, be sure to include the costs for labor, travel and lodging (if necessary),
expendable items (such as personal protective gear, sample containers, preservatives, etc.),
preparation of a health and safety plan, sample and equipment shipping, sample analysis,
assessment, and reporting. Some sampling plans (such as composite sampling) may require
fewer analyses and associated analytical costs, but might require more time to implement and
not achieve the project objectives. EPA's Data Quality Objectives Decision Error Feasibility
Trials Software (DEFT) (USEPA 2001 a) is one tool available that makes the process of
selecting the most resource effective design easier.
5.7 Preparing a QAPP or WAP
In this activity, the outputs of the DQO Process and the sampling design are combined in a
planning document such as a QAPP or WAP. The Agency has developed detailed guidance on
how to prepare a QAPP (see USEPA 1998a) or WAP (see USEPA 1994a). The minimum
requirements for a WAP are specified at 40 CFR §264.13. The following discussion is focused
on the elements of a QAPP; however, the information can be used to help develop a WAP.
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The QAPP is a critical planning document
for any environmental data collection
operation because it documents project
activities including how QA and QC
activities will be implemented during the
life cycle of a project. 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 data produced by the
operation will be of the type and quality
needed and expected by the data user.
QA, acknowledged to be a management
function emphasizing systems and
policies, aids the collection of data of
needed and expected quality appropriate
to support management decisions in a
resource-efficient manner.
Additional EPA Guidance on Preparing
a QAPP or WAP
Chapter One, SW-846
EPA Requirements for Quality Assurance Project
Plans, EPA QA/R-5 (replaces QAMS-005/80)
(USEPA2001b)
EPA Guidance for Quality Assurance Project Plans,
EPA QA/G-5 (EPA/600/R-98/018) (USEPA 1998a)
Guidance for Choosing a Sampling Design for
Environmental Data Collection, EPA QA/G-5S - Peer
Review Draft (EPA QA/G-5S) (USEPA 2000c)
Waste Analysis at Facilities That Generate, Treat,
Store, And Dispose Of Hazardous Wastes, a
Guidance Manual (USEPA 1994a)
The activities addressed in the QAPP cover the entire project life cycle, integrating elements of
the planning, implementation, and assessment phases. If the DQOs are documented (e.g., in a
memo or report format), include the DQO document as an attachment to the QAPP to help
document the technical basis for the project and to document any agreements made between
stakeholders.
As recommended in EPA QA/G-5 (USEPA 1998a), a QAPP is composed of four sections of
project-related information called "groups," which are subdivided into specific detailed
"elements." The elements and groups are summarized in the following subsections.
5.7.1 Project Management
The QAPP (or WAP) is prepared after completion of the DQO Process. Much of the following
guidance related to project management can be excerpted from the outputs of the DQO
Process.
The following group of QAPP elements covers the general areas of project management,
project history and objectives, and roles and responsibilities of the participants. The following
elements ensure that 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:
Title and approval sheet
Table of contents and document control format
Distribution list
Project/task organization and schedule (from DQO Step 1)
Problem definition/background (from DQO Step 1)
Project/task description (from DQO Step 1)
Quality objectives and criteria for measurement data (DQO Step 3)
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Special training requirements/certification
Documentation and records.
For some projects, it will be necessary to include the names and qualifications of the person(s)
who will obtain the samples (e.g., as required under 40 CFR §261.38(c)(7) in connection with
testing for the comparable fuels exclusion).
5.7.2 Measurement/Data Acquisition
This group of QAPP elements covers all aspects of measurement system design and
implementation, ensuring that appropriate methods for sampling, analysis, data handling, and
QC are employed and thoroughly documented. Apart from the sample design step (DQO Step
7), the following information should be included in the QAPP or incorporated by reference:
Sampling process design/experimental design (DQO Steps 5 and 7)
Sampling methods and SOPs
Sample handling and chain-of-custody requirements
Analytical methods and SOPs (DQO Step 3)
QC requirements;
Instrument/equipment testing, inspection, and maintenance requirements
Instrument calibration and frequency
Inspection/acceptance requirements for supplies and consumables
Data acquisition requirements (non-direct measurements)
Data management.
For some projects, under various circumstances it may be appropriate to include hard copies of
the SOPs in the QAPP, rather than incorporate the information by reference. For example,
under the performance-based measurement system (PBMS) approach, alternative sampling
and analytical methods can be used. Such methods can be reviewed and used more readily if
actual copies of the SOPs are included in the QAPP. Hard copies of SOPs also are critically
important when field analytical techniques are used. Field personnel must have detailed
instructions available to ensure that the methods are followed. If it is discovered that deviation
from an SOP is required due to site-specific circumstances, the deviations can be documented
more easily if hard copies of the SOPs are available in the field with QAPP.
5.7.3 Assessment/Oversight
The purpose of assessment is to ensure that the QAPP is implemented as prescribed. The
elements below address the activities for assessing the effectiveness of the implementation of
the project and the associated QA/QC activities:
Assessments and response actions
Reports to management.
5.7.4 Data Validation and Usability
Implementation of these elements ensures that the data conform to the specified criteria, thus
enabling reconciliation with the project's objectives. The following elements cover QA activities
that occur after the data collection phase of the project has been completed:
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Data review, verification, and validation requirements
Verification and validation methods
Reconciliation with DQOs.
5.7.5 Data Assessment
Historically, the focus of most QAPPs has been on analytical methods, sampling, data handling,
and quality control. Little attention has been paid to data assessment and interpretation. We
recommend that the QAPP address the data assessment steps that will be followed after data
verification and validation. While it may not be possible to specify the statistical test to be used
in advance of data generation, the statistical objective (identified in the DQO Process) should be
stated along with general procedures that will be used to test distributional assumptions and
select statistical tests. EPA's Guidance for Data Quality Assessment (USEPA 2000d) suggests
the following five-step methodology (see also Section 8 for a similar methodology):
1. Review the DQOs
2. Conduct a preliminary data review
3. Select the statistical test
4. Verify the assumptions of the test
5. Draw conclusions from the Data.
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. The final
decision on the specific need for these elements for project-specific QAPPs will be made by the
regulatory agency. Documents prepared prior to the QAPP (e.g., SOPs, test plans, and
sampling plans) can be appended or, in some cases, incorporated by reference.
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6 CONTROLLING VARIABILITY AND BIAS IN SAMPLING
The DQO Process allows you to identify the problem to be solved, set specific goals and
objectives, establish probability levels for making incorrect decisions, and develop a resource-
efficient data collection and analysis plan. While most of the sampling designs suggested in this
guidance incorporate some form of randomness so that unbiased estimates can be obtained
from the data, there are other equally important considerations (Myers 1997). Sampling and
analysis activities must also include use of correct devices and procedures to minimize or
control random variability and biases (collectively known as "error") that can be introduced in
field sampling, sample transport, subsampling, sample preparation, and analysis. Sampling
error can lead to incorrect conclusions irrespective of the quality of the analytical measurements
and the appropriateness of the statistical methods used to evaluate the data.
This section is organized into three subsections which respond to these questions:
1. What are the sources of error in sampling (Section 6.1)?
2. What is sampling theory (Section 6.2)?
3. How can you reduce or otherwise control sampling error in the field and
laboratory (Section 6.3)?
6.1 Sources of Random Variability and Bias in Sampling
In conducting sampling, we are interested in obtaining an estimate of a population parameter
(such as the mean, median, or a percentile); but an estimate of a parameter made from
measurements of samples always will include some random variability (or variances) and bias
(or a systematic shift away from the true value) due primarily to (1) the inherent variability of the
waste or media (the "between-sampling-unit variability") and (2) imprecision in the methods
used to collect and analyze the samples (the "within-sampling-unit variability") (USEPA 2001 e).
Errors caused by the sample collection process can be much greater than the preparation,
analytical, and data handling errors (van Ee, et al. 1990, Crockett, et al 1996) and can dominate
the overall uncertainty associated with a characterization study (Jenkins, et al. 1996 and 1997).
In fact, analytical errors are usually well-characterized, well-understood, and well-controlled by
laboratory QA/QC, whereas sampling and sample handling errors are not usually
well-characterized, well-understood, or well-controlled (Shefsky 1997). Because sampling error
contributes to overall error, it is important for field and laboratory personnel to understand the
sources of sampling errors and to take measures to control them in field sampling.
The two components of error - random variability and bias - are independent. This concept is
demonstrated in the "target" diagram (see Figure 7 in Section 2), in which random variability
(expressed as the variance, (J2) refers to the "degree of clustering" and bias (ju — x) relates
to the "amount of offset from the center of the target" (Myers 1997).
Random variability and bias occur at each stage of sampling. Variability occurs due to the
heterogeneity of the material sampled and random variations in the sampling and sample
handling procedures. In addition, bias can be introduced at each stage by the sampling device
(or the manner in which it is used), sample handling and transport, subsampling, and analysis.
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While it is common practice to calculate the variability of sample analysis results "after the fact,"
it is more difficult to identify the sources and potential impacts of systematic sampling bias. As
discussed in more detail below, it usually is best to understand the potential sources of error "up
front" and take measures to minimize them when planning and implementing the sampling and
analysis program.
Even though random variability and bias are independent, they are related quantitatively (see
Figure 23). Errors expressed as the variance can be added together to estimate overall or "total
study error." Biases can be added together to estimate overall bias (though sampling bias is
difficult to measure in practice). Conceptually, the sum of all the variances can be added to the
sum of all biases (which is then squared) and expressed as the mean square error (MSE(x})
which provides a quantitative way of measuring the degree of representativeness of the
samples. In practice, it is not necessary to try to calculate mean square error, however, we
suggest you understand the sources and impacts of variability and bias so you can take steps to
control them in sampling and improve the representativeness of the samples. (See Sections
5.2.4 and 5.2.5 of EPA's Guidance for Data Quality Assessment, EPA QA/G-9 - QAOO Update
(USEPA 2000d) for a more detailed discussion of how to address measurement variability and
bias in the sampling design).
Random Variability
7777
(J2 = Ol + 0] + 02a
where
Systematic Error (Bias)
(JT = Between-sampling-unit
variability (population
variability)
(Js = Sampling and subsampling
variability
2
O a — Analytical variability
MSE(Jc) =
bias = Sum of all biases
including
• Sampling bias (e.g., improper selection
and use of sampling devices; loss or
gain of constituents during sampling,
transport, storage, subsampling, and
sample preparation)
• Analytical bias
• Statistical bias
• Mistakes, blunders, sabotage
o2 + (bias
)2
Figure 23. Components of error and the additivity of variances and biases in sampling
and analysis
The relatively new science of sampling theory and practice (Myers 1997) provides a technically
based approach for addressing sampling errors (see Section 6.2). Sampling theory recognizes
that sampling errors arise from or are related to the size and distribution of particles in the
waste, the weight of the sample, the shape and orientation of the sampling device, the manner
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in which the sample is collected, sample handling, and the manner in which subsampling is
performed within the laboratory. Sampling theory applies to particulate solids, liquids, and
mixtures of solids and liquids. Understanding sampling theory does not allow us to completely
eliminate sampling and analytical errors, but sampling theory does allow us to identify the
sources and magnitudes of sampling errors so we can take steps to minimize those that are the
largest. In doing so, samples will be more precise and unbiased (i.e., more "representative"),
thus reducing the number of samples required (lowering costs) and improving our ability to
achieve the decision error rate specified in the DQOs.
6.2 Overview of Sampling Theory
A number of environmental scientists have recognized a set of sampling theories developed by
Dr. Pierre Gy (Gy 1982 and 1998) and others (Ingamells and Switzer 1973; Ingamells 1974;
Ingamells and Pitard 1986; Pitard 1989; and Visman 1969) as one set of tools for improving
sampling. These researchers have studied the sources of sampling error (particularly in the
sampling of particulate matter) and developed techniques for quantifying the amount of error
that can be introduced by the physical sampling process. The theories were originally
developed in support of mineral exploration and mining and more recently were adopted by EPA
for soil sampling (van Ee, et al. 1990; Mason 1992). Under some conditions, however, the
theories can be applied to waste sampling as a means for improving the efficiency of the
sampling and analysis process (Ramsey, et al. 1989).
As discussed in the context of this guidance, Gy's theories focus on minimizing error during the
physical collection of a sample of solid and liquid media and should not be confused with the
statistical sampling designs such as simple random, stratified random, etc. discussed in Section
5. Both sampling theory and sampling design, however, are critical elements in sampling: Gy's
theories facilitate collection of "correct" individual samples, while statistical sampling designs
allow us to conduct statistical analyses and make conclusions about the larger mass of waste or
environmental media (i.e., the decision unit).
The following three subsections describe key aspects of sampling theory including
heterogeneity, sampling errors, and the concept of sample support. The descriptions are mostly
qualitative and intended to provided the reader with an appreciation for the types and
complexities of sampling error. Detailed descriptions of the development and application of
sampling theory can be found in Sampling for Analytical Purposes (Gy 1998), Geostatistical
Error Management (Myers 1997), Pierre Gy's Sampling Theory and Sampling Practice (Pitard
1993), and in EPA's guidance document Preparation of Soil Sampling Protocols: Sampling
Techniques and Strategies (Mason 1992).
6.2.1 Heterogeneity
One of the underlying principles of sampling theory is that the medium to be sampled is not
uniform in its composition or in the distribution of constituents in the medium, rather, it is
heterogeneous. Heterogeneity causes the sampling errors.
Appropriate treatment of heterogeneity in sampling depends on the scale of observation. Large-
scale variations in a waste stream or site affect where and when we take samples. Small-scale
variations in a waste or media affect the size, shape, and orientation of individual field samples
and laboratory subsamples. Gy's theory identifies three major types of heterogeneity: (1) short-
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range (or small-scale) heterogeneity, (2) long-range (or large-scale) heterogeneity, and (3)
periodic heterogeneity:
Short-range heterogeneity refers to properties of the waste at the sample level or in
the immediate vicinity of a sample location. Two other types of heterogeneity are found
within short-range heterogeneity: one reflected by differences in the composition
between individual particles, the other having to do with the distribution of those particles
in the waste. Composition heterogeneity (also known as constitution heterogeneity) is
constant and cannot be altered except by particle size reduction (e.g., grinding or
crushing the material). The distribution heterogeneity plays an important role in
sampling because particles can separate into groups. Distribution heterogeneity can be
increased (e.g., by gravitational segregation of particles or liquids) and can be reduced
by homogenization (mixing) or by taking many small increments to form a sample.
Large-scale heterogeneity reflects local trends and plays an important role in deciding
whether to divide the population into smaller internally homogenous decision units or to
use a stratified sampling design. See Appendix C for a detailed description of large-
scale heterogeneity.
Periodic heterogeneity, another larger-scale phenomena, refers to cyclic phenomena
found in flowing streams or discharges. Understanding periodic heterogeneity can aid in
dividing a waste into separate waste streams or in establishing a stratified sampling
design.
Forming a conceptual model of the heterogeneity of a waste will help you to determine how to
address it in sampling.
6.2.2 Types of Sampling Error
Gy's theory (see also Mason 1992, Pitard 1993, and Gy 1998) identifies a number of different
types of error that can occur in sampling as a result of heterogeneity in the waste and failure to
correctly define the appropriate shape and volume of material for inclusion in the sample.
Understanding the types and sources of the errors is an important step toward avoiding them.
In qualitative terms, these errors include the following:
Fundamental error, which is caused by differences in the composition of
individual particles in the waste
Errors due to segregation and grouping of particles and the constituent
associated with the particles
Errors due to various types of trends including small-scale trends, large-scale
trends, or cycles
Errors due to defining (or delimiting) the sample space and extracting the sample
from the defined area
Errors due to preparation of the sample, including shipping and handling. [Note
that the term "preparation," as used here, describes all the activities that take
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place after the primary sample is obtained in the field and includes sample
containerization, preservation, handling, mixing, grinding, subsampling, and other
preparative steps taken prior to analysis (such as the "sample preparation
methods" as described in Chapters Three, Four, and Five of SW-846).]
Errors that can occur during sampling are described below.
6.2.2.1 Fundamental Error
The composition of a sample never perfectly matches the overall composition of the larger mass
from which is was obtained because the mass of an individual sample is always less than the
mass of the population and the population is never completely homogeneous. These conditions
result in a sampling error known as fundamental error. The error is referred to as
"fundamental" because it is an incompressible minimum sampling error that depends on the
composition, shape, fragment size distribution, and chemical properties of the material, and it is
not affected by homogenization or mixing. It arises when the constituent of interest is
concentrated in constituent "nuggets" in a less concentrated matrix, especially when the
constituent is present at a trace concentration level (e.g., less than 1 percent). This type of
sampling error occurs even when the nuggets are mixed as well as possible in the matrix (so
long as they are not dissolved). The fundamental error is the only error that remains when the
sampling operation is "perfect"; that is, when all parts of the sample are obtained in a
probabilistic manner and each part is independent.
"Population"
As a conceptual example of fundamental
error, consider a container filled with many
white marbles and a few black marbles
that have been mixed together well (Figure
24). If a small sample comprising only a
few marbles is picked at random, there is
a high probability they would all be white
(Sample "A" in Figure 24) and a small
chance that one or more would be black.
As the sample size becomes larger, the
distribution in the sample will reflect more
and more closely the parent population
(Sample "B" in Figure 24). The situation is
similar in a waste that contains rare highly
concentrated "nuggets" of a constituent of
concern. If a small sample is taken, it is
possible, and even likely, that no nuggets
of the constituent would be selected as
part of the sample. This would lead to a
major underestimate of the true parameter
of interest. It also is possible with a small
sample that a gross overestimate of the parameter of interest will occur if a nugget is included in
the sample because the nugget would comprise a relatively large proportion of the analytical
sample compared to the true population. To minimize fundamental error, the point is not to
simply "fish" for a black marble (the contaminant), but to sample for all of the fragments and
constituents such that the sample is a representation of the lot from which it is derived.
Sample A
Sample B
Figure 24. Effects of sample size on fundamental error. Small
samples such as "A" cause the constituent of interest to be
under-represented in most samples and over-represented in a
small proportion of samples. Larger samples such as "B" more
closely reflect the parent population.
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The fundamental error is never zero (unless the population is completely homogeneous or the
entire population is submitted for analysis) and it never "cancels out." It can be controlled by
taking larger physical samples; however, larger samples can be difficult to handle in the field
and within the laboratory, and they may pose practical constraints due to increased space
needed for storage. Furthermore, small samples (e.g., less than 1 gram) generally are required
for analytical purposes. To preserve the character of a large sample in the small analytical
sample, subsampling and particle size reduction strategies should be employed (see also
Section 7.3).
6.2.2.2
Grouping and Segregation Error
Grouping and segregation results from the short-range heterogeneity within and around the
area from which a sample is collected (i.e., the sampling location) and within the sample
container. This small-scale heterogeneity is caused by the tendency for some particles to
associate into groups of like particles due to gravitational separation, chemical partitioning,
differing moisture content, magnetism, or electrostatic charge. Grouping and segregation of
particles can lead to sampling bias.
Figure 25 depicts grouping of particles (at
"A") and segregation of particles (at "B")
within a sample location. The grouping of
particles at location "A" could result from
an affinity between like particles (for
example, due to electrostatic forces).
Analytical samples formed from just one
group of particles would yield biased
results.
The segregation of particles at location "B"
could result from gravitation separation
(e.g., during sample shipment). If the
contaminant of interest was associated
with only one class of particle (for
example, only the black diamond shapes),
then a sample collected from the top would
result in a different concentration than a
sample collected from the bottom, thus
biasing the sample.
Grouping
Segregation
Increments
(A)
Increments
(B)
Figure 25. How grouping and segregation of particles can
affect sampling results. Grouping and segregation error can be
minimized by taking many small increments.
Grouping and segregation error can be minimized by properly homogenizing and splitting the
sample. As an alternative, an individual sample can be formed by taking a number of
increments (small portions of media) in the immediate vicinity of the sampling location and
combining them into the final collected sample.1 Pitard (1993) suggests collecting between 10
and 25 increments as a means to control grouping and segregation error. These increments
are then combined to form an individual sample to be submitted to the laboratory for analysis.
This approach should not be confused with composite sampling, in which individual samples from different
times or locations are pooled and mixed into a single sample.
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The approach of taking multiple increments to form a sample is not recommended when volatile
constituents are of interest and may have practical limitations when sampling highly
heterogeneous wastes or debris containing very large fragments.
6.2.2.3 Increment Delimitation Error
Increment delimitation error occurs when the shape of the sampling device excludes or
discriminates against certain portions of the material to be sampled. For example, a sampling
device that only samples the top portion of a liquid effluent as it is leaves a discharge pipe
(leaving a portion of the flow unsampled) causes increment delimitation error. This type of error
is eliminated by choosing a sampling device capable of obtaining all of the flow for a fraction of
the time (see also Sections 6.3.2 and 6.3.3).
6.2.2.4 Increment Extraction Error
Increment extraction error occurs when portions of the sample are lost or extraneous materials
are included in the sample. For example, if the coring device is too small to accommodate a
large fragment of waste, particles that should be in the sample might get pushed aside, causing
sampling bias. Extraction error can be controlled through selection of devices designed to
accommodate the physical characteristics of the waste.
6.2.2.5 Preparation Error
This error results from the incorrect preservation, handling, mixing, grinding, and subsampling
that can result in loss, contamination, or altering of the sample such that it no longer is an
accurate representation of the material being sampled. Proper choice and implementation of
preparation methods controls this error.
6.2.3 The Concept of "Sample Support"
The weight, shape (length, width and height dimensions), and orientation of a sample describe
the "sample support." The term "support" has been used in sampling and statistical literature in
various ways, such as to describe the mass or volume of an "exposure unit" or "exposure area"
in the Superfund program -- similar to the "decision unit" described in the DQO Process.
Conceptually, there is a continuum of support from the decision unit level (e.g., an exposure
area of a waste site or a drum of solid waste) to the sample and subsample level down to the
molecular level. Because it is not possible to submit the entire decision unit for analysis,
samples must be submitted instead. For heterogeneous media, the sample support will have a
substantial effect on the reported measurement values.
Measures can be taken to ensure adequate size, shape, and orientation of a sample:
The appropriate size of a sample (either volume or mass) can be determined
based on the relationship that exists between the particle size distribution and
expected sampling error -- known as the fundamental error (see Section 6.2.2.1).
In the DQO Process, you can define the amount of fundamental error that is
acceptable (specified in terms of the standard deviation of the fundamental error)
and estimate the volume required for field samples. The sampling tool should
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have dimensions three or more times larger than that of the diameter of the
largest particles. Proper sizing of the sampling tool will help ensure that the
particle size distribution of the sampled material is represented in the sample
(see discussion at Section 6.3.1).
The appropriate shape and orientation of the sample are determined by the
sampling mode. For a one-dimensional waste (e.g., liquid flowing from a
discharge pipe or solids on a conveyor belt), the correct or "ideal" sample is an
undisturbed cross section delimited by two parallel planes (Pitard 1993, Gy 1998)
(see discussion at Section 6.3.2.1). For three-dimensional waste forms (such as
solids in a roll-off bin, piles, thick slabs, soil in drums, liquids in a tank, etc.), the
sampling problem is best treated as a series of overlapping two-dimensional
problems. The correct or ideal sample is an undisturbed core (Pitard 1993) that
captures the entire thickness of the waste (see discussion at Section 6.3.2.2).
6.3 Practical Guidance for Reducing Sampling Error
This section describes steps that can be taken to control sampling error. While the details of
sampling theory may appear complex and difficult to explain, in practice most sampling errors
can be minimized by observing a few simple rules that, when used, can greatly improve the
reliability of sampling results with little or no additional costs (Gy 1998):
Determine the optimal mass of each field sample. For particulate solids,
determine the appropriate sample weight based on the particle size distribution
and characteristics, and consider any practical constraints (see Section 6.3.1).
Also, determine additional amounts of the sampled material needed for split
samples, for field and laboratory quality control purposes, or for archiving.
Select the appropriate shape and orientation of the sample based on the
sampling design model identified in DQO Step 7 (see Section 6.3.2).
Select sampling devices and procedures that will minimize grouping and
segregation errors and increment delimitation and increment extraction errors
(see Sections 6.3.3 and 7.1).
Implement the sampling plan by obtaining the number of samples at the sampling locations and
times specified in the sampling design selected in DQO Step 7, and take measures to minimize
preparation errors during sample handling, subsampling, analysis, documentation, and
reporting. When collecting samples for analysis for volatile organic constituents, special
considerations are warranted to minimize bias due to loss of constituents (see Section 6.3.4).
Table 7 provides a summary of strategies that can be employed to minimize the various types of
sampling error.
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Table 7. Strategies for Minimizing Sampling Error
Type of Sampling Error
Strategy To Minimize or Reduce Error
Fundamental Error
To reduce variability caused by fundamental error, increase the volume of
the sample.
To reduce the volume of the sample and maintain low fundamental error,
perform particle-size reduction followed by subsampling.
When volatile constituents are of interest, do not grind or mix the sample.
Rather, take samples using a method that minimizes disturbances of the
sample material (see also Section 6.3.4).
Grouping and Segregation Error
To minimize grouping error, take many increments.
To minimize segregation error, homogenize the sample (but beware of
techniques that promote segregation)
Increment Delimitation/Extraction
Errors
Select sampling devices that delimit and extract the sample so that all
material that should be included in the sample is captured and retained by
the device (Pitard 1993, Myers 1997).
For one-dimensional wastes (e.g., flowing streams or waste on a
conveyor), the correct or "ideal" sample is an undisturbed cross section
delimited by two parallel planes (Pitard 1993, Gy 1998). To obtain such a
sample, use a device that can obtain "all of the flow for a fraction of the
time" (Gy 1998) (see also Section 6.3.2.1).
For three-dimensional wastes (e.g., solids in a roll-off bin), the waste can
be considered for practical purposes a series of overlapping two-
dimensional wastes. The correct or "ideal" sample is an undisturbed
vertical core (Pitard 1993, Gy 1998) that captures the full depth of interest.
Preparation Error
Take steps to prevent contamination of the sample during field handling
and shipment. Sample contamination can be checked through preparation
and analysis of field quality control samples such as field blanks, trip
blanks, and equipment rinsate blanks.
Prevent loss of volatile constituents through proper storage and handling.
Minimize chemical transformations via proper storage and
chemical/physical preservation.
Take care to avoid unintentional mistakes when labeling sample
containers, completing other documentation, and handling and weighing
samples.
6.3.1 Determining the Optimal Mass of a Sample
As part of the DQO Process (Step 4 - Define the Boundaries), we recommend that you
determine the appropriate size (i.e., the mass or volume), shape, and orientation of the primary
field sample. For heterogeneous materials, the size, shape, and orientation of each field
sample will affect the analytical result. To determine the optimal mass (or weight) of samples to
be collected in the field, you should consider several key factors:
The number and type of chemical and/or physical analyses to be performed on
each sample, including extra volumes required for QA/QC. (For example, SW-
846 Method 1311 (TCLP) specifies the minimum sample mass to be used for the
extraction.)
Practical constraints, such as the available volume of the material and the ability
to collect, transport, and store the samples
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The characteristics of the matrix (such as participate solid, sludge, liquid, debris,
oily waste, etc.)
Health and safety concerns (e.g., acutely toxic, corrosive, reactive, or ignitable
wastes should be transported and handled in safe quantities)
Availability of equipment and personnel to perform particle-size reduction (if
needed) in the field rather than within a laboratory.
Often, the weight (or mass) of a field sample is determined by "whatever will fit into the jar."
While this criterion may be adequate for some wastes or media, it can introduce serious biases
- especially in the case of sampling particulate solids.
If a sample of particulate material is to be representative, then it needs to be representative of
the largest particles of interest (Pitard 1993). This is relevant if the constituent of concern is not
uniformly distributed across all the particle size fractions. To obtain a sample representative of
the largest particles of interest, the sample must be of sufficient weight (or mass) to control the
amount of fundamental error introduced during sampling.
If the constituent(s) of concern is uniformly distributed throughout all the particle size fractions,
then determination of the optimal sample mass using Gy's approach will not improve the
representativeness of the sample. Homogeneous or uniform distribution of contaminants
among all particle sizes, however, is not a realistic assumption, especially for contaminated
soils. In contaminated soils, concentrations of metals tend to be higher in the clay- and silt-size
fractions and organic contaminants tend to be associated with organic matter and fines in the
soil.
The following material provides a "rule of thumb" approach for determining the particle-size
sample-weight relationship sufficient to maintain fundamental error (as measured by the
standard deviation of the fundamental error) within desired limits. A detailed quantitative
method is presented in Appendix D. Techniques for calculating the variance of the fundamental
error also are presented in Mason (1992), Pitard (1993), Myers (1997), and Gy (1998).
The variance of the fundamental error (s2FE) is directly proportional to the size of the largest
particle and inversely proportional to the mass of the sample.2 To calculate the appropriate
mass of the sample, Pitard (1989) proposed a "Quick Safety Rule" for use in environmental
sampling based on a standard deviation of the fundamental error of 5 percent ( SFE = ±5%):
Ms>10000t/3 Equation 18
where M s is the mass of the sample in grams (g) and d of the diameter of the largest particle
in centimeters (cm).
2 2 / — 2 /
In this section, we use the "relative variance" ( s / X ) and the "relative standard deviation" (sjX ). The
values are dimensionless and are useful for comparing results from different experiments.
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Alternatively, if we are willing to accept sw = +16% , we can use
Ms >
Equation 19
An important feature of the fundamental error is that it does not "cancel out." On the contrary,
the variance of the fundamental error adds together at each stage of subsampling. As pointed
out by Myers (1997), the fundamental error quickly can accumulate and exceed 50 percent, 100
percent, 200 percent, or greater unless it is controlled through particle-size reduction at each
stage of sampling and subsampling. The variance, s2FE , calculated at each stage of
subsampling and particle-size reduction, must be added together at the end to derive the total
S2FE . A example of how the variances of the fundamental error can be added together is
provided in Appendix D.
6.3.2 Obtaining the Correct Shape and Orientation of a Sample
When sampling heterogeneous materials, the shape and orientation of the sampling device can
affect the composition of the resulting samples and facilitate or impede achievement of DQOs.
The following two subsections provide guidance on selecting the appropriate shape and
orientation of samples obtained from a moving stream of material and a stationary batch or unit
of material.
6.3.2.1
Sampling of a Moving Stream of Material
Direction of Flow
Taking all of the flow part of the time.
Taking part of the flow all of the time.
In sampling a moving stream of material,
such as solids, liquids, and multi-phase
mixtures moving through a pipe, on a
conveyor, etc., the material can be treated
as a one-dimensional mass. That is, the
material is assumed to be linear in time or
space.
The correct or "ideal" sample is an
undisturbed cross section delimited by two
parallel planes (Pitard 1993, Gy 1998).
The approach is depicted in Figure 26 in
which all of the flow is collected for part of
the time. In practice, the condition can be
met by using "cross-stream" sampling
devices positioned at the discharge of a
conveyor, hose, duct, etc. (Pitard 1993).
Alternatively, in sampling solids from a
conveyor belt, a transverse cutter or flat
scoop (with vertical sides) can be used to obtain a sample, preferably with the conveyor stopped
(though this condition may not be practical for large industrial conveyors).
For sampling of liquids, if the entire stream cannot be obtained for a fraction of the time (e.g., at
the discharge point), then it may be necessary to introduce turbulence in the stream using
baffles and to obtain a portion of the mixed stream part of the time (Pitard 1993).
Taking part of the flow part of the time.
Figure 26. Three ways of obtaining a sample from a moving
stream. "A" is correct. "B" and "C" will obtain biased samples
unless the material is homogeneous (modified after Gy 1998).
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6.3.2.2
Sampling of a Stationary Batch of Material
Different Size
Coring Devices
Different Shape
and Orientation
I
c
o J
« A
V
•
• 1
,.„.
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If volatile constituents are of interest, the device should obtain samples in an
undisturbed state to minimize loss of volatile constituents.
The device should be constructed of materials that will not alter analyte
concentrations due to loss or gain of analytes via sorption, desorption,
degradation, or corrosion.
The device should retain the appropriate size (volume or mass) and shape of
sample, and obtain it in the orientation appropriate for the sampling condition --
preferably in one pass.
Other considerations not related to performance follow:
"Ease of use" of the sampling device under the conditions that will be
encountered in the field. This includes the ease of shipping to and from the site,
ease of deployment, and ease of decontamination.
The degree of hazard associated with the deployment of one sampling device
versus another (e.g., consider use of an extension pole instead of a boat to
sample from a waste lagoon).
Cost of the sampling device and of the labor (e.g., single vs. multiple operators)
for its deployment (including training) and maintenance.
6.3.3.2 Use and Limitations of Common Devices
Unfortunately, many sampling devices in common use today lack the properties required to
minimize certain types of sampling error. In fact, there are few devices available that satisfy all
the general performance goals stated above. Pitard (1993), however, has identified a number
of devices that can help minimize delimitation and extraction error (depending on the physical
form of the waste to be sampled). These devices include:
COLIWASA (or "composite liquid waste sampler") - for sampling free-flowing
liquids in drums or containers
Shelby tube or similar device - for obtaining core samples of solids
Kemmerer depth sampler - for obtaining discrete samples of liquids
Flat scoop (with vertical walls) - for subsampling solids on a flat surface.
Some devices in common use that can cause delimitation and extraction errors include the
following: auger, shovel, spoon, trowel, thief, and trier. In spite of the limitations of many
conventional sampling devices, it is necessary to use them under some circumstances
encountered in the field because there are few alternatives. When selecting a sampling tool,
choose the one that will introduce the least sampling error. In cases in which no such tool
exists, document the approach used and be aware of the types of errors likely introduced and
their possible impact on the sampling results. To the extent possible and practicable, minimize
sampling errors by applying the concepts presented in this chapter.
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6.3.4 Special Considerations for Sampling Waste and Soils for Volatile Organic
Compounds
In most contaminated soils and other solid waste materials, volatile organic compound (VOCs),
when present, coexist in gaseous, liquid, and solid (sorbed) phases. Of particular concern with
regard to the collection, handling, and storage of samples for VOC characterization is the
retention of the gaseous component. This phase exhibits molecular diffusion coefficients that
allow for the immediate loss of gas-phase VOCs from a freshly exposed surface and continued
losses from well within a porous matrix. Furthermore, once the gaseous phase becomes
depleted, nearly instantaneous volatilization from the liquid and sorbed phases occurs in an
attempt to restore the temporal equilibrium that often exists, thereby allowing the impact of this
loss mechanism to continue.
Another mechanism that can influence VOC concentrations in samples is biological
degradation. In general, this loss mechanism is not expected to be as large a source of
determinate error as volatilization. This premise is based on the observation that losses of an
order of magnitude can occur on a time scale of minutes to hours due solely to diffusion and
advection, whereas losses of a similar magnitude due to biological processes usually require
days to weeks. Furthermore, under aerobic conditions, which is typical of most samples that
are transported and stored, biological mechanisms favor the degradation of aromatic
hydrocarbons over halogenated compounds. Therefore, besides the slower rate of analyte loss,
biodegradation is compound selective.
To limit the influence of volatilization and biodegradation losses, which, if not addressed can
biased results by one or more orders of magnitude, it is currently recommended that sample
collection and preparation, however not necessarily preservation, follow one or the other of
these two protocols:
The immediate in-field transfer of a sample into a weighed volatile organic
analysis vial that either contains VOC-free water so that a vapor partitioning
(purge-and-trap or headspace) analysis can be performed without reopening or
that contains methanol for analyte extraction in preparation for analysis, or
The collection and up to 2-day storage of intact samples in airtight containers
before initiating one of the aforementioned sample preparation procedures.
In both cases, samples should be held at 4±2 °C while being transported from the sampling
location to the laboratory.
The Standard Guide for Sampling Waste and Solids for Volatile Organics (ASTM D 4547-98) is
recommended reading for those unfamiliar with the many challenges associated with collecting
and handling samples for VOC analysis.
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7 IMPLEMENTATION: SELECTING EQUIPMENT AND CONDUCTING SAMPLING
This section provides guidance on selecting appropriate sampling tools and devices (Section
7.1), conducting field sampling activities (Section 7.2), and using sample homogenization,
splitting, and subsampling techniques (Section 7.3).
7.1 Selecting Sampling Tools and Devices
The tools, devices, and methods used for
sampling waste materials will vary with the
form, consistency, and location of the
waste materials to be sampled. As part of
the DQO Process, you identify the location
For additional guidance on the selection and use of
sampling tools and devices, see:
40 CFR 261, Appendix I, Representative Sampling
Methods
for Waste and Contaminated Media Data Collection
Activities (ASTM D 6232)
(type of unit or other source description) . standard Gujde for Selectjon of Sampljng Equipment
from which the samples will be obtained
and the "dimension" of the sampling
problem (such as "one-dimensional" or
"two-dimensional"). In the DQO Process,
you also specify the appropriate size, shape, orientation and other characteristics for each
sample (called the "sample support"). In addition to the DQOs for the sample, you will identify
performance goals for the sampling device. You may need a device that meets the following
qualifications:
Minimizes delimitation and extraction errors so that it does not include material
that should not be in the sample, nor exclude material that should be in the
sample
Provides a largely undisturbed sample (e.g., one that minimizes the loss of
volatile constituents, if those are constituents of concern)
Is constructed of materials that are compatible with the media and the
constituents of concern (e.g., the materials of construction do not cause
constituent loss or gain due to sorption, desorption, degradation, or corrosion)
Is easy to use under the conditions of the sampling location, and the degree of
health or safety risks to workers is minimal
Is easy to decontaminate
Is cost-effective during use and maintenance.
Unfortunately, few devices will satisfy all of the above goals for a given waste or medium and
sampling design. When selecting a device, try first to choose one that will introduce the least
sampling error and satisfy other performance criteria established by the planning team, within
practical constraints.
Figure 28 summarizes the steps you can use to select an optimal device for obtaining samples.
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Using the outputs from the DQO Process, a
description of the medium to be sampled, and
knowledge of the site or location of sample
collection, Tables 8 and 9 (beginning on
pages 109 and 115 respectively) can be used
to quickly identify an appropriate sampling
device. For most situations, the information in
the tables will be sufficient to make an
equipment selection; however, if you need
additional guidance, review the more detailed
information provided in Appendix E or refer to
the references cited.
If desired, you can refer to the documents
(such as ASTM standards) referenced by
Table 8 for supplementary guidance specific
to sampling a specific medium and site, or
refer to those referenced by Table 9 for
supplementary guidance on a device.1 The
contents of the ASTM standards are
summarized in Appendix J. (For more
information on ASTM or purchasing their
publications, including the standards
referenced in this chapter, contact ASTM at:
ASTM, 100 Barr Harbor Drive, West
Conshohocken, PA 19428-2959, or by
telephone at 610-832-9585, via the World
Wde Web at http://www.astm.org.)
In particular, we recommend that you review
the guidance found in ASTM Standard D
6232, Standard Guide for Selection of
Sampling Equipment for Waste and
Contaminated Media Data Collection
Activities. Most of the information on
sampling devices found in this chapter and in
Tables 8 and 9 came from that standard. As
noted by the standard, it covers criteria that
should be considered when selecting sampling equipment for collecting environmental and
waste samples for waste management activities. It also describes many of the typical devices
used during such sampling.
Because each sampling situation is unique, the guidance in this chapter may not adequately
cover your specific sampling scenario. You may have to modify a part of the device or modify
the device application to improve its performance or to facilitate sample collection. For
Stepl
Identify the medium (e.g., liquid or
sludge) in Table 8 that best describes
the material to be sampled.
+
Step 2
Select the location or point of sample
collection (e.g., conveyor, drum, tank,
etc.) in Table 8 for the medium selected
in Step 1.
i
Step 3
Identify candidate sampling devices in
the third column of Table 8. For each,
review the information in Table 9 and the
device summaries in Appendix E.
i
Step 4
Select a sampling device based on its
ability to (1) obtain the correct size,
shape, and orientation of the samples,
and (2) meet other performance goals
specified by the planning team.
Figure 28. Steps for selecting a sampling device
ASTM is a consensus standards development organization. Consistent with the provisions of the National
Technology Transfer and Advancement Act of 1995 (NTTAA), Public Law 104-113, Section 12(d), which directs EPA
to use voluntary consensus standards to the extent possible, this guidance supports the use of and provides
references to ASTM standards applicable to waste sampling.
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example, you might use a rope or an extension handle on a device to access a particular
location within a waste management unit. In other cases, you may need auxiliary equipment
that will increase the cost or complexity of sampling operation (such as a drill rig to drive a split
barrel sampler or a power supply to run a pump). The physical state of the waste or design of
the unit also may affect how the equipment is deployed. You should address such variations as
part of your sampling plan and make sure that any modifications do not cause sampling bias.
Finally, other sampling devices not addressed in this chapter can and should be used if
appropriate (e.g., if the device meets the performance goals and is more practical). New or
innovative devices not discussed in this chapter also should be considered for use if they allow
you to meet the sampling objectives in a more cost-effective manner. In other words, we
encourage and recommend a performance-based approach for selecting sampling equipment.
7.1.1 Stepl: Identify the Waste Type or Medium to be Sampled
The first column of Table 8 (page 109) lists the media type or waste matrix commonly sampled
under RCRA. These media may include liquids, sludges or slurries, various unconsolidated
solids, consolidated solids and debris, soil, ground water, sediment, soil gas, and air. In
general, the types of media describe the physical state of the material to be sampled. The
physical characteristics of the waste or medium affect many aspects of sampling, including the
volume of material required, selection of the appropriate sampling device, how the device is
deployed, and the containers used for the samples. Table 10 provides an expanded description
of the media listed in Table 8.
7.1.2 Step 2: Identify the Site or Point of Sample Collection
In the second column of Table 8, identify the site or point of sample collection that best
describes where you plan to obtain the samples. The "site or point of sample collection" may
include (1) the point at which the waste is generated (e.g., as the waste exits a pipe, moves
along a conveyor, or is poured or placed into a container, tank, impoundment or other waste
management unit); (2) the unit in which the waste is stored (such as a drum, collection hopper,
tank, waste pile, surface impoundment, sack or bag) or transported (such as a drum, tanker
truck, or roll-off box); or (3) the environmental medium to be sampled (such as surface soil,
subsurface soil, ground water, surface water, soil gas, or air).
When testing a solid waste to determine if it should be characterized as a hazardous waste or to|
determine if the waste is restricted from land disposal, such a determination must be made at
the point of waste generation.
7.1.2.1 Drums and Sacks or Bags
Drums and sacks or bags are portable containers used to store, handle, or transport waste
materials and sometimes are used in waste disposal (e.g., drums in a landfill). "Drums" include
metal drums and pails, plastic drums, or durable fiberboard paper drums or pails (USEPA
1994a). Drums and pails may contain nearly the full range of media - liquids (single or multi-
layered), sludges, slurries, or solids. Sacks or bags include less rigid portable containers and
thus can contain only solids. The sampling approach (including number of samples, locations of
samples, sampling device, depth of samples) for these containers will depend on the number of
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containers to be sampled, waste accessibility, physical and chemical characteristics of the
waste, and component distribution within the containers.
Review ASTM Standards D 6063, Guide for Sampling Drums and Similar Containers by Field
Personnel, and D 5679, Practice for Sampling Consolidated Solids in Drums or Similar
Containers, for more information on the sampling of drums and sacks or bags. Other useful
guidance on sampling drums includes "Drum Sampling" (USEPA 1994b), issued by EPA's
Environmental Response Team.
7.1.2.2 Surface Impoundments
Surface impoundments include natural depressions, manmade excavations, or diked areas that
contain an accumulation of liquids or wastes containing free liquids and solids. Examples of
surface impoundments are ponds, lagoons, and holding, storage, settling, and aeration pits
(USEPA 1994a). The appropriate sampling device for sampling a surface impoundment will
depend on accessibility of the waste, the type and number of phases of the waste, the depth,
and chemical and physical characteristics of the waste.
7.7.2.3 Tanks
A tank is defined at § 260.10 as a stationary device, designed to contain an accumulation of
hazardous waste which is constructed primarily of non-earthen materials which provide
structural support. A container is defined at § 260.10 as a portable device, in which a material
is stored, transported, treated, disposed of, or otherwise handled. The distinction that a tank is
not a container is important because the regulations at 261.7 set forth conditions to distinguish
whether hazardous waste in a container is subject to regulation. Nevertheless, for the purpose
of selecting an appropriate sampling device, the term "tank" as used in Table 8 could include
other units such as tank trucks and tanker cars even though they are portable devices.
The selection of equipment for sampling the pipes and sampling ports of a tank system is
covered separately under those categories. The equipment used to sample a pipe or spigot can
be very different from that used to sample an open tank.
Tanks usually contain liquids (single or multi-layered), sludges, or slurries. In addition,
suspended solids or sediments may have settled in the bottom of the tank. When sampling
from a tank, one typically considers how to acquire a sufficient number of samples from different
locations (including depths) to adequately represent the entire content of the tank.
Waste accessibility and component distribution will affect the sampling strategy and equipment
selection. In addition to discharge valves near the bottom, most tanks have hatches or other
openings at the top. It is usually desirable to collect samples via a hatch or opening at the top
of the tank because of the potential of waste stratification in the tank (USEPA 1996b). In an
open tank, the size of the tank may restrict sampling to the perimeter of the tank. Usually, the
most appropriate type of sampling equipment for tanks depends on the design of the tanks and
the media contained within the tank.
You can find additional guidance on sampling tanks in "Tank Sampling" (USEPA 1994c), issued
by the EPA's Environmental Response Team.
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7.1.2.4 Pipes, Point Source Discharges, or Sampling Ports
For the purpose of this guidance, pipes or point source discharges include moving streams of
sludge or slurry discharging from a pipe opening, sluice, or other discharge point (such as the
point of waste generation). Sampling ports include controlled liquid discharge points that were
installed for the purpose of sampling, such as may be found on tank systems, a tank truck, or
leachate collection systems at waste piles or landfills.
A dipper also is used to sample liquids from a sampling port. Typically, it is passed through the
stream in one sweeping motion so that it is filled in one pass. In that instance, the size of the
dipper beaker should be related to the stream flow rate. If the cross-sectional area of the
stream is too large, more than one pass may be necessary to obtain a sample (USEPA 1993b).
Besides the use of a dipper or other typical sampling devices, sometimes the sample container
itself is used to sample a spigot or point source discharge. This eliminates the possibility of
contaminating the sample with intermediate collection equipment, such as a dipper (USEPA
1996b).
See ASTM D 5013-89 Standard Practices for Sampling Wastes from Pipes and Other Point
Discharges for more information on sampling at this location. Also see Gy (1998) and Pitard
(1989, 1993).
7.1.2.5 Storage Bins, Roll-Off Boxes, or Collection Hoppers
Discharges of unconsolidated solids from a process, such as filter cakes, often fall from the
process into a collection hopper or other type of open-topped storage container. Sometimes the
waste materials are combined into large a storage bin, such as a roll-off box or collection
hopper. A storage bin also may be used to collect consolidated solids, such as construction
debris. The waste can be sampled either as it is placed in the container or after a certain period
of accumulation, depending on the technical and regulatory objectives of the sampling program.
7.7.2.6 Waste Piles
Waste piles include the non-containerized accumulation of solid and nonflowing waste material
on land. The size of waste piles can range from small heaps to large aggregates of wastes.
Liners may underlie a waste pile, thereby preventing direct contact with the soil. As with other
scenarios, waste accessibility and heterogeneity will be key factors in the sampling design and
equipment selection. Besides the devices listed in this chapter, excavation equipment may be
needed at first to properly sample large piles. Waste piles may present unique sample
delimitation problems (Pitard 1993 and Myers 1997), and special considerations related to
sampling design may be necessary (such as the need to flatten the pile).
We recommend a review of ASTM Standard D 6009, Guide for Sampling Waste Piles for more
information. Another source of information on sampling waste piles is "Waste Pile Sampling"
(USEPA 1994d), issued by EPA's Environmental Response Team.
7.1.2.7 Con veyors
Solid process discharges are sometimes sampled from conveyors such as conveyor belts or
screw conveyors. Conveyor belts are open moving platforms used to transport material
106
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between locations. Solid or semi-solid wastes on a conveyor belt can be sampled with a flat
scoop or similar device (see also Section 6.3.2.1). Screw conveyors usually are enclosed
systems that require access via a sampling port, or they can be sampled at a discharge point.
See also ASTM D 5013 and Gy (1998, pages 43 through 56).
7.1.2.8 Structures and Debris
This guidance assumes that the sampling of structure or debris typically will include the
sampling of consolidated solids such as concrete, wood, or other structure debris. Appendix C
provides supplemental guidance on developing a sampling strategy for such heterogeneous
wastes. See also AFCEE (1995), Koski, et al. (1991), Rupp (1990), USEPA and USDOE
(1992), and ASTM Standard D 5956, Standard Guide For Sampling Strategies for
Heterogeneous Wastes.
7.1.2.9 Surface or Subsurface Soil
Selection of equipment for sampling soil is based on the depth of sampling, the grain-size
distribution, physical characteristics of the soil, and the chemical parameters of interest (such as
the need to analyze the samples for volatiles). Your sampling strategy should specify the depth
and interval (e.g., "0 to 6 inches below ground surface") of interest for the soil samples.
Simple manual techniques and equipment can be used for surface or shallow depth sampling.
To obtain samples of soil from greater depths, powered equipment (e.g., power augers or drill
rigs) will be required; however, those are not used for actual sample collection, but are used
solely to gain easier access to the required sample depth (USEPA 1996b). Once at the depth,
surface sampling devices may be used.
ASTM has developed many informative standards on the sampling of soil, including D 4700,
Standard Guide for Soil Sampling from the Vadose Zone, and D 4220, Standard Practices for
Preserving and Transporting Soil Samples. In addition, see EPA-published guidance such as
Preparation of Soil Sampling Protocols: Sampling Techniques and Strategies (Mason 1992) and
Description and Sampling of Contaminated Soils - A Field Pocket Guide (USEPA 1991 b).
7.1.3 Step 3: Consider Device-Specific Factors
After you identify the medium and site of sample collection, refer to the third column of Table 8
for the list of candidate sampling devices. We listed common devices that are appropriate for
the given media and site. Next, refer to the information in Table 9 for each of the candidate
devices to select the most appropriate one for your sampling effort.
Table 9 provides device-specific information to help you choose the appropriate device based
on the study objective and the DQOs established for volume (size), shape, depth, and
orientation of the sample, and sample type (discrete or composite, surface or at depth).
For easy reference, the devices are listed alphabetically in Table 9. Appendix E contains a
summary description of key features of each device and sources for other information. Under
the third column in Table 9, "Other Device-Specific Guidance," we have identified some of those
sources, especially relevant ASTM standards (see summaries of ASTM standards in Appendix
J).
107
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7.1.3.1 Sample Type
The column "Sample Type" Table 9 identifies whether the device can sample at surface only,
shallow or at a deeper profile (depth), and whether the device can obtain a discrete sample or a
composite sample. For example, a COLIWASA or drum thief can be used to sample a
container that is 3-feet deep, but a Kemmerer sampler may be required to sample the much
deeper depth of an impoundment. We also identify in this column whether the device collects a
undisturbed or disturbed solid sample. Also, the actual depth capacity may depend on the
design of the device. Some devices can be modified or varied to collect at different depths or
locations in a material. You should refer to the device summary in Appendix E if you need
specifics regarding the sampling depth available for a given device.
7.1.3.2 Sample Volume
The column for volume in Table 9 identifies the range of sample volume, in liters, that the device
can obtain. It may be possible to increase or decrease this value through modification of the
device. During the planning process, you should determine the correct volume of sample
needed. Volume is one of the components of sample "support" (that is, the size, shape, and
orientation of the sample).
7.1.3.3 Other Device-Specific Considerations
The last column of Table 9 notes other considerations for device selection. The comments
focus on those factors that may cause error to be introduced or that might increase the time or
cost of sampling. For some devices, the column includes comments on how easy the
equipment is to use, such as whether it needs a power source or is heavy, and whether it can
be decontaminated easily. The table also mentions whether the device is appropriate for
samples requiring the analysis of volatile organic constituents and any other important
considerations regarding analyte and device compatibility. The equipment should be
constructed of materials that are compatible with the waste and not susceptible to reactions that
might alter or bias the physical or chemical characteristics of the sample of the waste.
7.1.4 Step 4: Select the Sampling Device
Select the sampling device based on its ability to (1) obtain the correct size, shape, and
orientation of the samples (see Sections 6.3.1 and 6.3.2) and (2) meet any other performance
criteria specified by the planning team in the DQO Process (see Section 6.3.3.1). In addition,
samples to be analyzed for volatile organic constituents should be obtained using a sampling
technique that will minimize the loss of constituents and obtain a sample volume required for the
analytical method (see Section 6.3.4).
108
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Table 8. Device Selection Guide - Media and Site of Sample Collection
Media
(See Section 7.1.1)
Liquids, no distinct layer of
interest
Examples: Containerized
spent solvents, leachates or
other liquids discharged from a
pipe or spigot
Site or Point of
Sample
Collection
(See Section
7.1.2)
Drum
Surface
impoundment
Tank
Candidate Devices
(Listed Alphabetically. For
Device-Specific Information,
See Table 9)
COLIWASA
Dipper
Drum thief
Liquid grab sampler
Peristaltic pump
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
Valved drum sampler
Automatic sampler
Bacon bomb
Bailer
Bladder pump
Centrifugal sub-pump
Dipper
Displacement pump
Kemmerer sampler
Liquid grab sampler
Peristaltic pump
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
Bacon bomb
Bailer
COLIWASA
Dipper
Drum thief
Kemmerer sampler
Liquid grab sampler
Peristaltic pump
Plunger type sampler
Settleable solids profiler
Submersible pump
Swing jar sampler
Syringe sampler
Other Related
Guidance
ASTM D 5743
ASTM D 6063
EPA/ERT SOP 2009
(USEPA1994b)
ASTM D 6538
USEPA(1984, 1985,
and 1989c)
ASTM D 6063
ASTM D 5743
EPA/ERT SOP 2010
(USEPA1994C)
* Copies of EPA/ERT SOPs are available on the Internet at http://www.ert.orq/
109
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Table 8. Device Selection Guide - Media and Site of Sample Collection (Continued)
Media
(See Section 7.1.1)
Liquids, no distinct layer of
interest (continued)
Liquids, multi-layered, with
one or more distinct layers
of interest
Examples: Non-aqueous
phase liquids (NAPLs) in a
tank; mixtures of antifreeze in
a tank.
Site or Point of
Sample
Collection
(See Section
7.1.2)
Pipe, point
source discharge
Sampling port
(e.g., spigot)
Drum
Surface
impoundment
Tank
Candidate Devices
(Listed Alphabetically. For
Device-Specific Information,
See Table 9)
Automatic sampler
Bladder pump
Centrifugal submersible pump
Dipper
Displacement pump
Liquid grab sampler
Plunger type sampler
Sample container
Swing jar sampler
Beaker, bucket, sample container
Swing jar sampler
COLIWASA
Discrete level sampler
Drum thief
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
Valved drum sampler
Automatic sampler
Bacon bomb
Bailer (point source bailer)
Bladder pump
Centrifugal submersible pump
Discrete level sampler
Displacement pump
Peristaltic pump
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
COLIWASA
Centrifugal submersible pump
Bacon bomb
Bailer
Discrete level sampler
Peristaltic pump
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
Valved drum sampler
Other Related
Guidance
ASTMD5013
ASTM D 5743
ASTM D 6538
Gy 1998
Gy 1998
ASTM D 6063
ASTM D 6538
USEPA(1989c)
ASTM D 6063
ASTM D 5743
EPA/ERT SOP 2010
(USEPA1994C)
110
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Table 8. Device Selection Guide - Media and Site of Sample Collection (Continued)
Media
(See Section 7.1.1)
Sludges, slurries, and solid-
liquid suspensions
Examples: Paint sludge,
electroplating sludge, and ash
and water slurry.
Granular solids -
unconsolidated
Examples: Filter press cake,
powders, excavated (ex situ)
soil, incinerator ash
Site or Point of
Sample
Collection
(See Section
7.1.2)
Drum
Tank
Surface
impoundment
Pipe or conveyor
Drum
Sack or bag
Candidate Devices
(Listed Alphabetically. For
Device-Specific Information,
See Table 9)
COLIWASA
Dipper
Liquid grab sampler
Plunger type sampler
Settleable solids profiler
Swing jar sampler
Syringe sampler
COLIWASA
Dipper
Lidded sludge/water sampler
Liquid grab sampler
Plunger type sampler
Ponar dredge
Settleable solids profiler
Swing jar sampler
Syringe sampler
Dipper
Lidded sludge/water sampler
Liquid grab sampler
Peristaltic pump
Plunger type sampler
Ponar dredge
Settleable solids profiler
Swing jar sampler
Dipper or bucket
Scoop/trowel/shovel
Swing jar sampler
Bucket auger
Coring type sampler (w/valve)
Miniature core sampler
Modified syringe sampler
Trier
Scoop/trowel/shovel
Concentric tube thief
Miniature core sampler
Modified syringe sampler
Scoop/trowel/shovel
Trier
Other Related
Guidance
ASTM D 6063
ASTM D 6063
EPA/ERT2010
(USEPA1994C)
USEPA(1989c)
ASTM D 501 3
ASTM D 5680
ASTM D 6063
EPA/ERT SOP 2009
(USEPA1994b)
ASTM D 5680
ASTM D 6063
111
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Table 8. Device Selection Guide - Media and Site of Sample Collection (Continued)
Media
(See Section 7.1.1)
Granular solids -
unconsolidated (continued)
Other solids -
unconsolidated
Examples: Waste pellets,
catalysts, or large-grained
solids.
Site or Point of
Sample
Collection
(See Section
7.1.2)
Storage bin, roll-
off box, or
collection hopper
Waste pile
Pipe (e.g.,
vertical
discharge from
cyclone
centrifuge or
baghouse) or
conveyor
Drum
Sack or bag
Storage bin, roll-
off box, or
collection hopper
Waste pile
Conveyor
Candidate Devices
(Listed Alphabetically. For
Device-Specific Information,
See Table 9)
Bucket auger
Concentric tube thief
Coring type sampler (w/valve)
Miniature core sampler
Modified syringe sampler
Scoop/trowel
Trier
Bucket auger
Concentric tube thief
Coring type sampler (w/valve)
Miniature core sampler
Modified syringe sampler
Scoop/trowel/shovel
Thin-walled tube
Trier
Bucket, dipper, pan, or sample
container
Miniature core sampler
Scoop/trowel/shovel
Trier
Bucket auger
Scoop/trowel/shovel
Bucket auger
Scoop/trowel/shovel
Bucket auger
Scoop/trowel/shovel
Bucket auger
Scoop/trowel/shovel
Split barrel
Thin-walled tube
Scoop/trowel/shovel
Other Related
Guidance
ASTM D 5680
ASTM D 6063
ASTM D 6009
EPA/ERTSOP2017
(USEPA1994d)
ASTM D 501 3
Gy(1998)
Pitard(1993)
ASTM D 5680
ASTM D 6063
EPA/ERT SOP 2009
(USEPA1994b)
ASTM D 5680
ASTM D 6063
ASTM D 5680
ASTM D 6063
ASTM D 6009
EPA/ERT SOP 201 7
(USEPA1994d)
ASTM D5013
Gy(1998)
Pitard(1993)
112
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Table 8. Device Selection Guide - Media and Site of Sample Collection (Continued)
Media
(See Section 7.1.1)
Soil and other
unconsolidated geologic
material
Examples: In situ soil at a
land treatment unit or in situ
soil at a SWMU
Solids - consolidated
Examples: Concrete, wood,
architectural debris*
Site or Point of
Sample
Collection
(See Section
7.1.2)
Surface
Subsurface
Storage bin (e.g.,
roll-off box)
Waste pile
Structure
Candidate Devices
(Listed Alphabetically. For
Device-Specific Information,
See Table 9)
Bucket auger
Concentric tube thief
Coring type sampler
Miniature core sampler
Modified syringe sampler
Penetrating probe sampler
Scoop/trowel/shovel
Thin-Walled Tube
Trier
Bucket auger
Coring type sampler
Miniature core sampler
Mod. syringe sampler
Penetrating probe sampler
Shovel/scoop/shovel
Split barrel
Thin-walled tube
Penetrating probe sampler
Rotating coring device
Penetrating probe sampler
Rotating coring device
Split barrel
Rotating coring device
(See also Appendix C, Section
C.5)
Other Related
Guidance
ASTM D 5730
ASTM E 1727
ASTM D 4700
EISOPQA Manual
(USEPA1996b)
ASTM D 4700
ASTM D 5730
ASTM D 6169
ASTM D 6282
USEPA(1996b)
USEPA(1993c)
ASTM D 5679
ASTM D 5956
ASTM D 6063
USEPAandUSDOE
(1992)
ASTM D 6009
USEPAandUSDOE
(1992)
AFCEE(1995)
Koski, etal (1991)
USEPAandUSDOE
(1992)
* The term "debris" has a specific definition under 40 CFR 268.2(g) (Land Disposal Restrictions regulations) and
includes "solid material exceeding a 60 mm particle size that is intended for disposal and that is a manufactured
object; or plant or animal matter; or natural geologic material." § 268.2(g) also identifies materials that are not
debris. In general, debris includes materials of either a large particle size or variation in the items present.
113
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Table 8. Device Selection Guide - Media and Site of Sample Collection (Continued)
Selected References for Sampling of Other Media
Air
Example: BIF emissions
Chapter Ten SW-846
EISOPQA Manual (USEPA 1996b)
Sediment
Example: Surface
impoundment sediment
QA/QC Guidance for Sampling and Analysis of Sediments, Water, and Tissues
for Dredged Material Evaluations (USEPA 1995d)
Superfund Program Representative Sampling Guidance Volume 5; Water and
Sediment, Part I - Surface Water and Sediment, Interim Final Guidance
(USEPA 1995e)
Region 4 EISOPQA Manual (USEPA 1996b)
Sediment Sampling (USEPA 1994e)
ASTM D 4823; ASTM D 5387
Soil Gas or Vapor
Examples: Soil, soil water, or
gas in the vadose zone at a
waste disposal site
Subsurface Characterization and Monitoring Techniques - A Desk Reference
Guide (USEPA 1993c)
ASTM Standard Guide for Soil Gas Monitoring in the Vadose Zone (ASTM D
5314)
So/7 Gas Sampling (USEPA 1996c)
Ground Water
Example: Ground-water
monitoring wells at a landfill
RCRA Ground-Water Monitoring Draft Technical Guidance (USEPA 1992c)
Low-Flow (Minimal Drawdown) Ground-Water Sampling Procedures (Puls and
Barcelona 1996)
ASTM D4448-01 Standard Guide for Sampling Ground-Water Monitoring Wells
ASTM D 5092-90 Standard Practice for Design and Installation of Ground Water
Monitoring Wells in Aquifers
ASTM D 6286-98 Standard Guide for Selection of Drilling Methods for
Environmental Site Characterization
ASTM D 6282 Standard Guide for Direct Push Soil Sampling for Environmental
Site Characterizations
ASTM D 6771-02 Standard Practice for Low-Flow Purging and Sampling for
Wells and Devices Used for Ground-Water Quality Investigations
114
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Table 9. Device Selection Guide - Device-Specific Factors
Sampling
Device (Listed
in Alphabetical
Order)
Automatic
sampler
Bacon bomb
Bailer
Bladder pump
Bucket auger
Description,
Appendix E,
Section No.
E.1.1
E.3.1
E.7.1
E.1.2
E.5.1
Other Device-
Specific Guidance
(in Addition to
ASTM D 6232)
ASTM D 6538
EISOPQA Manual
(USEPA1996b)
USEPA1984
USEPA1994C
ASTM D 4448
USEPA1992C
USEPA1994C
ASTM D 4448
USEPA1992C
USEPA1996b
ASTM D 1452
ASTM D 4700
ASTM D 6063
Mason 1992
USEPA1993C
Sample Type
Shallow
(25 in.),
discrete or
composite
Depth,
discrete
Depth,
discrete
Depth,
discrete
Surface or
depth,
disturbed
Volume
(Liters per
Pass)
Unlimited
0.1 to 0.5
0.5 to 2.0
Unlimited
0.2 to 1.0
Comments
(For Example: Effects on Matrix, Operational
Considerations, Typical Uses)
Auto samplers are available to collect samples for volatile
organics analysis, provide a grab or composite sample, and may
be unattended. Need power source/battery. Commonly used at
waste water treatment plants. Must be knowledgeable of
compatibility of waste and sampler components.
For parameters that do not require a polytetrafluroethylene
(PTFE) sampler. Recommended for sampling of lakes, ponds,
large tanks, or lagoons. May be difficult to decontaminate and
materials of construction may not be compatible with sample
matrix.
Bailers are not recommended for sampling ground water for
trace constituent analysis due to sampling induced turbidity
(USEPA 1992c and Puls and Barcelona 1996). Unable to collect
samples from specific depths (unless a point-source bailer is
used). Available in a variety of sizes as either reusable or single
use devices. May be chemically incompatible with certain
matrices unless constructed of resistant material.
For purging or sampling of wells, surface impoundments, or
point discharges. Contact parts are made of PTFE, PVC and
stainless steel. Requires a power source, compressed gas, and
a controller. Difficult to decontaminate (based on design).
Suitable for samples requiring VOAs. May require a winch or
reel.
Easy and quick for shallow subsurface samples but not
recommended for VOAs. Requires considerable strength and
labor and destroys soil horizons.
115
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Table 9. Device Selection Guide - Device-Specific Factors (Continued)
Sampling
Device (listed
in alphabetical
order)
Centrifugal
submersible
pump
COLIWASA
Concentric tube
thief
Coring type
sampler (with or
without valve)
Dipper (or "pond
sampler")
Discrete level
sampler
Displacement
pumps
Description,
Appendix E,
Section
E.1.4
E.6.1
E.4.3
E.4.6
E.7.2
E.3.5
E.1.5
Other Device-
Specific Guidance
(in addition to
ASTM D 6232)
ASTM D 4448
ASTM D 4700
USEPA1992C
ASTM D 5495
ASTM D 5743
ASTM D 6063
USEPA1980
ASTM D 6063
USEPA1994d
ASTM D 4823
USEPA1989C
ASTM D 5358
ASTM D 501 3
USEPA1980
ASTM D 4448
Sample Type
Depth,
discrete
Shallow,
composite
Surface,
relatively
undisturbed,
selective
Surface or
depth,
disturbed
Shallow,
composite
Depth,
discrete
Depth,
discrete
Volume
(Liters Per
Pass)
Unlimited
0.5 to 3.0
0.5 to 1.0
0.2 to 1.5
0.5 to 1.0
0.2 to 0.5
Unlimited
Comments
(For Example: Effects on Matrix, Operational
Considerations, Typical Uses)
For purging or sampling wells, surface impoundments, or point
discharges. Contact parts are made of PTFE and stainless
steel. Requires a power source. Adjustable flow rate and easy
to decontaminate. Not compatible with liquids containing high
percent solids. May require a winch or reel.
Reusable and single use models available. Inexpensive. Glass
type devices may be difficult to decontaminate. Collects
undisturbed sample. For mixed solid/liquid media will collect
semi-liquid only. Not for high viscosity liquids.
Recommended for powdered or granular materials or wastes in
piles or in bags, drums or similar containers. Best used in dry,
unconsolidated materials. Not suitable for sampling large
particles due to narrow width of slot.
Designed for wet soils and sludge. May be equipped with a
plastic liner and caps. May be used for VOAs. Reusable and
easy to decontaminate.
For sampling liquids in surface impoundments. Inexpensive.
Not appropriate for sampling stratified waste if discrete
characterization needed.
Easy to decontaminate. Obtains samples from a discrete
interval. Limited by sample volume and liquids containing high
solids. Can be used to store and transport sample.
Can be used for purging or sampling of wells, impoundments, or
point discharges. Contact parts are made of PVC, stainless
steel, or PTFE to reduce risk of contamination when trace levels
or organics are of interest. Requires a power source and a large
gas source. May be difficult to decontaminate (piston
displacement type). May require a winch or reel to deploy.
116
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Table 9. Device Selection Guide - Device-Specific Factors (Continued)
Sampling
Device (listed
in alphabetical
order)
Drum thief
Kemmerer
sampler
Lidded
sludge/water
sampler
Liquid grab
sampler
Miniature core
sampler
Modified syringe
sampler
Description,
Appendix E,
Section
E.6.2
E.3.2
E.3.4
E.7.3
E.4.7
E.4.8
Other Device-
Specific Guidance
(in addition to
ASTM D 6232)
ASTM D 6063
ASTM D 5743
USEPA1994b
ASTM D 4547
ASTM D 641 8
ASTM D 4547
Sample Type
Shallow,
composite
Depth,
discrete
Discrete,
composite
Shallow,
discrete,
composite-
suspended
solids only
Discrete
Discrete
Volume
(Liters Per
Pass)
0.1 to 0.5
1.0 to 2.0
1.0
0.5 to 1.0
0.01 to 0.05
0.01 to 0.05
Comments
(For Example: Effects on Matrix, Operational
Considerations, Typical Uses)
Usually single use. If made of glass and reused,
decontamination may be difficult. Limited by length of sampler,
small volume of sample collected, and viscosity of fluids.
Recommended for lakes, ponds, large tanks or lagoons. May be
difficult to decontaminate. Materials may not be compatible with
sample matrix but all PTFE construction is available. Sample
container exposed to media at other depths while being lowered
to sample point.
1-L sample jar placed into device (low risk of contamination).
May sample at different depths and samples up to 40-percent
solids. Equipment is heavy and limited to one bottle size.
For sampling liquids or slurries. Can be capped and used to
transport sample. Easy to use. May be lowered to specific
depths. Compatibility with sample parameters is a concern.
Used to retrieve samples from surface soil, trench walls, or sub-
samples from soil cores. O-rings on plunger and cap minimize
loss of volatiles and allow device to be used to transport sample.
Designed for single use. Cannot be used on gravel or rocky
soils must avoid trapping air with samples.
Made by modifying a plastic, medical, single-use syringe. Used
to collect a sample from a material surface or to sub-sample a
core. The sample is transferred to a vial for transportation.
Inexpensive. Must ensure device is clean and compatible with
media to be sampled.
117
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Table 9. Device Selection Guide - Device-Specific Factors (Continued)
Sampling
Device (listed
in alphabetical
order)
Penetrating
probe sampler
Peristaltic pump
Plunger type
sampler
Ponar dredge
Rotating coring
device
Scoop
Settleable solids
profiler
Description,
Appendix E,
Section
E.4.1
E.1.3
E.6.4
E.2.1
E.5.2
E.7.5
E.6.5
Other Device-
Specific Guidance
(in addition to
ASTM D 6232)
USEPA1993C
ASTM D 4448
ASTM D 6063
USEPA1996b
ASTM D 5743
ASTM D 4387
ASTM D 4342
USEPA1994e
ASTM D 5679
ASTM D 5633
ASTM D 4700
ASTM D 6063
Sample Type
Discrete,
undisturbed
Shallow,
discrete or
composite-
suspended
solids only
Surface or
depth,
discrete
Bottom
surface, rocky
or soft,
disturbed
Surface or
depth,
undisturbed
Surface,
disturbed,
selective
Depth,
composite-
suspended
solids only
Volume
(Liters Per
Pass)
0.2 to 2.0
Unlimited
0.2 to
Unlimited
0.5 to 3.0
0.5 to 1.0
<0.1 to 0.6
1.3 to 4.0
Comments
(For Example: Effects on Matrix, Operational
Considerations, Typical Uses)
Used to sample soil vapor, soil, and ground water (pushed or
hydraulically driven). Versatile, make samples available for
onsite analysis and reduces investigation derived waste. Limited
by sample volume and composition of subsurface material.
Possible to collect samples from multiple depths up to 25 feet.
Decontamination of pump is not required and tubing is easy to
replace. Can collect samples for purgeable organics with
modified equipment, but may cause loss of VOAs.
Made of high-density polyethylene (HOPE) or PTFE with
optional glass sampling tubes. Used to collect a vertical column
of liquid. Either a reusable or single use device.
Decontamination may be difficult (with glass tubes).
One of the most effective samplers for general use on all types
of substrates (silt to granular material). May be difficult to
repeatedly collect representative samples. May be heavy.
May obtain a core of consolidated solid. Requires power and
water source and is difficult to operate. Sample integrity may be
affected.
Usually for surface soil and solid waste samples. Available in
different materials and simple to obtain. May bias sample
because of particle size. May exacerbate loss of VOCs.
Typically used at waste water treatment plants, waste settling
ponds, and impoundments to measure and sample settleable
solids. Easy to assemble, reusable and unbreakable under
normal use. Not recommended for caustics or high viscosity
materials.
118
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Table 9. Device Selection Guide - Device-Specific Factors (Continued)
Sampling
Device (listed
in alphabetical
order)
Shovel
Split barrel
sampler
Swing jar
sampler
Syringe sampler
Thin-walled tube
Trier
Trowel
Valved drum
sampler
Description,
Appendix E,
Section
E.7.5
E.4.2
E.7.4
E.3.3
E.4.5
E.4.4
E.7.5
E.6.3
Other Device-
Specific Guidance
(in addition to
ASTM D 6232)
ASTM D 4700
ASTMD 1586
ASTM D 4700
ASTM D 6063
ASTM D 5743
ASTM D 6063
ASTMD 1587
ASTM D 4823
ASTM D 4700
ASTM D 5451
ASTM D 6063
ASTM D 5633
ASTM D 4700
ASTM D 6063
Sample Type
Surface,
disturbed
Discrete,
undisturbed
Shallow,
composite
Shallow,
discrete,
disturbed
Surface or
depth,
undisturbed
Surface,
relatively
undisturbed,
selective
Surface,
disturbed,
selective
Shallow,
composite
Volume
(Liters Per
Pass)
1.0 to 5.0
0.5 to 30.0
0.5 to 1.0
0.2 to 0.5
0.5 to 5.0
0.1 to 0.5
0.1 to 0.6
0.3 to 1.6
Comments
(For Example: Effects on Matrix, Operational
Considerations, Typical Uses)
Used to collect surface material or large samples from waste
piles. Easy to decontaminate and rugged. Limited to surface
use and may exacerbate the loss of samples for VOAs.
May be driven manually, or mechanically by a drill rig with
trained personnel. May collect a sample at depth. A liner may
be used in the device to minimize disturbance or for samples
requiring VOAs.
Used to sample liquids, powders, or small solids at a distance up
to 12 feet. Adaptable to different container sizes. Not suitable
for discrete samples. Can sample a wide variety of locations.
Recommended for highly viscous liquids, sludges and tar-like
substances. Easy to decontaminate. Obtains samples at
discrete depths but limited to length of device. Waste must be
viscous enough to stay in sampler.
Useful for collecting an undisturbed sample (depends on
extension). May require a catcher to retain soil samples.
Inexpensive, easy to decontaminate. Samples for VOAs may be
biased when sample is extruded.
Recommended for powdered or granular materials or wastes in
piles or in bags, drums, or similar containers. Best for moist or
sticky materials. Will introduce sampling bias when used to
sample coarse-grained materials.
Usually for surface soil and solid waste samples. Available in
different materials and simple to obtain. May bias sample
because of particle size, and may exacerbate loss of VOAs.
Used to collect a vertical column of liquid. Available in various
materials for repeat or single use. High viscosity liquids may be
difficult to sample.
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Table 10. Descriptions of Media Listed in Table 8.
Media
Liquids - no distinct layer of
interest
Liquids - one or more distinct
layers of interest
Sludges or slurries
Granular solids, unconsolidated
Other solids, unconsolidated
Description
Liquids (aqueous or nonaqueous) that are or are not
stratified and samples from discrete intervals are not of
interest. Sampling devices for this medium do not need to
be designed to collect liquids at discrete depths.
Liquids (aqueous or nonaqueous) that are stratified with
distinct layers and collection of samples from discrete
intervals is of interest. Sampling devices for this media do
need to be designed to collect liquids at discrete depths.
Materials that are a mixture of liquids and solids and that
may be viscous or oily. Includes materials with suspended
solids.
Solids which are not cemented, or do not require significant
pressure to separate into particles, and are comprised of
relatively small particles or components.
Solids with larger particles than those covered by granular
solids. The sampling device needs to collect a larger
diameter or volume of sample to accommodate the larger
particles.
Examples
Containerized leachates or spent solvents; leachates or
other liquids released from a spigot or discharged from a
pipe.
Mixtures of antifreeze and used oil; light or dense non-
aqueous phase liquids and water in a container, such as a
tank.
Waste water treatment sludges from electroplating; slurry
created by combining solid waste incinerator ash and water.
Excavated (ex situ) soil in a staging pile; filter press cake;
fresh cement kiln dust; incinerator ash.*
Waste pellets or catalysts.
* For EPA-published guidance on the sampling of incinerator ash, see Guidance for the Sampling and Analysis of Municipal Waste Combustion Ash for the
Toxicity Characteristic (USEPA 1995f).
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Table 10. Descriptions of Media Listed in Table 8 (Continued).
Media
Soil (in-situ) and other
unconsolidated geologic material
Solids, consolidated
Air
Sediment
Soil gas or vapor
Ground water
Description
Soil in its original undisturbed location or other geologic
material that does not require significant pressure to
separate into particles. In situ soil sampling may be
conducted at subsurface or surface depths. Surface soils
generally are defined as soils between the ground surface
and 6 to 12 inches below the ground surface (USEPA
1996b); however, the definition of surface soils in State
programs may vary considerably from EPA's.
Cemented or otherwise dense solids that require significant
physical pressure to break apart into smaller parts.
For the purpose of RCRA sampling, air includes emissions
from stationary sources or indoor air.
Settled, unconsolidated solids beneath a flowing or standing
liquid layer.
Gas or vapor phase in the vadose zone. The vadose zone
is the hydrogeological region extending from the soil surface
to the top of the principal water table.
"Water below the land surface in a zone of saturation" (40
CFR 260.10). Water can also be present below the land
surface in the unsaturated (vadose) zone.
Examples
Subsurface soil at a land treatment unit; surface soil
contaminated by a chemical spill on top of the ground or soil
near a leak from an excavated underground storage tank.*
Concrete, wood, and architectural debris.
Emissions from boilers and industrial furnaces (BIFs).**
Sediment in a surface water body.
Soil gas overlying a waste disposal site.
Ground water in monitoring wells surrounding a hazardous
waste landfill.***
* Detailed guidance on soil sampling can be found in Preparation of Soil Sampling Protocols: Sampling Techniques and Strategies (Mason 1992), which
provides a discussion of the advantages and disadvantages of various sample collection methods for soil.
** See Chapter Ten of SW-846 for EPA-approved methods for sampling air under RCRA.
*** Detailed guidance on ground-water sampling can be found in RCRA Ground-Water Monitoring — Draft Technical Guidance (USEPA 1992c), which updates
technical information in Chapter Eleven of SW-846 (Rev. 0, Sept. 1986) and the Technical Enforcement Guidance Document (TEGD).
121
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7.2 Conducting Field Sampling Activities
This section provides guidance on performing field sampling activities that typically are
performed during implementation of the sampling plan. Additional guidance can be found in
Waste Analysis at Facilities That Generate, Treat, Store, and Dispose of Hazardous Wastes, a
Guidance Manual (USEPA 1994a), Environmental Investigations Standard Operating
Procedures and Quality Assurance Manual, U.S. EPA Region 4, May 1996 (USEPA 1996b),
other USEPA guidance cited in the reference section of this chapter, and various ASTM
standards summarized in Appendix J of this guidance. See also Appendix C of EPA's Guidance
for Quality Assurance Project Plans (USEPA 1998a). The latter document includes extensive
checklists, including the following:
Sample handling, preparation, and analysis checklist
QAPP review checklist
Chain-of-custody checklist.
In this section, we provide guidance on the following topics:
Sample containers (Section 7.2.1)
Sample preservation and holding times (Section 7.2.2)
Documentation of field activities (Section 7.2.3)
Field quality control samples (Section 7.2.4)
Sample identification and chain-of-custody procedures (Section 7.2.5)
Decontamination of equipment and personnel (Section 7.2.6)
Health and safety (Section 7.2.7)
Sample packaging and shipping (Section 7.2.8).
7.2.1 Selecting Sample Containers
All samples should be placed in containers of a
size and construction appropriate for the
volume of material specified in the sampling
plan and as appropriate for the requested
analyses. If sufficient sample volume is not
collected, the analysis of all requested parameters and complete quality control determinations
may not be possible. In addition, minimum sample volumes may be required to control
sampling errors (see Section 6). Chapters Two, Three, and Four of SW-846 identify the
appropriate containers for RCRA-related analyses by SW-846 methods.
It is important to understand that a single "sample" may need to be apportioned to more than
one container to satisfy the volume and preservation requirements specified by different
categories of analytical methods. Furthermore, the analytical plan may require transport of
portions of a sample to more than one laboratory.
Factors to consider when choosing containers are compatibility with the waste components,
cost, resistance to breakage, and volume. Containers must not distort, rupture, or leak as a
result of chemical reactions with constituents of waste samples. The containers must have
adequate wall thickness to withstand handling during sample collection and transport. For
analysis of non-volatile constituents, containers with wide mouths are often desirable to facilitate
122
Chapters Two, Three, and Four of SW-846 identify
some of the appropriate containers for RCRA-related
analyses by SW-846 methods.
-------
transfer of samples from the equipment. The containers must be large enough to contain the
optimum sample volume specified in the DQO Process.
You should store samples containing light-sensitive organic constituents in amber glass bottles
with TeflondMined lids. Polyethylene containers are not appropriate for use when the samples
are to be analyzed for organic constituents because the plastics could contribute organic
contaminants and potentially introduce bias. If liquid samples are to be submitted for analysis of
volatile compounds, you must store the samples in air-tight containers with zero head space.
You can store samples intended for metals and other inorganic constituent analyses in
polyethylene containers with polyethylene-lined lids. We recommend that you consult with a
chemist for further direction regarding chemical compatibility of available containers and the
media to be sampled. We recommend that an extra supply of containers be available at the
sampling location in case you want to collect more sample material than originally planned or
you need to retain splits of each sample.2
Always use clean sample containers of an assured quality. For container cleaning procedures
and additional container information, refer to the current iteration of Specifications and
Guidance for Contaminant-Free Sample Containers (USEPA 1992d). You may wish to
purchase pre-cleaned/quality assured bottles in lieu of cleaning your own bottles (USEPA
2001 g).
7.2.2 Sample Preservation and Holding Times
Samples are preserved to minimize any chemical or physical changes that might occur between
the time of sample collection and analysis. Preservation can be by physical means (e.g., kept at
a certain temperature) or chemical means (e.g., with the addition of chemical preservatives). If
a sample is not preserved properly, the levels of constituents of concern in the sample may be
altered through chemical, biological, or photo-degradation, or by leaching, sorption, or other
chemical or physical reactions within the sample container.
The appropriate method for preserving a sample will depend on the physical characteristics of
the sample (such as soil, waste, water, etc.), the concentration of constituents in the sample,
and the analysis to be performed on the sample. Addition of chemical preservatives may be
required for samples to be analyzed for certain parameters. You should not chemically
preserve highly concentrated samples. Samples with low concentrations, however, should be
preserved. You should consult with a chemist at the laboratory regarding the addition of
chemical preservatives and the possible impact on the concentration of constituents in the
sample. Also, be aware that addition of some chemical preservatives to highly concentrated
waste samples may result in a dangerous reaction.
Regardless of preservation measures, the concentrations of constituents within a sample can
degrade over time. Therefore, you also should adhere to sample holding times (time from
sample collection to analysis), particularly if the constituents of concern are volatiles in low
concentrations. Analytical data generated outside of the specified holding times are considered
to be minimum values only. You may use such data to demonstrate that a waste is hazardous
2 For example, when inspections are conducted under Section 3007 of RCRA (42 U.S.C. § 6927), and
samples are obtained, EPA must provide a split sample to the facility, upon request.
123
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where the value of a constituent-of-concern is above the regulatory threshold, but you cannot
use the data to demonstrate that a waste is not hazardous. Exceeding a holding time when the
results are above a decision level does not invalidate the data.
Appropriate sample preservation techniques and sample holding times for aqueous matrices are
listed in Chapters Two, Three, and Four of SW-846. You should also consult the methods to be
used during analysis of the sampled waste. In addition, Standard Guide for Sampling Waste
and Soil for Volatile Organic Compounds (ASTM D 4547-98) provides information regarding the
preservation of volatile organic levels in waste and soil samples.
7.2.3 Documentation of Field Activities
This section provides guidance on documenting field activities. Records of field activities should
be legible, identifiable, retrievable and protected against damage, deterioration, and loss. You
should record all documentation in waterproof, non-erasable ink. If you make an error in any of
these documents, make corrections by crossing a single line through the error and entering the
correct information adjacent to it. The corrections should then be initialed and dated. Stick-on
labels of information should not be removable without evidence of the tampering. Do not put
labels over previously recorded information.
Keep a dedicated logbook for each sampling project with the name of the project leader, team
members, and project name written inside the front cover. Document all aspects of sample
collection and handling in the logbook. Entries should be legible, accurate, and complete. The
language should be factual and objective.
You also should include information regarding sample collection equipment (use and
decontamination), field analytical equipment and the measurements, calculations and
calibration data, the name of the person who collected the sample, sample numbers, sample
location description and diagram or map, sample description, time of collection, climatic
conditions, and observations of any unusual events. Document the collection of QC samples
and any deviations from procedural documents, such as the QAPP and SOPs.
When videos, slides, or photographs are taken, you should number them to correspond to
logbook entries. The name of the photographer, date, time, site location, and site description
should be entered sequentially into the logbook as photos are taken. A series entry may be
used for rapid aperture settings and shutter speeds for photographs taken within the normal
automatic exposure range. Special lenses, films, filters, or other image enhancement
techniques must be noted in the logbook. Chain-of-custody procedures for photoimages
depend on the subject matter, type of film, and the processing it requires. Adequate logbook
notations and receipts may be used to account for routine film processing. Once developed, the
slides or photographic prints should be serially numbered corresponding to the logbook
descriptions and labeled (USEPA 1992e).
7.2.4 Field Quality Control Samples
Quality control samples are collected during field studies to monitor the performance of sample
collection and the risk of sampling bias or errors. Field QC samples could include the following:
124
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Equipment blank: A rinse sample of the decontaminated sampling equipment using
organic/analyte free water under field conditions to evaluate the effectiveness of
equipment decontamination or to detect sample cross contamination.
Trip blank: A sample prepared prior to the sampling event and stored with the samples
throughout the event. It is packaged for shipment with the samples and not opened until
the shipment reaches the laboratory. The sample is used to identify any contamination
that may be attributed to sample handling and shipment.
Field blank: A sample prepared in the field using organic/analyte free water to evaluate
the potential for contamination by site contaminants not associated with the sample
collected (e.g., airborne organic vapors)
Field split sample: Two or more representative portions taken from the same sample
and submitted for analysis to different laboratories. Field split samples are used to
estimate interlaboratory precision.
In addition to collecting field QC samples, other QC procedures include sample storage,
handling, and documentation protocols. These procedures are covered separately in the
following sections. In addition, Chapter One of SW-846, entitled "Quality Control", contains
guidance regarding both field and laboratory QA/QC. We also recommend reviewing the
following for information on field QA/QC:
EPA Guidance for Quality Assurance Project Plans (USEPA 1998a)
Standard Practice for Generation of Environmental Data Related to Waste
Management Activities: Quality Assurance and Quality Control Planning and
Implementation (ASTM D 5283-92).
7.2.5 Sample Identification and Chain-of-Custody Procedures
You should identify samples for laboratory analysis with sample tags or labels. An example of a
sample label is given in Figure 29.
Typically, information on the sample label
should include the sample identification
code or number, date, time of collection,
preservative used, media, location, initials
of the sampler, and analysis requested.
While not required, you may elect to seal
each sample container with a custody seal
(Figure 30).
You should use chain-of-custody
procedures to record the custody of the
samples. Chain-of-custody is the custody
of samples from time of collection through
shipment to analysis. A sample is in one's
custody if:
[Name of Sampling Organization]
Sample DpSTiptinn
Plant-
Date-
Time-
Media:
Sample Type-
Sampled By-
Sample ID No •
Location:
Station:
Preservative-
Figure 29. Sample label
125
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It is in the actual possession of an investigator
It is in the view of an investigator, after being in their physical possession
It is in the physical possession of an investigator, who secures it to prevent
tampering
It is placed in a designated secure area.
#&>*, UNITED STATES
£** ENVIRONMENTAL PROTECTION AGENCY
£ ^jltfr 1 OFFICIAL SAMPLE SEAL
»Jw/
Xo»a^v
SAMPLE No. IDATE
SIGNATURE
PRINT NAME AND TITLE (INSPECTOR, ANALYST™ TECHNICIAN)
a
2
<
W
LI
c
D
EPA FORM
15QO-2(R7-75)
Figure 30. Custody seal
All sample sets should be accompanied by a chain-of-custody form. This record also serves as
the sample logging mechanism for the laboratory sample custodian. Figure 31 illustrates the
content of a chain-of-custody form. When the possession of samples is transferred, both the
individual relinquishing the samples and the individual receiving the samples should sign, date,
and note the time on the chain-of-custody document. If you use overnight shipping service to
transport the samples, record the air bill number on the chain-of-custody form. This chain-of-
custody record represents the official documentation for all transfers of the sample custody until
the samples have arrived at the laboratory. The original form of the chain-of-custody record
should accompany each shipment. A copy should be retained by a representative of the
sampling team.
When sample custody is transferred between individuals, the samples or coolers containing the
samples are sealed with a custody seal. This seal cannot be removed or broken without
destruction of the seal, providing an indicator that custody has been terminated.
EPA's Superfund Program has developed software called Field Operations and Records
Management System (FORMS) II Lite™ that automates the printing of sample documentation in
the field, reduces time spent completing sample collection and transfer documentation, and
facilitates electronic capture of data prior to and during field sampling activities. For information
on FORMS II Lite™, see http://www.epa.gov/superfund/programs/clp/f2lite.htm.
For additional information on chain-of-custody procedures, we recommend ASTM D 4840,
Standard Guide for Sampling Chain-of-Custody Procedures.
126
-------
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US EPA RETGION 4
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• ATHENS, GEORGIA 30605-2720
CHAIN OF CUSTODY RECORD
PROJECT NO.
PROJECT LEADER
PROJECT NAME/LOCATION
1. SURFACE WATER
2. GROUND WATER
3. POTABLE WATER
4. WASTEWATER
5. LEACHATE
11. OTHER
SAMPLE TYPES
6. SOIL/SEDIMENT
7. SLUDGE
8. WASTE
lOiFISH
STATION NO-
SAMPLERS(SIGN)
STATION LOCATION/DESCRIPTION
CIRCLE/ADD J.
parameters desired.
List number
of containers
submitted.
LAB
USE
ONLY
RELINQUISHED BY:
(PRINT)
RECEIVED BY:
(PRINT)
RELINQUISHED BY:
(PRINT)
RECEIVED BY:
(PRINT)
(SIGN)
RELINQUISHED BY:
(PRINT)
RECEIVED BY:
(PRINT)
RELINQUISHED BY:
(PRINT)
RECEIVED BY:
(PRINT)
(SIGN)
N: Wh|te and Piik copies accompany sample shipment to laboratory; Pink copy retained by laboratory
White copy is returned to samplers; Yellow copy retained by samplers
'U.S. GPO 1989-732 0186
4-1790tf
•10/89)
-------
7.2.6 Decontamination of Equipment and Personnel
Decontamination of sampling equipment refers to the physical and chemical steps taken to
remove any chemical or material contamination. Equipment decontamination helps prevent
sampling bias. All equipment that comes in contact with the sampled material should be free of
components that could influence (contaminate) the true physical or chemical composition of the
material. Besides the equipment used to collect the samples, any containers or equipment
used for sample compositing or for field subsampling should be free of contamination.
Equipment decontamination also prevents cross-contamination of samples when the equipment
is used to collect more than one sample. Disposable equipment or the use of dedicated
equipment provides the most effective means of avoiding cross-contamination; however, the
use of such equipment is not always practical.
You should decontaminate equipment to a level that meets the minimum requirements for your
data collection effort. Your decontamination steps (e.g., use of solvents versus use of only soap
and water), therefore, should be selected based on the constituents present, their concentration
levels in the waste or materials sampled, and their potential to introduce bias in the sample
analysis results if not removed from the sampling equipment. You should describe the project-
specific decontamination procedures in your planning document for the sampling effort. In
addition, items used to clean the equipment, such as bottle brushes, should be free of
contamination.
The following procedure is an example of one you could use to decontaminate a sampling
device to be used for collecting samples for trace organic or inorganic constituent analyses
(from USEPA1996b):
1. Clean the device with tap water and soap, using a brush if necessary to remove
particulate matter and surface films.
2. Rinse thoroughly with tap water.
3. Rinse thoroughly with analyte- or organic-free water.
4. Rinse thoroughly with solvent. Do not solvent-rinse PVC or plastic items.
5. Rinse thoroughly with organic/analyte free water, or allow equipment to dry
completely.
6. Remove the equipment from the decontamination area. Equipment stored
overnight should be wrapped in aluminum foil and covered with clean, unused
plastic.
The specifications for the cleaning materials are as follows (you should justify and document the
use of substitutes):
"Soap" should be a phosphate-free laboratory detergent such as Liquinox®. It
must be kept in clean plastic, metal, or glass containers until used and poured
directly from the container when in use.
128
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"Solvent" should be pesticide-grade isopropanol. It must be stored in the
unopened original containers until used. It may be applied using the low
pressure nitrogen system fitted with a Teflon® nozzle, or using Teflon® squeeze
bottles. For equipment highly contaminated with organics (such as oily waste), a
laboratory-grade hexane may be a more suitable alternative to isopropanol.
"Tap water" may be used from any municipal water treatment system. Use of an
untreated potable water supply is not an acceptable substitute. Tap water may
be kept in clean tanks, hand pressure sprayers, squeeze bottles, or applied
directly from a hose or tap.
"Analyte free water" (deionized water) is tap water treated by passing it through a
standard deionizing resin column. At a minimum, it must contain no detectable
heavy metals or other inorganic compounds as defined by a standard ICP (or
equivalent) scan. It may be obtained by other methods as long as it meets the
analytical criteria. Analyte free water must be stored in clean glass, stainless
steel, or plastic containers that can be closed prior to use. It can be applied from
plastic squeeze bottles.
"Organic/analyte free water" is tap water that has been treated with activated
carbon and deionizing units. A portable system to produce such water under
field conditions is available. At a minimum, the water must meet the criteria of
analyte free water and not contain detectable pesticides, herbicides, or
extractable organic compounds, and no volatile organic compounds above
minimum detectable levels as determined for a given set of analyses.
Organic/analyte free water obtained by other methods is acceptable, as long as it
meets the analytical criteria. It must be stored in clean glass, Teflon®, or
stainless steel containers. It may be applied using Teflon® squeeze bottles or
with the portable system.
Clean the field equipment prior to field use. Designate a decontamination zone at the site and,
if necessary, construct a decontamination pad at a location free of surface contamination. You
should collect wastewater from decontamination (e.g., via a sump or pit) and remove it
frequently for appropriate treatment or disposal. The pad or area should not leak contaminated
water into the surrounding environment. You also should collect solvent rinses for proper
disposal.
You should always handle field-cleaned equipment in a manner that prevents recontamination.
For example, after decontamination but prior to use, store the equipment in a location away
from the cleaning area and in an area free of contaminants. If it is not immediately reused, you
should cover it with plastic or aluminum foil to prevent recontamination.
Decontamination will generate a quantity of wastes called investigation derived waste (IDW).
You should address the handling and disposal of IDW in your sampling plan. You must handle
this material in accordance with whether it is nonhazardous or suspected of, or known to be,
hazardous. You should minimize the generation of hazardous IDW and keep it separated from
nonhazardous IDW. For example, you should control the volume of spent solvents during
equipment decontamination by applying the minimum amount of liquid necessary and capturing
129
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it separately from the nonhazardous washwater. For additional guidance on handling IDW, see
Management of Investigation-Derived Wastes (USEPA 1992f).
Decontamination of personnel and their protective gear also is often necessary during
hazardous waste sampling. This important type of decontamination protects personnel from
chemical exposure and prevents cross-contamination when personnel change locations. The
level or degree of such decontamination will depend on site-specific considerations, such as the
health hazards posed by exposure to the sampled waste. You should address these
decontamination procedures in your health and safety plan.
For additional information regarding decontamination, see ASTM D 5088, Standard Practice for
Decontamination of Field Equipment Used at Nonradioactive Waste Sites. Another source of
additional information is "Sampling Equipment Decontamination" (USEPA 1994f), issued by
EPA's Environmental Response Team.
7.2.7 Health and Safety Considerations
Regulations published by the Occupational Safety and Health Administration (OSHA) at 29 CFR
Part 1910.120 govern workers at hazardous waste sites and include requirements for training,
equipment, medical monitoring, and other practices. Many sampling activities covered by this
guidance may require compliance with OSHA's health and safety regulations. Specific
guidance on worker health and safety is beyond the scope of this chapter; however,
development and use of a project-specific health and safety plan may be required. It is the
responsibility of the sampling team leader and others in charge to ensure worker safety.
Some important health and safety considerations follow:
Field personnel should be up-to-date in their health and safety training.
Field personnel should have a medical examination at the initiation of sampling
activities and routinely thereafter, as appropriate and as required by the OSHA
regulations. Unscheduled examinations should be performed in the event of an
accident or suspected exposure to hazardous materials.
Staff also should be aware of the common routes of exposure at a site and be
instructed in the proper use of safety equipment and protective clothing and
equipment. Safe areas should be designated for washing, drinking, and eating.
To minimize the impact of an emergency situation, field personnel should be
aware of basic first aid and have immediate access to a first aid kit.
The guidance manual Occupational Safety and Health Guidance Manual for Hazardous Waste
Site Activities (OSHA 1985, revised 1998) was jointly developed by the National Institute for
Occupational Safety and Health (NIOSH), OSHA, the United States Coast Guard (USCG), and
EPA. Its intended audience is those who are responsible for occupational safety and health
programs at hazardous waste sites.
130
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7.2.8 Sample Packaging and Shipping
During transport of waste samples, you should follow all State and Federal regulations
governing environmental sample packaging and shipment and ship according to U.S.
Department of Transportation (DOT) and International Air Transportation Association (IATA)
regulations. Minimum guidelines for sample packaging and shipping procedures follow in the
next subsections; however, the rules and regulations for sample packaging and shipping are
complex, and for some samples and shipping situations the procedures outlined below may
need to be exceeded.
7.2.8.1 Sample Packaging
You should package and label samples in an area free of contamination. You also should ship
or transport samples to a laboratory within a time frame that meets recommended sample
holding times for the respective analyses. Additional guidelines follow:
Aqueous samples for inorganic analysis and volatile organic analysis may require
chemical preservation. The specific preservation requirements will depend on
the analytical method to be used.
Make sure all lids/caps are tight and will not leak.
Make sure sample labels are intact and covered with a piece of clear tape for
protection.
Enclose the sample container in a clear plastic bag and seal the bag. Make sure
the sample labels are visible. If bubble wrap or other wrapping material will be
placed around the labeled containers, write the sample number and fraction (e.g.,
"BLH01-VOCs") so that it is visible on the outside of the wrap, then place the
wrapped container in a clear plastic bag and seal the bag.
Make sure that all samples that need to be kept cold (4 ± 2 °C) have been
thoroughly cooled before placing in packing material so that the packing material
serves to insulate the cold. Change the ice prior to shipment as needed. Ideally,
pack the cooled samples into shipping containers that have already been chilled.
(Of course, these precautions are not necessary if none of the samples in the
shipping container need to be kept cold.)
Any soil/sediment samples suspected to be of medium/high concentration or
containing dioxin must be enclosed in a metal can with a clipped or sealable lid
(e.g., paint cans) to achieve double containment of those samples. Place
suitable absorbent packing material around the sample container in the can.
Make sure the sample is securely stored in a can and the lid is sealed. Label the
outer metal container with the sample number and fraction of the sample inside.
Use clean waterproof metal or hard plastic ice chests or coolers that are in good
repair for shipping samples.
Remove the inapplicable previous shipping labels. Make sure any drain plugs
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are shut. Seal plugs shut on the inside and outside with a suitable tape such as
duct tape. Line the cooler with plastic (e.g., large heavy-duty garbage bag)
before inserting samples.
Ship samples at 4 ± 2 °C, place double-bagged ice on top of samples. Ice must
be sealed in double plastic bags to prevent melting ice from soaking the packing
material. Loose ice should not be poured into the cooler.
Conduct an inventory of sample numbers, fractions, and containers when placing
samples into the coolers. Check the inventory against the corresponding chain-
of-custody form before sealing the cooler to make sure that all samples and
containers are present.
Pack the lined shipping containers with noncombustible absorbent packing
material, such as vermiculite or rock wool. Place the packing material on the
bottom of the shipping container (inside the plastic liner) and around sample
bottles or metal cans to avoid breakage during shipment. Never use earth, ice,
paper, or styrofoam to pack samples. Earth is a contaminant, melted ice may
cause complications and allow the sample containers to bang together when the
shipping container is moved, and styrofoam presents a disposal problem (it also
may easily blow out of the shipping container at the site).
For samples that need to be shipped at 4 ± 2°C, place double-bagged ice on top
of samples and fill remaining space with packing material. If sample bottles have
been protected with packaging material such as bubble wrap, then some double-
bagged ice or ice packs also may be placed between samples.
Use tape to securely fasten the top of the plastic used to line the shipping
container. It is a good idea to then place a completed custody seal around the
top of the bag that contains the sample in case the outer seals placed across the
cooler lid are inadvertently damaged during shipment.
Enclose all sample documentation (i.e., chain-of-custody forms and cooler return
shipping documents) in a waterproof plastic bag, and tape the bag to the
underside of the cooler lid. This documentation should address all samples in
the cooler, but not address samples in any other cooler.
If more than one cooler is being used, place separate sample documentation in
each cooler. Instructions for returning the cooler should be documented inside
the cooler lid. Write a return name and address for the sample cooler on the
inside of the cooler lid in permanent ink to ensure return of the cooler.
Tape the cooler shut using strapping tape over the hinges. Place completed
custody seals across the top and sides of the cooler lid so that lid cannot be
opened without breaking the seal.
Place clear tape over the seal to prevent inadvertent damage to the seal during
shipment. Do not place clear tape over the seals in a manner that would allow
the seals to be lifted off with the tape and then reaffixed without breaking the
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seal.
For additional detailed guidance on sample documentation, packaging, and shipping, we
recommend the Contract Laboratory Program (CLP) Guidance for Field Samplers - Draft Final
(USEPA2001g).
7.2.8.2 Sample Shipping
In general, samples of drinking water, most ground waters and ambient surface waters, soil,
sediment, treated waste waters, and other low concentration samples can be shipped as
environmental samples; however, shipment of high concentration waste samples may require
shipment as dangerous goods (not as "hazardous waste"). Note that RCRA regulations
specifically exempt samples of hazardous waste from RCRA waste identification, manifest,
permitting, and notification requirements (see 40 CFR §261.4(d)). The shipment of samples to
and from a laboratory, however, must comply with U.S. DOT, U.S. Postal Service, or any other
applicable shipping requirements. If a sample is a hazardous waste, once received at the
laboratory, it must be managed as a hazardous waste.
In recent years, commercial overnight ._ ., .. ,. . , . . .tt.
J a For information on shipping dangerous goods visit the
International Air Transport Association (IATA)
Dangerous Goods Information Online at
http://www.iata.org/cargo/dg/index.htm
or call 1-800-716-6326.
shipping services have adopted the
regulations of the IATA for shipment of
dangerous goods by air. The IATA
Dangerous Goods Regulations contain all
provisions mandated by the International Civil
Aviation Organization and all rules universally
agreed to by airlines to correctly package and safely transport dangerous goods by air. Contact
IATA for a copy of the IATA Dangerous Goods Regulations and for assistance in locating
suppliers of specialized packaging for dangerous goods.
When shipping samples, perform the following activities:
Clearly label the cooler and fill out appropriate shipping papers.
Place return address labels clearly on the outside of the cooler.
If more than one cooler is being shipped, mark each cooler as "1 of 2," "2 of 2,"
etc.
Ship samples through a commercial carrier. Use appropriate packaging, mark
and label packages, and fill out all required government and commercial carrier
shipping papers according to DOT and IATA commercial carrier regulations.
Ship all samples by overnight delivery in accordance with DOT and IATA
regulations.
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7.3 Using Sample Homogenization, Splitting, and Subsampling Techniques
7.3.1 Homogenization Techniques
The objective of homogenization (mixing) is to minimize grouping and segregation of particles
so they are randomly distributed within the sample. While homogenization can reduce grouping
and segregation of particles, it will not eliminate it and will not make the material
"homogeneous." If homogenization is successful, subsamples of the homogenized material will
show less variability than if the material was not homogenized. Homogenization, combined with
a composite sampling strategy, can be an efficient method for improving the accuracy and
precision in sampling of particulate material (Jenkins, et al. 1996). Homogenization can be
applied to solids, liquids, slurries, and sludges.
Pitard (1993) recognizes two processes for homogenization:
Stationary processes - in which the material is not mixed but is redistributed so that
any correlation between the characteristics of individual fragments or particles is lost or
minimized. An example of this process is the collection of many small increments to
form an individual sample (ideally we would pick many individual particles at random to
form the sample, but this is not possible).
Dynamic processes - in which the material is mechanically mixed to remove or
minimize correlation between the characteristics of the fragment or particle and its
position within the sample. Examples of this process include mechanical mixing within a
container and use of magnetic stirrers in a beaker.
Note that the benefits of homogenization may be temporary because gravity-induced
segregation can occur during shipment, storage, and handling of samples. For this reason,
consider carrying out homogenization (mixing) immediately prior to analysis.
Some homogenization techniques work better than others. The strengths and limitations of
homogenization equipment and procedures (cone and quartering, riffle splitters, rotary splitters,
multiple cone splitters, and V-blenders) have been reviewed in the literature by Pitard (1993),
Schumacher, et al. (1991), ASTM (Standard D 6051-96), and others. The preferred techniques
for use within the laboratory follow:
Riffling (see also Section 7.3.2)
Fractional shoveling (see also Section 7.3.2)
Mechanical mixing
Cone and quartering
Magnetic stirrers (e.g., to homogenize the contents of an open beaker)
V-blenders.
Fractional shoveling and mechanical mixing also can be used in the field. Note that some
techniques for homogenization, such as riffling and fractional shoveling, also are used for
splitting and subsampling. Note that Pitard (1993) discourages the use of "sheet mixing" (also
called "mixing square") and vibratory spatulas because they tend to segregate particles of
different density and size.
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7.3.2 Sample Splitting
Splitting is employed when a field sample is significantly larger than the required analytical
sample. The goal of splitting is to reduce the mass of the retained sample and obtain an aliquot
of the field sample that reflects the average properties of the entire field sample. It is often
necessary to repeat the splitting process a number of times to achieve a sufficient reduction in
mass for analytical purposes.
Splitting can be used to generate a reduced mass aliquot that can be analyzed in its entirety or
a much reduced and homogenized mass from which an analytical or subsample can be
collected. ASTM's Standard Guide for Laboratory Subsampling of Media Related to Waste
Management Activities (ASTM D 6323-98), lists and discusses a variety of splitting equipment
(such as sectorial splitters and riffle splitters) and splitting procedures (such as cone and
quartering and the alternate scoop method). Gerlach, et al. (2002) also evaluated sample
splitting methods (riffle splitting, paper cone riffle splitting, fractional shoveling, coning and
quartering, and grab sampling) and found
that riffle splitting methods performed the
best.
A simple alternative to riffle splitting a
sample of solid media is a technique
called "fractional shoveling." To perform
fractional shoveling, deal out small
increments from the larger sample in
sequence into separate piles, randomly
select one of the piles and retain it as the
subsample (or retain more than one if a
portion of the sample is to be "split" with
another party and/or retained for archive
purposes), and reject the others (see
Figure 32).
7.3.3 Subsampling
Lot
Sample
Four
Sample
Five
Figure 32. Fractional shoveling as a sample splitting method
(after Pitard 1993)
The size of the sample submitted to the laboratory (either an individual sample or a composite)
by field personnel typically far exceeds that required for analysis. Consequently, Subsampling is
needed. A subsample is defined as "a portion of material taken from a larger quantity for the
purpose of estimating properties or the composition of the whole sample" (ASTM D 4547-98).
Taking a subsample may be as simple as collecting the required mass from a larger mass, or it
may involve one or more preparatory steps such as grinding, homogenization, and/or splitting of
the larger mass prior to removal of the subsample.
Specific procedures for maintaining sample integrity (e.g., minimizing fundamental error) during
splitting and Subsampling operations typically are not addressed in quality assurance, sampling,
or analytical plans, and error may be introduced unknowingly in Subsampling and sample
preparation. Many environmental laboratories do not have adequate SOPs for Subsampling;
therefore, it is important for the data users to provide the laboratory personnel clear instruction if
any special Subsampling or sample handling procedures are needed (such as instructions on
mixing of the sample prior to analysis, removing particles greater than a certain size, analyzing
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phases separately, etc.). If proper subsampling procedures are not specified in planning
documents, SOPs, or documents shipped with the samples, it may be difficult to assess the
usability of the results.
The following sections provide general guidance on obtaining subsamples of liquids, mixtures of
liquids and solids, and soils and solid media. For additional guidance and detailed procedures,
see Standard Guide for Composite Sampling and Field Subsampling for Environmental Waste
Management Activities (ASTM D 6051-96) and Standard Guide for Laboratory Subsampling of
Media Related to Waste Management Activities (ASTM D 6323-98).
7.3.3.1 Subsampling Liquids
In the case of subsampling a liquid, special precautions may be warranted if the liquid contains
suspended solids and/or the liquid comprises multiple liquid phases. In practice, samples may
contain solids and/or separate phases that are subject to gravitational action (Gy 1998). Even a
liquid that appears clear (absent of solids and without iridescence) may not be "homogeneous."
Subsampling of liquids (containing solids and/or in multiple phases) can be addressed by using
one or the other of two possible approaches:
Mixing the sample such that all phases are homogenized, and then taking a
subsample (using a pipette, for example)
Allowing all of the phases to separate followed by subsampling and analysis of
each phase separately.
Of course, the characteristics of the waste and the type of test must be considered. For
example, mixing of multi-phasic wastes to be analyzed for volatiles should be avoided due to
the potential loss of constituents. Some multi-phasic liquid wastes can form an emulsion when
mixed. Others, in spite of mixing, will quickly separate back into distinct phases.
7.3.3.2 Subsampling Mixtures of Liquids and Solids
If the sample is a mixture of liquids and solids, subsampling usually requires that the phases be
separated. The separate phases are then separately subsampled. Subsampling of the liquid
phase can be accomplished as described above, while subsampling of the solid phase should
be done according to sampling theory, as summarized below.
7.3.3.3 Subsampling Soils and Solid Media
To correctly subsample soil or solid media, use sampling tools and techniques that minimize
delimitation and extraction error. If the particles in the sample are too coarse to maintain
fundamental error within desired limits, it may be necessary to perform a series of steps of
particle size reduction followed by subsampling (see Appendix D). If the field sample mass is
equal to or less than the specified analytical size, the field sample can be analyzed in its
entirety. If the mass of the field sample is greater than the specified analytical sample size,
subsampling will be required.
One possible alternative to particle-size reduction prior to subsampling is to simply remove the
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coarse particles (e.g., via a sieve or visually) from the sample. This selective removal
technique is not recommended in situations in which the larger particles contribute to the overall
concentration of the constituent of concern in the waste. In other words, do not remove the
large particles if the constituents of concern tend to be concentrated in the large particles
relative to the smaller particles.
If the largest particle size of the field sample exceeds the allowable size for maintaining the
fundamental error specified by the DQO and the analyte of interest is volatile, it may be
necessary to analyze the sample as is and accept a large fundamental error. Guidance on
handling VOCs in samples can be found in Section 6.3.4 and in ASTM Standard D 4547-98.
The Standard Guide for Laboratory Subsampling of Media Related to Waste Management
Activities (ASTM D 6323-98) lists a variety of equipment for performing particle-size reduction
(e.g., cutting mills, jar mills, disc mills, dish and puck mills, mortar grinders and jaw crushers)
and tabulates their uses and limitations.
The techniques discussed below are most relevant to subsampling of solid particulate matter for
analysis of nonvolatile constituents. Mason (1992, page 5-7) provides a field procedure that
can be used to reduce the volume of a field soil sample for submission to the laboratory.
The issues regarding the subsampling of particulate-containing materials are identical to those
considered when collecting the original field samples and are as follows:
The tool used to collect the analytical sample must be correct and not
discriminate against any portion of the sample (in other words, the tool should not
introduce increment delimitation and increment extraction errors).
The mass of the subsample must be enough to accommodate the largest of the
particles contained within the parent sample (to reduce fundamental error).
The sample mass and the manner in which it is collected must accommodate the
short-term heterogeneity within the field sample (to reduce grouping and
segregation error).
The sampling tool must be constructed such
that its smallest dimension is at least three
times greater than the largest particle size
contained within the material being
subsampled. The construction of the
sampling tool must be such that it does not
discriminate against certain areas of the
material being sampled. For example,
Pitard (1993) argues that all scoops for
subsampling should be rectangular or
square in design with flat bottoms as
opposed to having curved surfaces (Figure
33).
Pitard (1993) and ASTM D 6323-98 suggest
Flat-bottom
Spatula
Figure 33. Example of correctly designed device for
subsampling. Flat bottom and vertical side walls minimize
increment delimitation error.
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subsampling from relatively flat elongated piles using a transversal subsampling technique that
employs a sampling scoop or spatula and a flat working surface (Figure 34(a)). The objective is
to convert the sampling problem to a one-dimensional approach. Specifically, Pitard (1993)
recommends the following procedure:
Empty the sample from the sample container onto a smooth and clean surface or
appropriate material.
Do not try to homogenize the sample, as this may promote segregation of
particles.
Reduce the sample by using the fractional shoveling technique (Figure 32) until a
sample 5 to 10 times larger than the analytical sample is obtained.
Shape the remaining material into an elongated pile with uniform width and
thickness (Figure 34(a)).
Take increments all across the pile through the entire thickness.
Reshape the pile perpendicular to its long axis, and continue to take increments
across the pile until the appropriate sample weight is reached.
Fractional shoveling and alternate scoop
techniques alone (Figure 32) also can be
used to generate subsamples.
When using these techniques, several
stages or iterations of subsampling
followed by particle size reduction may be
needed to minimize fundamental error
(also see Appendix D). At each stage,
the number of increments should be at
least 10 and preferably 25 to control
grouping and segregation (short-term
heterogeneity) within the sample. In the
final stage, however, where very small
analytical samples are required, the
number of increments required will be
much less.
(a)
INCORRECT
Figure 34. Correct (a) and incorrect (b) laboratory techniques
for obtaining subsamples of granular solid media ((a) modified
after Pitard 1993).
The subsampling procedures described
above offer a more correct and defensible alternative to an approach to subsampling in which
the analyst simply opens the sample jar or vial and removes a small increment from the top for
preparation and analysis (Figure 34(b)).
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8
ASSESSMENT: ANALYZING AND INTERPRETING DATA
This section presents guidance for the
assessment of sampling and analytical
results. In performing data assessment,
evaluate the data set to determine whether
the data are sufficient to make the
decisions identified in the DQO Process.
The data assessment process includes (1)
sampling assessment and analytical
assessment, and (2) data quality
assessment (DQA) (Figure 35) and follows
a series of logical steps to determine if the
data were collected as planned and to
reach conclusions about a waste relative to
RCRA requirements.
At the end of the process, EPA
recommends reconciliation with the DQOs
to ensure that they were achieved and to
decide whether additional data collection
activities are needed.
8.1
Data Verification and Validation
Data verification and validation are
performed to ensure that the sampling and
analysis protocols specified in the QAPP or
WAP were followed and that the
measurement systems performed in
accordance with the criteria specified in the
QAPP or WAP. The process is divided into
two parts:
ASSESSMENT
DATA VERIFICATION/VALIDATION
• Sampling Assessment
• Analytical Assessment
1
/ Verified and Validated Data /
;
DATA QUALITY ASSESSMENT
• Review DQOs and design
• Prepare data for statistical analysis
• Conduct preliminary data review and
check assumptions
• Select and perform statistical tests
• Draw conclusions and report results
1
/Conclusions Drawn from Data /
Figure 35. Elements of the quality assurance assessment
process (modified after USEPA 1998a)
sampling assessment (Section 8.1.1), and
analytical assessment (Section 8.1.2).
Guidance on analytical assessment is provided in Chapter One of SW-846 and in the individual
analytical methods. Additional guidance can be found in Guidance on Environmental Data
Verification and Data Validation EPA QA/G-8, published by EPA's Office of Environmental
Information (USEPA 2001c). For projects generating data for input into risk assessments, see
EPA's Guidance for Data Usability in Risk Assessment, Final (USEPA 1992g).
8.1.1 Sampling Assessment
Sampling assessment is the process of reviewing field sampling and sample handling methods
to check conformance with the requirements specified in the QAPP. Sampling assessment
activities include a review of the sampling design, sampling methods, documentation, sampling
handling and custody procedures, and preparation and use of quality control samples.
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The following types of information are useful in assessing the sampling activity:
Copies of the sampling plan, QAPP, and SOPs.
Copies of logbooks, chain-of-custody records, bench sheets, well logs, sampling
sequence logs, field instrument calibration records and performance records,
and/or other records (including electronic records such as calculations) that
describe and/or record all sampling operations, observations, and results
associated with samples (including all QC samples) while in the custody of the
sampling team. Records/results from the original sampling and any resampling,
regardless of reason, should be retained. Also, retain copies of the shipping
manifest and excess sample disposition (disposal) records describing the
ultimate fate of any sample material remaining after submission to the laboratory.
Copies of all records/comments associated with the sample team review of the
original data, senior staff review, and QA/QC review of the sampling activity.
Copies of any communication (telephone logs, faxes, E-mail, other records)
between the sampling team and the customer dealing with the samples and any
required resampling or reporting should be provided.
The following subsections outline the types of sampling information that should be assessed.
8.1.1.1 Sampling Design
Review the documentation of field activities to check if the number and type of samples called
for in the sampling plan were, in fact, obtained and collected from the correct locations. Perform
activities such as those described below:
Sampling Design: Document any deviations from the sampling plan made during
the field sampling effort and state what impact those modifications might have on
the sampling results.
Sample Locations/Times: Confirm that the locations of the samples in time or
space match those specified in the plan.
Number of Samples: Check for completeness in the sampling in terms of the
number of samples obtained compared to the number targeted. Note the cause
of the deficiencies such as structures covering planned locations, limited access
due to unanticipated events, samples lost in shipment or in the laboratory, etc.
Discrete versus Composite Samples: If composite sampling was employed,
confirm that each component sample was of equal mass or volume. If not,
determine if sufficient information is presented to allow adjustments to any
calculations made on the data. Both field and laboratory records should be
reviewed because compositing can occur at either location.
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8.1.1.2 Sampling Methods
Details of how a sample was obtained from its original time/space location are important for
properly interpreting the measurement results. Review the selection of sampling and ancillary
equipment and procedures (including equipment decontamination) for compliance with the
QAPP and sampling theory. 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. Note potentially unacceptable departures from the QAPP and assess their
potential impact on the quality and usefulness of the data. Comments from field surveillance on
deviations from written sampling plans also should be noted.
Sampling records should be reviewed to determine if the sample collection and field processing
were appropriate for the analytes being measured. For example, sampling for volatiles analysis
poses special problems due to the likely loss of volatiles during sample collection. Also,
determination of the appropriate "sample support" should be reviewed, whether it was obtained
correctly in the field, whether any large particles or fragments were excluded from the sample,
and whether any potential biases were introduced.
Laboratory subsampling and sample preparation protocols should be examined for the same
types of potential bias as the field procedures. When found, they should be discussed in the
assessment report.
8.1.1.3 Sample Handling and Custody Procedures
Details of how a sample is physically treated and handled between its original site or location
and the actual measurement site are extremely important. Sample handling activities should be
reviewed to confirm compliance with the QAPP or WAP for the following areas:
Sample containers
Preservation (physical and chemical)
Chain-of-custody procedures and documentation
Sample shipping and transport
Conditions for storage (before analysis)
Holding times.
8.1.1.4 Documentation
Field records generally consist of bound field notebooks with prenumbered pages, sample
collection forms, sample labels or tags, sample location maps, equipment maintenance and
calibration forms, chain-of-custody forms, sample analysis request forms, and field change
request forms. Documentation also may include maps used to document the location of sample
collection points or photographs or video to record sampling activities.
Review field records to verify they include the appropriate information to support technical
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interpretations, judgments, and discussions concerning project activities. Records should be
legible, identifiable, and retrievable and protected against damage, deterioration, or loss.
Especially note any documentation of deviations from SOPs and the QAPP.
8.1.1.5 Control Samples
Assess whether the control samples were collected or prepared as specified in the QAPP or
WAP. Control samples include blanks (e.g., trip, equipment, and laboratory), duplicates, spikes,
analytical standards, and reference materials that are used in different phases of the data
collection process from sampling through transportation, storage, and analysis. There are many
types of control samples, and the appropriate type and number of control samples to be used
will depend on the data quality specifications.
See Section 7.2.4 for guidance on the type of control samples for RCRA waste-testing
programs. Additional guidance on the preparation and use of QC samples can be found in the
following publications:
Test Methods for Evaluating Solid Waste, SW-846 (USEPA 1986a), Chapter One
EPA Guidance for Quality Assurance Project Plans, EPA QA/G-5 (USEPA
1998a), Appendix D
Contract Laboratory Program (CLP) Guidance for Field Samplers - Draft Final
(USEPA 2001g), Section 3.1.1.
8.1.2 Analytical Assessment
Analytical assessment includes an evaluation of analytical and method performance and
supporting documentation relative to the DQOs. Proper data review is necessary to minimize
decision errors caused by out-of-control laboratory processes or calculation or transcription
errors. The level and depth of analytical assessment is determined during the planning process
and is dependent on the types of analyses performed and the intended use of the data.
Analytical records needed to perform the assessment of laboratory activities may include the
following:
Contract Statement of Work requirements
SOPs
QAPP or WAP
Equipment maintenance documentation
Quality assurance information on precision, bias, method quantitation limits,
spike recovery, surrogate and internal standard recovery, laboratory control
standard recovery, checks on reagent purity, and checks on glassware
cleanliness
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Calibration records
Traceability of standards/reagents (which provide checks on equipment
cleanliness and laboratory handling procedures)
Sample management records
Raw data
Correspondence
Logbooks and documentation of deviation from procedures.
If data gaps are identified, then the assessor should prepare a list of missing information for
correspondence and discussion with the appropriate laboratory representative. At that time, the
laboratory should be requested to supply the information or to attest that it does not exist in any
form.
8.1.2.1 A nalytical Data Verification
The term data verification is confirmation by examination and provision of objective evidence
that specified requirements have been fulfilled. Data verification is the process of evaluating the
completeness, correctness, and conformance/compliance of a specific data set against the
method, procedural, or contractual requirements. The goal of data verification is to ensure that
the data are what they purport to be, that is, that the reported results reflect what was actually
done, and to document that the data fulfill specific requirements. When deficiencies in the data
are identified, then those deficiencies should be documented for the data user's review and,
where possible, resolved by corrective action (USEPA 2001 c).
Data verification may be performed by personnel involved with the collection of samples
or data, generation of analytical data, and/or by an external data verifier. The verification
process normally starts with a list of requirements that apply to an analytical data package. It
compares the laboratory data package to the requirements and produces a report that identifies
those requirements that were met and not met. Requirements that were not met can be
referred to as exceptions and may result in flagged data. Examples of the types of exceptions
that are found and reported are listed below:
Failure to analyze samples within the required holding times
Required steps not carried out by the laboratory (i.e., failure to maintain sample
custody, lack of proper signatures, etc.)
Procedures not conducted at the required frequency (i.e., too few blanks,
duplicates, etc.)
Contamination found in storage, extraction, or analysis of blanks
Procedures that did not meet pre-set acceptance criteria (poor laboratory control,
poor sample matrix spike recovery, unacceptable duplicate precision, etc).
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The verification report should detail all exceptions found with the data packages. If the
laboratory was able to provide the missing information or a suitable narrative explanation of the
exceptions, they should be made part of the report and included in the data package for use by
the people who determine the technical defensibility of the data.
8.1.2.2 Analytical Data Validation (Evaluation)
The term data validation (also known as "evaluation") is the confirmation by examination and
provision of objective evidence that the particular requirements for a specific intended use are
fulfilled. Data validation is an analyte- and sample-specific process that extends the evaluation
of data beyond method, procedural, or contractual compliance (i.e., data verification) to
determine the analytical quality of a specific data set. Data validation criteria are based upon
the measurement quality objectives developed in the QAPP or similar planning document, or
presented in the sampling or analytical method. Data validation includes a determination, where
possible, of the reasons for any failure to meet method, procedural, or contractual requirements,
and an evaluation of the impact of such failure on the overall data set (USEPA 2001 c)
Data validation includes inspection of the verified data and both field and analytical laboratory
data verification documentation; a review of the verified data to determine the analytical quality
of the data set; and the production of a data validation report and, where applicable, qualified
data. A focused data validation may also be required as a later step. The goals of data
validation are to evaluate the quality of the data, to ensure that all project requirements are met,
to determine the impact on data quality of those requirements that were not met, and to
document the results of the data validation and, if performed, the focused data validation. The
main focus of data validation is determining data quality in terms of accomplishment of
measurement quality objectives.
As in the data verification process, all planning documents and procedures not only must exist,
but they should also be readily available to the data validators. A data validator's job cannot be
completed properly without the knowledge of the specific project requirements. In many
cases, the field and analytical laboratory documents and records are validated by different
personnel. Because the data validation process requires knowledge of the type of information
to be validated, a person familiar with field activities usually is assigned to the validation of the
field documents and records. Similarly, a person with knowledge of analytical laboratory
analysis, such as a chemist (depending on the nature of the project), usually is assigned to the
validation of the analytical laboratory documents and records. The project requirements should
assist in defining the appropriate personnel to perform the data validation (USEPA 2001 c).
The personnel performing data validation should also be familiar with the project-specific data
quality indicators (DQIs) and associated measurement quality objectives. One of the goals of
the data validation process is to evaluate the quality of the data. In order to do so, certain data
quality attributes are defined and measured. DQIs (such as precision, bias, comparability,
sensitivity, representativeness, and completeness) are typically used as expressions of the
quality of the data (USEPA 2001 c).
The outputs that may result from data validation include validated data, a data validation report,
and a focused validation report. For detailed guidance on data validation, see Chapter One of
SW-846 and Guidance on Environmental Data Verification and Data Validation EPA QA/G-8
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(USEPA2001C).
8.2 Data Quality Assessment
Data quality assessment (DQA) is the
scientific and statistical evaluation of data
to determine if the data are of the right
type, quality, and quantity to support their
intended purpose (USEPA 2000d). The
focus of the DQA process is on the use of
statistical methods for environmental
decision making - though not every
environmental decisions necessarily must
be made based on the outcome of a
statistical test (see also Section 3). If the
sampling design established in the
planning process requires estimation of a
parameter or testing of a hypothesis, then
the DQA process can be used to evaluate
the sample analysis results.
The DQA process described in this section
includes five steps: (1) reviewing the DQOs
and study design, (2) preparing the data for
statistical analysis, (3) conducting a
preliminary review of the data and checking
statistical assumptions, (4) selecting and
performing statistical test, and (5) drawing
conclusions from the data (Figure 36).
Detailed guidance on the statistical
analysis of data can be found in Appendix
F. Additional guidance can be found in
Guidance for Data Quality Assessment,
EPA QA/G-9 (USEPA 2000d). A list of software tools to help you implement the DQA is
provided in Appendix H.
8.2.1 Review the DQOs and the Sampling Design
Review the DQO outputs to ensure that they are still applicable. Refer back to Sections 4 and 5
of this document for more information on the DQO Process or see USEPA 2000a or 2000b. A
clear understanding of the original project objectives, as determined during the systematic
planning process, is critical to selecting the appropriate statistical tests (if needed) and
interpreting the results relative to the applicable RCRA regulatory requirements.
8.2.2 Prepare Data for Statistical Analysis
After data validation and verification and before the data are available in a form for further
analysis, several intermediate steps usually are required. For most situations, EPA
DATA QUALITY ASSESSMENT
Review DQOs and Sampling Design
i
Prepare Data for Statistical Analysis
*
Conduct Preliminary Review of Data
and Check Statistical Assumptions
• Compute statistical quantities
(mean, standard deviation, etc.)
• Determine proportion of data
reported as "non-detect"
• Check distributional assumptions
• Check for outliers
i
Select and Perform the Statistical Test
*
Draw Conclusion from the Data
Figure 36. The DQA Process (modified from USEPA 2000d)
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recommends you prepare the data in computer-readable format. Steps in preparing data for
statistical analysis are outlined below (modified from Ott 1988):
1. Receive the verified and _, . _ . «.,«.....,
Steps in Preparing Data for Statistical
validated source from the QA
reports. Data are supplied to
the user in a variety of formats
and readiness for use,
depending on the size and
complexity of the study and the
types of analyses requested.
Most laboratories supply a QA
evaluation package that
Analysis
1. Receive the verified and validated data source.
2. Create a data base from the verified and validated
data source.
3. Check and edit the data base.
4. Create data files from the data base.
includes the verification/validation review, a narrative, tabulated summary forms
(including the results of analyses of field samples, laboratory standards, and QC
samples), copies of logbook pages, and copies of chain-of-custody records.
From this information, you can create a data base for statistical analysis.
2. Create a data base from the verified and validated data source. For most studies
in which statistical analyses are scheduled, a computer-readable data base is the
most efficient method for managing the data. The steps required to create the
data base and the format used will depend on the software systems used to
perform the analysis. For example, the data base may be as simple as a string
of concentration values for a single constituent input into a spreadsheet or word
processor (such as required for use of EPA's DataQUEST software (USEPA
1997b)), or it may be more complex, requiring multiple and related data inputs,
such as sample number, location coordinates, depth, date and time of collection,
constituent name and concentration, units of measurements, test method,
quantitation limit achieved, QC information, etc.
If the data base is created via manual data entry, the verified and validated data
should be checked for legibility. Any questions pertaining to illegible information
should be resolved before the data are entered. Any special coding
considerations, such as indicating values reported as "nondetect" should be
specified in a coding guide or in the QAPP. For very large projects, it may be
appropriate to prepare a separate detailed data management plan in advance.
3. Check and edit the data base. After creation of the data set, the data base
should be checked against the data source to verify accurate data entry and to
correct any errors discovered. Even if the data base is received from the
laboratory in electronic format, it should be checked for obvious errors, such as
unit errors, decimal errors, missing values, and quantitation limits.
4. Create data files from the data base. From the original data files, work files are
created for use within the statistical software package. This step could entail
separating data by constituent and by DQO decision unit and separating any
QA/QC data from the record data. When creating the final data files for use in
the statistical software, be sure to use a file naming and storage convention that
facilitates easy retrieval for future use, reference, or reporting.
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8.2.3 Conduct Preliminary Review of the Data and Check Statistical Assumptions
Many statistical tests and procedures require that certain assumptions be met for their use.
Failure to satisfy these assumptions can result in biased estimates of the parameter of interest;
therefore, it is important to conduct preliminary analyses of the data to learn about the
characteristics. EPA recommends that you compute statistical quantities, determine the
proportion of the data reported as "nondetect" for each constituent of concern, check whether
the data exhibit a normal distribution, then determine if there are any "outliers" that deserve a
closer look. The outputs of these activities are used to help select and perform the appropriate
statistical tests.
8.2.3.1 Statistical Quantities
To help "visualize" and summarize the data, calculate basic statistical quantities such as the:
Mean
Maximum
Percentiles
Variance
Standard deviation
Coefficient of variation.
Calculate the quantities for each constituent of concern. Example calculations of the mean,
variance, standard deviation, and standard error of the mean are given in Section 3. Detailed
guidance on the calculation of statistical quantities is provided in Chapter Two of EPA's QA/G-9
guidance document (USEPA 2000d). The useful quantities easily can be computed using
EPA's DataQUEST software (USEPA 1997b, see also Appendix H) or any similar statistical
software package.
When calculating statistical quantities, determine which data points were reported as below a
limit of detection or quantitation - known as "nondetects" (NDs). See also Section 8.2.4.2
("Treatment of Nondetects").
8.2.3.2 Checking Data for Normality
Check the data sets for normality by using graphical methods, such as histograms, box and
whisker plots, and normal probability plots (see also Section 3.1.3), or by using numerical tests,
such as the Shapiro-Wilk test for normality (see Appendix F). Table 11 provides a summary of
recommended methods. Detailed guidance on the use of graphical and statistical methods can
be found in USEPA 1989b, 1992b, 1997b, and 2000d.
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Table 11. Recommended Graphical and Statistical Methods for Checking Distributional Assumptions
Test
Use
Reference
Graphical Methods
Histograms and frequency plots
Normal probability plot
Box and Whisker Plot
Provides visual display of probability
or frequency distribution
Provides visual display of deviation
from expected normality
Provides visual display of potential
"outliers" or extreme values
See USEPA2000d. Construct via
EPA's DataQUEST software
(USEPA1997b)orusea
commercial software package.
See USEPA2000d. Construct via
EPA's DataQUEST software
(USEPA1997b)orusea
commercial software package.
See USEPA2000d. Construct via
EPA's DataQUEST software
(USEPA1997b)orusea
commercial software package.
Numerical Tests for Normality
Shapiro-WilkTest
Filliben's Statistic
Use for sample sizes of < 50
Use for sample sizes of > 50
See procedure in Appendix F,
Section F.1.2. This test also can
be performed using EPA's
DataQUEST software (USEPA
1997b).
See USEPA 2000d. This test can
be performed using EPA's
DataQUEST software (USEPA
1997b).
Graphical methods allow you to visualize the central tendency of the data, the variability in the
data, the location of extreme data values, and any obvious trends in the data. For example, a
symmetrical "mound" shape of a histogram is an indicator of an approximately normal
distribution. If a normal probability plot is constructed on the data (see Figure 5 in Section
3.1.3), a straight line plot usually is an indicator of normality. (Note that interpretation of a
probability plot depends on the method used to construct it. For example, in EPA's DataQUEST
software, normally distributed data will form an "S"-shaped curve rather than a straight line on a
normal probability plot.)
The Shapiro-Wilk test is recommended as a superior method for testing normality of the data.
The specific method for implementing the Shapiro-Wilk Test is provided in Appendix F. The
method also is described in Gilbert (1987), EPA's guidance on the statistical analysis of ground-
water monitoring data (USEPA 1992b), and can be performed with EPA's DataQUEST software
or other commercially available statistical software.
8.2.3.3
How To Assess "Outliers"
A measurement that is very different from other values in the data set is sometimes referred to
as an "outlier." EPA cautions that the term "outlier" be used advisedly, since a common reaction
to the presence of "outlying" values has been to "cleanse the data," thereby removing any
"outliers" prior to further analysis. In fact, such discrepant values can occur for many reasons,
148
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including (1) a catastrophic event such as a spill or process upset that impacts measurements
at the sampling point, (2) inconsistent sampling or analytical chemistry methodology that may
result in laboratory contamination or other anomalies, (3) errors in the transcription of data
values or decimal points, and (4) true but extreme hazardous constituent measurements.
While any one of these events can cause an apparent "outlier," it should be clear that the
appropriate response to an outlier will be very different depending on the origin. Because high
values due to contaminated media or waste are precisely what one may be trying to identify, it
would not be appropriate to eliminate such data in the guise of "screening for outliers."
Furthermore, depending on the form of the underlying population, unusually high concentrations
may be real but infrequent such as might be found in lognormally distributed data. Again, it
would not be appropriate to remove such data without adequate justification.
A statistical outlier is defined as a value originating from a different underlying population than
the rest of the data set. If the value is not consistent with the distributional behavior of the
remaining data and is "too far out in one of the tails" of the assumed underlying population, it
may test out as a statistical outlier. Defined as it is strictly in statistical terms, however, an
outlier test may identify values as discrepant when no physical reason can be given for the
aberrant behavior. One should be especially cautious about indiscriminate testing for statistical
outliers for this reason.
If an outlier is suspected, an initial and helpful step is to construct a probability plot of the data
set (see also Section 3.1.3 and USEPA 2000d). A probability plot is designed to judge whether
the sample data are consistent with an underlying normal population model. If the rest of the
data follow normality, but the outlier comes from a distinctly different population with higher (or
lower) concentrations, this behavior will tend to show up on a probability plot as a lone value
"out of line" with the remaining observations. If the data are lognormal instead, but the outlier is
again from a distinct population, a probability plot on the logged observations should be
constructed. Neither of these plots is a formal test; still, they provide invaluable visual evidence
as to whether the suspected outlier should really be considered as such.
Methods for conducting outlier tests are described in Chapter 4 of EPA's QA/G-9 guidance
document (USEPA 2000d), and statistical tests are available in the DataQUEST software (for
example, Rosner's Test and Walsh's Test) (USEPA 1997b).
8.2.4 Select and Perform Statistical Tests
This section provides guidance on how you can select the appropriate statistical test to make a
decision about the waste or media that is the subject of the study. It is important to select the
appropriate statistical test because decisions and conclusions derived from incorrectly used
statistics can be expensive (Singh, et al. 1997).
Prior to selecting the statistical test, consider the following factors:
The objectives of the study (identified in DQO Step 2)
Whether assumptions of the test are fulfilled
The nature of the underlying distribution
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The decision rule and null hypothesis (identified in DQO Step 5)
The relative performance of the candidate tests (for example, parametric tests
generally are more efficient than their nonparametric counterparts)
The proportion of the data that are reported as nondetects (NDs).
The decision-tree presented in Figure 37 provides a starting point for selecting the appropriate
statistical test. The statistical methods are offered as guidance and should not be used as a
"cook book" approach to data analysis. The methods presented here usually will be adequate
for the tests conducted under the specified conditions (see also Appendix F). An experienced
statistician should be consulted whenever there are questions.
Based on the study objective (DQO Step 2), determine which category of statistical tests to use.
Note the statistical methods recommended in the flow charts in Figure 38 and Figure 39 are for
use when the objective is to compare the parameter of interest to a fixed standard. Other
methods will be required if the objective is different (e.g., when comparing two populations,
detecting trends, and evaluating spatial patterns or relationships of sampling points).
8.2.4.1 Data Transformations in Statistical Tests
Users of this guidance may encounter data sets that show significant evidence of non-normality.
Due to the assumption of underlying normality in most parametric tests, a common statistical
strategy when encountering this predicament is to search for a mathematical transformation that
will lead to normally-distributed data on the transformed scale. Unfortunately, because of the
complexities associated with interpreting statistical results from data that have been
transformed to another scale and the common occurrence of lognormal patterns in
environmental data, EPA generally recommends that the choice of scale be limited to either the
original measurements (for normal data) or a log-transformed scale (for lognormal data). If
neither of these scales results in approximate normality, it is typically easiest and wisest to
switch to a nonparametric (or "distribution-free") version of the same test.
If a transformation to the log scale is needed, and a confidence limit on the mean is desired,
special techniques are required. If a data set exhibits a normal distribution on the log-
transformed scale, it is a common mistake to assume that a standard normal-based confidence
interval formula can be applied to the transformed data with the confidence interval endpoints
retransformed to the original scale to obtain the confidence interval on the mean. Invariably,
such an interval will be biased to the low side. In fact, the procedure just described actually
produces a confidence interval around the median of a lognormal population, rather than the
higher mean. To correctly account for this "transformation bias", special procedures are
required (Land 1971 and 1975, Gilbert 1987). See Section F.2.3 in Appendix F for detailed
guidance on calculating confidence limits for the mean of a lognormal population.
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Identify
the Decision
(DQO Step 2)
Test
Compliance
With a Fixed
Standard (e.g.,
TC or UTS)?
Compare
Two
Populations?
Evaluate
Spatial
Patterns?
Seek Other
Guidance for
Objectives Not
Discussed in
This Document.
1
Yes
r
Identify the Parameter
of Interest (DQO Step 5).
Yes
Yes
See EPA
QA/G-9
(USEPA
2000d)
Mean
Percentile or a "Not-to-
Exceed" Standard?
Go to Flow
Chart in Figure
"
Perform a
"Two-Sample"
Test.
Conduct Spatial
Analysis, such as a
Geostatistical
Study.
Go to Flow
Chart in Figure
. .
See Section
3.4.3.
See Section
3.4.4
Figure 37. Flow chart for selecting a statistical method
151
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Methods for Comparing the Mean to a Fixed Standard
(null hypothesis: concentration exceeds the standard)
Set Non-Detects Equal to
1/2 Detection Limit.
Yes
Are the
Data
Normally
Distributed?
See Cautionary Note
in Appendix F,
Section F.2.3.
1
r
Transform the Data
Using a Natural Log
V
Calculate Parametric \
UCL on the Mean (See |
Appendix F, Section I
F.2.1). I
Start (from Fig. 37)
Cohen's
Model OK?
(See
Appendix F,
Section
F.4.2).
Are the
Logged
Data
Normally
Distributed?
Calculate Cohen's
Adjusted Mean and
Standard Deviation.
Calculate UCL on the
Mean Using Land's H-
Statistic or Other
Appropriate Method (See
Appendix F, Section
V F.2.3).
\ ;
Calculate UCL on the
Mean Using the
Bootstrap or Jackknife
Method (See Appendix
F, Section F.2.4).
/ Calculate Cohen's \
I Adjusted UCL on the I
I Mean (See Appendix I
V F, Section F.4.2). /
/Use Regression on OrderN
/ Statistics, Helsel's Robust \
I Method, or Test for I
\ Proportions (See /
\ Appendix F, Sec. F.4.1). /
Figure 38. Flowchart of statistical methods for comparing the mean to a fixed standard (null hypothesis is "concentration exceeds the standard")
152
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Methods for Comparing an Upper Proportion or
Percentile To a Fixed Standard (null hypothesis:
concentration exceeds the standard)
Start (from Fig. 37)
Results
expressed
as pass/fail?
Set Non-Detects Equal to
1/2 Detection Limit
Use a
Nonparametric
Test
Are the
Data
Normally
Distributed?
Cohen's
Model OK?
(See
Appendix F,
Section
F.4.2).
Transform the
Data Using a
Natural Log
Are the
Logged Data
Normally
Distributed?
/ Calculate Parametric \
/ UCL on Upper \
Percentile (See
\ Appendix F, Section /
V """ )
Yes
(Calculate Cohen's
Adjusted UCL on the
Jpper Percentile (see
Appendix F, Section
F.4.2).
/ Apply an "Exceedance Rule" \
/ (see Appendix F, Section \
I F.3.2) or a One-Sample I
y Proportion Test (see Appendix /
F, Section F.3). /
Figure 39. Flowchart of statistical methods for comparing an upper proportion or percentile to a fixed standard (null hypothesis is "concentration exceeds the
standard")
153
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If the number of samples is small, it may not be possible to tell whether the distribution is
normal, lognormal, or any other specific function. You are urged not to read too much into small
data sets and not to attempt overly sophisticated evaluations of data distributions based on
limited information. If the distribution of data appears to be highly skewed, it is best to take
operational measures (such as more samples or samples of a larger physical size) to better
characterize the waste.
8.2.4.2 Treatment of Nondetects
If no more than approximately 15 percent of the samples for a given constituent are nondetect
(i.e., reported as below a detection or quantitation limit), the results of parametric statistical tests
will not be substantially affected if nondetects are replaced by half their detection limits (known
as a substitution method) (USEPA 1992b). When a larger percentage of the sample analysis
results are nondetect, however, the treatment of nondetects is more crucial to the outcome of
statistical procedures. Indeed, simple substitution methods (such as replacing the detection
limit with one-half the detection limit) tend to perform poorly in statistical tests when the
nondetect percentage is substantial (Gilliom and Helsel 1986, Helsel 1990).
Guidance on selecting an approach for handling nondetects in statistical intervals is given in
Appendix F, Section F.4. Guidance also is given in Section 4.7 of EPA's Guidance for Data
Quality Assessment Practical Methods for Data Analysis EPA QA/G-9 (USEPA 2000d).
8.2.5 Draw Conclusions and Report Results
The final step in the DQA Process is to draw conclusions from the data, determine if further
sampling is required, and report the results. This step brings the planning, implementation, and
assessment process "full circle" in that you attempt to resolve the problem and make the
decision identified in Steps 1 and 2 of the DQO Process.
In the DQO Process, you establish a "null hypothesis" and attempt to gather evidence via
sampling that will allow you to reject that hypothesis; otherwise, the null hypothesis must be
accepted. If the decision making process involves use of a statistical method (such as the
calculation of a statistical confidence limit or use of a statistical hypothesis test), then the
outcome of the statistical test should be reported along with the uncertainty associated with the
result. If other decision making criteria are used (such as use of a simple exceedance rule or a
"weight of evidence" approach), then the outcome of that decision making process should be
reported.
Detailed guidance on the use and interpretation of statistical methods for decision making can
be found in Appendix F. Additional guidance can found in EPA's Guidance for Data Quality
Assessment, EPA QA/G-9 (USEPA 2000d).
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Null Hypothesis: "Mean concentration exceeds the standard."
X
LCL UCL
LCL
Standard >
i i
i i i i i
I Conclusion: Mean is
1 less than the standard.
I
X
UCL
\ •
1 *
LCL
i
I
r Conclusion: Need to take more
samples, otherwise conclude
t mean exceeds the standard.
j Conclusion: Mean
1 exceeds the standard.
UCL
I i
1 1
Concentration
Figure 40. Using confidence limits on the mean to compare
waste concentrations to a fixed standard.
Most of the statistical methods suggested in this document involve the construction of one-sided
confidence limits (or bounds). The upper confidence limit, whether calculated on a mean,
median, or percentile, provides a value below which one can claim with specified confidence
that the true value of the parameter lies.
Figure 40 demonstrates how you can use
a confidence limit to test a hypothesis:
In the situation depicted at "A," the upper
confidence limit calculated from the
sample data is less than the applicable
standard and provides the evidence
needed to reject the null hypothesis. The
decision can be made that the waste
concentration is below the standard with
sufficient confidence and without further
analysis.
In situation "B," we cannot reject the null
hypothesis; however, because the
interval "straddles" the standard, it is
possible that the true mean lies below the
standard and a Type II (false acceptance)
error has been made (i.e., to conclude
the concentration is above the standard,
when in fact it is not). One possible remedy to this situation is to obtain more data to "tighten"
the confidence interval.
In situation "C," the Type II (false acceptance) decision error rate is satisfied and we must
conclude that the mean concentration exceeds the standard.
One simple method for checking the performance of the statistical test is use the information
obtained from the samples to retrospectively estimate the number of samples required. For
example, the sample variance can be input into the sample size equation used (see Section 5.4
and 5.5, DQO Process Step 7). (An example of this approach is presented in Appendix I.) 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. See EPA's Guidance for Data Quality Assessment,
EPA QA/G-9 (USEPA 2000d) for additional guidance on this topic.
Finally, if a simple exceedance rule is used to measure compliance with a standard, then
interpretation of the results is more straightforward. For example, if zero exceedances are
allowed, and one or more samples exceeds the standard, then there is evidence of
noncompliance with that standard (see Appendix F, Section F.3.2).
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APPENDIX A
GLOSSARY OF TERMS*
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. EPA recommends using the terms "precision" and "bias," rather than the term
"accuracy," to convey the information usually associated with accuracy. Pitard (1993) indicates
that a sample is accurate when the absolute value of the bias is smaller than an acceptable
standard of accuracy.
Action Level - The numerical value that causes the decision maker to choose one of the
alternative actions (for example, compliance or noncompliance). It may be a regulatory
threshold standard, such as the maximum contaminant level for drinking water, a risk-based
concentration level, a technological limitation, or a reference-based standard (ASTM D 5792-
95).
Alternative Hypothesis - See Hypothesis.
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 - A qualitative and quantitative evaluation of the documentation and
procedures associated with environmental measurements to verify that the resulting data are of
acceptable quality.
Baseline Condition - A tentative assumption to be proven either true or false. When
hypothesis testing is applied to a 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 measured value from its true value (this can
occur during sampling design, the sampling process, or laboratory analysis).
* The definitions in this appendix are from USEPA 1998a, 2000b, 2000e, and 2001 b, unless otherwise noted. Some
definitions were modified based on comments received from technical reviewers during development of this
document. These definitions do not constitute the Agency's official use of the terms for regulatory purposes and
should not be construed to alter or supplant other terms in use.
Note: Terms in italics also are defined in this glossary.
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Appendix A
Blank - A sample that is intended to contain none of the analytes of interest and is 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 blank is used to detect
contamination during sample handling preparation and/or analysis (see also Rinsate, Method
Blank, Trip Blank, and Field Blank).
Boundaries - The spatial and temporal limits and practical constraints under which
environmental data are collected. Boundaries specify the area or volume (spatial boundary) and
the time period (temporal boundary) to which the decision will apply. Samples are then
collected within these boundaries.
Calibration - 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 also is used to quantify instrument
measurements of a given concentration in a given sample.
Calibration Drift - The deviation in instrument response from a reference value over a period of
time before recalibration.
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.
Coefficient of Variation (CV) - A dimensionless quantity used to measure the spread of data
relative to the size of the numbers. For a normal distribution, the coefficient of variation is given
by six . Also known as the relative standard deviation (RSD).
Colocated Samples - Two or more portions collected as close as possible at the same point in
time and space so as to be considered identical. If obtained in the field, these samples also are
known as "field replicates."
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.
Component - An easily identified item such as a large crystal, an agglomerate, rod, container,
block, glove, piece of wood, or concrete (ASTM D 5956-96). An elementary part or a
constituent that can be separated and quantified by analysis (Pitard 1993).
Composite Sample - A physical combination of two or more samples (ASTM D 6233-98). A
sample collected across a temporal or spatial range that typically consists of a set of discrete
samples (or "individual" samples) that are combined or "composited." Area-wide or long-term
compositing should not be confused with localized compositing in which a sample of the desired
support is created from many small increments taken at a single location. Four types of
composite samples are listed below:
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Appendix A
1. Time Composite - a sample comprising a varying number of discrete samples
collected at equal time intervals during the compositing period. The time
composite sample is typically used to sample waste water or streams.
2. Flow Proportioned Composite (FPC) - a sample collected proportional to the flow
during the compositing period by either a time-varying/constant volume (TVCV)
or a time-constant/varying volume method (TCVV). The TVCV method typically
is used with automatic samplers that are paced by a flow meter. The TCVV
method is a manual method that individually proportions a series of discretely
collected samples. The FPC is typically used when sampling waste water.
3. Areal Composite - sample composited from individual equal-size sarnies
collected on an areal or horizontal cross-sectional basis. Each discrete sample
is collected in an identical manner. Examples include sediment composites from
quarter-point sampling of streams and soil samples from within grids.
4. Vertical Composite - a sample composited from individual equal samples
collected from a vertical cross section. Each discrete sample is collected in an
identical manner. Examples include vertical profiles of soil/sediment columns,
lakes, and estuaries (USEPA 1996c).
Confidence Level - The probability, usually expressed as a percent, that a confidence interval
will contain the parameter of interest (ASTM D 5792-95). Also known as the confidence
coefficient.
Confidence Limits - Upper and/or lower limit(s) within which the true value of a parameter is
likely to be contained with a stated probability or confidence (ASTM D 6233-98).
Conformance - An affirmative indication or judgment that a product or service has met the
requirements of the relevant specifications, 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.
Control Sample - A quality control sample introduced into a process to monitor the
performance of the system (from Chapter One, SW-846).
Data Collection Design - A design that specifies the configuration of the environmental
monitoring effort to satisfy the data quality objectives. It includes: the types of samples or
monitoring information to be collected; where, when, and under what conditions they should be
collected; what variables are to be measured; and the quality assurance/quality control (QA/QC)
components that ensure acceptable sampling design error and measurement error to meet the
decision error rates specified in the DQOs. The data collection design is the principal part of the
quality assurance project plan (QAPP).
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Data of Known Quality - Data that have the qualitative and quantitative components
associated with their derivation documented appropriately for their intended use, and when such
documentation is verifiable and defensible.
Data Quality Assessment (DQA) Process - A statistical and scientific evaluation of the data
set to assess the validity and performance of the data collection design and statistical test and
to establish whether a data set is adequate for its intended use.
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 (precision and bias are preferred terms),
comparability, completeness, and representativeness.
Data Quality Objectives (DQOs) - Qualitative and quantitative statements derived from the
DQO Process that clarify study 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. The key elements of the process include:
concisely defining the problem
identifying the decision to be made
identifying the key inputs to that decision
defining the boundaries of the study
developing the decision rule
specifying tolerable limits on potential decision errors
selecting the most resource efficient data collection design.
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 and understandable form. Data reduction 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.
Data Validation - See Validation.
Debris - Under 40 CFR 268.2(g) (Land Disposal Restrictions regulations) debris includes "solid
material exceeding a 60 mm particle size that is intended for disposal and that is a
manufactured object; or plant or animal matter; or natural geologic material." 268.2(g) also
identifies materials that are not debris. In general, debris includes materials of either a large
particle size or variation in the items present. When the constituent items are more than 2 or 3
inches in size or are of different compositions, representative sampling becomes more difficult.
Decision Error - An error made when drawing an inference from data in the context of
hypothesis testing such that variability or bias in the data mislead the decision maker to draw a
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conclusion that is inconsistent with the true or actual state of the population under study. See
also False Negative Decision Error, and False Positive Decision Error.
Decision Performance Curve - A graphical representation of the quality of a decision process.
In statistical terms it is known as a power curve or function (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.
Decision Unit - A volume or mass of material (such as waste or soil) about which a decision will
be made.
Defensible - The ability to withstand any reasonable challenge related to the veracity, integrity,
or quality of the logical, technical, or scientific approach taken in a decision-making process.
Design - Specifications, drawings, design criteria, and performance requirements. Also, the
result of deliberate planning, analysis, mathematical manipulations, and design processes (such
as experimental design and sampling design).
Detection Limit - 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. Detection limits are
analyte- and matrix-specific and may be laboratory-dependent.
Discrete Sample - A sample that represents a single location or short time interval. A discrete
sample can be composed of more than one increment. The term has the same meaning as
"individual sample."
Distribution - 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.
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 the variance of the total method, including sampling and
analysis. See also Colocated Sample and Field Duplicate Samples.
Dynamic Work Plan - A work plan that allows the project team to make decisions in the field
about how subsequent site activities will progress (for example, by use field analytical methods
that provide near real-time sample analysis results). Dynamic work plans provide the strategy
for how dynamic field activities will take place. As such, they document a flexible, adaptive
sampling and analytical strategy. (Adopted from EPA Superfund web site at
http://www.epa.gov/superfund/programs/dfa/dynwork.htm).
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 scientific literature.
Environmental Monitoring - The process of measuring or collecting environmental data for
evaluating a change in the environment (e.g., ground-water monitoring).
Environmental Processes - Manufactured or natural processes that produce discharges to or
that impact the ambient environment.
Equipment Blank - See Rinsate.
Estimate - A characteristic from the sample from which inferences about population parameters
can be made.
Evaluation - See validation.
Evidentiary Records - Records identified as part of litigation and subject to restricted access,
custody, use, and disposal.
False Negative (False Acceptance) Decision Error (/?) - A false negative decision error
occurs when the decision maker does not reject the null hypothesis when the null hypothesis
actually is false. In statistical terminology, a false negative decision error also is called a Type II
error. The measure of the size of the error is expressed as a probability, usually referred to as
"beta" (/?). This probability also is called the complement of power (where "power" is
expressed as (1 - /?)).
False Positive (False Rejection) Decision Error (a ) - A false positive decision error occurs
when a decision maker rejects the null hypothesis when the null hypothesis is true. In statistical
terminology, a false positive decision error also is called a Type I error. The measure of the
size of the error is expressed as a probability, usually referred to as "alpha" (a ), the "level of
significance," or "size of the critical region."
Field Blank - A blank used to provide information about contaminants that may be introduced
during sample collection, storage, and transport. The clean sample is carried to the sampling
site, exposed to sampling conditions, returned to the laboratory, and treated as an
environmental sample.
Field Duplicates - Independent samples that are collected as close as possible to the same
point in space and time. Two separate samples are taken from the same source, stored in
separate containers, and analyzed independently. These duplicates are useful in documenting
the precision of the sampling process (from Chapter One, SW-846, July 1992).
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
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used, for example, to determine the effect of the sample preservation, shipment, storage,
matrix, and preparation on analyte recovery efficiency (the analytical bias).
Field Split Samples - Two or more representative portions taken from the same sample and
usually submitted for analysis to different laboratories to estimate interlaboratory precision.
Fundamental Error - The fundamental error results when discrete units of the material to be
sampled have different compositions with respect to the property of interest. The error is
referred to as "fundamental" because it is an incompressible minimum sampling error that
depends on the mass, composition, shape, fragment size distribution, and liberation factor of
the material and is not affected by homogenization or mixing. The fundamental error is the only
error that remains when the sampling operation is "perfect," i.e., when all parts of the sample
are obtained in a probabilistic manner and each part is independent. The fundamental error is
never zero (unless the population is completely homogeneous or the entire population is
submitted for exhaustive analysis) and it never "cancels out." It can be reduced by taking larger
physical samples and by using particle-size reduction steps in preparing the analytical sample.
Geostatistics - A branch of statistics, originating in the mining industry and greatly developed in
the 1950s, that assesses the spatial correlation among samples and incorporates this
information into the estimates of population parameters.
Goodness-of-Fit Test - In general, the level of agreement between an observed set of values
and a set wholly or partly derived from a model of the data.
Grab Sample - A one-time sample taken from any part of the waste (62 FR 91, page 26047,
May 12, 1997).
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 Process.)
Gray Region - A range of values of the population parameter of interest (such as mean
contaminant concentration) within which the consequences of making a decision error are
relatively minor. The gray region is bounded on one side by the action level. The width of the
gray region is denoted by A in this guidance.
Guidance - A suggested practice that is not mandatory, but rather intended as an aid or
example in complying with a standard or requirement.
Guideline - A suggested practice that is nonmandatory in programs intended to comply with a
standard.
Hazardous Waste - Any waste material that satisfies the definition of "hazardous waste" as
given in 40 CFR Part 261, "Identification and Listing of Hazardous Waste."
Heterogeneity - The condition of the population under which items of the population are not
identical with respect to the parameter of interest (ASTM D 6233-98). (See Section 6.2.1).
Holding Time - The period of time a sample may be stored prior to its required analysis. While
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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.
Homogeneity - The condition of the population under which all items of the population are
identical with respect to the parameter of interest (ASTM D 6233-98). The condition of a
population or lot in which the elements of that population or lot are identical; it is an inaccessible
limit and depends on the "scale" of the elements.
Hot Spots - Strata that contain high concentrations of the characteristic of interest and are
relatively small in size when compared with the total size of the materials being sampled (ASTM
D 6009-96).
Hypothesis - A tentative assumption made to draw out and test its logical or empirical
consequences. In hypothesis testing, the hypothesis is labeled "null" (for the baseline
condition) or "alternative," depending on the decision maker's concerns for making a decision
error. The baseline condition is retained until overwhelming evidence indicates that the
baseline condition is false. See also baseline condition.
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.
Increment - A group of particles extracted from a batch of material in a single operation of the
sampling device. It is important to make a distinction between an increment and a sample that
is obtained by the reunion of several increments (from Pitard 1989).
Individual Sample - See Discrete Sample.
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 assessing
the precision and bias of the applied analytical method.
Item - An all-inclusive term used in place of the following: appurtenance, facility, sample,
assembly, component, equipment, material, module, part, product, structure, subassembly,
subsystem, system, unit, documented concepts, or data.
Laboratory Split Samples - Two or more representative portions taken from the same sample
for laboratory analysis. Often 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.
Limits on Decision Errors - The tolerable maximum decision error probabilities established by
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the decision maker. Potential economic, health, ecological, political, and social consequences
of decision errors should be considered when setting the limits.
Matrix Spike - A sample prepared by adding a known mass of a target analyte to a specified
amount of sample matrix 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) (x ) - 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 Square Error ( MSE) - A statistical term equivalent to the variance added to the square
of the bias. An overall measure of the representativeness of a sample.
Measurement Error - The difference between the true or actual state and that which is reported
from measurements.
Median - 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. The median
is the 50th percentile.
Medium - A substance (e.g., air, water, soil) that serves as a carrier of the analytes of interest.
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.
Method Blank - A blank prepared to represent the sample matrix as closely as possible and
analyzed exactly like the calibration standards, samples, and 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.
Natural Variability - The variability that is inherent or natural to the media, objects, or subjects
being studied.
Nonparametric - A term describing statistical methods that do not assume a particular
population probability distribution, and are therefore valid for data from any population with any
probability distribution, which can remain unknown (Conover 1999).
Null Hypothesis - See Hypothesis.
Observation - (1) 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. (2) A datum.
Outlier - An observation that is shown to have a low probability of belonging to a specified data
population.
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Parameter - A quantity, usually unknown, such as a mean or a standard deviation
characterizing a population. Commonly misused for "variable," "characteristic," or "property."
Participant - When used in the context of environmental programs, an organization, group, or
individual that takes part in the planning and design process and provides special knowledge or
skills to enable the planning and design process to meet its objective.
Percent Relative Standard Deviation (%RSD) - The quantity, 100(RSD)%.
Percentile - The specific value of a distribution that divides the distribution such that p percent
of the distribution is equal to or below that value. For example, if we say "the 95th percentile is
X," then it means that 95 percent of the values in the statistical sample are less than or equal to
X.
Planning Team - The group of people that will carry out the DQO Process. Members include
the decision maker (senior manager), representatives of other data users, senior program and
technical staff, someone with statistical expertise, and a QA/QC advisor (such as a QA
Manager).
Population -The total collection of objects, media, or people to be studied and from which a
sample is to be drawn. The totality of items or units under consideration (ASTM D 5956-96).
Precision - A measure of mutual agreement among individual measurements of the same
property, usually under prescribed similar conditions, expressed generally in terms of the
sample standard deviation. See also the definition for precision in Chapter One, SW-846.
Probabilistic Sample - See statistical sample.
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.
Qualified Data - Any data that have been modified or adjusted as part of statistical or
mathematical evaluation, data validation, or data verification operations.
Quality - The totality of features and characteristics of a product (including data) or service that
bears on its ability to meet the stated or implied needs and expectations of the user (i.e., fitness
for use).
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 Manager- The individual designated as the principal manager within the
organization having management oversight and responsibilities for planning, coordinating, and
assessing the effectiveness of the quality system for the organization.
Quality Assurance Project Plan (QAPP) - A formal document describing, in comprehensive
detail, the necessary QA, QC, and other technical activities that must be implemented to ensure
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that the results of the work performed will satisfy the stated performance criteria.
Quality Control (QC) - The overall system of technical activities that measures the attributes
and performance (quality characteristics) 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
intralaboratory or analyst-specific precision and bias or to assess the performance of all or a
portion of the measurement system.
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 - 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 QA and QC.
Random Error - The chance variation encountered in all measurement work, characterized by
the random occurrence of deviations from the mean value.
Range - The numerical difference between the minimum and maximum of a set of values.
Relative Standard Deviation - See Coefficient of Variation.
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; that is, within-rum precision of a method or set of
measurements.
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 usually are not associated with a probability level.
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Representative Sample - RCRA regulations define a representative sample as "a sample of a
universe or whole (e.g., waste pile, lagoon, ground water) which can be expected to exhibit the
average properties of the universe or whole" (40 CFR § 260.10).
Representativeness - A measure of the degree to which data accurately and precisely
represent a characteristic of a population, parameter variations at a sampling point, a process
condition, or an environmental condition.
Reproducible - The condition under which there is no statistically significant difference in the
results of measurements of the same sample made at different laboratories.
Reproducibility - The degree of agreement between independent test results produced by the
same method or set of measurements for very similar, but not identical, conditions (e.g., at
different times, by different technicians, using different glassware, laboratories, or samples); that
is, the between-run precision of a method or set of measurements.
Requirement - A formal statement of a need and the expected manner in which it is to be met.
Rinsate (Equipment Rinsate) - A sample of analyte-free medium (such as HPLC-grade water
for organics or reagent-grade deionized or distilled water for inorganics) which has been used to
rinse the sampling equipment. It is collected after completion of decontamination and prior to
sampling. This blank is useful in documenting the adequate decontamination of sampling
equipment (modified from Chapter One, SW-846).
Sample - A portion of material that is taken from a larger quantity for the purpose of estimating
the properties or the composition of the larger quantity (ASTM D 6233-98).
Sample Support - See Support.
Sampling - The process of obtaining representative samples and/or measurements of a
population or subset of a population.
Sampling Design Error - The error due to observing only a limited number of the total possible
values that make up the population being studied. It should be distinguished from: errors due
to imperfect selection; bias in response; and errors of observation, measurement, or recording,
etc.
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.
Sensitivity - The capability of a method or instrument to discriminate between measurement
responses representing different levels of a variable of interest (i.e., the slope of the calibration).
Set of Samples - More than one individual sample.
Split Samples - Two or more representative portions taken from one sample and often
analyzed by different analysts or laboratories as a type of QC sample used to assess analytical
variability and comparability.
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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 that has the same unit of
measurement as the mean. See variance.
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 (usually by the quality assurance officer) as the method for performing certain
routine or repetitive tasks.
Statistic - A function of the sample measurements; e.g., the sample mean or standard
deviation. A statistic usually, but not necessarily, serves as an estimate of a population
parameter. A summary value calculated from a sample of observations.
Statistical Sample - A set of samples or measurements selected by probabilistic means (i.e.,
by using some form of randomness). Also known as a probabilistic sample.
Statistical Test - Any statistical method that is used to determine the acceptance or rejection of
a hyothesis.
Stratum - A subgroup of a population separated in space or time, or both, from the remainder of
the population and being internally consistent with respect to a target constituent or property of
interest and different from adjacent portions of the population (ASTM D 5956-96).
Subsample - A portion of material taken from a larger quantity for the purpose of estimating
properties or the composition of the whole sample (ASTM D 4547-98).
Support - The physical volume or mass, orientation, and shape of a sample, subsample, or
decision unit.
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 method has been performed properly.
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 indepth analysis and
evaluation of documents, activities, material, data, or items that require technical verification or
validation for applicability, correctness, adequacy, completeness, and assurance that
established requirements are satisfied.
Total Study Error - The combination of sampling design error and measurement error.
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 project's quality.
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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. A trip blank is
used to document contamination attributable to shipping and field handling procedures. This
type of blank is useful in documenting contamination of volatile organics samples.
True - Being in accord with the actual state of affairs.
Type I Error (a ) - A Type I error occurs when a decision maker rejects the null hypothesis
when it is actually true. See also False Positive Decision Error.
Type II Error (/3) - A Type II error occurs when the decision maker fails to reject the null
hypothesis when it is actually false. See also False Negative Decision Error.
User - When used in the context of environmental programs, an organization, group, or
individual that utilizes the results or products from environmental programs. A user also may be
the client for whom the results or products were collected or created.
Vadose Zone - In soil, the unsaturated zone, limited above by the ground surface and below by
the saturated zone.
Validation - Confirmation by examination and provision of objective evidence that the particular
requirements for a specific intended use are fulfilled. In design and development, validation
concerns the process of examining a product or result to determine conformance to user needs.
Variable - The attribute of the environment that is indeterminant. A quantity which may take
any one of a specified set of values.
Variance - A measure of the variability or dispersion in (1) a population (population variance,
<72 ), or (2) a sample or set of subsamples (sample variance, s2). The variance is the second
moment of a frequency distribution taken about the arithmetic mean as the origin. For a normal
distribution, it is the sum of the squared deviations of the (population or sample) member
observation about the (population or sample) mean divided by the degrees of freedom (TV for
<72 , or n— 1 for s2).
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 B
SUMMARY OF RCRA REGULATORY DRIVERS FOR CONDUCTING
WASTE SAMPLING AND ANALYSIS
Through RCRA, Congress provided EPA with the framework to develop regulatory programs for
the management of solid and hazardous waste. The provisions of RCRA Subtitle C establish
the criteria for identifying hazardous waste and managing it from its point of generation to
ultimate disposal. EPA's regulations set out in 40 CFR Parts 260 to 279 are the primary
reference for information on the hazardous waste program. These regulations include
provisions for waste sampling and testing and environmental monitoring. Some of these RCRA
regulations require sampling and analysis, while others do not specify requirements and allow
sampling and analysis to be performed at the discretion of the waste handler or as specified in
individual facility permits.
Table B-1 provides a comprehensive listing of the regulatory citations, the applicable RCRA
standards, requirements for demonstrating attainment or compliance with the standards, and
relevant USEPA guidance documents. The table is divided into three major sections addressing
regulations for (1) hazardous waste identification, (2) land disposal restrictions, and (3) other
programs. The table is meant to be used as a general reference guide. Consult the latest 40
CFR, related Federal Register notices, and EPA's World Wide Web site (www.epa.gov) for new
or revised regulations and further clarification and definitive articulation of requirements. In
addition, because some states have requirements that differ from EPA regulations and
guidance, we recommend that you consult with a representative from your State if your State is
authorized to implement the regulation.
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
1
3
Q.
X'
DO
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Hazardous Waste Identification Program
§261.3(a)(2)(v) - Used oil rebuttable Used oil that contains more than
presumption (see also Part 279,
Subpart B and the Part 279
standards for generators,
transporters, processors, re-
refiners, and burners.)
1,000 parts per million (ppm) of total
halogens is presumed to have been
mixed with a regulated halogenated
hazardous waste (e.g., spent
halogenated solvents), and is
therefore subject to applicable
hazardous waste regulations. The
rebuttable presumption does not
apply to metalworking oils and oils
from refrigeration units, under some
circumstances.
A person may rebut this
presumption by demonstrating,
through analysis or other
documentation, that the used oil
has not been mixed with
halogenated hazardous waste. One
way of doing this is to show that the
used oil does not contain significant
concentrations of halogenated
hazardous constituents (50 FR
49176; November29, 1985). Ifthe
presumption is successfully
rebutted, then the used oil will be
subject to the used oil management
standards instead of the hazardous
waste regulations.
Hazardous Waste Management
System; Identification and Listing of
Hazardous Waste; Recycled Used
Oil Management Standards, 57 FR
41566; September 10, 1992
Part 279 Requirements: Used Oil
Management Standards,
EPA530-H-98-001
§261.3(c)(2)(ii)(C) - Generic
exclusion levels for K061, K062,
and F006 nonwastewater HTMR
residues
To be excluded from the definition
of hazardous waste, residues must
meet the generic exclusion levels
specified at §261.3(c)(2)(ii)(C)(7)
and exhibit no characteristics of
hazardous waste.
Testing requirements must be
incorporated in a facility's waste
analysis plan or a generator's self-
implementing waste analysis plan.
At a minimum, composite samples
of residues must be collected and
analyzed quarterly and/or when the
process or operation generating the
waste changes. Claimant has the
burden of proving by clear and
convincing evidence that the
material meets all of the exclusion
requirements.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
-------
Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Hazardous Waste Identification Program (continued)
§261.21- Characteristic of
Ignitability
CO
A solid waste exhibits the
characteristic of ignitability if a
representative sample of the waste
is: (1) A liquid having a flashpoint of
less than 140 degrees Fahrenheit
(60 degrees Centigrade); (2) A
non-liquid which causes fire through
friction, absorption of moisture, or
spontaneous chemical changes
and, when ignited, burns so
vigorously and persistently it
creates a hazard; (3) An ignitable
compressed gas; or (4) An oxidizer.
(Aqueous solutions with alcohol
content less than 24% are not
regulated.)
If a representative sample of the
waste exhibits the characteristic,
then the waste exhibits the
characteristic. Appendix I of 40
CFR Part 261 contains references
to representative sampling
methods; however a person may
employ an alternative method
without formally demonstrating
equivalency. Also, for those
methods specifically prescribed by
regulation, the generator can
petition the Agency for the use of
an alternative method (see 40 CFR
260.21).
See Chapters Seven and Eight in
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, IIA, IIB, III, and III A.
SW-846. (USEPA 1986a)
§261.22 - Characteristic of
Corrosivity
A solid waste exhibits the
characteristic of corrosivity if a
representative sample of the waste
is: (1) Aqueous, with a pH less than
or equal to 2, or greater than or
equal to 12.5; or (2) Liquid and
corrodes steel at a rate greater than
6.35 mm per year when applying a
National Association of Corrosion
Engineers Standard Test Method.
If a representative sample of the
waste exhibits the characteristic,
then the waste exhibits the
characteristic. Appendix I of 40
CFR Part 261 contains references
to representative sampling
methods; however a person may
employ an alternative method
without formally demonstrating
equivalency. Also, for those
methods specifically prescribed by
regulation, the generator can
petition the Agency for the use of
an alternative method (see 40 CFR
260.21).
See Chapters Seven and Eight in
Tesf Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Hazardous Waste Identification Program (continued)
§261.23 - Characteristic of
Reactivity
A solid waste exhibits the
characteristic of reactivity if a
representative sample of the waste:
(1) Is normally unstable and readily
undergoes violent change; (2)
Reacts violently with water; (3)
Forms potentially explosive
mixtures with water; (4) Generates
toxic gases, vapors, or fumes when
mixed with water; (5) Is a cyanide
or sulfide-bearing waste which,
when exposed to pH conditions
between 2 and 12.5, can generate
toxic gases, vapors, or fumes; (6) Is
capable of detonation or explosion if
subjected to a strong initiating
source or if heated under
confinement; (7) Is readily capable
of detonation or explosive
decomposition or reaction at
standard temperature and pressure;
or (8) Is a forbidden explosive as
defined by DOT.
EPA relies on these narrative
criterion to define reactive wastes.
Waste handlers should use their
knowledge to determine if a waste
is sufficiently reactive to be
regulated. Also, for those methods
specifically prescribed by
regulation, the generator can
petition the Agency for the use of
an alternative method (see 40 CFR
260.21).
EPA currently relies on narrative
standards to define reactive wastes,
and withdrew interim guidance
related to sulfide and cyanide levels
(see a Memorandum entitled,
Withdrawal of Cyanide and Sulfide
Reactivity Guidance" from David
Bussard and Barnes Johnson to
Diana Love, dated April 21, 1998).
§ 261.24 - Toxicity Characteristic
A solid waste exhibits the
characteristic of toxicity if the
extract of a representative sample
of the waste contains any of the
contaminants listed in Table 1 in
261.24, at or above the specified
regulatory levels. The extract
should be obtained through use of
the Toxicity Characteristic Leaching
Procedure (TCLP). If the waste
contains less than .5 percent
filterable solids, the waste itself,
after filtering, is considered to be
the extract.
Appendix I of 40 CFR Part 261
contains references to
representative sampling methods;
however, a person may employ an
alternative method without formally
demonstrating equivalency.
See Chapters Seven and Eight in
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, IIA, IIB, III, and III A.
SW-846. (USEPA 1986a)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Hazardous Waste Identification Program (continued)
§261.38(c)(8)(iii)(A) - Exclusion of
Comparable Fuels from the
Definition of Solid and Hazardous
Waste
For each waste for which an
exclusion is claimed, the generator
of the hazardous waste must test
for all of the constituents on
Appendix VIII to part 261, except
those that the generator
determines, based on testing or
knowledge, should not be present in
the waste. The generator is
required to document the basis for
each determination that a
constituent should not be present.
For waste to be eligible for
exclusion, a generator must
demonstrate that "each constituent
of concern is not present in the
waste above the specification level
at the 95% upper confidence limit
around the mean."
See the final rule from June
19,1998 (63 FR 33781)
For further information on the
comparable fuels exclusion, see the
following web site:
http://www.epa.gov/combustion/fast
rack/
Part 261-Appendix I -
Representative Sampling Methods
en
Provides sampling protocols for
obtaining a representative sample.
For the purposes of Subpart C, a
sample obtained using Appendix I
sampling methods will be
considered representative. The
Appendix I methods, however, are
not formally adopted (see comment
at§261.20(c)).
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
ASTM Standards
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Land Disposal Restriction Program
§268.6(b)(1) - Petitions to Allow
Land Disposal of a Waste
Prohibited Under Subpart C of Part
268 (No-Migration Petition)
The demonstration must meet the
following criteria: (1) All waste and
environmental sampling, test, and
analysis data must be accurate and
reproducible to the extent that
state-of-the-art techniques allow; (2)
All sampling, testing, and estimation
techniques for chemical and
physical properties of the waste and
all environmental parameters must
have been approved by the EPA
Administrator.
Waste analysis requirements
will be specific to the petition.
Sampling methods are specified
in the facility's Waste Analysis
Plan.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
Land Disposal Restrictions No
Migration Variances; Proposed
Rule. Federal Register, August 11,
1992 (USEPA 1992)
§268.40 - Land Disposal Restriction
(LDR) concentration-level standards
For total waste standards, all
hazardous constituents in the waste
or in the treatment residue must be
at or below the values in the table at
268.40. For waste extract
standards, the hazardous
constituents in the extract of the
waste or in the extract of the
treatment residue must be at or
below the values in the table at
268.40.
Sampling methods are specified
in the facility's Waste Analysis
Plan.
Compliance with the standards
for nonwastewater is measured
by an analysis of grab samples.
Compliance with wastewater
standards is based on composite
samples. No single sample may
exceed the applicable standard.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers for the Land Disposal Restriction Program (continued)
§268.44 - Land Disposal Restriction
Treatability Variance
If you are a generator or treatment
facility whose wastes cannot be
treated to achieve the established
treatment standards, or for which
treatment standards are not
appropriate, you may petition EPA
for a variance from the treatment
standard. A treatment variance
does not exempt your wastes from
treatment, but rather establishes an
alternative LDR treatment standard.
The application must demonstrate
that the treatment standard for the
waste in question is either
"unachievable" or "inappropriate."
Memorandum entitled "Use of Site-
Specific Land Disposal Restriction
Treatability Variances Under 40
CFR 268.44(h) During Cleanups"
(Available from the RCRA Call
Center or on EPA's web site at
http://www.epa.gov/epaoswer/hazw
aste/ldr/tv-rule/guidmem.txt
Variance Assistance Document:
Land Disposal Restrictions
Treatability Variances &
Determinations of Equivalent
Treatment (available from the
RCRA Call Center or on EPA's web
site at
http://www.epa.gov/epaoswer/hazw
aste/ldr/guidance2.pdf
§268.49(c)(1) - Alternative LDR
Treatment Standards for
Contaminated Soil
All constituents subject to treatment
must be treated as follows: (A) For
non-metals, treatment must achieve
90 percent reduction in total
constituent concentrations except
where treatment results in
concentrations less that 10 times
the Universal Treatment Standard
(UTS) at 268.48. (B) For metals,
treatment must achieve 90 percent
reduction in constituent
concentrations as measured in
TCLP leachate from the treated
media or 90 percent reduction in
total concentrations when a metal
removal technology is used, except
where treatment results in
concentrations less that 10 times
the UTS at 268.48.
Sampling methods are specified in
the facility's Waste Analysis Plan.
Guidance on Demonstrating
Compliance With the Land Disposal
Restrictions (LDR) Alternative Soil
Treatment Standards (USEPA
2002)
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations
§260.10 - Definitions
"Representative sample" means a
sample of a universe or whole (e.g.
waste pile, lagoon, ground water)
which can be expected to exhibit
the average properties of the
universe or whole.
Representative samples may be
required to measure compliance
with various provisions within the
RCRA regulations. See
requirements specified in the
applicable regulation or
implementation guidance.
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
Part 260 - Subpart C - Rulemaking
Petitions
In the section for petitions to amend
Part 261 to "delist" a hazardous
waste, the petitioner must
demonstrate that the waste does
not meet any of the criteria under
which the waste was listed as a
hazardous waste (§260.22).
Demonstration samples must
consist of enough representative
samples, but in no case less than
four samples, taken over a period of
time sufficient to represent the
variability or the uniformity of the
waste.
Petitions to Delist Hazardous
Waste-A Guidance Manual. 2nd ed.
(USEPA 1993d)
Region 6 RCRA Delisting Program
Guidance Manual for the Petitioner
(USEPA 1996d)
oo
Part 262 - Subpart A - Purpose,
Scope, and Applicability (including
§262.11 - Hazardous Waste
Determination)
Generators must make the following Generators must document their
determinations if a secondary
material is a solid waste: 1) whether
the solid waste is excluded from
regulation; 2) whether the waste is
a listed waste; and 3) whether the
waste is characteristic waste
(§262.11)
waste determination and land
disposal restriction determination.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
Part 262 - Subpart C - Pre-
Transport Requirements
Under §262.34(a)(4), if generators
are performing treatment within
their accumulation units, they must
comply with the waste analysis plan
requirements of §268.7(a)(5).
Generators must develop a waste
analysis plan (kept on-site for three
years) which details the treatment
they are performing to meet LDR
treatment standards and the type of
analysis they are performing to
show completion of treatment.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 264 - Subpart A - Purpose,
Scope, and Applicability
§264.1Q)(2) - In an exemption
established by the HWIR-media
rulemaking, remediation waste can
be exempt under circumstances
that require chemical and physical
analysis of a representative sample
of the hazardous remediation waste
to be managed at the site.
The analysis, at a minimum, must
contain all the information needed
to treat, store, or dispose of the
waste according to Part 264 and
Part 268. The waste analysis must
be accurate and up-to-date.
See the final Federal Register
notice from November 30, 1998 (63
FR 65873)
For further documentation, see the
following web site:
http://www.epa.gov/epaoswer/hazw
aste/id/h wirmdia.htm
Parts 264/265 - Subpart B -
General Facility Standards
CD
§264/265.13 - General waste
analysis requirements specify: (a)
Detailed chemical and physical
analysis of a representative sample
is required before an owner treats,
stores, or disposes of any
hazardous waste. Sampling
method may be those under Part
261; and (b) Owner/operator must
develop and follow a written waste-
analysis plan.
All requirements are case-by-case
and are determined in the facility
permit.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 264 - Subpart F - Groundwater
Monitoring
oo
o
Groundwater monitoring wells must
be properly installed so that
samples will yield representative
results. All monitoring wells must be
lined, or cased, in a manner that
maintains the integrity of the
monitoring well bore hole
(§264.97(c)). Poorly installed wells
may give false results.
There are specific monitoring
standards for all three sub-
programs:
Detection Monitoring
(§264.98);
Compliance Monitoring
(§264.99); and
Corrective Action Program
(§264.100).
The Corrective Action Program is
specific to the Groundwater
Monitoring Program.
At a minimum, there must be
procedures and techniques for
sample collection, sample
preservation and shipment,
analytical procedures, and chain-of-
custody control (§264.97(d)).
Sampling and analytical methods
must be appropriate for
groundwater sampling and
accurately measure the hazardous
constituents being analyzed. The
owner and operator must develop
an appropriate sampling procedure
and interval for each hazardous
constituent identified in the facility's
permit. The owner and operator
may use an alternate procedure if
approved by the RA. Requirements
and procedures for obtaining and
analyzing samples are detailed in
the facility permit, usually in a
Sampling and Analysis Plan.
Statistical Analysis of Ground-Water
Monitoring Data at RCRA Facilities
(Interim Final Guidance). Office of
Solid Waste (USEPA 1989b)
RCRA Ground-Water Monitoring:
Draft Technical Guidance. (USEPA
1992c)
Statistical Analysis of Ground-Water
Monitoring Data at RCRA Facilities
Addendum to Interim Final
Guidance (USEPA 1992b)
Methods for Evaluating the
Attainment of Cleanup Standards.
Volume 2: Ground Water (USEPA.
19921)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 265 - Subpart F - Ground-
water Monitoring
oo
To comply with Part 265, Subpart F,
the owner/operator must install,
operate, and maintain a ground-
water monitoring system capable of
representing the background
groundwater quality and detecting
any hazardous constituents that
have migrated from the waste
management area to the uppermost
aquifer. Under Part 265, Subpart F,
there are two types of groundwater
monitoring programs: an indicator
evaluation program designed to
detect the presence of a release,
and a ground-water quality
assessment program that evaluates
the nature and extent of
contamination.
To determine existing ground-water
conditions at an interim status
facility, the owner and operator
must install at least one well
hydraulically upgradientfrom the
waste management area. The
well(s) must be able to accurately
represent the background quality of
ground water in the uppermost
aquifer. The owner and operator
must install at least three wells
hydraulically downgradient at the
limit of the waste management
area, which are able to immediately
detect any statistically significant
evidence of a release. A separate
monitoring system for each
management unit is not required as
long as the criteria in §265.91 (a)
are met and the system is able to
detect any release at the edge of
the waste management area.
Statistical Analysis of Ground-Water
Monitoring Data at RCRA Facilities
(Interim Final Guidance). Office of
Solid Waste (USEPA 1989b)
RCRA Ground-Water Monitoring:
Draft Technical Guidance. (USEPA
1992c)
Statistical Analysis of Ground-Water
Monitoring Data at RCRA Facilities
Addendum to Interim Final
Guidance (USEPA 1992b)
Part 264/265 - Subpart G - Closure
and Post-Closure
The closure plan must include a
detailed description of the steps for
sampling and testing surrounding
soils and criteria for determining the
extent of decontamination required
to satisfy the closure performance
standards. (§264/265.112(b)(4))
All requirements are facility-specific
and are set forth in the facility
permit.
Closure/Postclosure Interim Status
Standards (40 CFR 265, Subpart
G): Standards Applicable to Owners
and Operators of Hazardous Waste
Treatment, Storage, and Disposal
Facilities Under RCRA, Subtitle C,
Section 3004
RCRA Guidance Manual for
Subpart G Closure and Postclosure
Care Standards and Subpart H Cost
Estimating Requirements (USEPA
1987)
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 264 - Subpart I - Use and
Management of Containers
Spilled or leaked waste and
accumulated precipitation must be
removed from the sump or
collection area in as timely a
manner as is necessary to prevent
overflow of the collection system
(§264.175).
oo
If the collected material is a
hazardous waste under part 261 of
this Chapter, it must be managed
as a hazardous waste in
accordance with all applicable
requirements of parts 262 through
266 of the chapter. If the collected
material is discharged through a
point source to waters of the United
States, it is subject to the
requirements of section 402 of the
Clean Water Act, as amended.
Testing scope and requirements are
site-specific and are set forth in the
facility waste analysis plan.
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
Guidance for Permit Writers:
Facilities Storing Hazardous Waste
in Containers, 11/2/82, PB88-105
689
Model RCRA Permit for Hazardous
Waste Management Facilities,
9/15/88, EPA530-SW-90-049
Parts 264/265 - Subpart J - Tank
Systems
Demonstrate the absence or
presence of free liquids in the
stored/treated waste using EPA
Method 9095 (Paint Filter Liquid
Tests) of SW-846 (§§264/265.196).
The Paint Filter Liquid Test is a
positive or negative test.
Method 9095 of Test Methods for
Evaluating Solid Waste,
Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and III A.
SW-846. (USEPA 1986a)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 264/265 - Subpart M - Land
Treatment
oo
CO
To demonstrate adequate treatment All requirements are facility-specific
(treatment demonstration), the
permittee must perform testing,
analytical, design, and operating
requirements. (§264.272)
Demonstration that food-chain
crops can be grown on a treatment
unit can include sample collection
with criteria for sample selection,
sample size, analytical methods,
and statistical procedures.
(§264/265.276)
Owner/operator must collect pore-
water samples and determine if
there has been a statistically
significant change over background
using procedures specified in the
permit. (§264/265.278)
During post-closure period, owner
may conduct pore-water and soil
sampling to determine if there has
been a statistically significant
change in the concentration of
hazardous constituents.
(§264/265.280)
and are set forth in the facility
permit.
See Chapters Twelve in Test
Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, IIA, IIB, III, and III A.
SW-846. (USEPA 1986a)
Guidance Manual on Hazardous
Waste Land Treatment
Closure/Postclosure (40 CFR Part
265), 4/14/87, PB87-183 695
Hazardous Waste Land Treatment,
4/15/83, SW-874
Permit Applicants' Guidance
Manual for Hazardous Waste Land
Treatment, Storage, and Disposal
Facilities; Final Draft, 5/15/84,
EPA530-SW-84-004
Permit Guidance Manual on
Hazardous Waste Land Treatment
Demonstrations, 7/15/86, EPA530-
SW-86-032
Permit Guidance Manual on
Unsaturated Zone Monitoring for
Hazardous Waste Land Treatment
Units, 10/15/86, EPA530-SW-86-
040
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 264 - Subpart O - Incinerators
There are waste analysis
requirements to verify that waste
fed to the incinerator is within
physical and chemical composition
limits specified in the permit.
(§§264/265.341)
The owner/operator must conduct
sampling and analysis of the waste
and exhaust emissions to verify that
the operating requirements
established in the permit achieve
the performance standards of
§264.343 (§§264/265.347)
All requirements are facility-specific
and are set forth in the facility
permit.
See Chapter Thirteen in Test
Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, IIA, IIB, III, and III A.
SW-846. (USEPA 1986a)
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Corrective Action for Solid Waste
Management Units
oo
en
EPA includes corrective action in
permits through the following
statutory citations:
Section 3008(h) - provides authority
to require corrective action at
interim status facilities
Section 3004(u) - requires
corrective action be addressed as a
condition of a facility's Part B permit
Section 3004(v) - provides authority
to require corrective action for
releases migrating beyond the
facility boundary
Section 3005(c)(3) - provides
authority to include additional
requirements in a facility's permit,
including corrective action
requirements
Section 7003 - gives EPA authority
to take action when contamination
presents an imminent hazard to
human health or the environment
Often the first activity in the
corrective action process is the
RCRA facility Assessment (RFA),
which identifies potential and actual
releases from solid waste
management units (SWMUs) and
make preliminary determinations
about releases, the need for
corrective action, and interim
measures. Another activity in the
corrective action process is the
RCRA Facility Investigation (RFI),
which takes place when a release
has been identified and further
investigation is necessary. The
purpose of the RFI is to gather
enough data to fully characterize
the nature, extent, and rate of
migration of contaminants to
determine the appropriate response
action. Once the implementing
agency has selected a remedy, the
facility enters the Corrective
Measures Implementation (CMI)
phase, in which the owner and
operator of the facility implements
the chosen remedy. Corrective
action may include various
sampling and monitoring
requirements.
There is a substantial body of
guidance and publications related to
RCRA corrective action. See the
following link for further information:
http://www.epa.gov/epaoswer/hazw
aste/ca/resou rce. htm
§264.552 - Corrective Action
Management Units
There are ground-water monitoring, All requirements are case-by-case
closure, and post-closure and are determined in the facility
requirements for CAMUs. permit.
There are numerous guidance 3
documents available. Seethe CD
following link for further information: §_
http://www.epa.qov/epaoswer/hazw X"
aste/ca/resou rce. htm 03
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40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Parts 264/265 - Subpart AA - Air
Emission Standards
The following types of units are
subject to the Subpart AA process
vent standards:
Units subject to the permitting
standards of Part 270 (i.e.,
permitted or interim status)
Recycling units located at
hazardous waste management
facilities otherwise subject to
the permitting standards of Part
270 (i.e., independent of the
recycling unit, the facility has a
RCRA permit or is in interim
status)
Less than 90-day large quantity
generator units.
Testing and statistical methods are
specified in the regulations at
§264.1034(b).
The primary source of guidance is
the regulations.
See also the final rulemakings that
promulgated the regulations:
June 21,1990 (55 FR 25494)
November 25, 1996 (62 FR 52641)
June 13, 1997 (62 FR 32462)
Parts 264/265 - Subpart BB - Air
Emission Standards
The following types of units are
subject to the Subpart BB
equipment leak standards:
• Units subject to the permitting
standards of Part 270 (i.e.,
permitted or interim status)
• Recycling units located at
hazardous waste management
facilities otherwise subject to the
permitting standards of Part 270
(i.e., independent of the recycling
unit, the facility already has a
RCRA permit or is in interim
status)
• Less than 90-day large quantity
generator units
The standards specify the type and
frequency of all inspection and
monitoring activities required.
These requirements vary depending
on the piece of equipment at the
facility. Testing and statistical
methods are specified in the
regulations at §264.1063(c).
The primary source of guidance is
the regulations.
See also the final rulemakings that
promulgated the regulations:
June 21,1990 (55 FR 25494)
June 13, 1997 (62 FR 32462)
-------
Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
§266.112 - Regulation of Residues
A residue from the burning or
processing of hazardous waste may
be exempt from hazardous waste
determination if the waste derived
residue is either: substantially
similar to normal residue or below
specific health based levels for both
metal and nonmetal constituents.
Concentrations must be determined
based on analysis of one or more
samples obtained over a 24-hour
period. Multiple samples may be
analyzed and composite samples
may be used provided the sampling
period does not exceed 24 hours. If
more than one sample is analyzed
to represent the 24-hour period, the
concentration shall be the arithmetic
mean of the concentrations in the
samples.
The regulations under §266.112
have specific sampling and analysis
requirements
Part 266, Appendix IX
oo
Part 270 - Subpart B - Permit
Application, Hazardous Waste
Permitting
Provides the corresponding permit
requirement to the general
requirements (including the
requirement for a waste analysis
plan) under §270.14. There are
also unit-specific waste analysis,
monitoring, and sampling
requirements incinerators (§270.19)
and boilers and industrial furnaces
(§270.22). There are also specific
requirements for dioxin listings
handled in waste piles (§270.18)
and landfills (§270.21).
The permittee must conduct
appropriate sampling procedures,
and retain results of all monitoring.
All requirements are facility specific
and are set forth in the permit and
waste analysis plan.
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
Part 270 - Subpart C - Conditions
Applicable to All Permits
Under §270.30, there are specific
requirements for monitoring and
recordkeeping. Section270.31
requires monitoring to be detailed in
the permit.
The permittee must conduct
appropriate sampling procedures,
and retain results of all monitoring.
All requirements are facility specific
and are set forth in the permit and
waste analysis plan.
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, IIA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
CD
3
Q.
X'
DO
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Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
1
3
Q.
X'
DO
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 270 - Subpart F - Special
Forms of Permits
Specifies sampling and monitoring
requirements based on trial burns
for incinerators (§270.62) and Boiler
and Industrial Furnaces (§270.66).
Waste analysis and sampling
requirements are site specific and
set forth in each facility's waste
analysis plan required under
264.13.
Test Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
Waste Analysis at Facilities That
Generate, Treat, Store, and
Dispose of Hazardous Wastes, a
Guidance Manual, EPA530-R-94-
024 (USEPA 1994a)
Part 273 - Universal Wastes
oo
00
Handlers and transporters of
universal wastes must determine if
any material resulting from a
release is a hazardous waste.
(§273.17(b) for small quantity
handlers, §273.37(b) for large
quantity handlers, and §273.54 for
transporters of universal wastes)
Also, if certain universal wastes are
dismantled, such as batteries or
thermostats, in certain cases the
resulting materials must be
characterized for hazardous waste
purposes. (§§273.13(a)(3) and
Sampling and analysis
requirements are identical to
hazardous waste identification
requirements.
Tesf Methods for Evaluating Solid
Waste, Physical/Chemical Methods,
Updates I, II, HA, IIB, III, and IIIA.
SW-846. (USEPA 1986a)
Universal Waste Final Rule, 60 FR
25492; May 11, 1995
Final rule adding Flourescent
Lamps, 64 FR 36465; July 6, 1999
-------
Table B-1. Summary of Waste Analysis Drivers for Major RCRA Regulatory Program Areas
40 CFR Citation and Description Applicable Standards
Requirements for Demonstrating Relevant USEPA Guidance
Attainment of or Compliance
With the Standards
Waste Analysis Drivers in Other RCRA Regulations (continued)
Part 279 - Standards for the
Management of Used Oil
Specifies sampling and analysis
procedures for owners or operators
of used-oil processing and re-
refining facilities.
Under §279.55, owners or
operators of used oil processing
and re-refining facilities must
develop and follow a written
analysis plan describing the
procedures that will be used to
comply with the analysis
requirements of §279.53 and/or
§279.72. The plan must be kept at
the facility.
Sampling: Part 261, Appendix I
Hazardous Waste Management
System; Identification and Listing of
Hazardous Waste; Recycled Used
Oil Management Standards, 57 FR
41566, September 10, 1992
Part 279 Requirements: Used Oil
Management Standards,
EPA530-H-98-001
oo
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190
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APPENDIX C
STRATEGIES FOR SAMPLING HETEROGENEOUS WASTES
C.1 Introduction
"Heterogeneous wastes" include structures, demolition debris, waste-construction materials,
containers (e.g., drums, tanks, and paint cans), solid waste from laboratories and manufacturing
processes, and post-consumer wastes (e.g., electronics components, battery casings, and
shredded automobiles) (USEPA and USDOE 1992). Heterogeneous wastes can pose
challenges in the development and implementation of a sampling program due to the physical
variety in size, shape, and composition of the material and the lack of tools and approaches for
sampling heterogeneous waste. The application of conventional sampling approaches to
heterogeneous waste is difficult and may not provide a representative sample.
To develop a sampling strategy for heterogeneous waste, it is first important to understand the
scale, type, and magnitude of the heterogeneity. This appendix provides an overview of large-
scale heterogeneity and provides some strategies that can be used to obtain samples of
heterogeneous wastes. See also Section 6.2.1 for a description of other types of heterogeneity
including short range (small-scale) heterogeneity (which includes distribution and constitution
heterogeneity).
Additional guidance on sampling heterogeneous waste can be found in the following
documents:
Characterizing Heterogeneous Wastes: Methods and Recommendations
(USEPA and USDOE 1992)
Standard Guide for Sampling Strategies for Heterogeneous Waste (ASTM D
5956-96)
Pierre Gy's Sampling Theory and Sampling Practice: Heterogeneity, Sampling
Correctness, and Statistical Process Control. 2nd ed. (Chapter 21) (Pitard 1993),
and
Geostatistical Error Management: Quantifying Uncertainty for Environmental
Sampling and Mapping (Myers 1997).
C.2 Types of Large-Scale Heterogeneity
The notion of heterogeneity is related to the scale of observation. An example given by Pitard
(1993) and Myers (1997) is that of a pile of sand. From a distance of a few feet, a pile of sand
appears to be uniform and homogeneous; however, at close range under magnification a pile of
sand is heterogeneous. Substantial differences are found between the individual grains in their
sizes, shapes, colors, densities, hardness, mineral composition, etc. For some materials, the
differences between individual grains or items are not measurable or are not significant relative
to the project objectives. In such a case, the degree of heterogeneity is so minor that for
practical purposes the material can be considered homogeneous. The Standard Guide for
Sampling Strategies for Heterogeneous Waste (ASTM D 5956-96) refers to this condition as
191
-------
Appendix C
"practical homogeneity," but recognizes that true homogeneity does not exist.
At a larger scale, such as an entire waste site, long-range (or large-scale) nonrandom
heterogeneity is of interest. Large-scale heterogeneity reflects local trends and plays an
important role in deciding whether to use a geostatistical appraisal to identify spatial patterns at
the site, to use stratified sampling design to estimate a parameter (such as the overall mean), or
to define the boundaries of the sampling problem so that it comprises two or more decision units
that are each internally relatively homogeneous.
Items, particles, or phases within a waste or site can be distributed in various ways to create
distinctly different types of heterogeneity. These types of heterogeneity include:
Random heterogeneity - occurs when dissimilar items are randomly distributed
throughout the population.
Non-random heterogeneity - occurs when dissimilar items are nonrandomly
distributed, resulting in the generation of strata. The term strata refers to
subgroups of a population separated in space, in time, or by component from the
remainder of the population. Strata are internally consistent with respect to a
target constituent or a property of interest and are different from adjacent
portions of the population.
The differences between items or particles that result in heterogeneity are due to differences in
their composition or properties. One of these properties - particle size - deserves special
consideration because significant differences in particle size are common and can complicate
sampling due to the fundamental error. Fundamental error can be reduced only through
particle-size reduction or the collection of sufficiently large samples. (Section 6 describes the
impacts that fundamental error and particle size can have on sampling error.)
Figure C-1 depicts populations exhibiting the three types of heterogeneity described in ASTM D
5956-96 Standard Guide for Sampling Strategies for Heterogeneous Waste: (1) homogeneous,
(2) randomly heterogeneous, (3) and nonrandomly heterogeneous populations. The drum-like
populations portray different types of spatial distributions while the populations being discharged
through the pipes represent different types of temporal distributions.
In the first scenario, very little spatial or temporal variation is found between the identical
particles of the "homogeneous" population; however, in the second scenario, spatial and
temporal variations are present due to the difference between the composition of the particles or
items that make up the waste. ASTM D 5956-96 refers to this as a "randomly heterogeneous"
population. In the third scenario, the overall composition of the particles or items remain the
same as in the second scenario, but the two different components have segregated into distinct
strata (e.g., due to gravity), with each strata being internally homogeneous. ASTM D 5956-96
refers to waste with this characteristic as "non-randomly heterogeneous."
C.3 Magnitude of Heterogeneity
The magnitude of heterogeneity is the degree to which there are differences in the characteristic
of interest between fragments, particles, or volumes within the population. The magnitude of
heterogeneity can range from that of a population whose items are so similar that it is practically
192
-------
Appendix C
POPULATION
Differences in
Spatial/Temporal
Distribution?
Flow E=>
/OOOOOOUCCCLV ,
Icccccccroxr Q_
...
Heterogeneity
ype
Homogeneous
WASTE DISCHARGE
Row
O
YES
Randomly
Heterogeneous
WASTE DISCHARGE
Flow
WASTE DISCHARGE
YES
Non-Randomly
Heterogeneous
Figure C-1. Different types of spatial and temporal heterogeneity.
homogeneous to a population whose items are all dissimilar. Statistical measures of dispersion,
the variance and standard deviation, are useful indicators of the degree of heterogeneity within
a waste or waste site (assuming sampling error is not a significant contributor to the variance -
an optimistic assumption).
If the waste exhibits nonrandom heterogeneity and a high magnitude of heterogeneity, then
consider segregating (e.g., at the point of generation) and managing the waste as two or more
separate decision units (if physically possible and allowed by regulations). This approach will
require prior knowledge (for example, from a pilot study) of the portions of the waste that fall into
each specified category (such as hazardous debris and nonhazardous debris).
C.4 Sampling Designs for Heterogeneous Wastes
The choice of a sampling design to characterize heterogeneous waste will depend upon the
regulatory objective of the study (e.g., waste identification or classification, site characterization,
etc.), the data quality objectives, the type and magnitude of the heterogeneity, and practical
considerations such as access to all portions of the waste, safety, and the availability of
equipment suitable for obtaining and preparing samples.
As described in Section 5 of this document, there are two general categories of sampling
designs: probability sampling design and authoritative (nonprobability) sampling designs.
Probability sampling refers to sampling designs in which all parts of the waste or media under
study have a known probability of being included in the sample. This assumption may be
difficult to support when sampling highly heterogeneous materials such as construction debris.
193
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Appendix C
All parts of a highly heterogeneous waste may not be accessible by conventional sampling
tools, limiting the ability to introduce some form of randomness into the sampling design.
Random Heterogeneous Waste: For random heterogeneous waste, a probability
sampling design such as simple random or systematic sampling can be used. At least
one of two sample collection strategies, however, also should be used to improve the
precision (reproducibility) of the sampling design: (1) take very large individual samples
(to increase the sample support), or (2) take many increments to form each individual
sample (i.e., use composite sampling). The concept of sample support is described in
Section 6.2.3. Composite sampling is discussed in Section 5.3.
Non-Random Heterogeneous Waste: For non-random heterogeneous wastes, one of
two strategies can be used to improve sampling: (1) If the objective is to estimate an
overall population parameter (such as the mean), then stratified random sampling could
be used. Stratified random sampling is discussed in detail in Section 5.2.2. (2) If the
objective is to characterize each stratum separately (e.g., to classify the stratum as
either a hazardous waste or a nonhazardous waste), then an appropriate approach is to
separate or divert each stratum at its point of generation into discrete, nonoverlapping
decision units and characterize and manage each decision unit separately (i.e., to avoid
mixing or managing hazardous waste with nonhazardous waste).
If some form of stratified sampling is used, then one of three types of stratification must be
considered. There are three types of stratification that can be used in sampling:
stratification by space
stratification by time
stratification by component.
The choice of the type of stratification will depend on the type and magnitude of heterogeneity
present in the population under consideration.
Figure C-2 depicts these different types of strata which are often generated by different
processes or a significant variant of the same process. The different origins of the strata usually
result in a different concentration or property distribution and different mean concentrations or
average properties. While stratification over time or space is widely understood, stratification by
component is less commonly employed. Some populations lack obvious spatial or temporal
stratification yet display high levels of heterogeneity. If these populations contain easily
identifiable components, such as bricks, gloves, pieces of wood or concrete, then it may be
advantageous to consider the population as consisting of a number of component strata. An
advantage of component stratification is that it can simplify the sampling and analytical process
and allow a mechanism for making inferences to a highly stratified population. Component
stratification shares many similarities with the gender or age stratification applied to
demographic data by pollsters (i.e., the members of a given age bracket belonging to the same
stratum regardless of where they reside). Component stratification is used by the mining
industry to assay gold ore and other commodities where the analyte of interest is found in
194
-------
Appendix C
COMPONENT
SPACE
TIME
10,000
14,000
11,000
20ppm
COMPONENT
( SOURCE)
5000 ppm
Figure C-2. Three different types of strata (from ASTM 5956-96)
discrete particles relative to a much greater mass of other materials.
Component stratification, although not commonly employed, is applicable to many waste
streams, including the more difficult-to-characterize waste streams such as building debris.
Additional guidance on stratification by component can be found in ASTM D 5956-96.
Table C-1 offers practical examples when wastes considered "non-randomly heterogeneous"
might be good candidates for stratification across space, time, or by component.
The stratification approach can result in a more precise estimate of the mean compared to
simple random sampling. However, keep in mind that greater precision is likely to be realized
only if a waste exhibits substantial nonrandom chemical heterogeneity and stratification
efficiently "divides" the waste into strata that exhibit maximum between-strata variability and
minimum within-strata variability. If that does not occur, stratified random sampling can produce
results that are less precise than in the case of simple random sampling; therefore, it is
reasonable to employ stratified sampling only if the distribution of chemical contaminants in a
waste is sufficiently known to allow an intelligent identification of the strata and at least two or
three samples can be collected in each stratum.
Note that failure to recognize separate strata might lead one to conclude incorrectly, via a
statistical test, that the underlying population is lognormal or some other right-skewed
distribution.
195
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Appendix C
Table C-1. Examples of Three Types of Stratification
Type of Stratification
Stratification Across Space
Stratification Across Time
Stratification by Component
Example Scenario
A risk-based cleanup action requires a site owner to remove the top two feet of
soil from a site. Prior to excavation, the waste hauler wants to know the average
concentration of the constituent of concern in the soil to be removed. The top
six inches of soil are known to be more highly contaminated than the remaining
18-inches of soil. Sampling of the soil might be carried out more efficiently by
stratifying the soil into two subpopulations - the upper six-inch portion and the
lower 18-inch portion.
A waste discharge from a pipe varies across time. If the objective is to estimate
the overall mean, then an appropriate sampling design might include
stratification across time.
Construction debris covered with lead-based paint in the same structure with
materials such as glass and unpainted wood could be sampled by components
(lead-based paint debris, glass, and unpainted wood). This strategy is known as
"stratification by component" (from ASTM D 5956-96).
C.5 Sampling Techniques for Heterogeneous Waste
Due to practical constraints, conventional sampling approaches may not be suitable for use in
sampling of heterogeneous wastes. For example, sampling of contaminated debris can pose
significant challenges due to the high degree of heterogeneity encountered. Methods used to
sample contaminated structures and debris have included the following:
Coring and cutting pieces of debris followed by crushing and grinding of the
matrix (either in the field or within the laboratory) so the laboratory can handle the
sample in a manner similar to a soil sample (Koski, et al 1991)
Drilling of the matrix (e.g., with a hand held drill) followed by collection of the
cuttings for analysis. This technique may require 20 to 50 drill sites in a local
area to obtain a sufficient volume for an individual field sample (Koski, et al 1991)
Grinding an entire structure via a tub grinder followed by conventional sampling
approaches (AFCEE 1995).
ASTM has published a guide for sampling debris for lead-based paint (LBP) in ASTM E1908-97
Standard Guide for Sample Selection of Debris Waste from a Building Renovation or Lead
Abatement Project for Toxicity Characteristic Leaching Procedure (TCLP) testing for Leachable
Lead (Pb).
Additional methods are described in Chapter Five, "Sample Acquisition," of Characterizing
Heterogeneous Wastes: Methods and Recommendations (USEPA and USDOE 1992) and in
Rupp(1990).
196
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APPENDIX D
A QUANTITATIVE APPROACH FOR CONTROLLING FUNDAMENTAL ERROR
This appendix provides a basic approach for determining the particle-size sample-weight
relationship sufficient to achieve the fundamental error level specified in the DQOs. The
procedure is based on that described by Pitard (1989, 1993), Gy (1998), and others; however, a
number of simplifying assumptions have been made for ease of use. The procedure described
in this appendix is applicable to sampling of granular solid media (including soil) to be analyzed
for nonvolatile constituents. It is not applicable to liquids, oily wastes, or debris.
The mathematical derivation of the equation for the fundamental error is complex and beyond
the scope of this guidance. Readers interested in the full documentation of the theory and
underlying mathematics are encouraged to review Gy (1982) and Pitard (1993). Several
authors have developed example calculations for the variance of the fundamental sampling
error for a "typical" contaminated soil to demonstrate its practical application.1 Examples found
in Mason (1992), and Myers (1997) may be particularly useful.
The equation for the variance of the fundamental error is extremely practical for optimization of
sampling protocols (Pitard 1993). In a relatively simple "rule of thumb" form, the equation for the
variance of the fundamental error (s2FE) is estimated by
Equation D.1
MsvaLC J
where
f = a dimensionless "shape" factor for the shape of particles in the material to be
sampled where cubic = 1.0, sphere = 0.523, flakes = 0.1, and needles = 1 to
10
/I = average density (gm/cm3) of the material
Ms = the sample weight (or mass of sample) in grams
aLC = proportion of the sample with a particle size less than or equal to the particle
size of interest
d = diameter of the largest fragment (or particle) in the waste, in centimeters.
Pitard's methodology suggests the particle size of interest should be set at 95 percent of the
largest particle in the population (or "lot"), such that aLC = 0.05. Equation D.1 then reduces to
2
2 1 o ,3
SFE = - 1 o<2 Equation D.2
M
1 It is important to note that discussion of the "variance of the fundamental error" refers to the relative
variance, which is the ratio of the sample variance over square of the sample mean ( s /x ). The relative variance
is useful for comparing results from different experiments.
197
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Appendix D
The equation demonstrates that the variance of the fundamental error is directly proportional to
the size of the largest particle and inversely proportional to the mass of the sample. To
calculate the appropriate mass of the sample, Equation D.2 easily can be rearranged as
-CO
Ma = - - 1 8d3 Equation D.3
Pitard (1989, 1993) proposed a "Quick Safety Rule" for use in environmental sampling using the
following input assumptions for Equation D.3:
f = 0.5 (dimensionless shape factor for a sphere)
/I = 2.7 (density of a waste in gm/cm3)
SFE = ±5% (standard deviation of the fundamental error).
By putting these assumed factors into Equation D.3, we get:
0.5x2.7 -
Ms = 2 1 Sd3 Equation D.4
Pitard (1993) rounds up, to yield the relationship
Ms > WOOOd3 Equation D.5
Alternatively, if we are willing to accept SFE = ±16% , Equation D.4 yields
Ms > WOOd3 Equation D.6
s
Equation D.4 was used to develop Table D-1 showing the maximum particle size that is allowed
for a given sample mass with the standard deviation of the fundamental error ( SFE )
prespecified at various levels (e.g., 5%, 10%, 16%, 20%, and 50%). A table such as this one
can be used to estimate the optimal weight of field samples and the optimal weight of
subsamples prepared within the laboratory. An alternative graphical method is presented by
Pitard (1993) and Myers (1997).
An important feature of the fundamental error is that it does not "cancel out." On the contrary,
the variance of the fundamental error adds together at each stage of subsampling. As pointed
out by Myers (1997), the fundamental error can quickly accumulate and exceed 50%, 100%,
200%, or greater unless it is controlled through particle-size reduction. The variance of the
fundamental error, s2FE , calculated at each stage of subsampling and particle-size reduction
must be added together at the end to derive the total SFE .
198
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Appendix D
Table D-1. Maximum Allowable Particle Size (cm) for a Given Sample Mass
for Selected Standard Deviations of the Fundamental Error
Sample Mass (g)
0.1
1
2
3
4
5
10
20
30
40
50
75
100
500
1000
5000
Maximum Allowable Particle Size d (cm)
SFE = 5%
0.02
0.05
0.06
0.07
0.07
0.08
0.10
0.13
0.15
0.16
0.17
0.20
0.22
0.37
0.47
0.80
SFE = 10%
0.03
0.07
0.09
0.11
0.12
0.13
0.16
0.20
0.23
0.25
0.27
0.31
0.35
0.59
0.74
1.27
SFE = 16%*
0.05
0.10
0.13
0.15
0.16
0.17
0.22
0.28
0.32
0.35
0.37
0.43
0.47
0.81
1.02
1.74
SFE = 20%
0.05
0.12
0.15
0.17
0.19
0.20
0.25
0.32
0.37
0.40
0.43
0.50
0.55
0.94
1.18
2.02
SFE = 50%
0.10
0.22
0.27
0.31
0.35
0.37
0.47
0.59
0.68
0.74
0.80
0.92
1.01
1.73
2.17
3.72
*A maximum standard deviation of the fundamental error of 16% has been recommended by Pitard (1993) and is
included in this table as a point of reference only. Project-specific fundamental error rates should be set in the DQO
Process.
Two important assumptions underlie the use of Table D-1:
1. The table is valid only if each and all steps of the sampling and subsampling
minimize other sampling error through use of careful and correct sampling
procedures
2. The table is valid only for wastes or soils with a shape factor (f) and density (/I)
similar to that used to derive the table; otherwise, waste-specific tables or
graphical methods (see Pitard 1993, Mason 1992, or Myers 1997) should be
used.
Hypothetical Example
Suppose we have a waste that is a particulate solid to be analyzed for total metals. The
laboratory requires an analytical sample of only 1 gram. The DQO planning team wants to
maintain the total standard deviation of the fundamental error ( SFE ) within ±16% . The sample
masses are determined at each stage of sampling and subsampling as follows:
Primary Stage:
Based on prior inspection of the waste, it is known that 95 percent of the
particles are 0.47 cm in diameter or less. Using Table D-1, we determine
that a field sample of 1,000 grams (or 1 Kg) will generate a fundamental
error SFF not greater than ±5% .
199
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Appendix D
Secondary Stage:
Final Stage:
After shipment of the 1,000-gram sample to the laboratory, particle-size
reduction to about 0.23 cm (2.36 mm or a No. 8 sieve) is performed, and a
30-gram subsample is taken. This step generates a fundamental error
SFE of ±10% .
A 1-gram subsample is required for the analysis. Particle-size reduction to
0.07 cm or less (e.g., a No. 30 sieve) is performed, and a 1-g subsample is
taken. This step generates a fundamental error SFF of ±10% .
The variance ( SFE ) from each stage is then summed to determine the total variance of the
fundamental error. As shown in Table D-2, the total standard deviation of the fundamental error
is less than ±16 percent and the DQO is achieved.
Table D-2. Example Calculation of the Total Variance of the Fundamental Error
Sampling and
Subsampling
Stage
Primary Stage
Secondary Stage
Final Stage
Mass (grams)
1000
30
1
d (cm)
0.47
0.23
0.07
SFE
.05
.10
.10
2
Sum of the variances of the fundamental errors ( SFE )
Total standard deviation of the fundamental error ( SFE ) (DQO = 16%)
SFE
.0025
.01
.01
SFE =0.0225
SF£ =0.1 5 or 15%
One final word of caution is provided regarding the use of the particle-size reduction and
subsampling routine outlined above. The approach can reduce bias and improve precision of
analyses for total constituent analyses, but the particle-size reduction steps may actually
introduce bias when used in conjunction with some leaching tests. For example, the TCLP
specifies a minimum sample mass of 100 grams (for nonvolatile extractions) and maximum
particle size of 9.5 mm. While this combination would generate a SFE of almost ±50 percent,
excessive particle-size reduction below 9.5 mm to lower SFE would increase the leachability of
the material during the test due to the increased surface area-to-volume ratio of smaller
particles. Therefore, it is important to remember that particle-size reduction to control
fundamental error is beneficial when total constituent analyses are performed, but may
introduce bias for some leaching tests. Furthermore, particle-size reduction below 9.5 mm is
not required by Method 1311 (TCLP) (except during Step 7.1.4, "Determination of Appropriate
Extraction Fluid").
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APPENDIX E
SAMPLING DEVICES
The key features of recommended sampling devices are summarized in this appendix. For
each sampling device, information is provided in a uniform format that includes a brief
description of the device and its use, advantages and limitations of the device, and a figure to
indicate the general design of the device. Each summary also identifies sources of other
guidance on each device, particularly any relevant ASTM standards.
Much of the information in this appendix was drawn from ASTM standards (see also Appendix J
for summaries of ASTM standards). In particular, much of the information came from ASTM D
6232, Standard Guide for Selection of Sampling Equipment for Waste and Contaminated Media
Data Collection Activities.
Internet Resource
Information on sampling devices can be found on the
Internet at the Federal Remediation Technologies
Roundtable site at http://www.frtr.gov/. The Field
Sampling and Analysis Technologies Matrix and
accompanying Reference Guide are intended as an initial
screening tool to provide users with an introduction to
innovative site characterization technologies and to
promote the use of potentially cost-effective methods for
onsite monitoring and measurement.
Devices not listed in this appendix or
described elsewhere in this chapter also
may be appropriate for use in RCRA-
related sampling. For example, other
more innovative or less common
technologies may allow you to meet your
performance goals and may be
appropriate for your sampling effort.
Therefore, we encourage and
recommend the selection and use of
sampling equipment based on a
performance-based approach. In future
revisions to this chapter, we will include new technologies, as appropriate.
This appendix is divided into subsections based on various categories of sampling technologies.
The categories are based on those listed in ASTM D 6232. The equipment categories covered
within this appendix are as follows:
E.1 Pumps and Siphons
E.2 Dredges
E.3 Discrete Depth Samplers
E.4 Push Coring Devices
E.5 Rotating Coring Devices
E.6 Liquid Profile Devices
E.7 Surface Sampling Devices
E.1 Pumps and Siphons
Pumps and siphons can be used to obtain samples of liquid wastes and ground water. For
detailed guidance on the selection and use of pumps for sampling of ground water, see RCRA
Ground-Water Monitoring: Draft Technical Guidance (USEPA 1992c).
In this section, you will find summaries for the following pumps or siphons:
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Appendix E
E.1.1
E.1.2
E.1.3
E.1.4
E.1.5
Automatic Sampler
Bladder Pump
Peristaltic Pump
Centrifugal Submersible Pump
Displacement Pumps
E.1.1 Automatic Sampler
An automatic sampler (see Figure E-1) is a type of pumping
device used to periodically collect samples. It is recommended
for sampling surface water and point discharges. It can be
used in waste-water collection systems and treatment plants
and in stream sampling investigations. An automatic sampler
designed for collection of samples for volatile organic analyses
is available.
An automatic sampler typically uses peristaltic pumps as the
sampling mechanism. It can be programmed to obtain
samples at specified intervals or to obtain a continuous
sample. It also can be programmed to collect time composite
or flow proportional samples.
Advantages
Can provide either grab sample or composite
samples overtime.
Figure E-1. Automatic sampler
Operates unattended, and it can be programmed to sample variable volumes at
variable times.
Limitations
Requires power to operate (either AC or battery power).
May be difficult to decontaminate.
May not operate correctly when sampling liquid streams containing a high
percentage of solids.
Highly contaminated water or waste can degrade sampler components.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232.
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Appendix E
Gas Inlet/Outlet
Discharge Line
Check Valve
- Gas Inlet/Outlet
— Discharge Line
Check Valve
Bladder
End Plug
Flexible
Bladder
E.1.2 Bladder Pump
The bladder pump is recommended for the
sampling of surface water, ground water, and
point discharges. It also can be used to
sample other liquids in surface impoundments.
A bladder pump consists of a flexible
membrane (bladder) enclosed by a rigid
sample container and can be constructed of a
variety of materials, such as neoprene, rubber,
stainless steel, nitrile, etc. There are two types
of bladder pumps - the squeeze type and the
expanding type (see Figure E-2). The squeeze
type has the bladder connected to the sample
discharge line. The chamber between the
bladder and the sampler body is connected to
the gas line. The expanding type has the
bladder connected to the gas line. In this type
of bladder pump, the chamber between the
bladder and the sampler body is connected to the sample discharge line.
During sampling, water enters the sampler through a check valve at the bottom of the device.
Compressed air or gas is then injected into the sampler. This causes the bladder to expand or
compress depending on the type of bladder pump. The inlet valve closes and the contents of
the sampler are forced through the top check valve into the discharge line. The top check valve
prevents water from re-entering the sampler. By removing the pressure, the process is
repeated to collect more sample. Automated sampling systems have been developed to control
the time between pressurization cycles.
_ Bladder
End Plug
Inlet Check
Valve
SQUEEZE TYPE
EXPANDING TYPE
Figure E-2. Bladder pump
Advantages
Limitations
Is suitable for sampling liquids containing volatile compounds.
Can collect samples up to a depth of 60 m (200 ft.) (ASTM D 6232).
Operation requires large volumes of compressed air or gas and a controller.
Requires a power source.
Can be heavy and difficult to operate.
Decontamination can be difficult.
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in
CM
Appendix E
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling Groundwater Monitoring Wells, ASJM D 4448
E.1.3 Peristaltic Pump
A peristaltic pump (Figure E-3) is a suction lift
pump used at the surface to collect liquid from
ground-water monitoring wells or surface
impoundments. It can be used for sampling
surface water, ground water, point discharges,
impounded liquids, and multi-layer liquid wastes.
A peristaltic pump consists of a rotor with ball
bearing rollers and it has a piece of flexible tubing
threaded around the pump rotor and connected to
two pieces of polytetrafluroethylene (PTFE) or
other suitable tubing. One end of the tubing is
placed in the sample. The other end is connected
to a sample container. Silicone tubing is
commonly used within the pumphead; however,
for organic sampling purposes, medical grade
silicone is recommended to avoid contamination of
the sample (ASTM D 4448). Fluorocarbon resin
tubing is also sometimes used for high hazard
materials and for samples to be analyzed for
organics (ASTM D 6063). The device can be modified to avoid contact of the sample with the
flexible tubing. In such a case, the pump is connected to a clean glass container using a PTFE
insert. A second PTFE tubing is used to connect the glass container to the sample source.
During operation, the rotor squeezes the flexible tubing, causing a vacuum to be applied to the
inlet tubing. The sample material is drawn up the inlet tubing and discharged through the outlet
end of the flexible tubing. In the modified peristaltic pump, the sample is emptied into the glass
container without coming in contact with the flexible tubing. To sample liquids from drums, the
peristaltic pump is first used to mix the sample. Both ends of the tubing are placed in the
sample and the pump is turned on. Once the drum contents are mixed, the sample is collected
as described above. To collect samples for organic volatile analyses, the PTFE tubing attached
to the intake end of the pump is filled with the sample and then disconnected from the pump.
The tube is then drained into the sample vials.
Figure E-3. Peristaltic pump
Advantages
Can collect samples from multiple depths and small diameter wells.
Easy to use and readily available.
204
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Appendix E
The pump itself does not need to be decontaminated. The tubing can be either
decontaminated or replaced.
Limitations
The drawing of a vacuum to lift the sample may cause the loss of volatile
contaminants.
Sampling depth cannot exceed about 7.6 m (25 ft.) (ASTM D 6232).
Requires a power source.
Flexible tubing may be incompatible with certain matrices.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling of Drums and Similar Containers by Field
Personnel, ASTM D 6063
Standard Guide for Sampling Groundwater Monitoring Wells, ASTM D 4448
E.1.4 Centrifugal Submersible Pump
Discharge hose
PTFE lined
polyethylene
The centrifugal submersible pump (Figure
E-4) is a type of pump used for purging and
sampling monitoring wells, sampling of
waste water from impoundments, and
sampling point discharges.
A centrifugal submersible pump uses a set
of impellers, powered by an electric motor,
to draw water up and through a discharge
hose. Parts in contact with liquid may be
made of PTFE and stainless steel. The
pump discharge hose can be made of
PTFE or other suitable material. The motor
cavity is filled with either air or deionized or
distilled water that may be replaced when
necessary. Flow rates for centrifugal
submersible pumps range from 100 ml_ per minute to 9 gallons per minute (ASTM D 6232).
During operation, water is drawn into the pump by a slight suction created by the rotation of the
impellers. The impellers work against fixed stator plates and pressurize the water which is
driven to the surface through the discharge hose. The speed at which the impellers are driven
controls the pressure and, thus, the flow rate.
Pump inlet
and impellers
Sealed motor
or water filled)
Figure E-4. Centrifugal submersible pump
205
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Limitations
Appendix E
Advantages
Can be constructed of materials (PTFE and stainless steel) that are chemically
resistant.
Can be used to pump liquids up to a 76 m (250 ft) head (ASTM D 6232).
Flow rate is adjustable.
May be incompatible with liquids containing a high percentage of solids.
May not be appropriate for collection of samples for volatile organics analysis.
Loss of volatiles can occur as a result of motor heating and sample
pressurization.
Requires an electric power source; e.g., either a 12 v (DC) or a 110/220 v (AC)
converter (ASTM D 6232).
May require a winch or reel system for portable use.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
E.1.5 Displacement Pumps
The displacement pump (Figure E-5) is a
type of pump used for the sampling of
surface water, ground water, point
discharges and other liquids (e.g., in
impoundments).
A displacement pump forces a discrete
column of water to the surface via a
mechanical lift. During sampling, water
enters the sampler through the check valve
at the bottom of the device. It is
commonly constructed of PVC, stainless
steel, or both. It also can be made of
PTFE to reduce the risk of contamination
when collecting samples with trace levels
of organic compounds. Two common
types of displacement pumps include the
air/gas and piston displacement pumps.
-0=-
-Gas Inlet/Outlet
Discharge Line
-Check Valve
- • Discharge Tube
Inlet Check Valve
AIR/GAS DISPLACEMENT PUMP
Actuating Rod
Rigid Discharge Pipe
One Way Flapper
'" Check Valve
- - Return Spring
Inlet Check Valve
PISTON DISPLACEMENT PUMP
Figure E-5. Displacement pump
The air/gas displacement pump uses compressed gas and it operates by applying positive
206
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Appendix E
pressure to the gas line. This causes the inlet check valve to close and the discharge line
check valve to open, forcing water up the discharge line to the surface. Removal of the gas
pressure causes the top valve to close and the bottom valve to open. Water enters the sampler
and the process is repeated.
The piston displacement pump uses an actuating rod powered from the surface or from an air or
electric actuator. The mechanically operated plunger delivers the sample to the surface at the
same time the chamber fills. It has a flap valve on the piston and an inlet check valve at the
bottom of the sampler.
Advantages
Can be constructed of PTFE to reduce the risk of contamination caused by
materials of construction when collecting samples for trace levels of organics.
Limitations
May be difficult to decontaminate.
Displacement pumps require large volumes of air or gas and a power source.
Loss of dissolved gases or sample contamination from the driving gas may occur
during sampling.
Displacement pumps may be heavy.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling Groundwater Monitoring Wells, ASTM D 4448
E.2 Dredges
Dredges include equipment that is often used to collect bottom material (e.g., sediments) from
beneath a layer of stationary or moving liquid. A variety of dredges are available including the
Ekman bottom grab sampler and the Ponar dredge. The Ponar dredge is described below.
E.2.1 Ponar Dredge
The ponar dredge is recommended for sampling sediment. It has paired jaws that penetrate the
substrate and close to retain the sample. The sample volume range is 0.5 to 3.0 liters (ASTM
D 6232).
207
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Appendix E
The Ponar dredge is lowered slowly with
controlled speed so that the dredge will
properly land and avoid blowout of the surface
layer to be sampled. The weight of the
dredge causes it to penetrate the substrate
surface. The slack in tension unlocks the
open jaws and allows the dredge to close as it
is raised. The dredge is raised slowly to
minimize disturbance and sample washout as
the dredge is retrieved through the liquid
column. The collected sample is emptied into
a suitable container. Auxiliary weight may be
added to the dredge to increase penetration.
Advantages
Figure E-6. Ponar dredge
Reusable
Limitations
Can obtain samples of most types of stationary sediments ranging from silt to
granular material
Available in a range of sizes and weights
Some models may be available in either stainless steel or brass.
Not capable of collecting undisturbed samples
May be difficult to decontaminate (depending upon the dredge's design and
characteristics of the sampled material)
Cannot collect a representative lift or repeatedly sample to the same depth and
position
Can be heavy and require a winch or portable crane to lift; however, a smaller
version, the petit Ponar, is available and can be operated by a hand-line (ASTM
D 4342).
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Collecting ofBenthic Macroinvertebrates with Ponar Grab
Sampler, ASTM D 4342
Standard Guide for Selecting Grab Sampling Devices for Collecting Benthic
Macroinvertebrates, ASTM D 4387
208
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Appendix E
"Sediment Sampling" (USEPA 1994e)
E.3 Discrete Depth Samplers
Discrete depth samplers include equipment that can collect samples at a specific depth. Such
samplers are sometimes used to collect samples from layered liquids in tanks or surface
impoundments. You will find summaries for the following discrete depth samplers
in this section:
E.3.1 Bacon Bomb
E.3.2 Kemmerer Sampler
E.3.3 Syringe Sampler
E.3.4 Lidded Sludge/Water Sampler
E.3.5 Discrete Level Sampler
Besides the samplers listed below, a self-purging, discrete depth sampler is available for
sampling ground-water monitoring wells. It fills when stopped at the desired depth and
eliminates the need for well purging. It samples directly into a 40-mL glass VOA sample vial
contained within the sampler; therefore, the loss of volatile organic compounds is minimized.
E.3.1 Bacon Bomb
A bacon bomb (Figure E-7) is a type of
discrete level sampler that provides a sample
from a specific depth in a stationary body of
water or waste. A bacon bomb is
recommended for sampling surface water and
is usually used to collect samples from a lake
or pond. It can also be used to collect liquid
waste samples from large tanks or lagoons. It
originally was designed to collect oil samples.
The sample volume range is from 0.1 to 0.5
liters (100 to 500 mL) (ASTM D 6232).
A bacon bomb has a cylindrical body
sometimes constructed of stainless steel, but
it is sometimes made of chrome-plated brass
and bronze. It is lowered into material by a primary support line and has an internal tapered
plunger that acts as a valve to admit the sample. A secondary line attached to the top of the
plunger opens and closes the plunger valve. The top cover has a locking mechanism to keep
the plunger closed after sampling. The bacon bomb remains closed until triggered to collect the
sample. Sample collection is triggered by raising the plunger line and allowing the sampler to
fill. The device is then closed by releasing the plunger line. It is returned to the surface by
raising the primary support line, and the sample is transferred directly to a container.
Figure E-7. Bacon bomb
209
-------
Appendix E
Advantages
Collects a discrete depth sample; it is not opened until the desired depth.
Easy to use, without physical requirement limitations.
Limitations
May be difficult to decontaminate due to design or construction materials.
Maximum sample capacity is only 500 ml_.
Materials of construction may not be compatible with parameters of concern.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
"Tank Sampling" (USEPA 1994c)
E.3.2 Kemmerer Sampler
A kemmerer sampler (Figure E-8) is a type of discrete level
sampler that provides a sample from a specific depth.
Recommended for sampling surface water, it is usually used to
collect samples from a lake or pond. It can also be used to
collect liquid waste samples from large tanks or lagoons. The
sample volume range is from 1 to 2 liters (ASTM D 6232).
The sampler comprises a stainless steel or brass cylinder with
rubber stoppers for the ends, but all PFTE construction also is
available. The ends are left open while being lowered in a
vertical position, allowing free passage of water or liquid through
the cylinder. When the device is at the designated depth, a
messenger is sent down a rope to close the stoppers at each
end. The cylinder is then raised and the sample is removed
through a valve to fill sample containers.
Advantages
Can collect a discrete depth sample.
Messenger
Rope/Cable
Trip Head
Upper Stopper
Chain
— Lower Stopper
Clamp
Drain Tube
Figure E-8. Kemmerer sampler
210
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Appendix E
Limitations
Provides correct delimitation and extraction of sample (Pitard 1989)
Easy to use
All PTFE construction is available.
May be difficult to decontaminate due to construction or materials.
The sampler is exposed to the medium at other depths while being lowered to a
sampling point at the desired depth.
Materials of construction may not be compatible with parameters of concern.
Other Guidance:
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
E.3.3 Syringe Sampler
CORING
TIP
•T HANDLE
LOCK NUT
INDICATOR
RING
CONTROL
ROD
A syringe sampler (Figure E-9) is a discrete depth
sampler used to sample liquids. With the optional
coring tip, it can be used as a coring device to
sample highly viscous liquids, sludges, and tar-
like substances. It is used to collect samples
from drums, tanks, and surface impoundments,
and it can also draw samples when only a small
amount remains at the bottom of a tank or drum.
The sample volume range is 0.2 to 0.5 liters
(ASTM D 6232).
A syringe sampler generally is constructed of a
piston assembly that comprises a T-handle,
safety locking nut, control rod, piston body
assembly, sampling tube assembly, and two tips
for the lower end (a closeable valve and a coring
tip). When used as a syringe, the sampler is
slowly lowered to the sampling point and the T-
handle is gradually raised to collect the sample.
Once the desired sample is obtained, the lock nut
is tightened to secure the piston rod and the
bottom valve is closed by pressing down on the sampler against the side or bottom of the
container. When used as a coring device, the sampler is slowly pushed down into the material.
Once the desired sample is obtained, the lock nut is tightened to secure the piston rod and the
sampler is removed from the media. The sample material is extruded into the sample container
by opening the bottom valve (if fitted), loosening the lock nut, and pushing the piston down.
VALVE
TIP
(OPEN)
| | VALVE
o
TIP
(CLOSED)
Figure E-9. Syringe sampler
211
-------
Appendix E
Advantages
The syringe sampler is easy to use and decontaminate.
The syringe sampler can sample at discrete depths, including the bottom of a
container.
Limitations
The syringe sampler can be used to depths of about 1.8 meters only (ASTM D
6232).
Material to be sampled must be viscous enough to remain in the device when the
coring tip is used; the valve tip is not recommended for viscous materials (ASTM
D 6063).
Other Guidance
Standard Guide for Sampling Single or Multilayered Liquids, ASTM D 5743
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling of Drums and Similar Containers by Field
Personnel, ASTM D 6063
E.3.4 Lidded Sludge/Water Sampler
A lidded sludge/water sampler (Figure E-10) is a
type of discrete depth device that provides a
sample from a specific depth. It is used to collect
sludges or waste fluids from tanks, tank trucks,
and ponds. It can sample liquids, multi-layer
liquid wastes, and mixed-phase solid/liquid
wastes. The typical sample volume is 1.0-liter
(ASTM D 6232).
A lidded sludge/water sampler comprises a
removable glass jar, sometimes fitted with a
cutter for sampling materials containing more
than 40-percent solids (ASTM D 6232), that is
mounted on a stainless steel device.
The sampler is lowered into the material to be
sampled and opened at the desired depth. The
top handle is rotated to upright the jar and open
and close the lid. Then, the device is carefully
retrieved from the material. The jar is removed
from the sampler by lifting it from the holder, and
Figure E-10. Lidded sludge/water sampler
212
-------
Appendix E
the jar serves as a sample container so there is no need to transfer the sample.
Advantages
The jar in the sampling device also serves as a sample container reducing the
risk of cross-contamination.
Bottles and lids are unique to each sample, therefore, decontamination of an
intermediate transfer container is not required.
Limitations
Heavy and limited to one bottle size
Thick sludge is difficult to sample (ASTM D 6232).
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data
Collection Activities, ASTM D
6232
E.3.5 Discrete Level Sampler
A discrete level sampler (Figure E-11) is a
dismountable cylindrical sampler fitted with a
manually-operated valve(s). It is recommended
for sampling surface water, ground water, point
discharges, liquids, and multi-layer liquids and is
used for sampling drums, tanks, containers,
wells, and surface impoundments. The typical
sample volume range is 0.2 to 0.5 liters (ASTM D
6232).
A discrete level sampler is made from PTFE and
stainless steel and is designed to be reusable. It
comprises a tube fitted with manually-operated
valve or valves, which are operated by a control
assembly attached to the upper end of the
sampler. This assembly consists of a rigid tube
and rod or a flexible tube and inner cable. The
standard level sampler has a manually operated
upper valve and a lower spring-retained bottom
dump valve. The dual valve model may be
emptied by opening the valves manually or with
a metering device attached to the lower end of
the sampler (not shown).
To control
Dump
valve
Upper
valve
PTFE
Body
STANDARD MODEL
Lower
valve
DUAL VALVE MODEL
Figure E-11. Discrete level sampler
213
-------
Appendix E
To collect a sample, the discrete level sampler is lowered into the sample material to the
desired sampling depth. The valve or valves are opened manually to collect the sample and
closed before retrieving the sampler. The standard model is emptied by pressing the dump
valve against the side of the sample container. The dual valve sampler is emptied by opening
the valves manually. Alternatively, the collected sample may be taken to the laboratory in the
sampler body by replacing the valves with solid PTFE end caps.
Advantages
Relatively easy to decontaminate and reuse
May be used to sample liquids in most environmental situations.
Can be remotely operated in hazardous environments.
Sample representativeness is not affected by liquids above the sampling point.
The sampling body can be used for sample storage and transport.
Limitations
Limited to sample chamber capacities of 240-475 ml_ (ASTM D 6232).
May be incompatible with liquids containing a high percentage of solids.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
E.4 Push Coring Devices
Push coring devices include equipment that use a pushing action to collect a vertical column of
a solid sample. You will find summaries for the following push coring devices in this section:
E.4.1 Penetrating Probe Sampler
E.4.2 Split Barrel Sampler
E.4.3 Concentric Tube Thief
E.4.4 Trier
E.4.5 Thin-Walled Tube
E.4.6 Coring Type Sampler (with Valve)
E.4.7 Miniature Core Sampler
E.4.8 Modified Syringe Sampler
214
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Appendix E
E.4.1 Penetrating Probe Sampler
The penetrating probe sampler (Figure E-12) is a
push coring device and, therefore, provides a core
sample. The probe sampler is recommended for
sampling soil and other solids. The sample
volume range is 0.2 to 2.0 liters (ASTM D 6232).
The probe sampler typically consists of single or
multiple threaded steel tubes, a threaded top cap,
and a detachable steel tip. The steel tubes are
approximately 1 inch or less in diameter.
Specialized attachments may be used for various
matrices. Some probes are equipped with
adjustable screens or retractable inner rods to
sample soil vapor or ground water.
Advantages
Easy to decontaminate and is
reusable.
Can provide samples for onsite
analysis (ASTM D 6232).
. Threaded
Top Cap
. Removable
Liner
One Piece
Probe Body
Coring Tip
Limitations
Versatile and may sample 15 to 20 F'9ure E-12- Probe sampler
locations a day for any combination
of matrices (ASTM D 6232).
Can reduce quantity of investigative derived wastes.
May be heavy and bulky depending on the size used.
Limited by composition of subsurface materials and accessibility to deeper depth
materials.
May be inappropriate for sampling materials that require mechanical strength to
penetrate.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
215
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Threaded Top Cap
Coring Tip
Appendix E
E.4.2 Split Barrel Sampler
A split barrel sampler (Figure E-13) is a
push coring device often used with a drill
rig to collect deep subsurface samples.
The device is recommended for soil
sampling, but can be used to sample other
solids. The materials to be sampled
should be moist enough to remain in the
sampler. The sample volume range is 0.5
to 30.0 liters (ASTM D 6232).
The sampler consists of a length of steel
tubing split longitudinally and equipped
with a drive shoe, made of steel, and a
drive head. The drive shoe is detachable
and should be replaced when dented or
distorted. The samplers are available in a
variety of diameters and lengths. The split
barrel is typically 18 to 30 inches in length
with an inside diameter of 1.5 to 2.5 inches
(ASTM D 4700, ASTM D 1586). The split F'9ure E-13- sPNt barrel samP|er
barrel sampler can be used to collect relatively undisturbed soil samples at considerable depths.
The split barrel sampler may be driven manually, but is usually driven with a drill rig drive weight
assembly or hydraulically pushed using rig hydraulics. The sampler is placed on the surface of
the material to be sampled, then pushed downward while being twisted slightly. Because
pushing by hand may be difficult, a drop hammer typically is attached to a drill rig used to finish
inserting the sampler. When the desired depth is reached the sampler is twisted again to break
the core; then, the sampler is pulled straight up and out of the material. The sample may be
removed from the barrel or the liner may be capped off for analysis. Barrels may be extended
to 5 inches in diameter (ASTM D 6232). Liners often are used when sampling for volatile
organic compounds or other trace constituents of interest. With a liner, the sample can be
removed with a minimum amount of disturbance. Liners must be compatible with the matrix and
compounds of interest; plastic liners may be inappropriate if analyzing for organics.
Optional Liner
Split Barrels, may be extended
with couplings and additional
Barrel sets
Auger Tip
Advantages
Limitations
Reusable, easily decontaminated, and easy to use.
Provides a relatively undisturbed sample, therefore, can minimize the loss of
volatile organic compounds.
Requires a drill or direct push rig for deep samples.
Made of steel and may penetrate underground objects such as a pipe or drum.
216
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Appendix E
Only accommodates samples that contain particles smaller than the opening of
the drive shoe (ASTM D 4700).
Other Guidance:
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Soil Sampling from the Vadose Zone, ASTM D 4700
Standard Test Method for Penetration Test and Split-Barrel Sampling of Soils,
ASTM D1586
E.4.3 Concentric Tube Thief
The concentric tube thief (also known as a grain
sampler) (Figure E-14) is a push coring device that the
user directly pushes into the material to be sampled. It
can be used to sample powdered or granular solids and
wastes in piles or in bags, drums, or similar containers.
The concentric tube thieves are generally 61 to 100 cm
(24 to 40 inches) long by 1.27 to 2.54 cm (Y2 to 1 inch) in
diameter (USEPA 1994i). The sample volume range is
0.5 to 1.0 liters (ASTM D 6232).
The concentric tube thief consists of two slotted
telescoping tubes, which are constructed of stainless
steel, brass, or other material. The outer tube has a
conical pointed tip on one end which allows the thief to
penetrate the material being sampled. The thief is
opened and closed by rotating the inner tube, and it is
inserted into the material while in the closed position.
Once inserted, the inner tube is rotated into the open
position and the device is wiggled to allow the material
to enter the open slots. The thief then is closed and
withdrawn.
Advantages
Is a good direct push sampler for dry
unconsolidated materials.
Easy to use.
Figure E-14. Concentric tube thief
217
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Appendix E
Limitations
May be difficult to decontaminate, depending on the matrix
Not recommended for sampling of moist or sticky materials.
Does not collect samples containing all particle sizes if the diameter of the
largest solid particle is greater than one-third of the slot width (ASTM D 6232).
Most useful when the solids are no greater than 0.6 cm (1/4-inch) in diameter
(USEPA 1994i).
Depth of sample is limited by the length of the thief.
Not recommended for use when volatiles are of interest. Collects a somewhat
disturbed sample, which may cause loss of some volatiles.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
"Waste Pile Sampling" (USEPA 1994d)
E.4.4 Trier
A trier (Figure E-15) is a push coring device that
resembles an elongated scoop and is used to
sample moist or sticky solids with a particle
diameter less than one-half the diameter of the
tube portion. The trier can be used to sample
soils and similar fine-grained cohesive materials.
The typical sample volume range is 0.1 to 0.5
liters (ASTM D 6232).
A trier comprises a handle connected to a tube
cut in half lengthwise, with a sharpened tip that
allows it to cut into the material. Triers are made
of stainless steel, PTFE-coated metal, or plastic.
One should be selected who materials of
construction are compatible with the sampled
material.
A trier, typically 61 to 100 cm long and 1.27 to
2.54 cm in diameter, is used as a vertical coring
device when a relatively complete and cylindrical
sample can be extracted.
The trier is pushed into the material to be
sampled and turned one or two times to cut a
\
61-100 cm
(24-60")
\
\
1.27-2.54 co (%-!")
Figure E-15. Trier
218
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Appendix E
core. The rotation is stopped with the open face pointing upward. The core is then carefully
removed from the hole, preventing overburden material from becoming a part of the sample.
The sample is inspected for irregularities (e.g., pebbles) or breakage. If breakage occurred and
if the core does not satisfy minimum length requirements, discard it and extract another from an
immediately adjacent location (ASTM D 5451). The sample is emptied into the appropriate
container for analysis.
Advantages
A good direct push sampler for moist or sticky materials.
Lightweight, easy to use, and easy to decontaminate for reuse.
Limitations
Limited to sample particle sizes within the diameter of the inserted tube and will
not collect particles greater than the slot width.
Not recommended for sampling of dry unconsolidated materials. (A concentric
tube thief is good for such materials.)
Only for surface sampling, and the depth of sample is limited by the length of the
trier.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Sampling Using a Trier Sampler, ASTM D 5451
Sampling of Drums and Similar Containers by Field Personnel, ASTM D 6063
Standard Practice for Sampling Unconsolidated Solids in Drums or Similar
Containers, ASTM D 5680
E.4.5 Thin-Walled Tube
A thin-walled tube (Figure E-16) is a type of push coring device recommended for sampling
cohesive, unconsolidated solids - particularly soil. It is not recommended for gravel or rocky
soil. The sample volume range is 0.5 to 5.0 liters (ASTM D 6232).
The tube generally is constructed of carbon stainless steel, but can be manufactured from other
metals (ASTM D 4700). It is commonly 30-inches long and is readily available in 2-, 3-, and 5-
inch outside diameters (ASTM D 4700). The tube is attached with set screws to a length of a
solid or tubular rod, and the upper end of the rod, or sampler head, is threaded to accept a
handle or extension rod. Typically, the length of the tube depends on the desired sampling
depth. Its advancing end is beveled and has a cutting edge with a smaller diameter than the
219
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Appendix E
tube inside diameter. The tube can be used
in conjunction with drills - from hand-held to
full-sized rigs.
The end of the sampler is pushed directly
into the media using a downward force on
the handle. It can be pushed downward by
hand, with a jack-like system, or with a
hydraulic piston. Once the desired depth is
reached, the tube is twisted to break the
continuity of the tip and is pulled from the
media. The sample material is extruded into
the sample container by forcing a rod through
the tube. A paring device has been
developed to remove the outer layer during
extrusion (ASTM D 4700). Plastic and PFTE
sealing caps for use after sampling are
available for the 2-, 3-, and 5-inch tubes.
Advantages
Limitations
Readily available,
inexpensive, and easy to use.
Reusable and can be
decontaminated.
Obtains a relatively
undisturbed sample.
,
ample
Head
Sail Valve
—Screw
Thin-Walled
Tube
Cutting End
Figure E-16. Thin-walled tube
Some thin-walled tubes are large and heavy.
The material to be sampled must be of a physical consistency (cohesive sold
material) to be cored and retrieved within the tube. It cannot be used to sample
gravel or rocky soils.
Some volatile loss is possible when the sample is removed from the tube.
The most disturbed portion in contact with the tube may be considered
unrepresentative. Shorter tubes provide less-disturbed samples than longer
tubes.
Materials with particles larger than one-third of the inner diameter of the tube
should not be sampled with a thin-walled tube.
220
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Appendix E
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Core Sampling of Submerged, Unconsolidated Sediments,
ASTM D 4823
Standard Practice for Thin-Walled Type Geotechnical Sampling of Soils, ASTM D
1587
Standard Guide for Soil Sampling from the Vadose Zone, ASTM D 4700
E.4.6 Coring Type Sampler (with Valve)
Top Cap
Coring Tip
w/Butterfly Valve
A coring type sampler with valve (Figure
E-17) is a type of push coring device
recommended for wet soil, and can also
be used to sample unconsolidated solid
waste, mixed-phase solid/liquid waste,
and free-flowing powders. The coring
device may be used in drums and small
containers as well as tanks, lagoons, and
waste impoundments. The sample
volume range is 0.2 to 1.5 liters (ASTM D
6232).
The coring type sampler with valve is a
stainless steel cylindrical sampler with a
coring tip, top cap, an extension with a
cross handle, and a non-return valve at
the lower end behind a coring or augering
tip. The valve is a retaining device to hold
the sample in place as the coring device is
removed. Samples are normally collected in an optional liner. It is operated by attaching a
handle or an extension with a handle to the top of the coring device. The corer is lowered to the
surface, pushed into the material being sampled and removed. The top cap is removed and the
contents emptied into a sample container. Alternatively, the liner can be removed (with the
sampled material retained inside) and capped on both ends for shipment to a laboratory.
Auger Tip
w/Butlerlly Valve
Valve open
Sampler Body
Figure E-17. Coring type sampler (with valve)
Advantages
Reusable and is easily decontaminated.
Provides a relatively undisturbed sample if not extruded.
Can be hand operated and does not require significant physical strength.
221
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Appendix E
Limitations
Can not be used in gravel, large particle sediments, or sludges.
When sampling for volatile organic compounds, it must be used with a liner and
capped to minimize the loss of volatiles.
Other Guidance
O-Ring
Plunger Rod
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Guide for Core Sampling Submerged, Unconsolidated Sediments, ASTM D 4823
£.4.7 Miniature Core Sampler
The miniature core sampler (Figure E-18) can be
used to collect soil and waste samples for volatile
organics analysis. These include devices such as
the Purge-and-Trap Soil Sampler™, the EnCore™
sampler, or a cut plastic syringe (see Section 6.0
of SW-846 Method 5035). A miniature core
sampler is a single-use push coring sampling
device that also can be used as an air-tight
sample storage and shipping container. It collects
a small contained subsample and is particularly
useful for the sampling and analysis of volatile
organic compounds.
It is recommended for sampling soil, from the
ground or the side of a trench, and may be used
for sampling sediment and unconsolidated solid
wastes. It cannot be used for sampling cemented
material, consolidated material, or material having
fragments coarse enough to interfere with proper
coring. The EnCore™ sampler can be used to
collect subsamples from soil cores and has a
sample volume range of 0.01 to 0.05 liters (ASTM
D 6232).
Viton O-Rings
Locking
Mechanism
Figure E-18.
sampler)
Miniature core sample (Encore1
The device is available from the manufacturer in two sizes for collection of 5- and 25-gram
samples (assuming a soil density of 1.7 g/cm3). The size is chosen based on the sample size
required by the analytical procedure.
SW-846 Method 5035, "Closed-System Purge-and-Trap and Extraction for Volatile Organics in
Soil and Waste Samples," recommends that samples not be stored in the device longer than 48
hours prior to sample preparation for analysis. The manufacturer's instructions for sample
extrusion should be followed carefully.
222
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Appendix E
Advantages
Maintains sample structure in a device that also can be used to store and
transport the sample directly to the laboratory.
Recommended for collecting samples for the analysis of volatile compounds. It
collects a relatively undisturbed sample that is contained prior to analysis to
minimize the loss of volatile compounds.
Usually is compatible with the chemicals and physical characteristics of the
sampled media.
No significant physical limitations for its use.
Cross-contamination should not be a concern if the miniature core sampler is
certified clean by the manufacturer and employed as a single-use device.
Limitations
Cannot be used to sample gravel or rocky soils.
Instructions must be followed carefully for proper use to avoid trapping air with
the sample and to ensure that the sample does not compromise the seals.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Using the Disposable EnCore™ Sampler for Sampling and
Storing Soil for Volatile Organic Analysis, ASTM D 6418
Standard Guide for Sampling Waste and Soils for Volatile Organic Compounds,
ASTM D 4547
223
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Appendix E
E.4.8 Modified Syringe Sampler
A modified syringe sampler (Figure E-19) is a
push coring sampling device constructed by the
user by modifying a plastic, single-use, medical
syringe. It can be used to provide a small, sub-
sample of soil, sediments, and unconsolidated
solid wastes. It is sometimes used to sub-sample
a larger core of soil. It is not recommended for
sampling cemented material, consolidated
material, or material having fragments coarse
enough to interfere with proper coring. Unlike the
EnCore™ sampler, it should not be used to store
and ship a sample to the laboratory. Instead, the
sample should be extruded into another
container. Although the modified syringe sampler
does not provide as contained a sample as the
EnCore™ sampler, it can be used for sampling
volatile compounds, as long as sample extrusion
into another container is quickly and carefully
executed. The modified syringe sample has a
volume range of 0.01 to 0.05 liters (ASTM D
6232).
Figure E-19. Modified syringe sampler
A modified syringe sampler is constructed by
cutting off the lower end of the syringe attachment for the needle. The rubber cap is removed
from the plunger, and the plunger is pushed in until it is flush with the cut end. For greater ease
in pushing into the solid matrix, the front edge sometimes can be sharpened (ASTM D 4547).
The syringe sampler is then pushed into the media to collect the sample, which then may be
placed in a glass VOA vial for storage and transport to the laboratory. The sample is
immediately extruded into the vial by gently pushing the plunger. The volume of material
collected should not cause excessive stress on the device during intrusion into the material, or
be so large that the sample falls apart easily during extrusion.
Advantages
Obtains a relatively undisturbed profile sample.
Can be used for the collection of samples for the analysis of volatile compounds
as long as sample extrusion is quickly and carefully executed.
No significant physical limitations for its use.
Low-cost, single-use device.
224
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Appendix E
Limitations
Cannot be used to sample gravel or rocky soils.
Material of construction may be incompatible with highly contaminated media.
Care is required to ensure that the device is clean before use.
The device cannot be used to store and transport a sample.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling Waste and Soils for Volatile Organic Compounds,
ASTM D 4547
E.5 Rotating Coring Devices
Rotating coring devices include equipment that obtains vertical columns of a solid sample
through a rotating action. Some of these devices (such as augers) also can be used for just
boring a hole for sample collection at a certain depth using another piece of equipment. You
will find summaries for the following rotating coring devices in this section:
E.5.1 Bucket Auger
E.5.2 Rotating Coring Device
E.5.1 Bucket Auger
The bucket auger (Figure E-20) is a hand-
operated rotating coring device generally
used to sample soil, sediment, or
unconsolidated solid waste. It can be
used to obtain samples from drums,
storage containers, and waste piles. The
sample volume range is 0.2 to 1.0 liters
(ASTM D 6232).
The cutting head of the auger bucket is
pushed and twisted by hand with a
downward force into the ground and
removed as the bucket is filled. The
empty auger is returned to the hole and
the procedure is repeated. The sequence
is continued until the required depth is
reached. The same bucket may be used
to advance the hole if the vertical sample is a composite of all intervals; however, discrete grab
Regular Auger
Mud Auger
Figure E-20. Bucket auger
225
-------
Appendix E
samples should be collected in separate clean auger buckets. The top several inches of
material should be removed from the bucket to minimize chances of cross-contamination of the
sample from fall-in material from the upper portions of the hole.
Note that hand augering may be difficult in tight clays or cemented sands. At depths
approaching 20 feet (6 m), the tension of hand auger extension rods may make operation of the
auger too difficult. Powered methods are recommended if deeper samples are required (ASTM
D 6232).
Advantages
Reusable and easy to decontaminate.
Easy to use and relatively quick for shallow subsurface samples.
Allows the use of various auger heads to sample a wide variety of soil conditions
(USEPA 1993c).
Provides a large volume of sample in a short time.
Limitations
Depth of sampling is limited to about 20 feet (6 m) below the surface.
Not suitable for obtaining undisturbed samples.
Requires considerable strength to operate and is labor intensive.
Not ideal for sampling soils for volatile organic compounds.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Soil Investigation and Sampling by Auger Borings, ASTM
D 1452
Standard Guide for Soil Sampling from the Vadose Zone, ASTM D 4700
Standard Practice for Sampling Unconsolidated Waste From Trucks, ASTM D
5658
Standard Guide for Sampling of Drums and Similar Containers by Field
Personnel, ASTM D 6063
"Waste Pile Sampling" (USEPA 1994d)
226
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Appendix E
"Sediment Sampling" (USEPA 1994e)
E.5.2 Rotating Coring Device
The rotating coring device (Figure E-21)
collects vertical columns of a solid sample
through a rotating action and can be used
in sampling consolidated solid waste, soil,
and sediment. The sample volume range
is 0.5 to 1.0 liters (ASTM D 6232).
The rotating coring device consists of a
diamond- or carbide-tipped open steel
cylinder attached to an electric drill. The
coring device may be operated with the
drill hand-held or with the drill mounted on
a stand. When on a portable stand, full-
depth core samples can be obtained. The
barrel length is usually 1- to 1.5-feet long
and the barrel diameter ranges from 2 to
6 inches (ASTM D 6232 and ASTM D
5679). The rotating coring device may be used for surface or depth samples.
Figure E-21. Rotating coring device
The rotating coring device is placed vertical to the surface of the media to be sampled, then
turned on before contact with the surface. Uniform and continuous pressure is supplied to the
device until the specified depth is reached. The coring device is then withdrawn and the sample
is placed into a container for analysis, or the tube itself may be capped and sent to the
laboratory. Capping the tube is preferred when sampling for volatile organic compounds. The
rotating tube must be cooled and lubricated with water between samples.
Advantages
Limitations
Easy to decontaminate.
Reusable.
Can obtain a solid core sample.
Requires a battery or other source of power.
Requires a supply of water, used for cooling the rotating tube.
Not easy to operate.
227
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Appendix E
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Sampling Consolidated Solids in Drums or Similar
Containers, ASTM D 5679
"Drum Sampling" (USEPA 1994b)
"Sediment Sampling" (USEPA 1994e)
E.6 Liquid Profile Devices
Liquid profile devices include equipment that can collect a vertical column of a liquid, sludge, or
slurry sample. You will find summaries for the following liquid profile devices in this section:
E.6.1 Composite Liquid Waste Sampler (COLIWASA)
E.6.2 Drum Thief
E.6.3 Valved Drum Sampler
E.6.4 Plunger Type Sampler
E.6.5 Settleable Solids Profiler (Sludge Judge)
E.6.1 COLIWASA (Composite Liquid Waste Sampler)
The COLIWASA (Figure E-22) is a type of
liquid profile sampling device used to
obtain a vertical column of sampled
material. A COLIWASA is recommended
for sampling liquids, multi-layer liquid
wastes, and mixed-phase solid/liquid
wastes and is commonly used to sample
containerized liquids, such as tanks and
drums. It also may be used for sampling
open bodies of stagnant liquids. The
sample volume range is 0.5 to 3 liters
(ASTM D 6232).
A COLIWASA can be constructed of
polyvinyl chloride (PVC), glass, metal,
PTFE or any other material compatible with
the sample being collected. In general, a
COLIWASA comprises a tube with a
tapered end and an inner rod that has
some type of stopper on the end. The
design can be modified or adapted to meet
the needs of the sampler. One
configuration comprises a piston valve
attached by an inner rod to a locking
Glass Rod
Operating Rod
Glass Body
Lock Nut
,_ Indicator/
Scraper Ring
Operating
Rod
— Sampler Body
Teflon® or
\J Glass plug
SINGLE USE
COLIWASA
Piston Valve
— Valve Body
REUSEABLE
POINT SAMPLER
Figure E-22. COLIWASA
228
-------
Appendix E
mechanism at the other end. Designs are available for specific sampling situations (i.e., drums,
tanks). COLIWASAs specifically designed for sampling liquids, viscous materials, and heavy
sludges are also available. COLIWASAs come in a variety of diameters (0.5 to 2 inches) and
lengths (4 to 20 feet) (ASTM D 6232).
COLIWASAs are available commercially with different types of stoppers and locking
mechanisms, but all have the same operating principle. To draw a sample, the COLIWASA is
slowly lowered into the sample at a right angle with the surface of the material. (If the
COLIWASA sampler is lowered too fast, the level of material inside and outside the sampler
may not be the same, causing incorrect proportions in the sample. In addition, the layers of
multi-layered materials may be disturbed.) The sampler is opened at both ends as it is lowered
to allow the material to flow through it. When the device reaches the desired sampling depth,
the sampler is closed by the stopper mechanism and both tubes are removed from the material.
The sampled material is then transferred to a sample container by opening the COLIWASA. A
COLIWASA can be reused following proper decontamination (reusable point sampler) or
disposed after use (single-use COLIWASA). The reusable point sampler is used in the same
way as the single use COLIWASA; however, it can also sample at a specific point in the liquid
column.
Advantages
Provides correct delimitation and extraction of waste (Pitard 1989).
Easy to use.
Inexpensive.
Reusable.
Single-use models are available.
Limitations
May break if made of glass and used in consolidated matrices.
Decontamination may be difficult.
The stopper may not allow collection of material in the bottom of a drum.
High viscosity fluids are difficult to sample.
Other Guidance
Standard Practice for Sampling with a Composite Liquid Waste Sampler
(COLIWASA), ASTM D 5495
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
229
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Appendix E
Standard Guide for Sampling Drums and Similar Containers by Field Personnel,
ASTM D 6063
Standard Practice for Sampling Single or Multilayered Liquids, With or Without
Solids, in Drums or Similar Containers, ASTM D 5743
"Drum Sampling" (USEPA 1994b)
"Tank Sampling" (USEPA 1994c)
/
A
E.6.2 Drum Thief
A drum thief (Figure E-23) is an open-ended tube and liquid
profile sampling device that provides a vertical column of the
sampled material. It is recommended for sampling liquids,
multi-layer liquid wastes, and mixed-phase solid/liquid wastes
and can be used to sample liquids in drums or similar
containers. The typical sample volume range is 0.1 to 0.5
liters (ASTM D 6232).
Drum thieves can be made of glass, stainless steel, or any
other suitable material. Drum thieves are typically 6 mm to
16 mm inside diameter and 48-inches long (USEPA 1994c).
To sample liquids with low surface tension, a narrow bailer
works best. In most cases, tubes with a 1-centimeter inside
diameter work best. Wider tubes can be used to sample
sludges.
The drum thief is lowered vertically into the material to be
sampled, inserted slowly to allow the level of material inside
and outside the tube to be approximately the same. This
avoids incorrect proportions in the sample. The upper end is
then sealed with the thumb or a rubber stopper to hold the sample in the tube as it is removed
from the container. The thief is emptied by removing the thumb or stopper.
Figure E-23. Drum thief
Advantages
Limitations
Easy to use and inexpensive.
Available in reusable and single-use models.
Sampling depth is limited to the length of the sampler.
May not collect material in the bottom of a drum. The depth of unsampled
material depends on the density, surface tension, and viscosity of the material
being sampled.
230
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Appendix E
Highly viscous materials are difficult to sample.
May be difficult to retain sample in the tube when sampling liquids of high
specific gravity.
If made of glass, may break if used in consolidated matrices. In addition, chips
and cracks in a glass drum thief may cause an imperfect seal.
Decontamination is difficult.
When sampling a drum, repeated use of the drum thief to obtain an adequate
volume of sample may disturb the drum contents.
Drum-size tubes have a small volume and sometimes require repeated use to
obtain a sample. Two or more people may be required to use larger sizes.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling of Drums and Similar Containers by Field
Personnel, ASTM D 6063
Standard Practice for Sampling Single or Multilayered Liquids, With or Without
Solids, in Drums or Similar Containers, ASTM D 5743
"Drum Sampling" (USEPA 1994b)
"Tank Sampling" (USEPA 1994c)
E.6.3 Valved Drum Sampler
A valved drum sampler (Figure E-24) is a liquid profile
device often used to sample liquids in drums or tanks and
provides a vertical column of the sampled material. A
valved drum sampler is recommended for sampling
liquids, multi-layered liquid wastes, and mixed-phase
solid/liquid wastes. The typical sample volume range is
0.3 to 1.6 liters (ASTM D 6232).
The sampler can be constructed from PTFE for reuse or
polypropylene for single use and comprises a tube fitted
with a top plug and a bottom valve. A sliding indicator
ring allows specific levels or fluids interfaces to be
identified.
The valved drum sampler is open at both ends during
231
Tethered
Top Plug
Top Cap
Indicator/
Scrapper Ring
Sampler
Body
Figure E-24. Valved drum sampler
-------
Appendix E
sample collection and lowered vertically into the material to be sampled. The sampler is
inserted slowly to allow the level of material inside and outside the tube to equalize. Once the
desired amount of sample is collected, the top plug and the bottom valve are closed. The top
plug is closed manually and the bottom valve is closed by pressing against the side or bottom of
the container. The sample is poured from the top of the sampler into a suitable container.
Advantages
Easy to use, inexpensive, and unbreakable.
Obtains samples to depths of about 8 feet (2.4 m) (ASTM D 6232).
Reusable if made from PTFE (single-use if made from polypropylene) (ASTM D
6232).
Somewhat difficult to decontaminate
The bottom valve may prevent collection of the bottom 1.25 cm of material
(ASTM D 6232).
High viscosity fluids are difficult to sample.
Other Guidance
Limitations
Standard Guide for Selection of Sampling
Equipment for Waste and Contaminated
Media Data Collection Activities, ASTM D
6232
E.6.4 Plunger Type Sampler
The plunger type sampler (Figure E-25) is a liquid profile
sampling device used to collect a vertical column of liquid
and is recommended for the sampling of single and multi-
layered liquids or mixtures of liquids and solids. The
plunger type sampler can be used to collect samples
from drums, surface impoundments, and tanks. Sample
volume is at least 0.2 liters and ultimately depends on the
size of the sample container (ASTM D 6232).
A plunger type sampler comprises a sample tube, sample
line or rod, head section, and plunger and is made of
HOPE, PTFE, or glass. A sample jar is connected to the
head section. The sample tube is lowered into the liquid
to the desired depth. The plunger is engaged into the
tube to secure the sample within the tube and the cord or
rod is raised to transfer the sample directly into the
Cap & Septa
|| Sample Rod
or Line
Universal
Jar Holder
Sample Tube
r— Scraper Ring
Plunger
Figure E-25. Plunger type sampler
232
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Appendix E
sampling bottle or jar. The plunger can be pushed back down the sampling tube to reset the
sampler.
Advantages
Limitations
Easy to use.
Provides a sealed collection system.
Relatively inexpensive and available in various lengths.
Care is needed when using a glass sampling tube.
Decontamination may be difficult, particularly when a glass sampling tube is
used.
Other Guidance:
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Sampling Single or Multilayered Liquids, With or Without
Solids, in Drums or Similar Containers, ASTM D 5743
E.6.5 Settleable Solids Profiler (Sludge Judge)
The settleable solids profiler (Figure E-26), also known
as the sludge judge, primarily is used to measure or
sample settleable (suspended) solids found in sewage
treatment plants, waste settling ponds and
impoundments containing waste. It also can be used to
sample drums and tanks. It has a sample volume range
of 1.3 to 4.0 liters (ASTM D 6232).
The sludge judge is made from clear PVC and has 1-
foot-depth markings on its 5-foot-long body sections. It
has a check valve on the lower section and a cord on
the upper section and is assembled using the threaded
connections of the sections to the length needed for the
sampling event. The sampler is lowered into the media
to allow it to fill. A tug on the cord sets the check valve
and it is removed from the sampled material. The level
of settleable solids can be measured using the
markings. It is emptied by pressing in the protruding pin
on the lower end.
e
T
, Cord
D
Dp M
j F
•
•
•
M
J
ddle l_c
Marking
Marking
Marking
Marking
/ Check Valve
5
wer
Figure E-26. Settleable solids profiler
233
-------
Appendix E
Advantages
Allows measurement of the liquid/settleable solids columns of any length.
Easy to assemble and use.
Unbreakable in normal use and reusable.
Limitations
Suitable for sampling noncaustic liquids only.
May be difficult to sample high viscosity materials.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
E.7 Surface Sampling Devices
Surface sampling devices include equipment that by design are limited to sample collection at
the surface of material or can sample material of limited depth or width only. You will find
summaries for the following surface sampling devices in this section:
E.7.1
E.7.2
E.7.3
E.7.4
E.7.5
E.7.1 Bailer
Bailer
Dipper
Liquid Grab Sampler
Swing Jar Sampler
Spoons, Scoops, Trowels, and Shovels
Bailers (Figure E-27) are designed for
obtaining samples of ground water;
however, they also can be used to obtain
samples of liquids and multi-layered liquid
wastes from tanks and surface
impoundments. Bailers are not suitable
for sampling sludges. The sample volume
range is 0.5 to 2 liters (ASTM D 6232).
A bailer is a hollow tube with a check valve
at the base (open bailer) or valves at both
ends (point-source bailer). A bailer can be
threaded in the middle so that extension
tubes can be added to increase the
sampling volume. It can be constructed of
stainless steel, PVC, PTFE, or any other
STANDARD
BAILER OF TEFLON
I
TOP FOR
VARIABLE CAPACITY
/POINT OF SOURCE
BAILER OF PVC
1 FOOT
MIDSECTION
MAY BE ADDED
HERE
Figure E-27. Bailer
234
-------
Appendix E
suitable material and is available in numerous sizes for use in a variety of well sizes. The bailer
is attached to a line and gradually lowered into the sample. As the bailer is lowered, the bottom
check valve allows water to flow through the tube. The bailer is then slowly raised to the
surface. The weight of the water closes the bottom check valve. A point-source bailer allows
sampling at a specific depth. The check valve at the top of the tube limits water or particles
from entering the bailer as it is retrieved.
The bailer is emptied either by pouring from the top or by a bottom emptying device. When
using a top-emptying bailer, the bailer should be tipped slightly to allow a slow discharge into
the sample container to minimize aeration. A bottom-emptying model has controlled flow
valves, which is good for collecting samples for volatile organic analysis since agitation of the
sample is minimal.
Advantages
Limitations
Easy to use, inexpensive, and does not require an external power source.
Can be constructed of almost any material that is compatible with the
parameters of interest.
Relatively easy to decontaminate between samples. Single-use models are
available.
Bottom-emptying bailers with control valves can be used to obtain samples for
volatile compound analysis.
Not designed to obtain samples from specific depths below liquid surface (unless
it is a point-source bailer).
If using a top-emptying bailer, the sample may become aerated if care is not
taken during transfer to the sample container.
May disturb the sample in a water column if it is lowered too rapidly.
High suspended solids' content or freezing temperatures can impact operation of
check valves.
One of the least preferred devices for obtaining samples of ground water for low
concentration analyses due to their imprecision and agitation of the sample (see
USEPA 1992a and Puls and Barcelona 1996).
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Guide for Sampling Groundwater Monitoring Wells, ASTM D 4448
235
-------
Appendix E
"Tank Sampling" (USEPA 1994c)
E.7.2 Dipper
A dipper (Figure E-28) is a type of surface
sampling device used to sample surface
samples from drums, surface
impoundments, tanks, pipes, and point
source discharges. Sampling points are
shallow (10 inches) and taken at, or just
below, the surface. The typical sample
volume range is 0.5 to 1.0 liters (ASTM D
6232).
A dipper comprises a glass, metal, or
plastic beaker clamped to the end of a
two- or three-piece telescoping aluminum
or fiberglass pole, which serves as a
handle. A dipper may vary in the number
of assembled pieces. Some dippers have Figure E-28. Dipper
an adjustable clamp attached to the end of
a piece of metal tubing. The tubing forms the handle; the clamp secures the beaker. Another
type of dipper is a stainless steel scoop clamped to a movable bracket that is attached to a
piece of rigid tube. The scoop may face either toward or away from the person collecting the
sample, and the angle of the scoop to the pipe is adjustable. The dipper, when attached to a
rigid tube, can reach easily 10 to 13 feet (3 to 4 m) away from the person collecting the samples
(ASTM D 6232).
The dipper is used by submerging the beaker end into the material slowly (to minimize surface
disturbance). It should be on its side so that the liquid runs into the container without swirling or
bubbling. The beaker is filled and rotated up, then brought slowly to the surface. Dippers and
their beakers should be compatible with the sampled material.
Advantages
Limitations
Inexpensive.
Easy to construct and adapt to the sampling scenario by modifying the length of
the tubing or the type of container.
Not appropriate for sampling subsurface layers or to characterize discrete layers
of stratified liquids.
Can only be used to collect surface samples.
236
-------
Appendix E
Other Guidance
Standard Practice for Sampling with a Dipper or Pond Sampler, ASTM D 5358
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Sampling Wastes from Pipes and Other Point Discharges,
ASTM D5013
E.7.3 Liquid Grab Sampler
Valve Finger
Ring
Access Port
Jar Valve
A liquid grab sampler (Figure E-29) is a
surface sampling device designed to
collect samplers at a specific shallow
depth beneath the liquid surface. It can
be used to collect samples of liquids or
slurries from surface impoundments,
tanks, and drums. Its sample volume
range is from 0.5 to 1.0 liters (ASTM D
6232).
The liquid grab sampler is usually made
from polypropylene or PTFE with an
aluminum or stainless steel handle and
stainless steel fittings. The sampling jar is
usually made of glass, although plastic
jars are available. The jar is threaded into
the sampler head assembly, then lowered
by the sampler to the desired sampling position beneath the liquid surface. The valve is then
opened by pulling up on a finger ring to fill the jar. The valve is closed before retrieving the
sample.
Figure E-29. Liquid grab sampler
Advantages
Limitations
Easy to use.
The sample jar can be capped and used for transport to the laboratory, thus
minimizing the loss of volatile organic compounds.
The closed sampler prevents contaminants in upper layers from compromising
the sample.
Care is required to prevent breakage of glass sample jar.
Materials of construction need to be compatible with the sampled media.
237
-------
Appendix E
Cannot be used to collect deep samples.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
E.7.4 Swing Sampler (Swing Jar Sampler)
The swing jar sampler (Figure E-30) is a surface sampler
that may be used to sample liquids, powders, or small
solids at distances of up to 12 feet (3.5 m). It can be
used to sample many different types of units, including
drums, surface impoundments, tanks, pipe/point source
discharges, sampling ports, and storage bins. It has a
sample volume range of 0.5 to 1.0 liters.
The swing jar sampler is normally used with high density
polyethylene sample jars and has an extendable
aluminum handle with a pivot at the juncture of the
handle and the jar holder. The jar is held in the holder
with an adjustable clamp. The pivot allows samples to be
collected at different angles.
Advantages
Handle
(Extendable)
Jar Clamp
Easy to use.
Figure E-30. Swing jar sampler
Easily adaptable to samples with jars of
different sizes and materials, which can be used to facilitate compatibility with the
material to be sampled.
Can be pivoted to collect samples at different angles.
Can sample from a wide variety of locations and units.
Limitations
Cannot collect discrete depth samples.
Care is required to prevent breakage when using a glass sample jar.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
238
-------
Appendix E
E.7.5 Spoons, Scoops, Trowels, and Shovels
Spoons, scoops, trowels, or shovels are types of
surface sampling devices used to sample sludge,
soil, powder, or solid wastes. The typical sample
volume range is 0.1 to 0.6 liters for scoops or
trowels and 1.0 to 5.0 Liters for shovels (ASTM D
6232). The typical sample volume for a spoon is
10 to 100 grams (USEPA 1993c).
Spoons, available in stainless steel or PTFE
(reusable) or in plastic (disposable), easily sample
small volumes of liquid or other waste from the
ground or a container.
Scoop samplers provide best results when the
material is uniform and may be the only sampler
possible for materials containing fragments or
chunks. The scoop size should be suitable for
the size and quantity of the collected material.
Scoops and trowels come in a variety of sizes and
materials, although unpainted stainless steel is
preferred (ASTM D 6232). Scoops may be
attached to an extension, similar to the dipper, in
order to reach a particular area. Scoops and
trowels are used by digging and rotating the
sampler. The scoop is used to remove a sample
and transfer it into a sample container.
Figure E-31. Scoops
Shovels, usually made from stainless steel or suitable plastic materials, are typically used to
collect surface samples or to remove overburden material so that a scoop may remove a
sample.
Advantages
A correctly designed scoop or spatula (i.e., with a flat bottom and vertical sides)
is one of the preferred devices for sampling a one-dimensional mass of granular
solids (see also Sections 6.3.2.1 and 7.3.3.3).
Spoons, scoops, trowels, and shovels are reusable, easy to decontaminate, and
do not require significant physical strength to use.
Spoons and scoops are inexpensive and readily available.
Spoons and scoops are easily transportable and often disposable -- hence, their
use can reduce sampling time.
Shovels are rugged and can be used to sample hard materials.
239
-------
Appendix E
Limitations
Spoons, scoops, trowels, and shovels are limited to shallow and surface
sampling.
Shovels may be awkward to handle and cannot be used to easily fill small
sample containers.
Sampling with a spoon, scoop, trowel, or shovel may cause loss of volatile
organic compounds through disturbance of the media.
Spoons, scoops, trowels, and shovels of incorrect design (e.g., with rounded
bottoms) can introduce bias by preferentially selecting certain particle sizes.
Other Guidance
Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities, ASTM D 6232
Standard Practice for Sampling with a Scoop, ASTM D 5633
"Waste Pile Sampling" (USEPA 1994d)
"Sediment Sampling" (USEPA 1994e).
240
-------
APPENDIX F
STATISTICAL METHODS
This appendix provides guidance on the statistical analysis of waste testing and environmental
monitoring data. You should select the statistical test during the Data Quality Assessment
(DQA) phase after you review the data quality objectives, the sampling design, and the
characteristics of the data set. See guidance provided in Section 8.
Additional Guidance on the Statistical Analysis of
Waste Testing and Environmental Monitoring Data
USEPA. 2000d. Guidance For Data Quality Assessment,
The statistical methods in this appendix are
appropriate for use in evaluating sample
analysis results when comparing
constituent concentrations in a waste or
environmental medium to a fixed standard.
Users of this guidance may have other
objectives such as comparing two
populations, detecting trends, or characterizing the spatial pattern of contamination. If so,
review other guidance or seek assistance from a professional environmental statistician.
Note that not all RCRA standards require the waste handler to use sampling, analysis, and
statistical tests to measure compliance. However, if sampling and analysis is used by the waste
handler to measure compliance with a RCRA standard, then statistical methods may be used to
help quantify uncertainty associated with the decisions made using the data - even where there
is no regulatory obligation to do so (see also Sections 2 and 3).
This appendix is divided into subsections that describe the following statistical methods:
F.1 Testing Distributional Assumptions
F. 1.1 Overview and Recommendations
F. 1.2 Shapiro-Wilk Test for Normality (n < 50)
F.2 Confidence Limits for the Mean
F.2.1 Confidence Limits for the Mean of a Normal Distribution
F.2.2 Confidence Limits for a Normal Mean When Composite Sampling Is Used
F.2.3 Confidence Limits for a Lognormal Mean
F.2.4 Confidence Limits for the Mean of a Non-normal or Unknown Distribution
F.3 Tests for a Proportion or a Percentile
F.3.1 Parametric Upper Confidence Limits for an Upper Percentile
F.3.2 Using a Simple Exceedance Rule Method for Determining Compliance
With A Fixed Standard
F.4 Treatment of Nondetects
F.4.1 Recommendations
F.4.2 Cohen's Adjustment
Table F-1 provides a summary of frequently used statistical equations. See Appendix G for
statistical tables used with these methods.
241
-------
Appendix F
Table F-1. Summary of Basic Statistical Terminology Applicable to Sampling Plans for Solid Waste
Terminology
Symbol Mathematical Equation
Equation
No.
Variable (e.g., barium or
endrin)
X
Individual measurement of
variable
Simple Random Sampling and Systematic Random Sampling
Mean of measurements
generated from the
samples (sample mean)
x n i=i
where n = number of sample measurements.
Variance of sample
1
s = •
-V -
Standard deviation of
sample
S = \S
Standard error (also
standard deviation of the
mean)
Approximate number of
samples to estimate the
mean (financial constraints
not considered) (See
Section 5.4.1)
)V
n =
n A2 2
where the " z " values are obtained from the last
row of Table G-1 in Appendix G.
Approximate number of
samples to test a proportion
against a fixed standard
(See Section 5.5.1).
n
n =
15
Number of samples to test
a proportion when the
decision rule specifies zero
nonconforming samples
(See Section 5.5.2).
n
« = log(«)/logO)
where p equals the proportion of the waste or
media exceeded by the largest sample
16
242
-------
Appendix F
Table F-1. (Continued)
Terminology
Symbol Mathematical Equation
Equation
No.
Stratified Random Sampling (Proportional Allocation)
Arithmetic mean of the
measurements generated
from the samples obtained
from each Mh stratum
X,.
xh,
where nh = number of sample measurements
obtained from each Mh stratum.
Variance of measurements
generated from the
samples obtained from
each Mh stratum
The weighting factor
assigned to each Mh
stratum when stratified
random sampling is used
Overall sample mean using
stratified random sampling
h=l
Standard error of the mean
for a stratified random
sample
10
Total number of samples to
collect from a solid waste to
estimate the mean using
stratified random sampling
(proportional allocation)
n
* * ~
A2
df\ y
h=\
11
Degrees of freedom
associated with the
f-quantile in Table G-1,
Appendix G, when stratified
random sampling is used
12
243
-------
Appendix F
F.1 Testing Distributional Assumptions
F.1.1 Overview and Recommendations
The assumption of normality is very important as it is the basis for many statistical tests. A
normal distribution is a reasonable model of the behavior of certain random phenomena and
often can 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 (such as the sample mean) behave as if they are normally distributed
variables. 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.
While assumption of a normal distribution is convenient for statistical testing purposes, it is not
always appropriate. Sometimes data are highly skewed. In environmental applications, it is not
unusual to encounter data that exhibit a lognormal distribution in which the natural logarithms of
the data exhibit a normal distribution. Statistical tests can be used to verify the assumption of
normality or lognormality, but the conclusion of lognormality should not be based on the
outcome of a statistical test alone. There are several physical phenomena that can cause the
underlying distribution to appear lognormal when in fact it is not. For example, Singh, et al.
(1997) note that the presence of a relatively small highly contaminated area in an otherwise
uncontaminated area can cause sampling results to indicate a lognormal distribution. In such a
situation, it may be more appropriate to treat the areas as two separate decision units or use a
stratified sampling design. In other cases, sampling bias may cause a population to appear
lognormal. For example, analytical results could be skewed if highly concentrated portions of
the waste are over- or under-represented by the sampling procedure.
There are many methods available for verifying the assumption of normality ranging from simple
to complex. This guidance recommends use of the Shapiro-Wilk test for normality. Use of the
test is appropriate when the number of samples (n) is 50 or less. For n greater than 50, an
alternative test for normality should be used. One alternative presented in EPA's QA/G-9
guidance (USEPA 2000d) and the DataQUEST software (USEPA 1997b) is Filliben's Statistic
(Filliben 1975). Refer to EPA's QA/G-9 (USEPA 2000d) guidance or EPA's statistical guidance
for ground-water monitoring data (USEPA 1989b and 1992b) for other graphical and statistical
goodness-of-fit tests.
F.1.2 Shapiro-Wilk Test for Normality ( n < 50)
Purpose and Background
This section provides the method for performing the Shapiro-Wilk test for normality. The test is
easily performed using statistical software such as EPA's DataQUEST freeware (USEPA
1997b); however, the test also can be performed manually, as described here.
The Shapiro-Wilk test is recommended as a superior method for testing normality of the data. It
is based on the premise that if the data are normally distributed, the ordered values should be
highly correlated with corresponding quantiles (z-scores) taken from a normal distribution
(Shapiro and Wilk 1965). In particular, the Shapiro-Wilk test gives substantial weight to
evidence of non-normality in the tails of a distribution, where the robustness of statistical tests
based on the normality assumption is most severely affected.
244
-------
Appendix F
The Shapiro-Wilk test statistic (W) will tend to be large when a probability plot of the data
indicates a nearly straight line. Only when the plotted data show significant bends or curves will
the test statistic be small. The Shapiro-Wilk test is considered to be one of the very best tests
of normality available (Miller 1986, Madansky 1988).
Procedure
Step 1. Order the data from least to greatest, labeling the observations as xt for
/ = 1... n . Using the notation x^ , let the y'th order statistic from any data set
represent the y'th smallest value.
Step 2. Compute the differences x,n_i+l) — x,^ for each / = !...«. Then determine
k as the greatest integer less than or equal to (n 12) .
Step 3. Use Table G-4 in Appendix G to determine the Shapiro-Wilk coefficients, an-i+\,
for / = !...«. Note that while these coefficients depend only on the sample size
(n), the order of the coefficients must be preserved when used in step 4 below.
The coefficients can be determined for any sample size from n = 3 up to n = 50.
Step 4. Compute the quantity b given by the following formula:
k k
b = Z~ibi = Z~ian-i+i(x(n-i+i) ~x(t)} Equation F.1
7=1 7=1
Note that the values bi are simply intermediate quantities represented by the
terms in the sum of the right-hand expression in the above equation.
Step 5. Calculate the standard deviation (s) of the data set. Then compute the Shapiro-
Wilk test statistic using the following formula:
Equation F.2
Step 6. Given the significance level (a) of the test (for example, 0.01 or 0.05),
determine the critical point of the Shapiro-Wilk test with n observations using
Table G-5 in Appendix G. Compare the Shapiro-Wilk statistic (W) against the
critical point (wc). If the test statistic exceeds the critical point, accept normality
as a reasonable model for the underlying population; however, if W < wc, reject
the null hypothesis of normality at the a -level and decide that another
distributional model would provide a better fit.
An example calculation of the Shapiro-Wilk test for normality is presented in Box F.1.
245
-------
Appendix F
Box F.1. Example Calculation of the Shapiro-Wilk Test for Normality
Use the Shapiro-Wilk test for normality to determine whether the following data set, representing the total
concentration of nickel in a solid waste, follows a normal distribution: 58.8, 19,39,3.1, 1,81.5, 151,942,262,
331, 27, 85.6, 56, 14, 21.4, 10, 8.7, 64.4, 578, and 637.
Solution
Step 1.
Step 2.
Step 3.
Step 4.
Step 5.
Order the data from smallest to largest and list, as in Table F-2. Also list the data in reverse
order alongside the first column.
Compute the differences
X,_i+^ — X(i-> i
in column 4 of the table by subtracting column 2
from column 3. Because the total number of samples is n = 20 , the largest integer less than
or equal to (n 1 2) is k = 1 0 .
Look up the coefficients
from Table G-4 in Appendix G and list in column 4.
Multiply the differences in column 4 by the coefficients in column 5 and add the first k
products (bi ) to get quantity bi , using Equation F.1.
6 = [.4734(941. 0)+.3211(633.9) + --- .0140(2.8)] = 932.88
Compute the standard deviation of the sample, s = 259.72, then use Equation F.2 to calculate
the Shapiro-Wilk test statistic:
w=
932.88
259.72V19"
= 0.679
Step 6.
Use Table G-5 in Appendix G to determine the .01-level critical point for the Shapiro-Wilk test
when n =20. This gives Wc =0.868. Then, compare the observed value of W = 0.679 to
the 1-percent critical point. Since W < 0.868, the sample shows significant evidence of non-
normality by the Shapiro-Wlktest. The data should be transformed using natural logs and
rechecked using the Shapiro-Wilk test before proceeding with further statistical analysis.
246
-------
Appendix F
Table F-2. Example Calculation of the Shapiro-Wilk Test (see example in Box F.1)
*(„-)- *co «„-,* bt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
3.1
8.7
10
14
19
21.4
27
39
56
58.8
64.4
81.5
85.6
151
262
331
578
637
942
942
637
578
331
262
151
85.6
81.5
64.4
58.8
56
39
27
21.4
19
14
10
8.7
3.1
1
941
634
569
321
248
132
64.2
54.5
25.4
2.8
-2.8
-25.4
-54.5
-64.2
-132.0
-248.0
-321.0
-569.3
-633.9
-941.0
0.4734
0.3211
0.2565
0.2085
0.1686
0.1334
0.1013
0.0711
0.0422
0.0140
445.47
203.55
146.03
66.93
41.81
17.61
6.5
3.87
1.07
0.04
b = 932.88
F.2 Confidence Limits for the Mean
When a fixed standard or limit is meant to represent an average or mean concentration level,
attainment of the standard can be measured using a confidence limit on the mean. A
confidence limit is then compared with the fixed compliance limit. Under the null hypothesis that
the mean concentration in the waste exceeds the standard unless proven otherwise, statistically
significant evidence of compliance with the standard is shown if and only if the entire confidence
interval lies below the standard. By implication, the key test then involves comparing the upper
confidence limit (UCL) to the standard. In other words, the entire confidence interval must lie
below the standard for the waste to be compliant with the standard. If the UCL exceeds the
regulatory limit, on the other hand, we cannot conclude the mean concentration is below the
standard.
F.2.1 Confidence Limits for the Mean of a Normal Distribution
Requirements and Assumptions
Confidence intervals for the mean of a normal distribution should be constructed only if the data
pass a test of approximate normality or at least are reasonably symmetric. It is strongly
recommended that a confidence interval not be constructed with less than four measurements,
though the actual number of samples should be determined as part of the planning process.
The reason for this is two-fold: (1) the formula for a normal-based confidence interval on the
247
-------
Appendix F
mean involves calculation of the sample standard deviation (s), which is used as an estimate of
the underlying population standard deviation (this estimate may not be particularly accurate
when the sample size is smaller than four), and (2) the confidence interval formula also involves
a Student's f-quantile based on n - 1 degrees of freedom, where n equals the number of
samples used in the calculation (see Table G-1 in Appendix G). When n is quite small, the t-
quantile will be relatively large, leading to a much wider confidence interval than would be
expected with a larger n. For example, at a 90-percent confidence level, the appropriate t-
quantile would be t = 3.078 for n = 2, t = 1 .638 for n = 4, and t = 1 .41 5 for n = 8.
Procedure
Step 1 . Check the « sample concentrations for normality. If the normal model is
acceptable, calculate the mean (x) and standard deviation (s) of the data set. If
the lognormal model provides a better fit, see Section F.2.3.
Step 2. Given the desired level of confidence, (I — a), calculate the upper confidence
limit as follows:
UCL = x + tl_a ~ —j= Equation F.3
where tl_a # is obtained from a Student's ttable (Table G-1) with the
appropriate degrees of freedom. If simple random or systematic sampling is
used, then df = n — 1 .
If stratified random sampling is used, calculate the UCL as follows:
UCLst = xst + t,_a^s-st Equation F.4
where xst is the overall mean from Equation 8, the df is obtained from Equation
1 1 , and the standard error (s~ ) is obtained from Equation 9 (see also Table F-
xst
1 for these equations).
Step 3. Compare the UCL calculated in Step 2 to the fixed standard. If the UCL is less
than the standard, then you can conclude, with 100(1 — a )% confidence, that
the mean concentration of the constituent of concern is less than the standard.
If, however, the upper confidence bound is greater than the standard, then there
is not sufficient evidence that the mean is less than the standard.
An example calculation of the UCL on the mean is provided in Box F.2.
248
-------
Appendix F
Box F.2. Example Calculation of the UCL for a Normal Mean
A generator obtains ten samples of waste to demonstrate that the waste qualifies for the comparable fuels
exclusion under 40 CFR 261.38. The samples are obtained using a simple random sampling design. Analysis of
the samples for lead generated the following results: 16, 17.5, 21, 22, 23, 24, 24.5, 27, 31, and 38 ppm. The
regulation requires comparison of a 95% UCL on the mean to the specification level. The specification level is 31
ppm.
Solution
Step 1. Using the Shapiro-Wilk test, we confirmed that the normal model is acceptable. The mean is calculated
as 24.4 ppm and the standard deviation as 6.44 ppm.
Step 2. The RCRA regulations at 40 CFR 261.38(c)(8)(iii)(A) require that the determination be made with a level
of confidence, 100( 1 — a )%, of 95 percent. We turn to Table G-1 (Appendix G) and find the Student's t
value is 1.833 for n — 1 = 9 degrees of freedom. The UCL is calculated as follows:
644
UCL = 24.4 + 1.833-== = 28.1 - 28
vio
Step 3. We compare the limit calculated in step 2 to the fixed standard. Because the UCL (28 ppm) is less than
the regulatory level (31 ppm), we can conclude with at least 95-percent confidence that the mean
concentration of the constituent in the waste is less than 31 ppm.
F.2.2 Confidence Limits for a Normal Mean When Composite Sampling Is Used
If a composite sampling strategy has been employed to obtain a more precise estimate of the
mean, confidence limits can be calculated from the analytical results using the same procedure
outlined above in Section F.2.1, except that n represents the number of composite samples and
s represents the standard deviation of the n composite samples.
F.2.3 Confidence Limits for a Lognormal Mean
If the results of a test for normality indicate the data set may have a lognormal distribution, and
a confidence limit on the mean is desired, then a special approach is required. It is not correct
to simply transform the data to the log scale, calculate a normal-based mean and confidence
interval on the logged data, and transform the results back to the original scale. It is a common
mistake to do so. Invariably, a transformation bias will be introduced and the approach will
underestimate the mean and UCL. In fact, the procedure just described actually produces a
confidence interval around the median of a lognormal population rather than the higher-valued
mean.
To calculate a UCL on the mean for data that exhibit a lognormal distribution, this guidance
recommends use of a procedure developed by Land (1971, 1975); however, as noted below,
Land's procedure should be used with caution because it relies heavily on the lognormal
assumption, and if that assumption is not true, the results may be substantially biased.
Requirements and Assumptions
Confidence intervals for the mean of a lognormal distribution should be constructed only if the
data pass a test of approximate normality on the log-scale. While many environmental
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populations tend to follow the lognormal distribution, it is usually wisest to first test the data for
normality on the original scale. If such a test fails, the data can then be transformed to the log-
scale and retested.
Cautionary Note: Even if a data set passes a test for normality on the log scale, do not
proceed with calculation of the confidence limits using Land's procedure until you have
considered the following:
The skewness of the data set may be due to biased sampling, mixed distributions
of multiple populations, or outliers, and not necessarily due to lognormally
distributed data (see Singh, et al. 1997). Review the sampling approach, the
physical characteristics of the waste or media, and recheck any unusually high
values before computing the confidence limits. Where there is spatial clustering
of sample data, declustering and distribution weighting techniques (Myers 1997)
may also be appropriate.
If the number of samples («) is small, the confidence interval obtained by Land's
procedure could be remarkably wide. Singh, et al. (1997) have recommended
that Land's procedure not be used for cases in which the number of samples is
less than 30. They argue that in many cases the resulting UCL will be an order
of magnitude larger than the maximum observed data value. Even higher values
for the UCL could be generated if the coefficient of variation (CV or the standard
deviation divided by the mean) is greater than 1.
If the lognormal distribution is the best fit, and the number of samples («) is small, then Land's
method (provided below) can still be used, though a "penalty" will be paid for the small sample
size. If the number of samples is small and the distribution is skewed to the right, one of the
following alternative approaches should be considered: (1) Simply treat the data set as if the
parent distribution were normal and use the parametric Student-f method to calculate
confidence limits using the untransformed (original scale) data (as described in Section F.2.1).
If, however, this normal theory approach is used with highly skewed data, the actual confidence
level achieved by the test will be less than that desired (Porter, et al. 1997); (2) UCLs on the
mean could be constructed using procedures such as the "bootstrap" or the "jackknife," as
recommended by Singh, et al. (1997) (see Section F.2.4).
The approach for Land's "H-statistic" method is given below:
Procedure
Step 1. Test the data for normality on the log-scale. After determining that the lognormal
distribution is a good fit, transform the data via logarithms (the natural log is
used) and denote the transformed measurements by yt.
Step 2. Compute the sample mean and the standard deviation (sy ) from the log-scale
measurements.
Step 3. Obtain Land's bias-correction factor(s) (//1-a) from Table G-6 in Appendix G,
where the correct factor depends on the sample size («), the log-scale sample
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standard deviation (sy), and the desired confidence level (1 — a ).1
Step 4. Plug all these factors into the equations given below for the UCL.
( s H^ a\
UCL,_a = exp y+ .5s2y + y/—^ Equation F.5
Step 5. Compare the UCL against the fixed standard. If the UCL is less than the
standard, then you can conclude with 100( 1 — a)% confidence that the mean
concentration of the constituent of concern is less than the standard. If, however,
the upper confidence bound is greater than the standard, then there is not
sufficient evidence that the mean is less than the standard.
An example calculation of the UCL on a lognormal mean is given in Box F.3.
Box F.3: Example Calculation of the UCL on a Lognormal Mean
This example is modified after an example provided in Supplemental Guidance to RAGS: Calculating the
Concentration Term (USEPA 1992a).
The concentration of lead (total in mg/Kg) in 31 soil samples obtained using a simple random sampling design
are: 1, 3, 13, 14, 18, 20, 21, 36, 37, 41, 42, 45, 48, 59, 60, 110, 110, 111, 111, 136, 137, 140, 141, 160, 161, 200,
201, 230, 400, 1300, and 1400. Using these data, calculate a 90% UCL on the mean.
Solution
Step 1. Using the Shapiro-Wilk test, the natural logarithms of the data set are shown to exhibit a normal
distribution. The data are then transformed to natural logs.
Step 2. The mean of logged data is y = 4.397 . The standard deviation is S = 1.509 .
Step 3. The bias-correction factor (Hl_a = 2.282 ) is obtained from Table G-6 for n = 3 1 and a confidence
level of 90 percent.
Step 4. Plug the factors into the equation for the upper (UCL) confidence limit.
UCLt_a = expf 4.222 + 0.5(1.509)' +
= exp(5.989) = 399 mg / kg
Step 5. The 90-percent UCL on the mean is 399 mg/kg.
1 For a more extensive tabulation of Land's factors, see Land (1975) or Tables A10 through A13 in Gilbert
(1987).
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F.2.4 Confidence Limits for the Mean of a Non-normal or Unknown Distribution
If the assumption of a normal or lognormal distribution cannot be justified, then you may
construct a UCL on the mean using one of several alternative methods described in this section.
Bootstrap or Jackknife Methods: Bootstrap and jackknife procedures, as discussed by Efron
(1981) and Miller (1974), typically are nonparametric statistical techniques which can be used to
reduce the bias of point estimates and construct approximate confidence intervals for
parameters such as the population mean. These procedures require no assumptions regarding
the statistical distribution (e.g., normal or lognormal) for the underlying population.
Using a computer, the bootstrap method randomly samples n values with replacement from the
original set of n random observations. For each bootstrap sample, the mean (or some other
statistic) is calculated. This process of "resampling" is repeated hundreds or perhaps
thousands of times and the multiple estimates of the mean are used to define the confidence
limits on the mean. The jackknife approximates the bootstrap. Rather than resampling
randomly from the entire sample like the bootstrap does, the jackknife takes the entire sample
except for one value, and then calculates the statistic of interest. It repeats the process, each
time leaving out a different value, and each time recalculating the test statistic.
Both the bootstrap and the jackknife methods require a great deal of computer power, and,
historically have not been widely adopted by environmental statisticians (Singh, et al. 1997).
However, with advances in computer power and availability of software, computationally
intensive statistical procedures have become more practical and accessible. Users of this
guidance interested in applying a "resampling" method such as the bootstrap or jackknife should
check the capabilities of available software packages and consult with a professional statistician
on the correct use and application of the procedures.
Nonparametric Confidence Limits: If the data are not assumed to follow a particular
distribution, then it may not be possible to calculate a UCL on the mean using normal theory
techniques. If, however, the data are non-normal but approximately symmetric, a
nonparametric UCL on the median (or the 50th percentile) may serve as a reasonable alternative
to calculation of a parametric UCL on the mean. One severe limitation of this approach is that it
involves changing the parameter of interest (as determined in the DQO Process) from the mean
to the median, potentially biasing the result if the distribution of the data is not symmetric.
Accordingly, the procedure should be used with caution.
Lookup tables can be used to determine the confidence limits on the median (50th percentile).
For example, see Conover (1999, Table A3) or Gilbert (1987, Table A14). In general, when the
sample size is very small (e.g., less than about nine or ten samples) and the required level of
confidence is high (e.g., 95 to 99 percent), the tables will designate the maximum value in the
data set as the upper confidence limit. Conover (1999, page 143) gives a large sample
approximation for a confidence interval on a proportion (quantile). Methods also are given in
Gilbert (1987, page 173), Hahn and Meeker (1991, page 83), and USEPA (1992i, page 5-30).
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F.3 Tests for a Proportion or Percentile
Some RCRA standards represent concentrations that should rarely or never be exceeded for
the waste or media to comply with the standard. To measure compliance with such a standard,
a waste handler may want to know with some specified level of confidence that a high
proportion of the waste complies with the standard (or conversely, that at most only a small
proportion of all possible samples could exceed the standard). Two approaches are given for
measuring compliance with such a standard:
1. Under the assumption of a normal distribution, use a parametric UCL on a
percentile to demonstrate that the true/?th percentile (xp) concentration in the set
of all possible samples is less than the concentration standard. The method is
given below in Section F.3.1.
2. By far, the simplest method for testing proportions is to use an "exceedance rule"
in which the proportion of the population with concentrations less than the
standard can be estimated based on the total number of sample values and the
number of those (if any) that exceed the standard. The exceedance rule method
is given below in Section F.3.2.
If the number of samples is relatively large, then a "one-sample proportion test" also can be
used to test a proportion against a fixed standard. The one-sample proportion test is described
in Section 3.2.2.1 in Guidance for Data Quality Assessment, EPA QA/G-9 (QAOO Update)
(USEPA2000d).
F.3.1 Parametric Upper Confidence Limits for an Upper Percentile
If the study objective is to demonstrate that the true/?th percentile (xp) concentration in the set of
all possible samples (of a given sample support) is less than the applicable standard or Action
Level, then a UCL on the upper percentile can be used to determine attainment of the standard.
Requirements and Assumptions
The formulas for constructing parametric UCL on an upper percentile assume that the data are
at least approximately normally distributed. Therefore, such a limit should be constructed only if
the data pass a test of normality. If the data are best fit by a lognormal distribution instead, the
observations should first be transformed to the log-scale. Unlike confidence limits for a
lognormal mean, no special equations are required to construct similar limits on an upper
percentile. The same formula used when the data are normally distributed can be applied to the
log-scale data. The only additional step is that the confidence interval limits must be re-
exponentiated before comparing them against the regulatory standard.
It is strongly recommended that a confidence limit not be constructed with less than four
measurements, and preferably more (the actual number, however, should be determined during
Step Seven of the DQO Process). There are three reasons for this: (1) the formula for a
normal-based confidence interval on an upper percentile involves calculation of the sample
standard deviation, s, which is used as an estimate of the underlying population standard
deviation. This estimate may not be accurate when fewer than four samples are used. (2) The
confidence interval formula also involves a special factor K ("kappa"), which depends on both
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the desired confidence level (1 — a ) and the number of samples, n, used in the calculation.
When n is quite small, the K factor is more extreme, leading to a much wider confidence
interval than would be expected with a larger«. For example, at a confidence level of 90
percent, the appropriate K factor for an upper one-sided limit on the 99th percentile is K =
18.50 when n = 2, K = 5.438 when n = 4, and K = 3.783 when n = 8. (3) The third reason is
that the power of the test for normality or lognormality is very low with a small number of
samples.
Procedure
Step 1.
Step 2.
Step 3.
First test the data for normality on the original scale. If a test of normality is
passed, calculate the limit on the raw measurements. If the data violate the
assumption of normality, but pass a test of lognormality, calculate the limit using
the log-scale data.
If the data are normal, compute the mean and standard deviation of the raw data.
If the data are consistent with lognormality instead, compute the mean and
standard deviation after first transforming the data to the log-scale.
Given the percentile (p) being estimated, the sample size (n), and the desired
confidence level (I — a), use Table G-2 (in Appendix G) to determine the K
factor(s) needed to construct the appropriate UCL. A one-sided upper
confidence bound is then computed with the formula
a(xp) = x + s'K\-a P Equation F.6
where Kl_a is the upper 1-a factor for the/?th percentile with n sample
measurements.
Again, if the data are lognormal instead of normal, the same formula would be
used but with the log-scale mean and standard deviation substituted for the raw-
scale values. Then the limit must be exponentiated to get the final upper
confidence bound, as in the following formula for an upper bound with
(l-a)100% confidence:
Equation F.7
Step 4. Compare the upper (1 — #)100% confidence bound against the fixed standard.
If the upper limit exceeds the standard, then the standard is not met.
An example calculation of the UCL on a percentile is given in Box F.4.
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Appendix F
Box F.4. Example Calculation of a UCL on an Upper Percentile To Classify a Solid Waste
A secondary lead smelter produces a slag that under some operating conditions exhibits the Toxicity
Characteristic (TC) for lead. The facility owner needs to classify a batch of waste as either hazardous or
nonhazardous at the point of waste generation. During the planning process, the owner determined based on
previous sampling studies that the constituent of interest is lead, TCLP results for lead tend to exhibit a normal
distribution, and a sample size often 200-gram samples (not including QC samples) should satisfy the study
objectives. The TC regulatory level for lead is 5 mg/L. The owner wants to determine, with 90-percent
confidence, whether a large proportion (e.g., at least 95 percent) of all possible samples of the waste will be
below the regulatory limit.
At the point of waste generation, the facility representative takes a series of systematic samples of the waste.
The following sample analysis results were generated for ten samples analyzed for lead via the TCLP and SW-
846 Method 6010B: <0.5, 0.55, 0.60, 0.80, 0.90, 1.00, 1.50, 1.80, 2.00, and 3.00 mg/L.
Calculate a 90-percent upper confidence limit on the 95th percentile.
Solution
Step 1. Based on the shape of the histogram and normal probability plot, the data were judged to exhibit a
normal distribution. Therefore, we proceed with the calculation on the original (untransformed) scale.
Step 2. One value (10% of the measurements) is reported below the quantitation limit of 0.5 mg/L so we
replace that value with half the quantitation limit (0.25 mg/L) (see also Section F.4). The mean and
standard deviation of the data set are then calculated as X = 1.24 mg/L and s = 0.836.
Step 3. Use Table G-2 (in Appendix G) to determine the K factor for n = 10 needed to construct a 90-percent
UCL on the 95th percentile. The table indicates K = 2.568 . Plug X , S , and K into Equation F.6,
as follows:
f/L090(jc095) = 1.24 + (0.836)(2.568) = 3.39 - 3.4mg/L
Step 4. All of the sample analysis results are less than the TC regulatory limit of 5 mg/L TCLP for lead, and the
owner concludes that the waste is a nonhazardous waste under RCRA. The owner also can conclude
with at least 90-percent confidence that at least 95 percent of all possible sample analysis results
representing the batch of waste in the roll-off bin are nonhazardous.
F.3.2 Using a Simple Exceedance Rule Method for Determining Compliance With A
Fixed Standard
Some RCRA standards represent concentration limits that should never or rarely be exceeded
or waste properties that should never or rarely be exhibited for the waste to comply with the
standard. One of the simplest nonparametric methods for determining compliance with such a
standard is to use an "exceedance rule" (USEPA 1989a). To apply this method, simply require
that a number of samples be acquired and that zero or a small number (e.g., one) of the
concentration measurements be allowed to exceed the standard. This kind of rule is easy to
implement and evaluate once the data are collected. It only requires specification of a number
of samples and the number of exceedances allowed (usually zero, for example, for compliance
with the LDR concentration level treatment standards). Alternately, one can specify the
statistical performance criteria in advance and then determine the number of samples required.
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Appendix F
Requirements and Assumptions for Use of an Exceedance Rule
The method given here is a simple nonparametric method and requires only the ability to
identify the number of samples in the data set and whether each sample analysis result
complies with the applicable standard or does not comply with the standard. Unfortunately, this
ease of use comes with a price. Compared to parametric methods that assume underlying
normality or lognormality of the data, the nonparametric method given here requires significantly
more samples to achieve the same level of confidence.
Procedure
Step 1: Specify the degree of confidence desired, 100(l- #)% , and the proportion (p)
of the population that must comply with the standard.
Step 2: If the decision rule permits no exceedance of the standard for any single sample
in a set of samples, then obtain and analyze the number of samples (ri) indicated
in Table G-3a in Appendix G.
If the decision rule permits a single exceedance of the standard in a set of
samples, then obtain and analyze the number of samples (ri) indicated in Table
G-3b in Appendix G.
Step 3: Based on the number of samples obtained and the statistical performance
required, determine whether the applicable standard has been attained.
An example application of the exceedance rule is Box F.5.
Box F.5: Example Application of a Simple Exceedance Rule
A facility has treated nonwastewater F003 solvent waste containing carbon disulfide to attain the LDR UTS.
Samples of the treatment residue are obtained systematically as the waste treatment is completed. The treater
wants to have at least 90% confidence that at least 90% of the batch of treated waste attains the standard. To
comply with the LDR regulations, no samples can exceed the UTS. TCLP analyses for carbon disulfide in the
treated waste are required to measure compliance with the treatment standard of 4.8 mg/L TCLP.
From Table G-3a we find that for a confidence level (1 — a ) of .90 (or 90%) and a proportion of .90, at least 22
samples are required. All sample analysis results must be less than or equal to the UTS of 4.8 mg/L TCLP for
the statistical performance criteria to be achieved.
If only 9 samples are obtained (with all sample analysis results less than or equal to the standard), what level of
confidence can the treater have that at least 90-percent (orp = 0.90) of all possible samples drawn from the
waste meet the treatment standard?
From Table G-3a we find for;? = 0.90 and n = 9, 1 - CX =0.60. Therefore, the 100(1-«)% confidence level
equals only 60 percent.
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Appendix F
F.4 Treatment of Nondetects in Statistical Tests
Data generated from chemical analysis may fall below a limit of detection of the analytical
procedure. These measurement data generally are described as "nondetects", (rather than as
zero or not present) and the appropriate limit of detection - such as a quantitation limit - usually
is reported. Data sets that include both detected and nondetected results are called "censored"
data in the statistical literature.
If a relatively small proportion of the data are reported below detection limit values, replacing the
nondetects with a small number (between zero and the detection limit) 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 in which relatively large amounts of data
below the detection limit exist, one may need only to consider whether the chemical was
detected as above some level or not.
F.4.1 Recommendations
If no more than approximately 15 percent of the sample analysis results are nondetect for a
given constituent, then the results of parametric statistical tests will not be substantially affected
if nondetects are replaced by half their detection limits (USEPA 1992b).2 When more than
approximately 15 percent of the samples are nondetect, however, the handling of nondetects is
more crucial to the outcome of statistical procedures. Indeed, simple substitution methods tend
to perform poorly in statistical tests when the nondetect percentage is substantial (Gilliom and
Helsel 1986). If the percentage of nondetects is between approximately 15 percent and 50
percent, we recommend use of Cohen's Adjustment (see method below).
The conditions for use of Cohen's method, however, are limited (see method given below) and
numerous alternative techniques for imputing left-censored data should be considered if the
conditions for use of Cohen's method do not apply. Other methods available include iterative
techniques, regression on order statistics (ROS) methods, bias-corrected maximum likelihood
estimator (MLE), restricted MLE, modified probability plotting, Winsorization, and lognormalized
statistics (EPA Delta log). A modified probability plotting method called Helsel's Robust Method
(Helsel 1990) is a popular method that should be considered. Most of the above methods can
be performed using publicly available software entitled UnCensor© v. 4.0 (Newman et al. 1995).
Although EPA's Office of Solid Waste has not reviewed or tested this software, users of this
guidance may be interested in investigating its use.
If the percentage of nondetects is greater than 50 percent, then the regression on order
statistics method or Helsel's Robust Method should be considered. As an alternative, EPA's
Guidance for Data Quality Assessment EPA QA/G-9 (USEPA 2000d) suggests the use of a test
for proportions when the percentage of nondetects is in the range of greater than 50 percent to
90 percent.
This guidance does not advocate a specific method for imputing or replacing values that lie
2 Additional experience and research for EPA supporting development of guidance on the statistical analysis
of ground-water monitoring data indicates that if the percentage of nondetects is as high as 20 to 25 percent, the
results of parametric statistical tests may not be substantially affected if the nondetects are replaced with half their
detection limits (Cameron 1999).
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Appendix F
below the limit of detection, however, whichever method is selected should be adequately
supported. Table F-3 provides a summary of approaches for handling nondetects in statistical
intervals.
Table F-3. Guidance for Handling Nondetects In Statistical Intervals
Percentage of Data Reported as
"Nondetect"
Recommended Treatment of Data Set
15% to 50%
> 50%
Replace nondetects with DL/2
Cohen's adjustment, regression order statistics,
or Helsel's Robust Method
Regression on order statistics, Helsel's Robust
Method, or a test for proportions
Even with a small proportion of nondetects, care should be taken when choosing which value
should be used as the "detection limit". There are important differences between the method
detection limit and the quantitation limit (QL) in characterizing "nondetect" concentrations. Many
nondetects are characterized by analytical laboratories with one of three data qualifier flags: "U,"
"J," or "E." Samples with a "U" data qualifier represent "undetected" measurements, meaning
that the signal characteristic of that analyte could not be observed or distinguished from
"background noise" during lab analysis. Inorganic samples with an "E" flag and organic samples
with a "J" flag may or may not be reported with an estimated concentration. If no concentration
estimate is reported, these samples represent "detected but not quantified" measurements. In
this case, the actual concentration is assumed to be positive, falling somewhere between zero
and the QL. Because the actual concentration is unknown, the suggested substitution for
parametric statistical procedures is to replace each nondetect qualified with an "E" or "J" with
one-half the QL. Note, however, that "E" and "J" samples reported with estimated
concentrations should be treated, for statistical purposes, as valid measurements. In other
words, substitution of one-half the QL is not recommended for samples for which an estimated
concentration is provided.
As a general rule, nondetect concentrations should not be assumed to be bounded above by
the MDL. The MDL is usually estimated on the basis of ideal laboratory conditions with analyte
samples that may or may not account for matrix or other interferences encountered when
analyzing specific, actual field samples. For this reason, the QL typically should be taken as the
most reasonable upper bound for nondetects when imputing specific concentration values to
these measurements.
If a constituent is reported only as "not detected" and a detection limit is not provided, then
review the raw data package to determine if a detection limit was provided. If not, identify the
analytical method used and consult a qualified chemist for guidance on an appropriate QL.
F.4.2 Cohen's Adjustment
If a confidence limit is used to compare waste concentrations to a fixed standard, and a
significant fraction of the observed measurements in the data set are reported as nondetects,
simple substitution techniques (such as putting in half the detection limit for each nondetect) can
lead to biased estimates of the mean or standard deviation and inaccurate confidence limits.
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Appendix F
By using the detection limit and the pattern seen in the detected values, Cohen's method
(Cohen 1959) attempts to reconstruct the key features of the original population, providing
explicit estimates of the population mean and standard deviation. These, in turn, can be used
to calculate confidence intervals, where Cohen's adjusted estimates are used as replacements
for the sample mean and sample standard deviation.
Requirements and Assumptions
Cohen's Adjustment assumes that the common underlying population is normal. As such, the
technique should only be used when the observed sample data approximately fit a normal
model. Because the presence of a large fraction of nondetects will make explicit normality
testing difficult, if not impossible, the most helpful diagnostic aid may be to construct a censored
probability plot on the detected measurements. If the censored probability plot is clearly linear
on the original measurement scale but not on the log-scale, assume normality for purposes of
computing Cohen's Adjustment. If, however, the censored probability plot is clearly linear on
the log-scale, but not on the original scale, assume the common underlying population is
lognormal instead; then compute Cohen's Adjustment to the estimated mean and standard
deviation on the log-scale measurements and construct the desired statistical interval using the
algorithm for lognormally-distributed observations (see also Gilbert 1987, page 182).
When more than 50 percent of the observations are nondetect, the accuracy of Cohen's method
breaks down substantially, getting worse as the percentage of nondetects increases. Because
of this drawback, EPA does not recommend the use of Cohen's adjustment when more than
half the data are nondetect. In such circumstances, one should consider an alternate statistical
method (see Section F.4.1).
One other requirement of Cohen's method is that there be just a single censoring point. As
discussed previously, data sets with multiple detection or quantitation limits may require a more
sophisticated treatment.
Procedure
Step 1. Divide the data set into two groups: detects and nondetects. If the total sample
size equals n, let m represent the number of detects and (n - m) represent the
number of nondetects. Denote the /th detected measurement by jc., then
compute the mean and sample variance of the group of detects (i.e., above the
quantitation limit data) using the following formulas:
i m
xd= —^ x. Equation F.8
and
m- 1
m
t? - mx2d
Equation F.9
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Appendix F
Step 2. Denote the single censoring point (e.g., the quantitation limit) by QL. Then
compute the two intermediate quantities, h and 7 , necessary to derive Cohen's
adjustment via the following equations:
h = (n-ni)/n Equation F.10
and
y = s2d/(xd-QLf Equation F.11
Step 3. Use the intermediate quantities, h and 7 to determine Cohen's adjustment
^
parameter /I from Table G-7 in Appendix G. For example, if h = 0.4 and j =
0.30, then A = 0.6713.
^
Step 4. Using the adjustment parameter /I found in step 3, compute adjusted estimates
of the mean and standard deviation with the following formulas:
^.
x = xd - A(xd - QL) Equation F.12
and
+ A(xd - QL)2 Equation F.13
Step 5. Once the adjusted estimates for the population mean and standard deviation are
derived, these values can be substituted for the sample mean and standard
deviation in formulas for the desired confidence limit.
An example calculation using Cohen's method is given in Box F.6.
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Appendix F
Box F.6. An Example of Cohen's Method
To determine attainment of a cleanup standard at SWMU, 24 random soil samples were obtained and analyzed
for pentachlorophenol. Eight of the 24 values (33%) were below the matrix/laboratory-specific quantitation limit
of 1 mg/L. The 24 values are <1.0, <1.0, <1.0, <1.0, <1.0, <1.0, <1.0, <1.0, 1.1, 1.5, 1.9, 2.0, 2.5, 2.6, 3.1, 3.3,
3.2, 3.2, 3.3, 3.4, 3.5, 3.8, 4.5, 5.8 mg/L. Cohen's Method will be used to adjust the sample mean and standard
deviation for use in constructing a UCL on the mean to determine if the cleanup has attained the site-specific
risk-based cleanup standard of 5.0 mg/kg.
Solution
Step 1: The sample mean of the™ = 16 values greater than the quantitation limit \sxd = 3.044
2
Step 2: The sample variance of the 16 quantified values is sd = 1.325.
Step 3: /z = (24-16)/ 24 = 0.333 and J = 1.3257(3.044 - 1.0)2 = 0.317
^
Step 4: Table G-7 of Appendix G was used for h = 0.333 and J = 0.317 to find the value of /I . Since the
^
table does not contain these entries exactly, double linear interpolation was used to estimate A =
0.5223.
Step 5: The adjusted sample mean and standard deviation are then estimated as follows:
X = 3.044 - 0.5223 (3.044 -1.0) = 1.976 ~ 2.0 and
s = ^1-325 + 0.5223(3.044 - l.O)2 = 1.873 « 1.9
261
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262
-------
APPENDIX G
STATISTICAL TABLES
Table G-1. Critical Values of Student's t Distribution (One-Tailed)
Degrees
of
Freedom
(see note)
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
CO
0.70
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
0.75
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
0.688
0.687
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
0.80
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
t values
0.85
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
for (I — a)
0.90
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
1.340
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
or(l-fi)
0.95
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
0.975
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
0.99
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
0.995
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: For simple random or systematic sampling, degrees of freedom (df
collected from a solid waste and analyzed, less one (in other words, df =
used, calculate df using Equation 12 or 14 in Section 5.4.2.2.
) are equal to the number of samples (n
n — \). If stratified random sampling is
The last row of the table ( °« degrees of freedom) gives the critical values for a standard normal distribution ( z
For example, the z value for 1 — ot, where a = 0.10 is found in the last row as 1.282.
263
-------
Appendix G
Table G-2. Factors (K ) for Parametric Upper Confidence Bounds on Upper Percentiles (p)
n
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
55
60
65
70
75
80
85
90
95
100
I- a °-800
3.417
2.016
1.675
1.514
1.417
1.352
1.304
1.266
1.237
1.212
1.192
1.174
1.159
1.145
1.133
1.123
1.113
1.104
1.096
1.089
1.082
1.076
1.070
1.065
1.060
1.055
1.051
1.047
1.043
1.039
1.035
1.032
1.029
1.026
1.023
1.020
1.017
1.015
1.013
1.010
1.008
1.006
1.004
1.002
1.000
0.998
0.996
0.994
0.993
0.985
0.978
0.972
0.967
0.963
0.959
0.955
0.951
0.948
0.945
0.900
6.987
3.039
2.295
1.976
1.795
1.676
1.590
1.525
1.474
1.433
1.398
1.368
1.343
1.321
1.301
1.284
1.268
1.254
1.241
1.229
1.218
1.208
1.199
1.190
1.182
1.174
1.167
1.160
1.154
1.148
1.143
1.137
1.132
1.127
1.123
1.118
1.114
1.110
1.106
1.103
1.099
1.096
1.092
1.089
1.086
1.083
1.080
1.078
1.075
1.063
1.052
1.043
1.035
1.028
1.022
1.016
1.011
1.006
1.001
p = 0.80
0.950
14.051
4.424
3.026
2.483
2.191
2.005
1.875
1.779
1.703
1.643
1.593
1.551
1.514
1.483
1.455
1.431
1.409
1.389
1.371
1.355
1.340
1.326
1.313
1.302
1.291
1.280
1.271
1.262
1.253
1.245
1.237
1.230
1.223
1.217
1.211
1.205
1.199
1.194
1.188
1.183
1.179
1.174
1.170
1.165
1.161
1.157
1.154
1.150
1.146
1.130
1.116
1.104
1.094
1.084
1.076
1.068
1.061
1.055
1.049
0.975
28.140
6.343
3.915
3.058
2.621
2.353
2.170
2.036
1.933
1.851
1.784
1.728
1.681
1.639
1.603
1.572
1.543
1.518
1.495
1.474
1.455
1.437
1.421
1.406
1.392
1.379
1.367
1.355
1.344
1.334
1.325
1.316
1.307
1.299
1.291
1.284
1.277
1.270
1.263
1.257
1.251
1.246
1.240
1.235
1.230
1.225
1.220
1.216
1.211
1.191
1.174
1.159
1.146
1.135
1.124
1.115
1.106
1.098
1.091
0.990
70.376
10.111
5.417
3.958
3.262
2.854
2.584
2.391
2.246
2.131
2.039
1.963
1.898
1.843
1.795
1.753
1.716
1.682
1.652
1.625
1.600
1.577
1.556
1.537
1.519
1.502
1.486
1.472
1.458
1.445
1.433
1.422
1.411
1.400
1.391
1.381
1.372
1.364
1.356
1.348
1.341
1.333
1.327
1.320
1.314
1.308
1.302
1.296
1.291
1.266
1.245
1.226
1.210
1.196
1.183
1.171
1.161
1.151
1.142
p = 0.90
0.800
5.049
2.871
2.372
2.145
2.012
1.923
1.859
1.809
1.770
1.738
1.711
1.689
1.669
1.652
1.637
1.623
1.611
1.600
1.590
1.581
1.572
1.564
1.557
1.550
1.544
1.538
1.533
1.528
1.523
1.518
1.514
1.510
1.506
1.502
1.498
1.495
1.492
1.489
1.486
1.483
1.480
1.477
1.475
1.472
1.470
1.468
1.465
1.463
1.461
1.452
1.444
1.437
1.430
1.425
1.420
1.415
1.411
1.408
1.404
0.900
10.253
4.258
3.188
2.742
2.494
2.333
2.219
2.133
2.066
2.011
1.966
1.928
1.895
1.867
1.842
1.819
1.800
1.782
1.765
1.750
1.737
1.724
1.712
1.702
1.691
1.682
1.673
1.665
1.657
1.650
1.643
1.636
1.630
1.624
1.618
1.613
1.608
1.603
1.598
1.593
1.589
1.585
1.581
1.577
1.573
1.570
1.566
1.563
1.559
1.545
1.532
1.521
1.511
1.503
1.495
1.488
1.481
1.475
1.470
0.950
20.581
6.155
4.162
3.407
3.006
2.755
2.582
2.454
2.355
2.275
2.210
2.155
2.109
2.068
2.033
2.002
1.974
1.949
1.926
1.905
1.886
1.869
1.853
1.838
1.824
1.811
1.799
1.788
1.777
1.767
1.758
1.749
1.740
1.732
1.725
1.717
1.710
1.704
1.697
1.691
1.685
1.680
1.674
1.669
1.664
1.659
1.654
1.650
1.646
1.626
1.609
1.594
1.581
1.570
1.559
1.550
1.542
1.534
1.527
0.975
41 .201
8.797
5.354
4.166
3.568
3.206
2.960
2.783
2.647
2.540
2.452
2.379
2.317
2.264
2.218
2.177
2.141
2.108
2.079
2.053
2.028
2.006
1.985
1.966
1.949
1.932
1.917
1.903
1.889
1.877
1.865
1.853
1.843
1.833
1.823
1.814
1.805
1.797
1.789
1.781
1.774
1.767
1.760
1.753
1.747
1.741
1.735
1.730
1.724
1.700
1.679
1.661
1.645
1.630
1.618
1.606
1.596
1.586
1.578
0.990
103.029
13.995
7.380
5.362
4.411
3.859
3.497
3.240
3.048
2.898
2.777
2.677
2.593
2.521
2.459
2.405
2.357
2.314
2.276
2.241
2.209
2.180
2.154
2.129
2.106
2.085
2.065
2.047
2.030
2.014
1.998
1.984
1.970
1.957
1.945
1.934
1.922
1.912
1.902
1.892
1.883
1.874
1.865
1.857
1.849
1.842
1.835
1.828
1.821
1.790
1.764
1.741
1.722
1.704
1.688
1.674
1.661
1.650
1.639
264
-------
Appendix G
Table G-2. Factors (K ) for Parametric Upper Confidence Bounds on Upper Percentiles (p) (continued)
n
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
55
60
65
70
75
80
85
90
95
100
I- a °-800
6.464
3.604
2.968
2.683
2.517
2.407
2.328
2.268
2.220
2.182
2.149
2.122
2.098
2.078
2.059
2.043
2.029
2.016
2.004
1.993
1.983
1.973
1.965
1.957
1.949
1.943
1.936
1.930
1.924
1.919
1.914
1.909
1.904
1.900
1.895
1.891
1.888
1.884
1.880
1.877
1.874
1.871
1.868
1.865
1.862
1.859
1.857
1.854
1.852
1.841
1.832
1.823
1.816
1.810
1.804
1.799
1.794
1.790
1.786
0.900
13.090
5.311
3.957
3.400
3.092
2.894
2.754
2.650
2.568
2.503
2.448
2.402
2.363
2.329
2.299
2.272
2.249
2.227
2.208
2.190
2.174
2.159
2.145
2.132
2.120
2.109
2.099
2.089
2.080
2.071
2.063
2.055
2.048
2.041
2.034
2.028
2.022
2.016
2.010
2.005
2.000
1.995
1.990
1.986
1.981
1.977
1.973
1.969
1.965
1.948
1.933
1.920
1.909
1.899
1.890
1.882
1.874
1.867
1.861
p = 0.95
0.950
26.260
7.656
5.144
4.203
3.708
3.399
3.187
3.031
2.911
2.815
2.736
2.671
2.614
2.566
2.524
2.486
2.453
2.423
2.396
2.371
2.349
2.328
2.309
2.292
2.275
2.260
2.246
2.232
2.220
2.208
2.197
2.186
2.176
2.167
2.158
2.149
2.141
2.133
2.125
2.118
2.111
2.105
2.098
2.092
2.086
2.081
2.075
2.070
2.065
2.042
2.022
2.005
1.990
1.976
1.964
1.954
1.944
1.935
1.927
0.975
52.559
10.927
6.602
5.124
4.385
3.940
3.640
3.424
3.259
3.129
3.023
2.936
2.861
2.797
2.742
2.693
2.650
2.611
2.576
2.544
2.515
2.489
2.465
2.442
2.421
2.402
2.384
2.367
2.351
2.336
2.322
2.308
2.296
2.284
2.272
2.262
2.251
2.241
2.232
2.223
2.214
2.206
2.198
2.190
2.183
2.176
2.169
2.163
2.156
2.128
2.103
2.082
2.063
2.047
2.032
2.019
2.006
1.995
1.985
0.990
131.426
17.370
9.083
6.578
5.406
4.728
4.285
3.972
3.738
3.556
3.410
3.290
3.189
3.102
3.028
2.963
2.905
2.854
2.808
2.766
2.729
2.694
2.662
2.633
2.606
2.581
2.558
2.536
2.515
2.496
2.478
2.461
2.445
2.430
2.415
2.402
2.389
2.376
2.364
2.353
2.342
2.331
2.321
2.312
2.303
2.294
2.285
2.277
2.269
2.233
2.202
2.176
2.153
2.132
2.114
2.097
2.082
2.069
2.056
p = 0.99
0.800
9.156
5.010
4.110
3.711
3.482
3.331
3.224
3.142
3.078
3.026
2.982
2.946
2.914
2.887
2.863
2.841
2.822
2.804
2.789
2.774
2.761
2.749
2.738
2.727
2.718
2.708
2.700
2.692
2.684
2.677
2.671
2.664
2.658
2.652
2.647
2.642
2.637
2.632
2.627
2.623
2.619
2.615
2.611
2.607
2.604
2.600
2.597
2.594
2.590
2.576
2.564
2.554
2.544
2.536
2.528
2.522
2.516
2.510
2.505
0.900
18.500
7.340
5.438
4.666
4.243
3.972
3.783
3.641
3.532
3.443
3.371
3.309
3.257
3.212
3.172
3.137
3.105
3.077
3.052
3.028
3.007
2.987
2.969
2.952
2.937
2.922
2.909
2.896
2.884
2.872
2.862
2.852
2.842
2.833
2.824
2.816
2.808
2.800
2.793
2.786
2.780
2.773
2.767
2.761
2.756
2.750
2.745
2.740
2.735
2.713
2.694
2.677
2.662
2.649
2.638
2.627
2.618
2.609
2.601
0.950
37.094
10.553
7.042
5.741
5.062
4.642
4.354
4.143
3.981
3.852
3.747
3.659
3.585
3.520
3.464
3.414
3.370
3.331
3.295
3.263
3.233
3.206
3.181
3.158
3.136
3.116
3.098
3.080
3.064
3.048
3.034
3.020
3.007
2.995
2.983
2.972
2.961
2.951
2.941
2.932
2.923
2.914
2.906
2.898
2.890
2.883
2.876
2.869
2.862
2.833
2.807
2.785
2.765
2.748
2.733
2.719
2.706
2.695
2.684
0.975
74.234
15.043
9.018
6.980
5.967
5.361
4.954
4.662
4.440
4.265
4.124
4.006
3.907
3.822
3.749
3.684
3.627
3.575
3.529
3.487
3.449
3.414
3.382
3.353
3.325
3.300
3.276
3.254
3.233
3.213
3.195
3.178
3.161
3.145
3.131
3.116
3.103
3.090
3.078
3.066
3.055
3.044
3.034
3.024
3.014
3.005
2.996
2.988
2.980
2.943
2.911
2.883
2.859
2.838
2.819
2.802
2.786
2.772
2.759
0.990
185.617
23.896
12.387
8.939
7.335
6.412
5.812
5.389
5.074
4.829
4.633
4.472
4.337
4.222
4.123
4.037
3.960
3.892
3.832
3.777
3.727
3.681
3.640
3.601
3.566
3.533
3.502
3.473
3.447
3.421
3.398
3.375
3.354
3.334
3.315
3.297
3.280
3.264
3.249
3.234
3.220
3.206
3.193
3.180
3.168
3.157
3.146
3.135
3.125
3.078
3.038
3.004
2.974
2.947
2.924
2.902
2.883
2.866
2.850
265
-------
Appendix G
Table G-3a. Sample Size Required to Demonstrate With At Least 100(1 - a)% Confidence That At Least
100p°/o of a Lot or Batch of Waste Complies With the Applicable Standard (No Samples Exceeding the Standard)
p
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
0.99
l-a
0.50
1
2
2
2
2
3
4
5
7
14
69
0.55
2
2
2
2
3
3
4
5
8
16
80
0.60
2
2
2
3
3
4
5
6
9
18
92
0.65
2
2
3
3
3
4
5
7
10
21
105
0.70
2
3
3
3
4
5
6
8
12
24
120
0.75
2
3
3
4
4
5
7
9
14
28
138
0.80
3
3
4
4
5
6
8
10
16
32
161
0.85
3
4
4
5
6
7
9
12
19
37
189
0.90
4
4
5
6
7
9
11
15
22
45
230
0.95
5
6
6
7
9
11
14
19
29
59
299
0.99
7
8
10
11
13
17
21
29
44
90
459
Table G-3b. Sample Size Required to Demonstrate Wth At Least 1 00(1 - #)% Confidence That At Least
1 00/7% of a Lot or Batch of Waste Complies Wth the Applicable Standard (One Sample Exceeding the Standard)
P
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
0.99
0.50
3
4
4
5
6
7
9
11
17
34
168
0.55
4
4
5
5
6
7
9
12
19
37
184
0.60
4
4
5
6
7
8
10
13
20
40
202
0.65
4
5
5
6
7
9
11
15
22
44
222
0.70
5
5
6
7
8
9
12
16
24
49
244
l-a
0.75
5
6
6
7
9
10
13
18
27
53
269
0.80
5
6
7
8
9
11
14
19
29
59
299
0.85
6
7
8
9
10
13
16
22
33
67
337
0.90
7
8
9
10
12
15
18
25
38
77
388
0.95
8
9
10
12
14
18
22
30
46
93
473
0.99
11
12
14
16
20
24
31
42
64
130
662
266
-------
Appendix G
Table G-4. Coefficients [ctn_j+l ] for the Shapiro-Wilk Test for Normality
i\n
1
2
3
4
5
i\n
1
2
3
4
5
6
7
8
9
10
i\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
2
.7071
11
.5601
.3315
.2260
.1429
.0695
.0000
21
.4643
.3185
.2578
.2119
.1736
.1399
.1092
.0804
.0530
.0263
.0000
3
.7071
.0000
12
.5475
.3325
.2347
.1586
.0922
.0303
22
.4590
.3156
.2571
.2131
.1764
.1443
.1150
.0878
.0618
.0368
.0122
4
.6872
.1677
13
.5359
.3325
.2412
.1707
.1099
.0539
.0000
23
.4542
.3126
.2563
.2139
.1787
.1480
.1201
.0941
.0696
.0459
.0228
.0000
5
.6646
.2413
.0000
14
.5251
.3318
.2460
.1802
.1240
.0727
.0240
24
.4493
.3098
.2554
.2145
.1807
.1512
.1245
.0997
.0764
.0539
.0321
.0107
6
.6431
.2806
.0875
15
.5150
.3306
.2495
.1878
.1353
.0880
.0433
.0000
25
.4450
.3069
.2543
.2148
.1822
.1539
.1283
.1046
.0823
.0610
.0403
.0200
.0000
7
.6233
.3031
.1401
.0000
16
.5056
.3290
.2521
.1939
.1447
.1005
.0593
.0196
26
.4407
.3043
.2533
.2151
.1836
.1563
.1316
.1089
.0876
.0672
.0476
.0284
.0094
8
.6052
.3164
.1743
.0561
17
.4968
.3273
.2540
.1988
.1524
.1109
.0725
.0359
.0000
27
.4366
.3018
.2522
.2152
.1848
.1584
.1346
.1128
.0923
.0728
.0540
.0358
.0178
.0000
9
.5888
.3244
.1976
.0947
.0000
18
.4886
.3253
.2553
.2027
.1587
.1197
.0837
.0496
.0163
28
.4328
.2992
.2510
.2151
.1857
.1601
.1372
.1162
.0965
.0778
.0598
.0424
.0253
.0084
10
.5739
.3291
.2141
.1224
.0399
19
.4808
.3232
.2561
.2059
.1641
.1271
.0932
.0612
.0303
.0000
29
.4291
.2968
.2499
.2150
.1864
.1616
.1395
.1192
.1002
.0822
.0650
.0483
.0320
.0159
.0000
20
.4734
.3211
.2565
.2085
.1686
.1334
.1013
.0711
.0422
.0140
30
.4254
.2944
.2487
.2148
.1870
.1630
.1415
.1219
.1036
.0862
.0697
.0537
.0381
.0227
.0076
Source: After Shapiro and Wilk (1965)
267
-------
Appendix G
Table G-4. Coefficients
for the Shapiro-Wilk Test for Normality (Continued)
i\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
i\n
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
31
.4220
.2921
.2475
.2145
.1874
.1641
.1433
.1243
.1066
.0899
.0739
.0585
.0435
.0289
.0144
.0000
41
.3940
.2719
.2357
.2091
.1876
.1693
.1531
.1384
.1249
.1123
.1004
.0891
.0782
.0677
.0575
.0476
.0379
.0283
.0188
.0094
.0000
32
.4188
.2898
.2463
.2141
.1878
.1651
.1449
.1265
.1093
.0931
.0777
.0629
.0485
.0344
.0206
.0068
42
.3917
.2701
.2345
.2085
.1874
.1694
.1535
.1392
.1259
.1136
.1020
.0909
.0804
.0701
.0602
.0506
.0411
.0318
.0227
.0136
.0045
33
.4156
.2876
.2451
.2137
.1880
.1660
.1463
.1284
.1118
.0961
.0812
.0669
.0530
.0395
.0262
.0131
.0000
43
.3894
.2628
.2334
.2078
.1871
.1695
.1539
.1398
.1269
.1149
.1035
.0927
.0824
.0724
.0628
.0534
.0442
.0352
.0263
.0175
.0087
.0000
34
.4127
.2854
.2439
.2132
.1882
.1667
.1475
.1301
.1140
.0988
.0844
.0706
.0572
.0441
.0314
.0187
.0062
44
.3872
.2667
.2323
.2072
.1868
.1695
.1542
.1405
.1278
.1160
.1049
.0943
.0842
.0745
.0651
.0560
.0471
.0383
.0296
.0211
.0126
.0042
35
.4096
.2834
.2427
.2127
.1883
.1673
.1487
.1317
.1160
.1013
.0873
.0739
.0610
.0484
.0361
.0239
.0119
.0000
45
.3850
.2651
.2313
.2065
.1865
.1695
.1545
.1410
.1286
.1170
.1062
.0959
.0860
.0775
.0673
.0584
.0497
.0412
.0328
.0245
.0163
.0081
.0000
36
.4068
.2813
.2415
.2121
.1883
.1678
.1496
.1331
.1179
.1036
.0900
.0770
.0645
.0523
.0404
.0287
.0172
.0057
46
.3830
.2635
.2302
.2058
.1862
.1695
.1548
.1415
.1293
.1180
.1073
.0972
.0876
.0785
.0694
.0607
.0522
.0439
.0357
.0277
.0197
.0118
.0039
37
.4040
.2794
.2403
.2116
.1883
.1683
.1505
.1344
.1196
.1056
.0924
.0798
.0677
.0559
.0444
.0331
.0220
.0110
.0000
47
.3808
.2620
.2291
.2052
.1859
.1695
.1550
.1420
.1300
.1189
.1085
.0986
.0892
.0801
.0713
.0628
.0546
.0465
.0385
.0307
.0229
.0153
.0076
.0000
38
.4015
.2774
.2391
.2110
.1881
.1686
.1513
.1356
.1211
.1075
.0947
.0824
.0706
.0592
.0481
.0372
.0264
.0158
.0053
48
.3789
.2604
.2281
.2045
.1855
.1693
.1551
.1423
.1306
.1197
.1095
.0998
.0906
.0817
.0731
.0648
.0568
.0489
.0411
.0335
.0259
.0185
.0111
.0037
39
.3989
.2755
.2380
.2104
.1880
.1689
.1520
.1366
.1225
.1092
.0967
.0848
.0733
.0622
.0515
.0409
.0305
.0203
.0101
.0000
49
.3770
.2589
.2271
.2038
.1851
.1692
.1553
.1427
.1312
.1205
.1105
.1010
.0919
.0832
.0748
.0667
.0588
.0511
.0436
.0361
.0288
.0215
.0143
.0071
.0000
40
.3964
.2737
.2368
.2098
.1878
.1691
.1526
.1376
.1237
.1108
.0986
.0870
.0759
.0651
.0546
.0444
.0343
.0244
.0146
.0049
50
.3751
.2574
.2260
.2032
.1847
.1691
.1554
.1430
.1317
.1212
.1113
.1020
.0932
.0846
.0764
.0685
.0608
.0532
.0459
.0386
.0314
.0244
.0174
.0104
.0035
268
-------
Appendix G
Table G-5. Of -Level Critical Points for the Shapiro-Wilk 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
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
a
0.01
0.753
0.687
0.686
0.713
0.730
0.749
0.764
0.781
0.792
0.805
0.814
0.825
0.835
0.844
0.851
0.858
0.863
0.868
0.873
0.878
0.881
0.884
0.888
0.891
0.894
0.896
0.898
0.900
0.902
0.904
0.906
0.908
0.910
0.912
0.914
0.916
0.917
0.919
0.920
0.922
0.923
0.924
0.926
0.927
0.928
0.929
0.929
0.930
0.05
0.767
0.748
0.762
0.788
0.803
0.818
0.829
0.842
0.850
0.859
0.866
0.874
0.881
0.887
0.892
0.897
0.901
0.905
0.908
0.911
0.914
0.916
0.918
0.920
0.923
0.924
0.926
0.927
0.929
0.930
0.931
0.933
0.934
0.935
0.936
0.938
0.939
0.940
0.941
0.942
0.943
0.944
0.945
0.945
0.946
0.947
0.947
0.947
Source: After Shapiro and Wilk (1965)
269
-------
Appendix G
Table G-6. Values of ffl_a = //0 90 for Calculating a One-Sided 90-Percent UCL on a Lognormal Mean
sy
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.25
1.50
1.75
2.00
2.50
3.00
3.50
4.00
4.50
5.00
6.00
7.00
8.00
9.00
10.0
n
3
1.686
1.885
2.156
2.521
2.990
3.542
4.136
4.742
5.349
5.955
7.466
8.973
10.48
11.98
14.99
18.00
21.00
24.00
27.01
30.01
36.02
42.02
48.03
54.03
60.04
5
1.438
1.522
1.627
1.755
1.907
2.084
2.284
2.503
2.736
2.980
3.617
4.276
4.944
5.619
6.979
8.346
9.717
11.09
12.47
13.84
16.60
19.35
22.11
24.87
27.63
7
1.381
1.442
1.517
1.607
1.712
1.834
1.970
2.119
2.280
2.450
2.904
3.383
3.877
4.380
5.401
6.434
7.473
8.516
9.562
10.61
12.71
14.81
16.91
19.02
21.12
10
1.349
1.396
1.453
1.523
1.604
1.696
1.800
1.914
2.036
2.167
2.518
2.896
3.289
3.693
4.518
5.359
6.208
7.062
7.919
8.779
10.50
12.23
13.96
15.70
17.43
12
1.338
1.380
1.432
1.494
1.567
1.650
1.743
1.845
1.955
2.073
2.391
2.733
3.092
3.461
4.220
4.994
5.778
6.566
7.360
8.155
9.751
11.35
12.96
14.56
16.17
15
1.328
1.365
1.411
1.467
1.532
1.606
1.690
1.781
1.880
1.985
2.271
2.581
2.907
3.244
3.938
4.650
5.370
6.097
6.829
7.563
9.037
10.52
12.00
13.48
14.97
21
1.317
1.348
1.388
1.437
1.494
1.558
1.631
1.710
1.797
1.889
2.141
2.415
2.705
3.005
3.629
4.270
4.921
5.580
6.243
6.909
8.248
9.592
10.94
12.29
13.64
31
1.308
1.335
1.370
1.412
1.462
1.519
1.583
1.654
1.731
1.812
2.036
2.282
2.543
2.814
3.380
3.964
4.559
5.161
5.763
6.379
7.607
8.842
10.08
11.32
12.56
51
1.301
1.324
1.354
1.390
1.434
1.485
1.541
1.604
1.672
1.745
1.946
2.166
2.402
2.648
3.163
3.697
4.242
4.796
5.354
5.916
7.048
8.186
9.329
10.48
11.62
101
1.295
1.314
1.339
1.371
1.409
1.454
1.504
1.560
1.621
1.686
1.866
2.066
2.279
2.503
2.974
3.463
3.965
4.474
4.989
5.508
6.555
7.607
8.665
9.725
10.79
Source: Land (1975)
270
-------
Appendix G
Table G-7. Values of the Parameter A for Cohen's Adjustment for Nondetected Values
h
7
' .01 .02 .03 .04 .05 .06 .07 .08 .09 .10 .15 .20
.00 .010100 .020400 .030902 .041583 .052507 .063625 .074953 .08649 .09824 .11020 .17342 .24268
.05 .010551 .021294 .032225 .043350 .054670 .066159 .077909 .08983 .10197 .11431 .17925 .25033
.10 .010950 .022082 .033398 .044902 .056596 .068483 .080563 .09285 .10534 .11804 .18479 .25741
.15 .011310 .022798 .034466 .046318 .058356 .070586 .083009 .09563 .10845 .12148 .18985 .26405
.20 .011642 .023459 .035453 .047829 .059990 .072539 .085280 .09822 .11135 .12469 .19460 .27031
.25 .011952 .024076 .036377 .048858 .061522 .074372 .087413 .10065 .11408 .12772 .19910 .27626
.30 .012243 .024658 .037249 .050018 .062969 .076106 .089433 .10295 .11667 .13059 .20338 .28193
.35 .012520 .025211 .038077 .051120 .064345 .077736 .091355 .10515 .11914 .13333 .20747 .28737
.40 .012784 .025738 .038866 .052173 .065660 .079332 .093193 .10725 .12150 .13595 .21129 .29250
.45 .013036 .026243 .039624 .053182 .066921 .080845 .094958 .10926 .12377 .13847 .21517 .29765
.50 .013279 .026728 .040352 .054153 .068135 .082301 .096657 .11121 .12595 .14090 .21882 .30253
.55 .013513 .027196 .041054 .055089 .069306 .083708 .098298 .11208 .12806 .14325 .22225 .30725
.60 .013739 .027849 .041733 .055995 .070439 .085068 .099887 .11490 .13011 .14552 .22578 .31184
.65 .013958 .028087 .042391 .056874 .071538 .086388 .10143 .11666 .13209 .14773 .22910 .31630
.70 .014171 .028513 .043030 .057726 .072505 .087670 .10292 .11837 .13402 .14987 .23234 .32065
.75 .014378 .029927 .043652 .058556 .073643 .088917 .10438 .12004 .13590 .15196 .23550 .32489
.80 .014579 .029330 .044258 .059364 .074655 .090133 .10580 .12167 .13775 .15400 .23858 .32903
.85 .014773 .029723 .044848 .060153 .075642 .091319 .10719 .12225 .13952 .15599 .24158 .33307
.90 .014967 .030107 .045425 .060923 .075606 .092477 .10854 .12480 .14126 .15793 .24452 .33703
.95 .015154 .030483 .045989 .061676 .077549 .093611 .10987 .12632 .14297 .15983 .24740 .34091
1.00 .015338 .030850 .046540 .062413 .078471 .094720 .11116 .12780 .14465 .16170 .25022 .34471
271
-------
Appendix G
Table G-7. Values of the Parameter A for Cohen's Adjustment for Nondetected Values (Continued)
7
.05
.10
.15
.20
.25
.30
.35
.40
.45
.50
.55
.60
.65
.70
.75
.80
.85
.90
.95
1.00
.25
.32793
.33662
.34480
.35255
.35993
.36700
.37379
.38033
.38665
.39276
.39679
.40447
.41008
.41555
.42090
.42612
.431 22
.43622
.44112
.44592
.30
.4130
.4233
.4330
.4422
.4510
.4595
.4676
.4735
.4831
.4904
.4976
.5045
.5114
.5180
.5245
.5308
.5370
.5430
.5490
.5548
.35
.5066
.5184
.5296
.5403
.5506
.5604
.5699
.5791
.5880
.5967
.6061
.6133
.6213
.6291
.6367
.6441
.6515
.6586
.6656
.6724
.40
.6101
.6234
.6361
.6483
.6600
.6713
.6821
.6927
.7029
.7129
.7225
.7320
.7412
.7502
.7590
.7676
.7781
.7844
.7925
.8005
.45
.7252
.7400
.7542
.7673
.7810
.7937
.8060
.8179
.8295
.8408
.8517
.8625
.8729
.8832
.8932
.9031
.9127
.9222
.9314
.9406
h
.50
.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
.55
.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
1.166
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
1.358
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
1.585
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
2.203
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
3.314
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
272
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APPENDIX H
STATISTICAL SOFTWARE
Since publication of Chapter Nine ("Sampling Plan") of SW-846 in 1986, great advances have
been made in desktop computer hardware and software. In implementing the procedures
recommended in this chapter, you should take advantage of the powerful statistical software
now available for low cost or no cost. A number of useful "freeware" packages are available
from EPA and other organizations, and many are downloadable from the Internet.
Commercially available software also may be used.
This appendix provides a list of software that you might find useful. EPA Guidance for Quality
Assurance Project Plans, EPA QA/G-5 (USEPA 1998a) also provides an extensive list of
software that can assist you in developing and preparing a quality assurance project plan.
Title
Decision Error
Feasibility Trials
(DEFT)*
GeoEAS*
Sampling Design Software
Description
This software package allows quick generation of cost information about
several simple sampling designs based on DQO constraints, which 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 (USEPA
2000b), which provides general guidance to organizations developing data
quality criteria and performance specifications for decision making. The Data
Quality Objectives Decision Error Feasibility Trials Software (DEFT) - User's
Guide (EPA/240/B-0 1/007) contains detailed instructions on how to use
DEFT software and provides background information on the sampling
designs that the software uses.
Download from EPA's World Wide Web site at:
http://www.epa.qov/qualitv/qa docs.html.
Geostatistical Environmental Assessment Software (GeoEAS) (USEPA
1991b) 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.
GeoEAS Version 1.2.1 (April 1989) software and documentation is available
from EPA's Web site at http://www.epa.qov/ada/csmos/models/qeoeas.html
Also available on EPA's CD-ROM Site Characterization Library Volume 1 (Release 2) (USEPA 1998c)
273
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Appendix H
Sampling Design Software (Continued)
Title
Visual Sample Plan
(VSP)
Description
ELIPGRID-PC ELIPGRID-PC is a program for the design and analysis of sampling grids for
locating elliptical targets (e.g., contamination "hot spots"). It computes the
probability of success in locating targets based on the assumed size, shape,
and orientation of the targets, as well as the specified grid spacing. It also
can be used to compute a grid spacing from a specified success probability,
compute cost information associated with specified sampling grids,
determine the size of the smallest "hot spot" detected given a particular grid,
and create graphs of the results.
Information, software, and user's guide are available on the World Wide Web
at: http://dqo.pnl.gov/software/elipgrid.htm The site is operated for the U.S.
Department of Energy Office of Environmental Management by the Pacific
Northwest National Laboratory.
DQO-PRO This software comprises a series of programs with a user interface such as a
common calculator and it is accessed using Microsoft Windows. DQO-PRO
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
3. Determining the sampling grid necessary to detect "hot spots."
DQO-PRO facilitates understanding the significance of DQOs by showing the
relationships between numbers of samples and DQO parameters, such as
(1) confidence levels versus numbers of false positive or negative
conclusions; (2) tolerable error versus analyte concentration, standard
deviation, etc., and (3) confidence levels versus sampling area grid size. The
user has only to type in his or her requirements and the calculator instantly
provides the answers.
Contact: Information and software are available on the Internet at the
American Chemical Society, Division of Environmental Chemistry Web site at
http://www.acs-envchem.duq.edu/dqopro.htm
VSP provides statistical solutions for optimizing the sampling design. The
software can answer two important questions in sample planning: (1) How
many samples are needed? VSP can quickly calculate the number of
samples needed for various scenarios at different costs. (2) Where should
the samples be taken? Sample placement based on personal judgment is
prone to bias. VSP provides random or grided sampling locations overlaid
on the site map.
Information and software available at http://dqo.pnl.gov/VSP/lndex.htm
VSP was developed in part by Department of Energy's (DOE's) National
Analytical Management Program (NAMP) and through a joint effort between
Pacific Northwest National Laboratory (PNNL) and Advanced
Infrastructure Management Technologies (AIMTech).
274
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Appendix H
Data Quality Assessment Software
Title
Description
DataQUEST This software 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 (USEPA 2000d) which demonstrates the use of
the DQA Process in evaluating environmental data sets.
Download from EPA's World Wide Web site at
http://www.epa.gov/quality/qa docs.html
ASSESS 1.01 a"
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.
Available on EPA's CD-ROM Site Characterization Library Volume 1
(Release 2) (USEPA 1998c)
MTCASfaf This software package is published by the Washington Department of
Ecology and can be used to calculate sample sizes (for both normal and
lognormal distributions), basic statistical quantities, and confidence intervals.
Requires MS Excel 97.
The USEPA Office of Solid Waste has not evaluated this software for use in
connection with RCRA programs, however, users of this guidance may wish
to review the software for possible application to some of the concepts
described in this document.
Available from Washington Department of Ecology's "Site Cleanup,
Sediments, and Underground Storage Tanks" World Wide Web site at
http://www.ecv.wa.qov/proqrams/tcp/tools/toolmain.html
' Also available on EPA's CD-ROM Site Characterization Library Volume 1 (Release 2) (USEPA 1998c)
275
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276
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APPENDIX I
EXAMPLES OF PLANNING, IMPLEMENTATION, AND ASSESSMENT
FOR RCRA WASTE SAMPLING
This appendix presents the following two hypothetical examples of planning, implementation,
and assessment for RCRA waste sampling:
Example 1: Sampling soil in a RCRA Solid Waste Management Unit (SWMU) to
confirm attainment of the cleanup standard (using the mean to measure
compliance with a standard)
Example 2: Sampling of a process waste to make a hazardous waste determination
(using a maximum or upper percentile to measure compliance with a
standard).
Example 1: Sampling Soil at a RCRA SWMU to Confirm Attainment of a Cleanup
Standard
Introduction
In this example, the owner of a permitted TSDF completed removal of contaminated soil at a
SWMU as required under the facility's RCRA permit under EPA's RCRA Corrective Action
Program. The permit required the facility owner to conduct sampling and analysis to determine
if the remaining soil attains the facility-specific risk-based standard specified in the permit. This
hypothetical example describes how the planning, implementation, and assessment activities
were conducted.
Planning Phase
The planning phase included implementation of EPA's systematic planning process known as
the Data Quality Objectives (DQO) Process and preparation of a quality assurance project plan
(QAPP). A DQO planning team was assembled, and the DQO Process was implemented
following EPA's guidance in Guidance for the Data Quality Objectives Process for Hazardous
Waste Site Operations EPA QA/G-4HW (\JSEPA 2000a), Guidance for the Data Quality
Objectives Process EPA QA/G-4 (USEPA 2000b), and Chapter Nine of SW-846.
The outputs of the seven steps of the DQO Process are outlined below.
DQO Step 1: Stating the Problem
The DQO planning team included the facility owner, a technical project manager,
a chemist, environmental technician (sampler), and a facility engineer familiar
with statistical methods. As part of the DQO Process, the team consulted with
their state regulator to determine if the State has any additional regulations or
guidance that applies. A state guidance document provided recommendations
for the parameter of interest and the acceptable Type I decision error rate.
277
-------
Appendix I Example 1
A concise description of the problem was developed as follows: The facility
conducted a soil removal action at the SWMU. Soil with concentrations greater
than the risk-based cleanup standard of 10 mg/kg of pentachlorophenol (PCP)
was excavated for off-site disposal. Removal was guided by the results of grab
samples analyzed for PCP using a semi-quantitative field analytical method.
The conceptual site model (CSM) assumed that the PCP migrated downward
into the soil, and that if a soil layer were found to be "clean," then the underlying
soil layer also would be assumed "clean."
The technical staff were given six weeks to complete the study and submit a draft
report to the regulatory agency.
DQO Step 2: Identifying Possible Decisions
Decision statement: The study objective was to determine if the soil remaining in
the SWMU after removal of the contaminated soil attained the cleanup standard.
If the standard is attained, then the area will be backfilled with clean fill and
reserved for future industrial development. If the standard is not attained, then
the next layer of soil within the SWMU will be removed.
DQO Step 3: Identifying Inputs to the Decision
The sample analysis results for total PCP (in mg/kg) in soil were used to decide
whether or not the soil attained the cleanup. PCP was designated as the only
constituent of concern, and its distribution within the SWMU was assumed to be
random. The risk-based cleanup level for PCP in soil was set at 10 mg/kg.
The decision was based on the concentrations in the top six-inch layer of soil
across the entire SWMU. The study was designed to determine whether the
entire unit attains the standards, or does not.
The chemist identified two candidate analytical methods for measuring PCP
concentrations in soil: (1) SW-846 Method 4010A "Screening For
Pentachlorophenol By Immunoassay" ($20/analysis), and (2) SW-846 Method
8270 (and prep method 3550) ($110/analysis). The project chemist confirmed
that both methods were capable of achieving a quantitation limit well below the
action level of 10 mg/kg. During Step 7 of the DQO Process, the chemist
revisited this step to select a final method and prepare method performance
criteria as part of the overall specification of decision performance criteria.
The planning team identified the need to specify the size, shape, and orientation
of each sample to satisfy the acceptable sampling error (specified in DQO
Process Step 7) and to enable selection of the appropriate sampling device
(during development of the QAPP). Because the soil exists in a relatively flat
stationary three-dimensional unit, it was considered a series of overlapping two-
dimensional surfaces for the purposes of sampling. The correct orientation, size,
278
-------
Example 1 Appendix I
and shape of each sample was a vertical core capturing the full six-inch
thickness of the soil unit. The minimum mass of each primary field sample was
determined during DQO Process Step 7 using the particle size-weight
relationship required to control fundamental error at an acceptable level.
DQO Step 4: Defining Boundaries
The dimensions of the SWMU were approximately 125 feet by 80 feet (10,000
square feet). The SWMU was relatively flat. The depth of interest was limited to
the top six inches of soil in the unit after removal of the contaminated soil. The
spatial boundary of the SWMU was defined by the obvious excavation and by
wooden stakes at the corners of the excavation.
The soil within the study boundary was loamy sand with a maximum particle size
of about 1.5 mm (0.15 cm).
The project team planned to collect samples within a reasonable time frame, and
degradation or transformation of the PCP over the investigation period was not a
concern.
DQO Step 5: Developing Decision Rules
The population parameter of interest was the mean. The mean was selected as
the parameter of interest because the risk-based cleanup standard (Action Level)
was derived based upon long-term average health effects predicted from
exposures to the contaminated soil.
The risk-based action level was 10 mg/kg total pentachlorophenol (PCP) in soil.
The decision rule was then established as follows: "If the mean concentration for
PCP in the soil is less than 10 mg/kg, then the cleanup standard is attained.
Otherwise, the SWMU will be considered contaminated and additional remedial
action will be required."
DQO Step 6: Specifying Limits on Decision Errors
The major sources of variability (measured as the relative variance) were
identified as within-sample unit variability (s^) (including analytical imprecision
and Gy's fundamental error) and between-sample unit variability (s% ) (or
population variability). The total study variance (s^ ) , expressed as the relative
variance, was estimated using the following relationship:
= s + s
+ s2a
279
-------
Appendix I Example 1
where s2 = between-unit variance (population variance), s2 = sample collection
imprecision (estimated by Gy's fundamental error, s2FE), and s2a = analytical
imprecision (determined from the measurement of laboratory control samples
with concentrations near the Action Level).
Sample analysis results for eight samples of soil excavated from the previous lift
gave a standard deviation and mean of s = 7.1 and x = 10.9 respectively. The
total study relative standard deviation ( ST ) was then estimated as 0.65.
The relative standard deviation (RSD) of the sampling error (ss) was estimated
as 0.10 (as estimated by Gy's fundamental error), based a maximum observed
particle size of approximately 1.5 mm (0.15 cm) and a sample mass of 10 grams.
The RSD for the analytical imprecision (sa) associated with the field screening
method (SW-846 Method 401OA - "Screening For Pentachlorophenol By
Immunoassay") was estimated from replicate measurements as 0.40.
The between-unit (population) relative standard deviation (sb) was then
estimated as:
= V(.65)2-(.102 + .402) = 0.50
Two potential decision errors could be made based on interpreting sampling and
analytical data:
Decision Error A: Concluding that the mean PCP concentration within the
SWMU was less than 10 mg/kg when it was truly greater than 10 mg/kg,
or
Decision Error B: Concluding that the mean PCP concentration within the
SWMU was greater than 10 mg/kg when it was truly less than 10 mg/kg.
The consequences of Decision Error A, incorrectly deciding the SWMU was
"clean" (mean PCP concentration less than 10 mg/kg), would leave contaminated
soil undetected and would likely increase health risks for onsite workers and
pose potential future legal problems for the owner.
The consequences of Decision Error B, incorrectly deciding the SWMU was "not
clean" (mean PCP concentration greater than or equal to 10 mg/kg), would cause
the needless expenditure of resources (e.g., funding, time, backhoe and
operator, soil disposal, sampling crew labor, and analytical capacity) for
unnecessary further remedial action.
280
-------
Example 1 Appendix I
Error A, incorrectly deciding that the mean PCP concentration is less than the
action level of 10 mg/kg, posed more severe consequences for human health
plus liability and compliance concerns. Consequently, the baseline condition
chosen for the SWMU was that the mean PCP concentration within the SWMU is
truly greater than or equal to the action level of 10 mg/kg.
Table 1-1. Null Hypothesis and Possible Decision Errors for Example 1
Possible Decision Errors
"Null Hypothesis"
(baseline condition) Type I Error (CC ), Type II Error ( p),
False Rejection False Acceptance
The true mean concentration Concluding the site is "clean" Concluding the site is still
of PCP in the SWMU is when, in fact, it is contaminated when, in fact, it
greater than or equal to the contaminated. is "clean."
risk-based cleanup standard
(i.e., the SWMU is
contaminated).
Next, it was necessary to specify the boundaries of the gray regions. The gray
region defines a range that is less than the action limit, but too close to the Action
Level to be considered "clean," given uncertainty in the data. When the null
hypothesis (baseline condition) assumes that the site is contaminated (as in this
example), the upper limit of the gray region is bounded by the Action Level; the
lower limit is determined by the decision maker. The project team sets the lower
bound of the gray region at 7.5 mg/kg, with the understanding that this bound
could be modified after review of the outputs of Step 7 of the DQO Process.
The planning team set the acceptable probability of making a Type I (false
rejection) error at 5 percent (a= 0.05) based on guidance provided by the State
regulatory agency. In other words, the team was willing to accept a 5 percent
chance of concluding the SWMU was clean, if in fact it was not. While a Type II
(false acceptance) error could prove to be costly to the company, environmental
protection and permit compliance are judged to be most important. The planning
team decides to set the Type II error rate at only 20 percent.
The information collected in Step 6 of the DQO Process is summarized below.
281
-------
Appendix I Example 1
Table I-2. Initial Outputs of Step 6 of the DQO Process
Needed Parameter Output
Action Level (AL) 10 mg/kg
Gray Region 7.5-10 mg/kg (width of gray region, A = 2.5)
Relative Width of Gray Region (10 - 7.5)17.5 = 0.33
Null Hypothesis (H0) Mean (PCP) > 10 mg/kg
False Rejection Decision Error Limit $ = 0 05
(probability of a Type I error)
False Acceptance Decision Error Limit /? — ft ?0
(probability of a Type II error) "
DQO Step 7: Optimizing the Data Collection Design
1. Review outputs from the first six steps of the DQO Process. The project
team reviewed the outputs of the first six steps of the DQO Process. They
expected the PCP concentration to be near the cleanup standard (Action Level);
thus, it was decided that a probabilistic sampling design would be used so that
the results could be stated with a known probability of making a decision error.
2. Consider various data collection designs. The objective of this step was to
find cost-effective design alternatives that balance the number of samples and
the measurement performance, given the feasible choices for sampling designs
and measurement methods. Based on characterization data from the excavated
soil, the planning team assumed that the between-sample unit variability or
population variability would remain relatively stable at approximately sb = 0.50,
independent of the sampling and analytical methods used. The planning team
investigated various combinations of sampling and analytical methods (with
varying associated levels of precision and cost) as a means find the optimal
study design.
The planning team considered three probabilistic sampling designs: simple
random, stratified random, and systematic (grid-based) designs. A composite
sampling strategy also was considered. All designs allowed for an estimate of
the mean to be made. Because the existence of strata was not expected
(although could be discovered during the investigation), the stratified design was
eliminated from consideration. A simple random design is the simplest of the
probabilistic sampling methods, but it may not provide very even coverage of the
SWMU; thus, if spatial variability becomes a concern, then it may go undetected
with a simple random design. The systematic design provides more even
coverage of the SWMU and typically is easy to implement.
The practical considerations were considered for each alternative design,
including site access and conditions, equipment selection/use, experience
282
-------
Example 1 Appendix I
needed, special analytical needs, health and safety requirements, and
scheduling. There were no significant practical constraints that would limit the
use of either the systematic or the simple random sampling designs; however,
the systematic design was preferred because it provides sampling locations that
are easier to survey and locate in the field, and it provides better spatial
coverage. Ultimately, two sampling designs were evaluated: a systematic
sampling design and a systematic sampling design that incorporates composite
sampling.
The acceptable mass of each primary field sample was determined using the
particle size-weight relationship required to control fundamental error. The soil in
the SWMU is a granular solid, and the 95th percentile particle size (d) was
estimated at 1.5 mm (0.15 cm). To maintain the relative standard deviation of
the fundamental error at 0.10, a sample mass of at least 8.2 grams was required
(using Equation D.4 in Appendix D). To maintain the relative standard deviation
of the fundamental error at 0.05, a sample mass of at least 30 grams would be
required. There were no practical constraints on obtaining samples of these
sizes.
Next, it was necessary to estimate unit costs for sampling and analysis. Based
on prior experience, the project team estimated the cost of collecting a grab
sample at $40 - plus an additional $30 per sample for documentation,
processing of field screening samples, and $60 per sample for documentation,
processing, and shipment for samples sent for fixed laboratory analysis.
3. Select the optimal number of samples. Using the initial outputs of Step 6, the
appropriate number of samples was calculated for each sampling design:
For the systematic sampling design (without compositing), the following formula
was used (Equation 8 from Section 5.4.1):
(Zl_a + Zl_ff)24 z2
/!= -T- +^
where
ZI_Q; = the /?th quantile of the standard normal distribution (from
the last row of Table G-1, Appendix G), where a is the
probability of making a Type I error (the significance level
of the test) set in DQO Step 6.
ZI_P = the pth quantile of the standard normal distribution (from
the last row of Table G-1, Appendix G), where ft is the
probability of making a Type II error set in DQO Step 6.
ST = an estimate of the total study relative standard deviation.
A = the width of the gray region from DQO Step 6 (expressed
as the relative error in this example).
283
-------
Appendix I Example 1
[EPA's DEFT software could be used to calculate the appropriate number of
samples (see Data Quality Objectives Decision Error Feasibility Trials Software
(DEFT) - User's Guide, USEPA 2001 h). Note, however, that the DEFT program
asks for the bounds of the gray region specified in absolute units. If the planning
team uses the relative standard deviation (or coefficient of variation) in the
sample size equation rather than the absolute standard deviation, then the
bounds of the gray region also must be input into DEFT as relative values. Thus,
the Action Level would be set equal to 1, and the other bound of the gray region
would be set equal to 1 - (relative width of gray region) or 1 + (relative width of
gray region) depending what baseline condition is selected.]
Note that if there were more than one constituent of concern, then the
appropriate number of samples would need to be calculated for each constituent
using preliminary estimates of their standard deviations. The number of samples
would then be determined by the highest number of samples obtained for any
single constituent of concern.
The sample size for systematic composite sampling also was evaluated. In
comparison to non-composite sampling, composite sampling can have the effect
of minimizing between-sample variation, thereby reducing somewhat the total
number of composite samples that must be submitted for analysis. In addition,
composite samples are expected to generate normally distributed data thereby
allowing the team to apply normal theory statistical methods. To estimate the
sample size, the planning team again required an estimate of the standard
deviation. However, since the original estimate of the standard deviation was
based on available individual or "grab" sample data rather than composite
samples, it was necessary to adjust the variance term in the sample size
equation for the appropriate number of composite samples. In the sample size
equation, the between-unit (population) component of variance (s%) was
replaced with s^/g .where g is the number of individual or "grab" samples
used to form each composite. Sample sizes were then calculated assuming
g=4.
Table I-3 and Table I-4 summarize the inputs and outputs of Step 7 of the DQO
Process and provides the estimated costs for the various sampling and analysis
designs evaluated.
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Example 1
Table I-3. Summary of Inputs for Candidate Sampling Designs
Appendix I
Parameter
Systematic
Sampling - Fixed
Lab Analyses
Systematic
Sampling - Field
Analyses
Systematic
Composite
Sampling - Fixed
Lab Analyses
Systematic
Composite
Sampling - Field
Analyses
Inputs
Sampling Costs
Collection Cost (per
"grab")
Documentation,
processing, shipment
Analytical Costs
SW-846 Method
3550/8270 (fixed lab)
SW-846 Method
401 OA (field
screening)
Relative Width of Gray
Region (A )
Null Hypothesis (H0)
False Rejection Decision
Error Limit
False Acceptance
Decision Error Limit
Relative Std. Dev.
Sampling (Ss)
Analytical (Sa), SW-
846 Method 8270
Analytical ( Sa ) SW-
846 Method 401 OA
"Population" ( Sfr )
Total Study
/ 2 2 2
ST = ^ss +sa+sb
$40 ea.
$60 ea.
$110 ea.
NA
0.33
Mean(PCP) > 10
mg/kg
a = 0.05
p = 0.20
0.10
0.10
NA
0.50
0.52
$40 ea.
$30 ea.
$110 ea.*
$20 ea.
0.33
Mean(PCP) > 10
mg/kg
a =0.05
P = 0.20
0.10
NA
0.40
0.50
0.65
$40 ea.
$60 ea.
$110 ea.
NA
0.33
Mean(PCP) > 10
mg/kg
a =0.05
P = 0.20
0.10
0.10
NA
0.50
0.29**
$40 ea.
$30 ea.
$110 ea.*
$20 ea.
0.33
Mean(PCP) > 10
mg/kg
a =0.05
P = 0.20
0.10
NA
0.40
0.50
0.48**
NA: Not applicable
* Assumes 20-percent of all field analyses must be confirmed via fix laboratory method.
2
** For composite sampling, the total study relative standard deviation (ST ) was estimated by replacing Sb with
2 /
Sb I g , where g = the number of "grabs" per composite.
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Example 1
Table I-4. Summary of Outputs for Candidate Sampling Designs
Systematic Systematic Systematic Systematic
p . Sampling - Fixed Sampling - Field Composite Composite
arame er Lab Analyses Analyses Sampling - Fixed Sampling - Field
Lab Analyses Analyses
Outputs
Number of Samples ( n )
Cost Estimate
"Grab" Sampling
17 25 6 15
$40x17 $40x25 $40x4x6 $40x4x15
Documentation,
processing, and
shipment
SW-846 Method
3550/8270 (fixed lab)
SW-846 Method
401OA (field
screening)
$60x17
$110x17
NA
($30 x 25) +
($60 x 5)
(see note 2)
$110x5
(see note 2)
$20 x 25
(see note 1)
$60x6
$110x6
NA
(see note 1)
($30 x 15) +
($60 x 3)
(see note 2)
$110x3
(see note 2)
$20x15
Cost
$3,570
$3,100
$1,980
$3,660
1. The calculation assumes four grabs per composite sample.
2. The calculation includes costs for shipment and analysis of 20% of field screening samples for fixed laboratory
analysis.
NA: Not applicable
4. Select a resource-effective design. It was determined that all of the systematic
designs and systematic composite sampling designs would meet the statistical
performance requirements for the study in estimating the mean PCP
concentration in the SWMU. The project team selected the systematic
composite sampling design - with fixed laboratory analysis - based on the cost
savings projected over the other sampling designs.
The planning team decided that one additional field quality control sample (an
equipment rinsate blank), analyzed by SW-846 Method 8720, was required to
demonstrate whether the sampling equipment was free of contamination.
The outputs of the DQO Process were summarized in a memo report which was
then used help prepare the QAPP.
5. Prepare a QAPP. The operational details of the sampling and analytical
activities were documented in the QAPP using EPA Guidance for Quality
Assurance Project Plans, EPA QA/G-5 (USEPA 1998a) and Chapter One of SW-
846 for guidance.
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Implementation Phase
The QAPP was implemented in accordance with the schedule, sampling plan, and safety plan.
The exact location of each field sample was established using a grid on a map of the SWMU.
The start point for constructing the grid was selected at random.
The QAPP established the following DQOs and performance goals for the sampling equipment:
The correct orientation and shape of each sample is a vertical core.
Each sample must capture the full depth of interest (six inches).
The minimum mass of each sample is 10 g.
The device must be constructed of materials that will not alter analyte
concentrations due to loss or gain of analytes via sorption, desorption,
degradation, or corrosion.
The device must be easy to use, safe, and low cost.
A sampling device was selecting using the four-steps described in Figure 28 in Section 7.1.
Step 1 - Identify the Medium to be Sampled
The material to be sampled is a soil. Using Table 8 in Section 7.1, we find the media
descriptor that most closely matches the waste in the first column of the table: "Soil and
other unconsolidated geologic material."
Step 2 - Select the Sample Location
The second column of Table 8 in Section 7.1 provides a list of possible sampling sites
(or units types) for soil (i.e., surface or subsurface). In this example, the sampling
location is surface soil and "Surface" is found in the second column in the table.
Step 3 - Identify Candidate Sampling Devices
The third column of Table 8 in Section 7.1 provides a list of candidate sampling devices.
For the waste stream in this example, the list includes bucket auger, concentric tube
thief, coring type sampler, miniature core sampler, modified syringe, penetrating probe
sampler, sampling scoop/trowel/shovel, thin-walled tube, and trier.
Step 4 - Select Devices
Sampling devices were selected from the list of candidate sampling devices after review
of Table 9 in Section 7.1. Selection of the equipment was made after consideration of
the DQOs for the sample support (i.e., required volume, depth, shape, and orientation),
the performance goals established for the sampling device, ease of use and
decontamination, worker safety issues, cost, and any practical considerations.
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Example 1
Table I-5 demonstrates how the DQOs and performance goals can be used together to
narrow the candidate devices down to just one or two.
Table I-5. Using DQOs and Performance Goals to Select a Final Sampling Device
Candidate
Devices
Bucket auger
Concentric tube
thief
Coring Type
Sampler
Miniature core
sampler
Modified syringe
sampling
Penetrating
Probe Sampler
Scoop, trowel,
or shovel
Thin-walled tube
Trier
Data Quality Objectives and Performance Goals
Required Depth
6 inches
Y
Y
Y
Y
N
Y
Y
Y
Y
Orientation and
Shape
Vertical
undisturbed core
N
N
N
Y
N
Y
N
Y
N
Sample
Volume
>10g
Y
Y
Y
N
N
Y
Y
Y
Y
Operational
Considerations
Device is portable,
safe, & low cost?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Desired Material
of Construction
Stainless or
carbon steel
Y
Y
Y
N
N
Y
Y
Y
Y
Key: Y = The device is capable of achieving the specified DQO or performance goal.
N = The device is not capable of achieving the DQO or performance goal.
The "penetrating probe sampler" and the "thin-walled tube" were identified as the
preferred devices because they could satisfy all of the DQOs and performance goals for
the sampling devices. The penetrating probe was selected because it was easy to use
and was readily available to the field sampling crew.
A penetrating probe sampler was then used to take the field samples at each location on
the systematic square grid (see Figure 1-1). Each composite sample was formed by
pooling and mixing individual samples collected from within each of four quadrants. The
process was repeated until six composite samples were obtained. Because the total
mass of each individual (grab) sample used to form composite samples exceeded that
required by the laboratory for analysis, a field subsampling routine was used to reduce
the volume of material submitted to the laboratory.
The field samples and associated field QC samples were submitted to the laboratory
where a subsample was taken from each field sample for analysis. The samples were
analyzed in accordance with the QAPP.
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Appendix I
Boundary of SWMU
-------
Appendix I Example 1
2. Prepare the data for statistical analysis. The summary of the verified and
validated data were received in hard-copy format and an electronic data base
was created by manual data entry into spreadsheet software. The data base
was checked by a second person for accuracy. The results for the data
collection effort are listed in Table I-6. A data file was created in a format
suitable for import into EPA's DataQUEST software.
Table I-6. Soil Sample Analysis Results for PGP (mg/kg)
Sample Identification
1
2
3
4
5
6
Result (POP, mg/kg)
8.0
8.0
7.0
6.0
10.5
7.5
3. Conduct preliminary analysis of data and check distributional
assumptions: Using EPA's DataQUEST, statistical quantities were computed as
shown in Figure I-2.
STATISTICAL QUANTITIES
Number of Observations: 6
Minimum: 6.000
Mean: 7.833
Variance : 2.267
Range : 4.500
Coefficient of Variation: 0
Coefficient of Skewness : 0
Coefficient of Kurtosis: -0
Percentiles :
1st: 6.000 75th:
7th: 6.000
90th: 10.500
10th: 6.000 95th:
25th: 7.000 99th:
50th: 7.750 (median)
Maximum
Median :
Std De:
IQR: 1
.192
. 783
. 087
8.000
10 .500
10.500
10.500
7 . 750
1.506
000
Figure 1-2. Statistical quantities using DataQUEST software
On a normal probability plot, the data plot as a straight line, indicating
approximate normality (see Figure 1-3).
290
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Example 1
Appendix I
Normal Probability Plot
.0
ro
.a
o
.95
.80
.50
.20
.05
.01
.001
Average: 7.833
StDev: 1.506
N: 6
POP (mg/kg)
Figure I-3. Normal probability plot
The data also were checked for normality by the Shapiro-Wilk test. Using the
DataQUEST software, the Shapiro-Wilk test was performed at the 0.05 percent
significant level. The Shapiro-Wilk test did not reject the null hypothesis of
normality (see Figure I-4).
Shapiro-Wilk Test
Null Hypothesis: 'Data are normally distributed'
Sample Value: 0.914
Tabled Value: 0.788
There is not enough evidence to reject the
assumption of normality with a 5% significance
level.
Figure 1-4. Results of the Shapiro-Wilk test using EPA's DataQUEST software
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4. Select and perform the statistical test. The analysis of the data showed there
were no "non-detects" and a normal distribution was an acceptable model. Using
the guidance in Figure 38 (Section 8.2.4), a parametric upper confidence limit
(UCL) on the mean was selected as the correct statistic to compare to the
regulatory level. The 95% UCL on the mean was calculated as follows:
= 7.833+2.0151 L5°6
= 9.1 mg/kg
The tabulated "t value" (2.015) was obtained from Table G-1 in Appendix G and
based on a 95-percent one-tailed confidence interval with a = 0.05 and 5
degrees of freedom.
5. Draw conclusions and report results: The 95% UCL for the mean of the
sample analysis results for PCP, 9.1 mg/kg, was less than the specified cleanup
level of 10 mg/kg. Thus, the null hypothesis was rejected, and the owner made
the determination that the soil remaining in the SWMU attains the cleanup
standard for PCP based on the established decision rule.
A summary report including a description of all planning, implementation, and
assessment activities was submitted to the regulatory agency for review.
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Example 2 Appendix I
Example 2: Sampling of a Process Waste to Make a Hazardous Waste Determination
Introduction
An aircraft manufacturing and maintenance facility strips paint from parts before
remanufacturing them. The facility recently switched its paint stripping process from a solvent-
based system to use of an abrasive plastic blasting media (PBM). The waste solvent,
contaminated with stripped paint, had to be managed as a hazardous waste. The facility owner
changed the process to reduce - or possibly eliminate - the generation of hazardous waste from
this operation and thereby reduce environmental risks and lower waste treatment and disposal
costs.
The plant operators thought the spent PBM could include heavy metals such as chromium and
cadmium from the paint, and therefore there was a need to make a hazardous waste
determination in order to comply with the RCRA regulations at 40 CFR Part 262.11. The facility
owner determined that the spent PBM is a solid waste under RCRA but not a listed hazardous
waste. The facility owner then needed to determine if the solid waste exhibits any of the
characteristics of hazardous waste: ignitability (§261.21), corrosivity (§261.22), reactivity
(§261.23), or toxicity (§261.24). Using process and materials knowledge, the owner determined
that the waste blasting media would not exhibit the characteristics of ignitability, corrosivity, or
reactivity. The facility owner elected to conduct waste testing to determine if the waste blasting
media exhibits the characteristic of toxicity.
This hypothetical example describes how the planning, implementation, and assessment
activities were conducted.
Planning Phase
The planning phase comprises the Data Quality Objectives (DQO) Process and preparation of a
quality assurance project plan (QAPP) including a sampling and analysis plan. A DQO planning
team was assembled and the DQO Process was implemented following EPA's guidance in
Guidance for the Data Quality Objectives Process EPA QA/G-4 (USEPA 2000b) and SW-846.
The outputs of the seven steps of the DQO Process are outlined below.
DQO Step 1: Stating the Problem
The DQO planning team included the plant manager, a technical project
manager, a consulting chemist, and the paint stripping booth operator who also
served as the sampler.
The conceptual model of the waste generation process was developed as
follows: The de-painting operation consists of a walk-in blast booth with a
reclamation floor. After blasting, the plastic blast media, mixed with paint fines, is
passed through a reclamation system; the reusable media is separated out for
reloading to the blast unit, while the spent media and paint waste is discharged to
a container.
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Appendix I Example 2
A concise description of the problem was developed as follows: The problem was
described as determining whether the new waste stream (the spent plastic
blasting media and waste paint) should be classified as a hazardous waste that
requires treatment and subsequent disposal in a RCRA Subtitle C landfill (at
$300 per ton), or whether it is a nonhazardous industrial waste that can be land-
disposed in an industrial landfill (at $55 per ton).
The plant manager gave the plant staff and consultant 60 days to complete the
study. The turn-around time was established to minimize the amount of time that
the waste was stored at the facility while the data were being generated, and to
allow adequate time to have the waste shipped off site - if it were found to be a
hazardous waste - within the 90-day accumulation time specified at 40 CFR Part
262.34(a).
DQO Step 2: Identifying Possible Decisions
Decision statement: The decision statement was determining whether the spent
PBM paint waste was hazardous under the RCRA regulations.
Alternative actions: If the waste was hazardous, then treatment and subsequent
disposal in a RCRA landfill would be required.
DQO Step 3: Identifying Inputs to the Decision
The decision was to be based on the quantity of waste generated over
approximately a one-month period, but not to exceed the quantity placed in a
single 10-cubic yard roll off box.
Based on process and materials knowledge, the team specified cadmium and
chromium as the constituents of concern.
To resolve the decision statement, the planning team needed to determine if,
using the Toxicity Characteristic Leaching Procedure (TCLP) SW-846 Method
1311, the extract from a representative sample of the waste contained the
constituents of concern at concentrations equal to or greater than their regulatory
levels as required by the RCRA regulations at 40 CFR 261.24. The chemist
noted, however, that the TCLP method allows the following: "If a total analysis of
the waste demonstrates that individual analytes are not present in the waste, or
that they are present but at such low concentrations that the appropriate
regulatory levels could not possibly be exceeded, the TCLP need not be run."
With that flexibility in mind, the planning team identified a candidate method for
total analysis (including SW-846 Method 3050B/6010), and noted that the TCLP
would be required if the total analysis indicated TC levels could be exceeded.
The project chemist found that SW-846 Methods 301OA (prep) and 601 OB were
suitable for analysis of the TCLP extracts at quantitation limits at or below the
applicable regulatory levels.
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Example 2 Appendix I
The minimum sample "support" was determined as follows: Method 1311 (TCLP)
specifies a minimum sample mass of 100 grams for analysis of nonvolatile
constituents and a maximum particle size of 9.5 mm. The waste stream,
composed of dry fine to medium-grained plastic and paint chips, was well within
the particle size requirements of the TCLP. During Step 7 of the DQO Process,
the planning team revisited this step to determine whether a sample mass larger
than 100-grams would be necessary to satisfy the overall decision performance
criteria.
DQO Step 4: Defining Boundaries
The paint stripping operation includes a blast booth, a PBM reclamation unit, and
a waste collection roll-off box that complies with the applicable container
requirements of Subparts I and CC of 40 CFR part 265. The spent blast media
and paint waste is discharged to the roll-off box from the reclamation unit. Each
discharge event was considered a "batch" for the purposes of the waste
classification study.
When testing a solid waste to determine if it exhibits a characteristic of
hazardous waste, the determination must be made when management of the
solid waste would potentially be subject to the RCRA hazardous waste
regulations at 40 CFR Part 262 through 265. Accordingly, the planning team
decided samples should be obtained at the point where the waste discharges
from the reclamation unit into the roll-off container (i.e., the point of generation).
Until such time that the generator determined that the waste is not a hazardous
waste, the generator complied with the applicable pre-transport requirements at
40 CFR Part 262 - Subpart C (i.e., packaging, labeling, marking, and
accumulation time).
The boundary of the decision was set as the extent of time over which the
decision applies. The boundary would change only if there were a process or
materials change that would alter the composition of the waste. Such a process
or materials change could include, for example, a change in the composition,
particle size or particle shape of the blasting media, or a significant change in the
application (pressure) rate of the blast media.
DQO Step 5: Developing Decision Rules
The planning team reviewed the RCRA regulations at for the Toxicity
Characteristic at 40 CFR 261.24 and found the regulation does not specify a
parameter of interest (such as the mean or a percentile). They observed,
however, that the Toxicity Characteristic (TC) regulatory levels specified in Table
1 of Part 261.24 represent "maximum" concentrations that cannot be equaled or
exceeded; otherwise, the solid waste must be classified as hazardous. While the
regulations for hazardous waste determination do not require the use of any
statistical test to make a hazardous waste determination, the planning team
decided to use a high percentile value as a reasonable approximation of the
maximum TCLP sample analysis result that could be obtained from a sample of
the waste. Their objective was to "prove the negative" - that is, to demonstrate
295
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Appendix I Example 2
with a desired level of confidence that the vast majority of the waste was
nonhazardous. The upper 90th percentile was selected. The team specified an
additional constraint that no single sample could exceed the standard.
Otherwise, there may be evidence that the waste is hazardous at least part of the
time.
The Action Levels were set at the TC regulatory limits specified in Table 1 of 40
CFR Part 261.24:
Cadmium: 1.0 mg/L TCLP
Chromium: 5.0 mg/L TCLP
The decision rule was then established as follows: "If the upper 90th percentile
TCLP concentration for cadmium or chromium in the waste and all samples
analysis results are less than their respective action levels of 1.0 and 5.0 mg/L
TCLP, then the waste can be classified as nonhazardous waste under RCRA;
otherwise, the waste will be considered a hazardous waste."
DQO Step 6: Specifying Limits on Decision Errors
The null hypothesis was that the waste is hazardous, i.e., the true proportion (P)
of samples with concentrations of cadmium or chromium less than their
regulatory thresholds is less than 0.90, or Ho: P < 0.90.
Two potential decision errors could be made based on interpreting sampling and
analytical data:
Decision Error A: Concluding that the true proportion (P) of the waste that
is nonhazardous was greater than 0.90 when it was truly less than 0.90,
or
Decision Error B: Concluding that the true proportion (P) of the waste that
is nonhazardous was less than 0.90 when it was truly greater than 0.90.
The consequences of Decision Error A - incorrectly deciding the waste was
nonhazardous - would lead the facility to ship untreated hazardous waste off site
for disposal in solid waste landfill, likely increase health risks for onsite workers,
and pose potential future legal problems for the owner.
The consequences of Decision Error B - incorrectly deciding the waste was
hazardous when in fact it is not hazardous - would cause the needless costs for
treatment and disposal, but with no negative environmental consequences.
Error A, incorrectly deciding that a hazardous waste is a nonhazardous waste,
posed more severe consequences for the generator in terms of liability and
compliance concerns. Consequently, the baseline condition (null hypothesis)
chosen was that the true proportion of waste that is nonhazardous is less than 90
percent.
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Example 2 Appendix I
Table 1-7. Null Hypothesis and Possible Decision Errors for Example 2
Possible Decision Errors
"Null Hypothesis"
(baseline condition) Type I Error (CC ), Type II Error (p),
False Rejection False Acceptance
The true proportion (P) of Concluding the waste is Concluding the waste is
waste that is nonhazardous is nonhazardous when, in fact, it hazardous when, in fact, it is
less than 0.90. is hazardous. nonhazardous.
Next, it was necessary to specify the boundaries of the gray region. When the
null hypothesis (baseline condition) assumes that the waste is hazardous (as in
this example), one limit of the gray region is bounded by the Action Level and the
other limit is set at a point where it is desirable to control the Type II (false
acceptance) error. The project team set one bound of the gray region at 0.90
(the Action Level). Since a "no exceedance" criterion is included in the decision
rule, the other bound of the gray region is effectively set at 1.
The DQO planning team then sets the acceptable probability of making a Type I
(false rejection) error at 10 percent (a = 0.10). In other words, they are willing
to accept a 10 percent chance of concluding the waste is nonhazardous when at
least a portion of the waste is hazardous. The use of the exceedance rule
method does not require specification of the Type II (false acceptance) error rate.
The information collected in Step 6 of the DQO Process is summarized below.
Table I-8. Initial Outputs of Step 6 of the DQO Process - Example 2
Needed Parameter Output
Action Level 0.90
Gray Region 0.90 to 1.0 (A = 0.10)
Null Hypothesis (H0) P < 0.90
False Rejection Decision Error Limit CX = 010
(probability of a Type I error)
False Acceptance Decision Error Limit Not specified
(probability of a Type II error)
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Appendix I Example 2
DQO Step 7: Optimizing the Data Collection Design
Review outputs from the first six steps of the DQO Process. The planning
team reviewed the outputs of the first six steps of the DQO Process.
Consider various data collection designs. The DQO planning team
considered two probabilistic sampling designs: simple random and systematic
(random within time intervals). Both the simple random and the systematic
design would allow the facility owner to estimate whether a high percentage of
the waste complies with the standard. The team also considered using an
authoritative "biased" sampling design to estimate the high end or "worst case"
waste characteristics.
Two analytical plans were then considered: One in which the full TCLP would be
performed on each sample, and one in which TCLP concentrations could be
estimated from total concentration by comparing each total sample analysis
result to 20 times the TC regulatory limit (to account for the 20:1 dilution used in
the TCLP).
The laboratory requested a sample mass of at least 300 grams (per sample) to
allow the laboratory to perform the preliminary analyses required by the TCLP
and to provide sufficient mass to perform the full TCLP (if required).
The practical considerations were then evaluated for each alternative design,
including access to sampling locations, worker safety, equipment selection/use,
experience needed, special analytical needs, and scheduling.
Select the optimal number of samples. Since the decision rule specified no
exceedance of the standard in any sample, the number of samples was
determined from Table G-3a in Appendix G. The table is based on the formula
n = log(#)/log(/?). For a desired p = 0.90 and (!-#) = 0.90, the number
of samples (n) for a simple random or systematic sampling design was 22.
The team also considered how many samples might be required if a
nonprobabilistic authoritative sampling design were used. Some members of the
planning team thought that significantly fewer samples (e.g., four) could be used
to make a hazardous waste determination, and they pointed out that the RCRA
regulations do not require statistical sampling for waste classification. On the
other hand, other members of the planning team argued against the authoritative
design. They argued that there was insufficient knowledge of the waste to
implement authoritative sampling and noted that a few samples taken in a non-
probabilistic manner would limit their ability to quantify any possible decision
errors.
Select a resource-effective design. The planning team evaluated the
sampling and analytical design options and costs. The following table
summarizes the estimated costs for the four sampling designs evaluated.
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Appendix I
Table 1-9. Estimated Costs for Implementing Candidate Sampling Designs
Sample collection cost (per
Simple Random
or Systematic
Sampling (total
metals only)
$50
Simple Random
or Systematic
Sampling (TCLP
metals)
$50
Authoritative
(Biased)
Sampling (total
metals only)
$50
Authoritative
(Biased)
Sampling (TCLP
metals)
$50
sample)
Analysis cost
• SW-846 Methods 3050B/
601 OB (totaled and Cr)
(per sample)
• SW-846 TCLP Method
1311. Extract analyzed
by SW-846 Methods
3010A/6010B(per
sample)
Number of samples
$40
$40
22
$220
22
$220
Total Estimated Cost
$1,980
$5,940
$360
$1,080
While the authoritative design with total metals analysis offered the least cost
compared to the probabilistic designs, the team decided that they did not have
sufficient knowledge of the waste, its leaching characteristics, or the process yet
to use an authoritative sampling approach with total metals analysis only.
Furthermore, the team needed to quantify the probability of making a decision
error. The planning team selected the systematic design with total metals
analysis for Cd and Cr with the condition that if any total sample analysis result
indicated the maximum theoretical TCLP result could exceed the TC limit, then
the TCLP would be performed for that sample. This approach was selected for
its ease of implementation, it would provide adequate waste knowledge for future
waste management decisions (assuming no change in the waste generation
process), and would satisfy other cost and performance objectives specified by
the planning team.
Prepare a QAPP/SAP. The operational details of the sampling and analytical
activities are documented in a Quality Assurance Project Plan and Sampling and
Analysis Plan (QAPP/SAP).
Implementation Phase
The QAPP/SAP was implemented in accordance with the schedule and the facility's safety
program. Based on the rate of waste generation, it was estimated that the roll-off box would be
filled in about 30 work days assuming one "batch" of waste was placed in the roll off box each
day. It was decided to obtain one random sample from each batch as the waste was discharge
from the reclamation unit to the roll-off container (i.e., at the point of waste generation). See
Figure I-5.
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Appendix I
Example 2
Random Sampling Within Batches
Batch 1 Batch 2, etc
Point of waste
generation and
sampling point
If hazardous,
accumulation less
than 90 days prior to
shipment off site per
40 CFR Part 262.34(a).
Not to scale
Figure I-5. Systematic sampling design with random sampling times selected
within each batch
The QAPP/SAP established the following DQOs and performance goals for the equipment.
The sampling device must meet the following criteria:
Be able to obtain a minimum mass of 300 grams for each sample
Be constructed of materials that will not alter analyte concentrations due to loss
or gain of analytes via sorption, desorption, degradation, or corrosion
Be easy to use, safe, and low cost
Be capable of obtaining increments of the waste at the discharge drop without
introducing sampling bias.
The following four steps were taken to select the sampling device (from Section 7.1):
Step 1 - Identify the Medium To Be Sampled
Based on a prior inspection, it was known that the waste is a unconsolidated dry granular solid.
Using Table 8 in Section 7.1, we find the media descriptor that most closely matches the waste
in the first column of the table: "Other Solids - Unconsolidated."
Step 2 - Select the Sample Location
The second column of Table 8 provides a list of common sampling locations for unconsolidated
solids. The discharge drop opening is four inches wide, and the waste is released downward
into the collection box. "Pipe or Conveyor" found in the table is the closest match to the
300
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Example 2
Appendix I
configuration of the waste discharge point.
Step 3 - Identify Candidate Sampling Devices
The third column of Table 8 provides a list of candidate sampling devices for sampling solids
from a pip or conveyor. For this waste stream, the list of devices for sampling a pipe or
conveyor includes bucket, dipper, pan, sample container, miniature core sampler,
scoop/trowel/shovel, and trier. The planning team immediately eliminated miniature core
sampler, scoop/trowel/shovel, and trier because they are not suitable for obtaining samples from
a falling stream or vertical discharge.
Step 4 - Select Devices
From the list of candidate sampling devices, one device was selected for use in the field from
Table 9 in Section 7.1. Selection of the equipment was made after consideration of the DQOs
for the sample support (i.e., required volume, width, shape, and orientation), the performance
goals established for the sampling device, ease of use and decontamination, worker safety
issues, cost, and any practical considerations. Table 1-10 demonstrates how the DQOs and
performance goals were used to narrow the candidate devices down to just one or two.
Table 1-10. Using DQOs and Performance Goals To Select a Final Sampling Device
Candidate
Devices
Bucket
Dipper
Pan
Sample
container
Data Quality Objectives and Performance Goals
Required
Width
4 inches
Y
N
Y
N
Orientation and
Shape
Cross-section of
entire stream
Y
Y
Y
N
Sample
Volume
>300g
Y
Y
Y
Y
Operational
Considerations
Device is
portable, safe,
and low cost?
Y
Y
Y
Y
Desired
Material of
Construction
Polyethylene
or PTFE
Y
Y
Y
Y
Key: Y = The device is capable of achieving the specified DQO or performance goal.
N = The device is not capable of achieving the specified DQO or performance goal.
The sampling mode was "one-dimensional," that is, the material is relatively linear in time and
space. The ideal sampling device would obtain a sample of constant thickness and must be
capable of obtaining the entire width of the stream for a fraction of the time (see discussion at
Section 6.3.2.1). Either a bucket or pan wide enough (preferably 3 times the width of the
stream) to obtain all of the flow for a fraction of the time are identified as suitable devices
because they are capable of achieving all the performance goals.
A flat 12-inch wide polyethylene pan with vertical sides was used to collect each primary field
sample. Each primary field sample was approximately 2 kilograms, therefore, the field team
used the "fractional shoveling" technique (see Section 7.3.2) to reduce the sample mass to a
subsample of approximately 300 grams. The field samples (each in a 32-oz jar) and associated
301
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Appendix I Example 2
field QC samples were submitted to the laboratory in accordance with the sample handling and
shipping instructions specified in the QAPP/SAP.
A total of 30 samples were obtained by the time the roll-off box was filled, so it was necessary to
randomly select 22 samples from the set of 30 for laboratory analysis.
All 22 samples were first analyzed for total cadmium and chromium to determine if the
maximum theoretical TCLP concentration in any one sample could exceed the applicable TC
limit. Samples whose maximum theoretical TCLP value exceeded the applicable TC limit were
then analyzed using the full TCLP.
For the TCLP samples, no particle-size reduction was required for the sample extraction
because the maximum particle size in the waste passed through a 9.5 mm sieve (the maximum
particle size allowed for the TCLP). (On a small subsample of the waste, however, particle size
reduction to 1 mm was required to determine the TCLP extract type (I or II)). A 100-gram
subsample was taken from each field sample for TCLP analysis.
Assessment Phase
Data Verification and Validation
Sampling and analytical records were reviewed to check compliance with the QAPP/SAP. The
data collected during the study met the DQOs. Sampling and analytical error were minimized
through the use of a statistical sampling design, correct field sampling and subsampling
procedures, and adherence to the requirements of the analytical methods. The material that
was sampled did not present any special problems concerning access to sampling locations,
equipment usage, particle-size distribution, or matrix interferences. Quantitation limits achieved
for total cadmium and chromium were 5 mg/kg and 10 mg/kg respectively. Quantitation limits
achieved for cadmium and chromium in the TCLP extract were 0.10 mg/Land 1.0 mg/L
respectively. The analytical package was validated and the data generated were judged
acceptable for their intended purpose.
Data Quality Assessment
DQA was performed using the approach outlined in Section 9.8.2 and EPA QA/G-9 (USEPA
2000d):
1. Review DQOs and sampling design. The DQO planning team reviewed the
original objectives: "If the upper 90th percentile TCLP concentration for cadmium
or chromium in the waste and all samples analysis results are less than their
respective action levels of 1.0 and 5.0 mg/L TCLP, then the waste can be
classified as nonhazardous waste under RCRA; otherwise, the waste will be
considered a hazardous waste."
2. Prepare the data for statistical analysis. The summary of the verified and
validated data were received in hard copy format, and summarized in a table.
The table was checked by a second person for accuracy. The results for the
data collection effort are listed in Table 1-11.
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Example 2
Appendix I
Table 1-11. Total and TCLP Sample Analysis Results
Sample No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Cadmium
Total (mg/kg)
<5
6
29
<5
<5
7
7
13
<5
<5
36
<5
<5
<5
<5
9
<5
<5
<5
20
<5
<5
Total/ 20
(TC limit = 1 mg/L)
<0.25
0.3
1.45
(full TCLP = 0.72)
<0.25
<0.25
0.35
0.35
0.65
<0.25
<0.25
1.8
(full TCLP = 0.8)
<0.25
<0.25
<0.25
<0.25
0.45
<0.25
<0.25
<0.25
1
(full TCLP = <0. 10)
<0.25
<0.25
Chromium
Total (mg/kg)
11
<10
<10
<10
42
<10
<10
26
19
<10
<10
<10
<10
12
<10
<10
<10
<10
31
<10
<10
<10
Total 120
(TC limit = 5 mg/L)
0.55
<0.5
<0.5
<0.5
2.1
<0.5
<0.5
1.3
0.95
<0.5
<0.5
<0.5
<0.5
0.6
<0.5
<0.5
<0.5
<0.5
1.55
<0.5
<0.5
<0.5
3. Conduct preliminary analysis of data and check distributional
assumptions. To use the nonparametric "exceedance rule" no distributional
assumptions are required. The only requirements are a random sample, and that
the quantitation limit is less than the applicable standard. These requirements
were met.
4. Select and perform the statistical test. The maximum TCLP sample analysis
results for cadmium and chromium were compared to their respective TC
regulatory limits. While several of the total results indicated the maximum
theoretical TCLP result could exceed the regulatory limit, subsequent analysis of
the TCLP extracts from these samples indicated the TCLP concentrations were
below the regulatory limits.
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Appendix I Example 2
5. Draw conclusions and report results. All 22 sample analysis results were less
than the applicable TC limits, therefore the owner concluded with at least 90-
percent confidence that at least 90-percent of all possible samples of the waste
would be below the TC regulatory levels. Based on the decision rule established
for the study, the owner decided to manage the waste as a nonhazardous
waste.1
A summary report including a description of all planning, implementation, and
assessment activities was placed in the operating record.
1 Note that if fewer than 22 samples were analyzed -for example, due to a lost sample - and all sample
analysis results indicated concentrations less than the applicable standard, then one still could conclude that 90-
percent of all possible samples are less than the standard but with a lower level of confidence. See Section 5.5.2,
Equation 17.
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APPENDIX J
SUMMARIES OF ASTM STANDARDS
ASTM (the American Society for Testing and Materials) is one of the entities that can provide
additional useful information on sampling. This appendix references many of the standards
published by ASTM that are related to sampling.
ASTM is a not-for-profit organization that provides a forum for writing standards for materials,
products, systems, and services. The Society develops and publishes standard test methods,
specifications, practices, guides, classifications, and terminology.
Each ASTM standard is developed within the
consensus principles of the Society and meets
the approved requirements of its procedures.
The voluntary, full-consensus approach brings
together people with diverse backgrounds and
knowledge. The standards undergo intense
round-robin testing. Strict balloting and due
process procedures guarantee accurate, up-
to-date information.
Contact ASTM
For more information on ASTM or how to purchase
their publications, including the standards referenced
by this appendix, contact them at: ASTM, 100 Barr
Harbor Drive, West Conshohocken, PA 19428-2959;
telephone: 610-832-9585; World Wide Web:
http://www.astm.org.
To help you determine which ASTM standards may be most useful, this appendix includes text
found in the scope of each standard. The standards, listed in alpha-numerical order, each deal
in some way with sample collection. ASTM has future plans to publish these standards together
in one volume on sampling.
D 140 Standard Practice for Sampling Bituminous Materials
This practice applies to the sampling of bituminous materials at points of manufacture, storage,
or delivery.
D 346 Standard Practice for Collection and Preparation of Coke Samples for Laboratory
Analysis
This practice covers procedures for the collection and reduction of samples of coke to be used
for physical tests, chemical analyses, and the determination of total moisture.
D 420 Guide to Site Characterization for Engineering. Design, and Construction
Purposes
This guide refers to ASTM methods by which soil, rock, and ground-water conditions may be
determined. The objective of the investigation should be to identify and locate, both horizontally
and vertically, significant soil and rock types and ground-water conditions present within a given
site area and to establish the characteristics of the subsurface materials by sampling or in situ
testing, or both.
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Appendix J
D 1452 Standard Practice for Soil Investigation and Sampling by Auger Borings
This practice covers equipment and procedures for the use of earth augers in shallow
geotechnical exploration. It does not apply to sectional continuous flight augers. This practice
applies to any purpose for which disturbed samples can be used. Augers are valuable in
connection with ground water level determinations, to help indicate changes in strata, and in the
advancement of a hole for spoon and tube sampling.
D 1586 Standard Test Method for Penetration Test and Split-Barrel Sampling of Soils
This test method describes the procedure, generally known as the Standard Penetration Test,
for driving a split-barrel sampler. The procedure is used to obtain a representative soil sample
and to measure the resistance of the soil to penetration of the sampler.
D 1587 Standard Practice for Thin-Walled Tube Geotechnical Sampling of Soils
This practice covers a procedure for using a thin-walled metal tube to recover relatively
undisturbed soil samples suitable for laboratory tests of structural properties. Thin-walled tubes
used in piston, plug, or rotary-type samplers, such as the Denison or Pitcher sampler, should
comply with the portions of this practice that describe the thin-walled tubes. This practice is
used when it is necessary to obtain a relatively undisturbed sample. It does not apply to liners
used within the above samplers.
D 2113 Standard Practice for Diamond Core Drilling for Site Investigation
This practice describes equipment and procedures for diamond core drilling to secure core
samples of rock and some soils that are too hard to sample by soil-sampling methods. This
method is described in the context of obtaining data for foundation design and geotechnical
engineering purposes rather than for mineral and mining exploration.
D 2234 Standard Practice for Collection of a Gross Sample of Coal
This practice covers procedures for the collection of a gross sample of coal under various
conditions of sampling. The practice describes general and special purpose sampling
procedures for coals by size and condition of preparation (e.g., mechanically cleaned coal or
raw coal) and by sampling characteristics. The sample is to be crushed and further prepared
for analysis in accordance with ASTM Method D 2013. This practice also gives procedures for
dividing large samples before any crushing.
D 3213 Standard Practices for Handling. Storing, and Preparing Soft Undisturbed Marine
Soil
These practices cover methods for project/cruise reporting; and for the handling, transporting
and storing of soft cohesive undisturbed marine soil. The practices also cover procedures for
preparing soil specimens for triaxial strength, and procedures for consolidation testing. These
practices may include the handling and transporting of sediment specimens contaminated with
hazardous materials and samples subject to quarantine regulations.
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Appendix J
D 3326 Standard Practice for Preparation of Samples for Identification of Waterborne
Oils
This practice covers the preparation for analysis of waterborne oils recovered from water. The
identification is based on the comparison of physical and chemical characteristics of the
waterborne oils with oils from suspect sources. These oils may be of petroleum or
vegetable/animal origin, or both. The practice covers the following seven procedures (A through
G): Procedure A, for samples of more than 50-mL volume containing significant quantities of
hydrocarbons with boiling points above 280°C; Procedure B, for samples containing significant
quantities of hydrocarbons with boiling points above 280°C; Procedure C, for waterborne oils
containing significant amounts of components boiling below 280°C and to mixtures of these and
higher boiling components; Procedure D, for samples containing both petroleum and
vegetable/animal derived oils; Procedure E, for samples of light crudes and medium distillate
fuels; Procedure F, for thin films of oil-on-water; and Procedure G, for oil-soaked samples.
D 3370 Standard Practices for Sampling Water from Closed Conduits
These practices cover the equipment and methods for sampling water from closed conduits
(e.g., process streams) for chemical, physical, and microbiological analyses. It provides
practices for grab sampling, composite sampling, and continual sampling of closed conduits.
D 3550 Standard Practice for Ring-Lined Barrel Sampling of Soils
This practice covers a procedure for using a ring-lined barrel sampler to obtain representative
samples of soil for identification purposes and other laboratory tests. In cases in which it has
been established that the quality of the sample is adequate, this practice provides shear and
consolidation specimens that can be used directly in the test apparatus without prior trimming.
Some types of soils may gain or lose significant shear strength or compressibility, or both, as a
result of sampling. In cases like these, suitable comparison tests should be made to evaluate
the effect of sample disturbance on shear strength and compressibility. This practice is not
intended to be used as a penetration test; however, the force required to achieve penetration or
a blow count, when driving is necessary, is recommended as supplemental information.
D 3665 Standard Practice for Random Sampling of Construction Materials
This practice covers the determination of random locations (or timing) at which samples of
construction materials can be taken. For the exact physical procedures for securing the
sample, such as a description of the sampling tool, the number of increments needed for a
sample, or the size of the sample, reference should be made to the appropriate standard
method.
D 3975 Standard Practice for Development and Use (Preparation) of Samples for
Collaborative Testing of Methods for Analysis of Sediments
This practice establishes uniform general procedures for the development, preparation, and use
of samples in the collaborative testing of methods for chemical analysis of sediments and
similar materials. The principles of this practice are applicable to aqueous samples with
suitable technical modifications.
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Appendix J
D 3976 Standard Practice for Preparation of Sediment Samples for Chemical Analysis
This practice describes standard procedures for preparing test samples (including the removal
of occluded water and moisture) of field samples collected from locations such as streams,
rivers, ponds, lakes, and oceans. These procedures are applicable to the determination of
volatile, semivolatile, and nonvolatile constituents of sediments.
D 3694 Standard Practices for Preparation of Sample Containers and for Preservation of
Organic Constituents
These practices cover the various means of (1) preparing sample containers used for collection
of waters to be analyzed for organic constituents and (2) preservation of such samples from the
time of sample collection until the time of analysis. The sample preservation practice depends
on the specific analysis to be conducted. Preservation practices are listed with the
corresponding applicable general and specific constituent test method. The preservation
method for waterborne oils is given in Practice D 3325. Use of the information given will make it
possible to choose the minimum number of sample preservation practices necessary to ensure
the integrity of a sample designated for multiple analysis.
D 4136 Standard Practice for Sampling Phytoplankton with Water-Sampling Bottles
This practice covers the procedures for obtaining quantitative samples of a phytoplankton
community by the use of water-sampling bottles.
D 4220 Standard Practices for Preserving and Transporting Soil Samples
These practices cover procedures for preserving soil samples immediately after they are
obtained in the field and accompanying procedures for transporting and handling the samples.
These practices are not intended to address requirements applicable to transporting of soil
samples known or suspected to contain hazardous materials.
D 4342 Standard Practice for Collecting of Benthic Macroinvertebrates with Ponar Grab
Sampler
This practice covers the procedures for obtaining qualitative or quantitative samples of
macroinvertebrates inhabiting a wide range of bottom substrate types (e.g., coarse sand, fine
gravel, clay, mud, marl, and similar substrates. The Ponar grab sampler is used in freshwater
lakes, rivers, estuaries, reservoirs, oceans, and similar habitats.
D 4343 Standard Practice for Collecting Benthic Macroinvertebrates with Ekman Grab
Sampler
This practice covers the procedures for obtaining qualitative or quantitative samples of
macroinvertebrates inhabiting soft sediments. The Ekman grab sampler is used in freshwater
lakes, reservoirs, and, usually, small bodies of water.
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Appendix J
D 4387 Standard Guide for Selecting Grab Sampling Devices for Collecting Benthic
Macroinvertebrates
This guide covers the selection of grab sampling devices for collecting benthic
macroinvertebrates. Qualitative and quantitative samples of macroinvertebrates in sediments or
substrates are usually taken by grab samplers. The guide discusses the advantages and
limitations of the Ponar, Peterson, Ekman and other grab samplers.
D 4411 Standard Guide for Sampling Fluvial Sediment in Motion
This guide covers the equipment and basic procedures for sampling to determine discharge of
sediment transported by moving liquids. Equipment and procedures were originally developed
to sample mineral sediments transported by rivers but they also are applicable to sampling a
variety of sediments transported in open channels or closed conduits. Procedures do not apply
to sediments transported by flotation. This guide does not pertain directly to sampling to
determine nondischarge-weighted concentrations, which in special instances are of interest.
However, much of the descriptive information on sampler requirements and sediment transport
phenomena is applicable in sampling for these concentrations and the guide briefly specifies
suitable equipment.
D 4448 Standard Guide for Sampling Groundwater Monitoring Wells
This guide covers procedures for obtaining valid representative samples from ground-water
monitoring wells. The scope is limited to sampling and "in the field" preservation and does not
include well location, depth, well development, design and construction, screening, or analytical
procedures. This guide provides a review of many of the most commonly used methods for
sampling ground-water quality monitoring wells and is not intended to serve as a ground-water
monitoring plan for any specific application. Because of the large and ever-increasing number
of options available, no single guide can be viewed as comprehensive. The practitioner must
make every effort to ensure that the methods used, whether or not they are addressed in this
guide, are adequate to satisfy the monitoring objectives at each site.
D 4489 Standard Practices for Sampling of Waterborne Oils
These practices describe the procedures to be used in collecting samples of waterborne oils, oil
found on adjoining shorelines, or oil-soaked debris, for comparison of oils by spectroscopic and
chromatographic techniques, and for elemental analyses. Two practices are described.
Practice A involves "grab sampling" macro oil samples. Practice B involves sampling most
types of waterborne oils and is particularly applicable in sampling thin oil films or slicks. Practice
selection will be dictated by the physical characteristics and the location of the spilled oil.
Specifically, the two practices are (1) Practice A, for grab sampling thick layers of oil, viscous
oils or oil soaked debris, oil globules, tar balls, or stranded oil, and (2) Practice B, for
TFE-fluorocarbon polymer strip samplers. Each of the two practices collect oil samples with a
minimum of water, thereby reducing the possibility of chemical, physical, or biological alteration
by prolonged contact with water between the time of collection and analysis.
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Appendix J
D 4547 Standard Guide for Sampling Waste and Soils for Volatile Organic Compounds
This guide describes recommended procedures for the collection, handling, and preparation of
solid waste, soil, and sediment subsamples for subsequent determination of volatile organic
compounds (VOCs). This class of compounds includes low molecular weight aromatics,
hydrocarbons, halogenated hydrocarbons, ketones, acetates, nitriles, acrylates, ethers, and
sulfides with boiling points below 200°C that are insoluble or slightly soluble in water. Methods
of subsample collection, handling, and preparation for analysis are described. This guide does
not cover the details of sampling design, laboratory preparation of containers, and the analysis
of the subsamples.
D 4687 Standard Guide for General Planning of Waste Sampling
This guide provides information for formulating and planning the many aspects of waste
sampling that are common to most waste-sampling situations. This guide addresses the
following aspects of sampling: Sampling plans, safety plans, quality assurance considerations,
general sampling considerations, preservation and containerization, cleaning equipment,
labeling and shipping procedures, and chain-of-custody procedures. This guide does not
provide comprehensive sampling procedures for these aspects, nor does it serve as a guide to
any specific application.
D 4696 Standard Guide for Pore-Liquid Sampling from the Vadose Zone
This guide discusses equipment and procedures used for sampling pore-liquid from the vadose
zone (unsaturated zone). The guide is limited to in-situ techniques and does not include soil
core collection and extraction methods for obtaining samples. The term "pore-liquid" is
applicable to any liquid from aqueous pore-liquid to oil, however, all of the samplers described
in this guide are designed to sample aqueous pore-liquids only. The abilities of these samplers
to collect other pore-liquids may be quite different than those described. Some of the samplers
described in the guide currently are not commercially available. These samplers are presented
because they may have been available in the past, and may be encountered at sites with
established vadose zone monitoring programs. In addition, some of these designs are
particularly suited to specific situations. If needed, these samplers could be fabricated.
D 4700 Standard Guide for Soil Sampling from the Vadose Zone
This guide addresses procedures that may be used for obtaining soil samples from the vadose
zone (unsaturated zone). Samples can be collected for a variety of reasons, including the
following:
Stratigraphic description
Hydraulic conductivity testing
Moisture content measurement
Moisture release curve construction
Geotechnical testing
Soil gas analyses
Microorganism extraction
Pore-liquid and soil chemical analyses.
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Appendix J
This guide focuses on methods that provide soil samples for chemical analyses of the soil or
contained liquids or contaminants. Comments on how methods may be modified for other
objectives, however, also are included. This guide does not describe sampling methods for
lithified deposits and rocks (e.g., sandstone, shale, tuff, granite).
D 4823 Standard Guide for Core Sampling Submerged. Unconsolidated Sediments
This guide covers core-sampling terminology, advantages and disadvantages of various core
samplers, core distortions that may occur during sampling, techniques for detecting and
minimizing core distortions, and methods for dissecting and preserving sediment cores. In this
guide, sampling procedures and equipment are divided into the following categories (based on
water depth): sampling in depths shallower than 0.5 m, sampling in depths between 0.5 m and
10m, and sampling in depths exceeding 10m. Each category is divided into two sections: (1)
equipment for collecting short cores and (2) equipment for collecting long cores. This guide
also emphasizes general principles. Only in a few instances are step-by-step instructions given.
Because core sampling is a field-based operation, methods and equipment usually must be
modified to suit local conditions. Drawings of samplers are included to show sizes and
proportions. These samplers are offered primarily as examples (or generic representations) of
equipment that can be purchased commercially or built from plans in technical journals. This
guide is a brief summary of published scientific articles and engineering reports, and the
references are listed. These documents provide operational details that are not given in the
guide but are nevertheless essential to the successful planning and completion of core sampling
projects.
D 4840 Standard Guide for Sampling Chain-of-Custody Procedures
This guide contains a comprehensive discussion of potential requirements for a sample
chain-of-custody program and describes the procedures involved in sample chain-of-custody.
The purpose of these procedures is to provide accountability for and documentation of sample
integrity from the time of sample collection until sample disposal. These procedures are
intended to document sample possession during each stage of a sample's life cycle, that is,
during collection, shipment, storage, and the process of analysis. Sample chain of custody is
just one aspect of the larger issue of data defensibility. A sufficient chain-of-custody process
(i.e., one that provides sufficient evidence of sample integrity in a legal or regulatory setting) is
situationally dependent. The procedures presented in this guide are generally considered
sufficient to assure legal defensibility of sample integrity. In a given situation, less stringent
measures may be adequate. It is the responsibility of the users of this guide to determine their
exact needs. Legal counsel may be needed to make this determination.
D 4854 Standard Guide for Estimating the Magnitude of Variability from Expected
Sources in Sampling Plans
The guide explains how to estimate the contributions of the variability of lot sampling units,
laboratory sampling units, and specimens to the variation of the test result of a sampling plan.
The guide explains how to combine the estimates of the variability from the three sources to
obtain an estimate of the variability of the sampling plan results. The guide is applicable to all
sampling plans that produce variables data. It is not applicable to plans that produce attribute
data, since such plans do not take specimens in stages, but require that specimens be taken at
random from all of the individual items in the lot.
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Appendix J
D 4916 Standard Practice for Mechanical Auger Sampling
This practice describes procedures for the collection of an increment, partial sample, or gross
sample of material using mechanical augers. Reduction and division of the material by
mechanical equipment at the auger also is covered.
D 5013 Standard Practices for Sampling Wastes from Pipes and Other Point Discharges
These practices provide guidance for obtaining samples of waste at discharge points from
pipes, sluiceways, conduits, and conveyor belts. The following are included: Practice A -
Liquid or Slurry Discharges, and Practice B - Solid or Semisolid Discharges. These practices
are intended for situations in which there are no other applicable ASTM sampling methods for
the specific industry. These practices do not address flow and time-proportional samplers and
other automatic sampling devices. Samples are taken from a flowing waste stream or moving
waste mass and, therefore, are descriptive only within a certain period. The length of the period
for which a sample is descriptive will depend on the sampling frequency and compositing
scheme.
D 5088 Standard Practice for Decontamination of Field Eguipment Used at
Nonradioactive Waste Sites
This practice covers the decontamination of field equipment used in the sampling of soils, soil
gas, sludges, surface water, and ground water at waste sites that are to undergo both physical
and chemical analyses. This practice is applicable only at sites at which chemical (organic and
inorganic) wastes are a concern and is not intended for use at radioactive or mixed (chemical
and radioactive) waste sites. Procedures are included for the decontamination of equipment
that comes into contact with the sample matrix (sample contacting equipment) and for ancillary
equipment that has not contacted the portion of sample to be analyzed (nonsample contacting
equipment). This practice is based on recognized methods by which equipment may be
decontaminated. When collecting environmental matrix samples, one should become familiar
with the site-specific conditions. Based on these conditions and the purpose of the sampling
effort, the most suitable method of decontamination can be selected to maximize the integrity of
analytical and physical testing results. This practice is applicable to most conventional sampling
equipment constructed of metallic and synthetic materials. The manufacturer of a specific
sampling apparatus should be contacted if there is concern regarding the reactivity of a
decontamination rinsing agent with the equipment.
D 5092 Standard Practice for Design and Installation of Ground Water Monitoring Wells
in Aguifers
This practice addresses the selection and characterization (by defining soil, rock types, and
hydraulic gradients) of the target monitoring zone as an integral component of monitoring well
design and installation. The development of a conceptual hydrogeologic model for the intended
monitoring zone(s) is recommended prior to the design and installation of a monitoring well.
The guidelines are based on recognized methods by which monitoring wells may be designed
and installed for the purpose of detecting the presence or absence of a contaminant, and
collecting representative ground water quality data. The design standards and installation
procedures in the practice are applicable to both detection and assessment monitoring
programs for facilities. The recommended monitoring well design, as presented in this practice,
312
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Appendix J
is based on the assumption that the objective of the program is to obtain representative ground-
water information and water quality samples from aquifers. Monitoring wells constructed
following this practice should produce relatively turbidity-free samples for granular aquifer
materials ranging from gravels to silty sand and sufficiently permeable consolidated and
fractured strata. Strata having grain sizes smaller than the recommended design for the
smallest diameter filter pack materials should be monitored by alternative monitoring well
designs not addressed by this practice.
D 5283 Standard Practice for Generation of Environmental Data Related to Waste
Management Activities Quality Assurance and Quality Control Planning and
Implementation
This practice addresses the planning and implementation of the sampling and analysis aspects
of environmental data generation activities. It defines the criteria that must be considered to
assure the quality of the field and analytical aspects of environmental data generation activities.
Environmental data include, but are not limited to, the results from analyses of samples of air,
soil, water, biota, waste, or any combinations thereof. DQOs should be adopted prior to
application of this practice. Data generated in accordance with this practice are subject to a
final assessment to determine whether the DQOs were met. For example, many screening
activities do not require all of the mandatory quality assurance and quality control steps found in
this practice to generate data adequate to meet the project DQOs. The extent to which all of the
requirements must be met remains a matter of technical judgment as it relates to the
established DQOs. This practice presents extensive management requirements designed to
ensure high-quality environmental data.
D 5314 Standard Guide for Soil Gas Monitoring in the Vadose Zone
This guide covers information pertaining to a broad spectrum of practices and applications of
soil atmosphere sampling, including sample recovery and handling, sample analysis, data
interpretation, and data reporting. This guide can increase the awareness of soil gas monitoring
practitioners concerning important aspects of the behavior of the soil-water-gas contaminant
system in which this monitoring is performed, as well as inform them of the variety of available
techniques of each aspect of the practice. Appropriate applications of soil gas monitoring are
identified, as are the purposes of the various applications. Emphasis is placed on soil gas
contaminant determinations in certain application examples. This guide suggests a variety of
approaches useful in monitoring vadose zone contaminants with instructions that offer direction
to those who generate and use soil gas data. This guide does not recommend a standard
practice to follow in all cases, nor does it recommend definite courses of action. The success of
any one soil gas monitoring methodology is strongly dependent upon the environment in which
it is applied.
D 5358 Standard Practice for Sampling with a Dipper or Pond Sampler
This practice describes the procedure and equipment for taking surface samples of water or
other liquids using a dipper. A pond sampler or dipper with an extension handle allows the
operator to sample streams, ponds, waste pits, and lagoons as far as 15 feet from the bank or
other secure footing. The dipper is useful in filling a sample bottle without contaminating the
outside of the bottle.
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D 5387 Standard Guide for Elements of a Complete Data Set for Non-Cohesive
Sediments
This guide covers criteria for a complete sediment data set, and it provides guidelines for the
collection of non-cohesive sediment alluvial data. This guide describes what parameters should
be measured and stored to obtain a complete sediment and hydraulic data set that could be
used to compute sediment transport using any prominently known sediment-transport
equations.
D 5451 Standard Practice for Sampling Using a Trier Sampler
This practice covers sampling using a trier. A trier resembles an elongated scoop, and is used
to collect samples of granular or powdered materials that are moist or sticky and have a particle
diameter less than one-half the diameter of the trier. The trier can be used as a vertical coring
device only when it is certain that a relatively complete and cylindrical sample can be extracted.
D 5495 Standard Practice for Sampling with a Composite Liquid Waste Sampler
(COLIWASA)
This practice describes the procedure for sampling liquids with the composite liquid waste
sampler (COLIWASA). The COLIWASA is an appropriate device for obtaining a representative
sample from stratified or unstratified liquids. Its most common use is for sampling containerized
liquids, such as tanks, barrels, and drums. It may also be used for pools and other open bodies
of stagnant liquid. (A limitation of the COLIWASA is that the stopper mechanism may not allow
collection of approximately the bottom inch of material, depending on construction of the
stopper.) The COLIWASA should not be used to sample flowing or moving liquids.
D 5608 Standard Practice for Decontamination of Field Equipment Used at Low Level
Radioactive Waste Sites
This practice covers the decontamination of field equipment used in the sampling of soils, soil
gas, sludges, surface water, and ground water at waste sites known or suspected of containing
low-level radioactive wastes. This practice is applicable at sites where low-level radioactive
wastes are known or suspected to exist. By itself or in conjunction with Practice D 5088, this
practice may also be applicable for the decontamination of equipment used in the vicinity of
known or suspected transuranic or mixed wastes. Procedures are contained in this practice for
the decontamination of equipment that comes into contact with the sample matrix (sample
contacting equipment), and for ancillary equipment that has not contacted the sample, but may
have become contaminated during use (noncontacting equipment). This practice is applicable
to most conventional sampling equipment constructed of metallic and hard and smooth
synthetic materials. Materials with rough or porous surfaces, or having a high sorption rate,
should not be used in radioactive-waste sampling due to the difficulties with decontamination.
In those cases in which sampling will be periodically performed, such as sampling of wells,
consideration should be given to the use of dedicated sampling equipment if legitimate
concerns exist for the production of undesirable or unmanageable waste byproducts, or both,
during the decontamination of tools and equipment. This practice does not address regulatory
requirements for personnel protection or decontamination, or for the handling, labeling,
shipping, or storing of wastes, or samples. Specific radiological release requirements and limits
must be determined by users in accordance with local, State and Federal regulations.
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D 5633 Standard Practice for Sampling with a Scoop
This procedure covers the method and equipment used to collect surface and near-surface
samples of soils and physically similar materials using a scoop. This practice is applicable to
rapid screening programs, pilot studies, and other semi-quantitative investigations. The practice
describes how a shovel is used to remove the top layers of soil to the appropriate sample depth
and either a disposable scoop or a reusable scoop is used to collect and place the sample in
the sample container.
D 5658 Standard Practice for Sampling Unconsolidated Waste from Trucks
This practice covers several methods for collecting waste samples from trucks. These methods
are adapted specifically for sampling unconsolidated solid wastes in bulk loads using several
types of sampling equipment.
D 5679 Standard Practice for Sampling Consolidated Solids in Drums or Similar
Containers
This practice covers typical equipment and methods for collecting samples of consolidated
solids in drums or similar containers. These methods are adapted specifically for sampling
drums having a volume of 110 U.S. gallons (416 L) or less, and are applicable to a hazardous
material, product, or waste.
D 5680 Standard Practice for Sampling Unconsolidated Solids in Drums or Similar
Containers
This practice covers typical equipment and methods for collecting samples of unconsolidated
solids in drums or similar containers. These methods are adapted specifically for sampling
drums having a volume of 110 U.S. gallons (416 L) or less, and are applicable to a hazardous
material, product, or waste.
D 5730 Standard Guide for Site Characterization for Environmental Purposes with
Emphasis on Soil. Rock, the Vadose Zone and Ground Water
This guide covers a general approach to planning field investigations that is useful for any type
of environmental investigation with a primary focus on the subsurface and major factors
affecting the surface and subsurface environment. Generally, such investigations should
identify and locate, both horizontally and vertically, significant soil and rock masses and ground-
water conditions present within a given site area and establish the characteristics of the
subsurface materials by sampling or in situ testing, or both. The extent of characterization and
specific methods used will be determined by the environmental objectives and data quality
requirements of the investigation. This guide focuses on field methods for determining site
characteristics and collection of samples for further physical and chemical characterization. It
does not address special considerations required for characterization of karst and fractured rock
terrain.
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D 5743 Standard Practice for Sampling Single or Multilayered Liquids, with or without
Solids, in Drums or Similar Containers
This practice covers typical equipment and methods for collecting samples of single or
multilayered liquids, with or without solids, in drums or similar containers. These methods are
adapted specifically for sampling drums having a volume of 110 gallons (416 L) or less, and are
applicable to a hazardous material, product, or waste.
D 5792 Standard Practice for Generation of Environmental Data Related to Waste
Management Activities: Development of Data Quality Objectives
This practice covers the development of data quality objectives (DQOs) for the acquisition of
environmental data. Optimization of sampling and analysis design is a part of the DQO
Process. This practice describes the DQO Process in detail. The various strategies for design
optimization are too numerous to include in this practice. Many other documents outline
alternatives for optimizing sampling and analysis design, therefore, only an overview of design
optimization is included. Some design aspects are included in the examples for illustration
purposes.
D 5903 Standard Guide for Planning and Preparing for a Groundwater Sampling Event
This guide covers planning and preparing for a ground-water sampling event. It includes
technical and administrative considerations and procedures. Example checklists are also
provided as appendices. This guide may not cover every consideration and procedure that is
necessary before all ground-water sampling projects. This guide focuses on sampling of
ground water from monitoring wells; however, most of the guidance herein can apply to the
sampling of springs as well.
D 5911 Standard Practice for Minimum Set of Data Elements to Identify a Soil Sampling
Site
This practice covers what information should be obtained to uniquely identify any soil sampling
or examination site where an absolute and recoverable location is necessary for quality control
of the study, such as for a waste disposal project. The minimum set of data elements was
developed considering the needs for informational data bases, such as geographic information
systems. Other distinguishing details, such as individual site characteristics, help in singularly
cataloging the site. For studies that are not environmentally regulated, such as for an
agricultural or preconstruction survey, the data specifications established by a client and the
project manager may be different from that of the minimum set. As used in this practice, a soil
sampling site is meant to be a single point, not a geographic area or property, located by an X,
Y, and Z coordinate position at land surface or a fixed datum. All soil data collected for the site
are directly related to the coordinate position, e.g., a sample is collected from a certain number
of feet (or meters) or sampled from a certain interval to feet (or meters) below the X, Y, and Z
coordinate position. A soil sampling site can include a test well, augered or bored hole,
excavation, grab sample, test pit, sidewall sample, stream bed, or any other site where samples
of the soil can be collected or examined for the purpose intended. Samples of soil (sediment)
filtered from the water of streams, rivers, or lakes are not in the scope of this practice.
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D 5956 Standard Guide for Sampling Strategies for Heterogeneous Wastes
This guide is a practical nonmathematical discussion for heterogeneous waste sampling
strategies. This guide is consistent with the particulate material sampling theory, as well as
inferential statistics, and may serve as an introduction to the statistical treatment of sampling
issues. This guide does not provide comprehensive sampling procedures, nor does it serve as
a guide to any specification.
D 6001 Standard Guide for Direct-Push Water Sampling for Geoenvironmental
Investigations
This guide reviews methods for sampling ground water at discrete points or in increments by
insertion of sampling devices by static force or impact without drilling and removal of cuttings.
By directly pushing the sampler, the soil is displaced and helps to form an annular seal above
the sampling zone. Direct-push water sampling can be one-time or multiple-sampling events.
Methods for obtaining water samples for water quality analysis and detection of contaminants
are presented. Field test methods described in this guide include installation of temporary well
points and insertion of water samplers using a variety of insertion methods. The insertion
methods include (1) soil probing using combinations of impact, percussion, or vibratory driving
with or without additions of smooth static force; (2) smooth static force from the surface using
hydraulic penetrometer or drilling equipment and incremental drilling combined with direct-push
water sampling events. Methods for borehole abandonment by grouting are also addressed.
D 6008 Standard Practice for Conducting Environmental Baseline Surveys
The purpose of this practice is to define good commercial and customary practice in the United
States for conducting an environmental baseline survey (EBS). Such surveys are conducted to
determine certain elements of the environmental condition of Federal real property, including
excess and surplus property at closing and realigning military installations. This effort is
conducted to fulfill certain requirements of the Comprehensive Environmental Response
Compensation and Liability Act of 1980 (CERCLA) section 120(h), as amended by the
Community Environmental Response Facilitation Act of 1992 (CERFA). As such, this practice is
intended to help a user to gather and analyze data and information in order to classify property
into seven environmental condition of property area types (in accordance with the Standard
Classification of Environmental Condition of Property Area Types). Once documented, the EBS
is used to support Findings of Suitability to Lease, or uncontaminated property determinations,
or a combination thereof, pursuant to the requirements of CERFA. Users of this practice should
note that it does not address (except where explicitly noted) requirements of CERFA. The
practice also does not address (except where explicitly noted) requirements for appropriate and
timely regulatory consultation or concurrence, or both, during the conduct of the EBS or during
the identification and use of the standard environmental condition of property area types.
D 6009 Standard Guide for Sampling Waste Piles
This guide provides guidance for obtaining representative samples from waste piles. Guidance
is provided for site evaluation, sampling design, selection of equipment, and data interpretation.
Waste piles include areas used primarily for waste storage or disposal, including above-grade
dry land disposal units. This guide can be applied to sampling municipal waste piles, and it
addresses how the choice of sampling design and sampling methods depends on specific
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features of the pile.
D 6044 Standard Guide for Representative Sampling for Management of Waste and
Contaminated Media
This guide covers the definition of representativeness in environmental sampling, identifies
sources that can affect representativeness (especially bias), and describes the attributes that a
representative sample or a representative set of samples should possess. For convenience,
the term "representative sample" is used in this guide to denote both a representative sample
and a representative set of samples, unless otherwise qualified in the text. This guide outlines a
process by which a representative sample may be obtained from a population, and it describes
the attributes of a representative sample and presents a general methodology for obtaining
representative samples. It does not, however, provide specific or comprehensive sampling
procedures. It is the user's responsibility to ensure that proper and adequate procedures are
used.
D 6051 Standard Guide for Composite Sampling and Field Subsampling for
Environmental Waste Management Activities
This guide discusses the advantages and appropriate use of composite sampling, field
procedures and techniques to mix the composite sample and procedures to collect an unbiased
and precise subsample from a larger sample. Compositing and subsampling are key links in the
chain of sampling and analytical events that must be performed in compliance with project
objectives and instructions to ensure that the resulting data are representative. This guide
discusses the advantages and limitations of using composite samples in designing sampling
plans for characterization of wastes (mainly solid) and potentially contaminated media. This
guide assumes that an appropriate sampling device is selected to collect an unbiased sample.
It does not address where samples should be collected (depends on the objectives), selection
of sampling equipment, bias introduced by selection of inappropriate sampling equipment,
sample collection procedures or collection of a representative specimen from a sample, or
statistical interpretation of resultant data and devices designed to dynamically sample process
waste streams. It also does not provide sufficient information to statistically design an optimized
sampling plan, or to determine the number of samples to collect or to calculate the optimum
number of samples to composite to achieve specified data quality objectives. The mixing and
subsampling described in this guide is expected to cause significant losses of volatile
constituents. Specialized procedures should be used for compositing samples for determination
of volatiles.
D 6063 Standard Guide for Sampling of Drums and Similar Containers by Field
Personnel
This guide covers information, including flow charts, for field personnel to follow in order to
collect samples from drums and similar containers. The purpose of this guide is to help field
personnel in planning and obtaining samples from drums and similar containers, using
equipment and techniques that will ensure that the objectives of the sampling activity will be
met. It can also be used as a training tool.
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D 6169 Standard Guide for Selection of Soil and Rock Sampling Devices Used With Drill
Rigs for Environmental Investigations
This guide covers the selection of soil and rock sampling devices used with drill rigs for the
purpose of characterizing in situ physical and hydraulic properties, chemical characteristics,
subsurface lithology, stratigraphy, and structure, and hydrogeologic units in environmental
investigations.
D 6232 Standard Guide for Selection of Sampling Equipment for Waste and
Contaminated Media Data Collection Activities
This guide covers criteria that should be considered when selecting sampling equipment for
collecting environmental and waste samples for waste management activities. This guide
includes a list of equipment that is used and is readily available. Many specialized sampling
devices are not specifically included in this guide, however, the factors that should be weighed
when choosing any piece of equipment are covered and remain the same for the selection of
any piece of equipment. Sampling equipment described in this guide include automatic
samplers, pumps, bailers, tubes, scoops, spoons, shovels, dredges, and coring and augering
devices. The selection of sampling locations is outside the scope of this guide.
D 6233 Standard Guide for Data Assessment for Environmental Waste Management
Activities
This guide covers a practical strategy for examining an environmental project data collection
effort and the resulting data to determine conformance with the project plan and impact on data
usability. This guide also leads the user through a logical sequence to determine which
statistical protocols should be applied to the data.
D 6250 Standard Practice for Derivation of Decision Point and Confidence Limit for
Statistical Testing of Mean Concentration in Waste Management Decisions
This practice covers a logical basis for the derivation of a decision point and confidence limit
when the mean concentration is used for making environmental waste management decisions.
The determination of a decision point or confidence limit should be made in the context of the
defined problem. The main focus of this practice is on the determination of a decision point. In
environmental management decisions, the derivation of a decision point allows a direct
comparison of a sample mean against this decision point. Similar decisions can be made by
comparing a confidence limit against a concentration limit. This practice focuses on making
environmental decisions using this kind of statistical comparison. Other factors, such as any
qualitative information that also may be important to decision making, are not considered in the
practice. This standard derives the decision point and confidence limit in the framework of a
statistical test of hypothesis under three different presumptions. The relationship between
decision point and confidence limit also is described.
D 6282 Standard Guide for Direct Push Soil Sampling for Environmental Site
Characterizations
This guide addresses direct push soil samplers, which may be driven into the ground from
the surface or through pre-bored holes. The samplers can be continuous or discrete interval
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units. The samplers are advanced to the depth of interest by a combination of static push, or
impacts from hammers, or vibratory methods, or a combination thereof. Field methods
described in this guide include the use of discreet and continuous sampling tools, split and solid
barrel samplers and thin walled tubes with or without fixed piston style apparatus. Insertion
methods described include static push, impact, percussion, other vibratory/sonic driving, and
combinations of these methods using direct push equipment adapted to drilling rigs, cone
penetrometer units, and specially designed percussion/direct push combination machines.
Hammers described by this guide for providing force for insertion include drop style,
hydraulically activated, air activated and mechanical lift devices. The guide does not cover open
chambered samplers operated by hand such as augers, agricultural samplers operated at
shallow depths, or side wall samplers.
D 6286 Standard Guide for Selection of Drilling Methods for Environmental Site
Characterization
This guide provides descriptions of various drilling methods for environmental site
characterization, along with the advantages and disadvantages associated with each method.
This guide is intended to aid in the selection of drilling method(s) for environmental soil and rock
borings and the installation of monitoring wells and other water-quality monitoring devices. This
guide does not address methods of well construction, well development, or well completion.
D 6311 Standard Guide for Generation of Environmental Data Related to Waste
Management Activities: Selection and Optimization of Sampling Design
This guide provides practical information on the selection and optimization of sample designs in
waste management sampling activities, within the context of the requirements established by
the data quality objectives or other planning process. Specifically, this document provides (1)
guidance for the selection of sampling designs; (2) techniques to optimize candidate designs;
and (3) descriptions of the variables that need to be balanced in choosing the final optimized
design.
D 6323 Standard Guide for Laboratory Subsampling of Media Related to Waste
Management Activities
This guide covers common techniques for obtaining representative subsamples from a sample
received at a laboratory for analysis. These samples may include solids, sludges, liquids, or
multilayered liquids (with or without solids). The procedures and techniques discussed in this
guide depend upon the sample matrix, the type of sample preparation and analysis performed,
the characteristic(s) of interest, and the project specific instructions or data quality objectives.
This guide includes several sample homogenization techniques, including mixing and grinding,
as well as information on how to obtain a specimen or split laboratory samples. This guide does
not apply to air or gas sampling.
D 6418 Standard Practice for Using the Disposable EnCore™ Sampler for Sampling and
Storing Soil for Volatile Organic Analysis
This practice provides a procedure for using the disposable EnCore™ sampler to collect and
store a soil sample of approximately 5 grams or 25 grams for volatile organic analysis. The
EnCore™ sampler is designed to collect and hold a soil sample during shipment to the
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laboratory. It consists of a coring body/storage chamber, O-ring sealed plunger, and O-ring
sealed cap. In performing the practice, the integrity of the soil sample structure is maintained
and there is very limited exposure of the sample to the atmosphere. Laboratory subsampling is
not required; the sample is expelled directly from the sampler body into the appropriate
container for analysis.
D 6538 Standard Guide for Sampling Wastewater With Automatic Samplers
This guide covers the selection and use of automatic wastewater samplers including procedures
for their use in obtaining representative samples. Automatic wastewater samplers are intended
for the unattended collection of samples that are representative of the parameters of interest in
the wastewater body. While this guide primarily addresses the sampling of wastewater, the
same automatic samplers may be used to sample process streams and natural water bodies.
D 6582 Standard Guide for Ranked Set Sampling: Efficient Estimation of a Mean
Concentration in Environmental Sampling
This guide describes ranked set sampling, discusses its relative advantages over simple
random sampling, and provides examples of potential applications in environmental sampling.
Ranked set sampling is useful and cost-effective when there is an auxiliary variable, which can
be inexpensively measured relative to the primary variable, and when the auxiliary variable has
correlation with the primary variable. The resultant estimation of the mean concentration is
unbiased, more precise than simple random sampling, and more representative of the
population under a wide variety of conditions.
D 6771 Standard Practice for Low-Flow Purging and Sampling for Wells and Devices
Used for Ground-Water Quality Investigations
This practice covers the method for purging and sampling wells and devices used for
ground-water quality investigations and monitoring programs known as low-flow purging and
sampling. The method is also known by the terms minimal drawdown purging or low-stress
purging. The method could be used for other types of ground-water sampling programs but
these uses are not specifically addressed in this practice. This practice applies only to wells
sampled at the wellhead. This practice does not address sampling of wells containing either
light or dense non-aqueous-phase liquids (LNAPLs or DNAPLs).
E 122 Standard Practice for Choice of Sample Size to Estimate the Average for a
Characteristic of a Lot or Process
This practice covers methods for calculating the sample size (the number of units to include in a
random sample from a lot of material) in order to estimate, with a prescribed precision, an
average of some characteristic for that lot or process. The characteristic may be either a
numerical value of some property or the fraction of nonconforming units with respect to an
attribute. If sampling from a process, the process must be in a state of statistical control for the
results to have predictive value.
E 178 Standard Practice for Dealing with Outlying Observations
This practice covers outlying observations in samples and how to test the statistical significance
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of them. An outlying observation, or "outlier," is an observation that appears to deviate
markedly from other members of the sample in which it occurs. An outlying observation may be
merely an extreme manifestation of the random variability inherent in the data. If this is true, the
value should be retained and processed in the same manner as the other observations in the
sample. On the other hand, an outlying observation may be the result of gross deviation from
prescribed experimental procedure or an error in calculating or recording the numerical value.
In such cases, it may be desirable to institute an investigation to ascertain the reason for the
aberrant value. The observation may even actually be rejected as a result of the investigation,
though not necessarily so. At any rate, in subsequent data analysis the outlier or outliers
probably will be recognized as being from a different population than that of the other sample
values. The procedures covered herein apply primarily to the simplest kind of experimental
data; that is, replicate measurements of some property of a given material, or observations in a
supposedly single random sample. Nevertheless, the tests suggested do cover a wide enough
range of cases in practice to have broad utility.
E 300 Standard Practice for Sampling Industrial Chemicals
This practice covers procedures for sampling several classes of industrial chemicals, as well as
recommendations for determining the number and location of such samples to ensure
representativeness in accordance with accepted probability sampling principles. Although this
practice describes specific procedures for sampling various liquids, solids, and slurries, in bulk
or in packages, these recommendations only outline the principles to be observed. They should
not take precedence over specific sampling instructions contained in other ASTM product or
method standards.
E 1402 Standard Terminology Relating to Sampling
This standard includes those items related to statistical aspects of sampling. It is applicable to
sampling in any matrix and provides definitions, descriptions, discussions, and comparisons of
trends.
E 1727 Standard Practice for Field Collection of Soil Samples for Lead Determination by
Atomic Spectrometry Techniques
This practice covers the collection of soil samples using coring and scooping methods. Soil
samples are collected in a manner that will permit subsequent digestion and determination of
lead using laboratory analysis techniques such as Inductively Coupled Plasma Atomic Emission
Spectrometry (ICP-AES), Flame Atomic Absorption Spectrometry (FAAS), and Graphite
Furnace Atomic Absorption Spectrometry (GFAAS).
F 301 Standard Practice for Open Bottle Tap Sampling of Liquid Streams
This practice covers a general method to take samples of liquid streams in such a way so that
the samples are representative of the liquid in the sampled stream and that the sample
acquisition process does not interfere with any operations taking place in the stream. The
practice is particularly applicable for sampling the feed and filtrate streams around a filter
medium. The practice includes consideration of potential limits in the sample size or sample
flow rate observation capability of the device used to measure particle content in the sample.
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REFERENCES
Note: Due to the dynamic nature of the Internet, the location and content of World Wide Web sites given in this
document may change over time. If you find a broken link to an EPA document, use the search engine at
http://www.epa.gov/ to find the document. Links to web sites outside the U.S. EPA web site are provided for the
convenience of the user, and the U.S. EPA does not exercise any editorial control over the information you may find
at these external web sites.
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Demolition Debris." Pro-Act Fact Sheet TI5040. Brooks Air Force Base, TX.
American Society for Quality (ASQ). 1988. Sampling Procedures and Tables for Inspection of
Isolated Lots by Attributes. American National Standard ANSI/ASQC Standard Q3-1988.
Milwaukee, Wisconsin.
ASQ. 1993. Sampling Procedures and Tables for Inspection By Attributes. American National
Standard ANSI/ASQC Z1.4-1993. Milwaukee, Wisconsin.
American Society for Testing and Materials (ASTM) D 1452-80. 1980. Standard Practice for Soil
Investigation and Sampling by Auger Borings. West Conshohocken, PA.
http://www.astm.org/
ASTM D 1586-84. 1984. Standard Test Method for Penetration Test and Split-Barrel Sampling
of Soils. West Conshohocken, PA.
ASTM D 1587-94. 1994. Standard Practice for Thin-Walled Tube Geotechnical Sampling of
Soils. West Conshohocken, PA.
ASTM D 3665-95. 1995. Standard Practice for Random Sampling of Construction Materials.
West Conshohocken, PA.
ASTM D 4220-95. 1995. Standard Practices for Preserving and Transporting Soil Samples.
West Conshohocken, PA.
ASTM D 4342-84. 1984. Standard Practice for Collecting ofBenthic Macroinvertebrates with
PonarGrab Sampler. West Conshohocken, PA.
ASTM D 4387-97. Standard Guide for Selecting Grab Sampling Devices for Collecting Benthic
Macroinvertebrates. West Conshohocken, PA.
ASTM D 4448-85a. 1985. Standard Guide for Sampling Groundwater Monitoring Wells. West
Conshohocken, PA.
ASTM D 4489-95. 1995. Standard Practices for Sampling ofWaterborne Oils. West
Conshohocken, PA.
ASTM D 4547-98. 1998. Standard Guide for Sampling Waste and Soils for Volatile Organics.
West Conshohocken, PA.
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References
ASTM D 4700-91. 1991. Standard Guide for Soil Sampling from the Vadose Zone. West
Conshohocken, PA.
ASTM D 4823-95. 1995. Standard Guide for Core Sampling Submerged, Unconsolidated
Sediments. West Conshohocken, PA.
ASTM D 4840-95. 1995. Standard Guide for Sampling Chain-of-Custody Procedures. West
Conshohocken, PA.
ASTM D 5013-89. 1989. Standard Practices for Sampling Wastes from Pipes and Other Point
Discharges. West Conshohocken, PA.
ASTM D 5088-90. 1990. Standard Practice for Decontamination of Field Equipment Used at
Nonradioactive Waste Sites. West Conshohocken, PA.
ASTM D 5092-90. 1990. Standard Practice for Design and Installation of Ground Water
Monitoring Wells in Aquifers. West Conshohocken, PA.
ASTM D 5283-92. 1992. Standard Practice for Generation of Environmental Data Related to
Waste Management Activities Quality Assurance and Quality Control Planning and
Implementation. West Conshohocken, PA.
ASTM D 5314-92. 1992. Standard Guide for Soil Gas Monitoring in the Vadose Zone. West
Conshohocken, PA.
ASTM D 5358-93. 1993. Standard Practice for Sampling with a Dipper or Pond Sampler. West
Conshohocken, PA.
ASTM D 5387-93. 1993. Standard Guide for Elements of a Complete Data Set for Non-
Cohesive Sediments. West Conshohocken, PA.
ASTM D 5451-93. 1993. Standard Practice for Sampling Using a Trier Sampler. West
Conshohocken, PA.
ASTM D 5495-94. 1994. Standard Practice for Sampling with a Composite Liquid Waste
Sampler (COLIWASA). West Conshohocken, PA.
ASTM D 5633-94. 1994. Standard Practice for Sampling with a Scoop. West
Conshohocken, PA.
ASTM D 5658-95. 1995. Standard Practice for Sampling Unconsolidated Waste from Trucks.
West Conshohocken, PA.
ASTM D 5679-95a. 1995. Standard Practice for Sampling Consolidated Solids in Drums or
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Office of Environmental Information, Washington, D.C. September 2001.
USEPA. 2001f. EPA Requirements for Quality Management Plans, EPA QA/R-2. EPA/240/B-
01/002. Office of Environmental Information. Washington, DC. March.
http://www.epa.gov/qualitv/qa docs.html
USEPA. 2001 g. Contract Laboratory Program (CLP) Guidance for Field Samplers - Draft Final.
OSWER 9240.0-35. EPA540-R-00-003. Office of Solid Waste and Emergency
Response. June, http://www.epa.qov/oerrpaqe/superfund/proqrams/clp/quidance.htm
USEPA. 2002a. Guidance on Demonstrating Compliance With the Land Disposal Restrictions
(LDR) Alternative Soil Treatment Standards, Final Guidance. EPA530-R-02-003. Office
of Solid Waste. July, http://www.epa.gov/epaoswer/hazwaste/ldr/soil f4.pdf
USEPA and USDOE. 1992. Characterizing Heterogeneous Wastes: Methods and
Recommendations. EPA/600/R-92/033. NTIS PB 92-216894. EPA Office of Research
and Development, Las Vegas, NV and USDOE Office of Technology Development,
Washington, DC.
335
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References
van Ee, J.J., L.J. Blume, and T.H. Starks. 1990. A Rationale for the Assessment of Errors in the
Sampling of Soils. EPA 600/4-90/013. Environmental Monitoring Systems Laboratory.
Las Vegas, NV.
Visman, J. 1969. "A General Sampling Theory. Materials Research and Standards."
MTRSA 9(11):8-13.
Wald, A. 1973. Sequential Analysis. New York: Dover Publications.
Williams, L.R., R.W. Leggett, M.L. Espegren, and C.A. Little. 1989. "Optimization of Sampling
for the Determination of Mean Radium 226 Concentration in Surface Soil."
Environmental Monitoring and Assessment 12:83-96. Dordrecht, the Netherlands:
Kluwer Academic Publishers.
336
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INDEX
Note: Bold page numbers indicate where the primary discussion of the subject is given.
Acceptance sampling, 27
Accuracy, 22, 57, 134, 157-158, 160
Action level, 22, 31, 35, 39-41, 45-47, 49, 51, 54, 61-
63, 72, 78-79, 81-82, 84, 157, 163, 253, 278-282,
284, 296-297, 302
Additivity of errors in sampling and analysis
of biases, 89
of variances, 89
Alpha ( a ), 42, 83
Alternative hypothesis, 43, 157
Analytical methods, 1, 12, 36, 40, 51, 70, 86-87, 108,
122, 131, 139, 144, 161, 164, 169
Analytical design, 50, 51, 183, 298
Arithmetic mean, 77, 165, 170, 187, 243
ASTM, 2, 16, 17, 35, 60, 63-65, 69, 74, 84, 101, 103,
106, 107, 122, 124-126, 130, 134-137, 157-159,
163-164, 166, 168-169, 175, 191-192, 195-196,
201-240
how to contact and obtain standards, 103
summaries of standards, 305-322
Attribute, 27, 39, 311, 321
Auger, bucket, 100, 111-113, 115, 225-226, 287-288
Automatic sampler, 109-110, 159, 202, 319, 321
Auxiliary variable, 54, 60, 321
Background, 15, 24, 28, 33, 37, 41, 42, 44, 181, 183
Bacon bomb sampler, 109, 110, 115, 209
Bailer, 109, 110, 115, 230, 234-235, 319
Beta (ft), 42, 162
Bias, 22-24, 41, 49-50, 88-89, 95, 108, 118, 119, 123,
128, 141, 142, 144, 150, 157, 160, 164-165, 167-
168, 200, 240, 249, 252, 274, 318
analytical, 23, 89, 163
sampling, 23, 89, 93-94, 104, 119, 124, 128, 244,
300
statistical, 23, 89
Binomial distribution, 18
Bladder pump, 109, 110, 115,202-203
Bootstrap, 152,250, 252
Bottles, see containers
Boundaries
defining, 15, 26, 30, 36-37, 45, 49, 52, 59, 63, 66,
76,79, 82, 158, 160,279,295
spatial, 14, 23, 32, 36-37, 39, 49 ,158
temporal, 14, 23, 32, 36-38, 49, 158
Box and whisker plot, 147, 148
Bucket, 110-112, 301
Calibration, 23, 86, 124, 140-143, 158
Central limit theorem (CLT), 67, 244
Centrifugal pump, 109, 110, 116, 205
CERCLA, 2 , 317
Chain-of-custody, 4, 86, 122, 124, 125-127, 132, 139-
141, 143, 146, 158, 180,310, 311
Cleanup (of a waste site), 8, 13, 28, 32, 33, 37-40, 43-
44, 51, 57, 62, 64, 68, 79, 82, 196, 261, 277
Closure, 7, 8, 10,61, 181, 185
Coefficient of variation (CV), 147, 158, 250, 284
Cohen's Adjustment, 152-153, 241, 257-261
COLIWASA, 100, 108-111, 116, 228-229, 314
Component stratification, 58, 194-196
Comparing,
populations, 24, 28, 150
to a fixed standard, 24, 25, 27, 65, 71, 150, 152,
153, 155, 241, 242, 247-249, 251, 253-255,
258
Composite sample, 64-73, 80, 108, 115, 140, 158-9,
172, 187, 249, 284, 288-289, 318
Composite sampling, 52, 64-73
advantages, 65
approach, 66-67
limitations, 65-66
number of samples, 73
simple random, 67
systematic, 68-69
Computer codes, see software
Conceptual site model (CSM), 32
Cone and quartering, 134
Confidence interval, 25-27, 61-62, 70, 150, 155, 247-
250, 252-254, 259
Confidence level, 47-48, 61, 74, 84, 159
Confidence limits, 25, 69, 155, 159
for a lognormal mean, 75, 249
for a normal mean using simple random or
systematic sampling, 247-249
for a normal mean using stratified random
sampling, 248
for a percentile, 253-255
nonparametric confidence limits, 252
using composite sampling, 249
Consensus standard, 17, 103, 159
Containers, sample, 23, 62, 84, 96, 104, 122-123,
128, 131-132, 138, 141
Control samples, 74, 96, 124-125, 139, 142, 280
duplicate, 51, 74, 142, 143, 161, 162
equipment blank, 51, 74, 96, 125, 142, 162, 286
field blank, 51,74, 96, 125, 162
rinsate, 96, 168, 286
spikes, 74, 142, 143, 162, 163
trip blank, 51, 74, 96, 125, 142, 162
Conveyor, 37, 52, 60, 95, 96, 98, 103, 104, 106-107,
111, 112, 312
belt, 52,95, 98, 106-107, 312
screw, 106-107
Coring type sampler, 111-113, 116, 214, 221
Corrosivity, 7, 8, 13, 26, 27, 35, 40, 66, 173, 293
Corrective action (RCRA), 1, 8, 10, 29, 40, 44, 79,
185, 277
337
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Index
Data quality assessment, 1, 2, 4, 139, 145,160,
241,275,289, 302
Data quality objectives, 1, 2, 10, 24, 25, 145, 154, 160
process, 30-87, 160
seven steps, 30
Data (a/so see distributions)
collection design, 38, 50, 51, 159
gaps, 50, 143
DataQUEST software, 146-149, 244, 270
Debris, 10, 58, 94, 97, 104, 106, 107, 113, 121, 160,
191-196
sampling methods, 191-196
Decision error, 31, 38, 41-48, 73, 75, 76, 82, 142,
155, 160
Decision maker, 28, 31, 32, 39-41, 43, 45, 49
Decision unit, 4, 15, 16, 26, 38-39, 41, 47-49, 57, 67,
68, 76, 79, 81, 82, 84, 90, 91, 94, 99, 146, 161,
193, 194,244
Decision rule, 30, 39-41, 49, 76, 79, 82, 83, 150, 279,
295
Decision support, see Decision Unit
Decontamination, 23, 51, 100, 117, 118, 122, 124,
125, 128-130, 141, 312, 314
DEFT software, 31, 45, 73, 84, 273, 284
Degrees of freedom (df), 268
simple random or systematic sampling, 248, 249
stratified random sampling, 78, 79, 243
Delta ( A ), 45
Detection limit, 40, 161, 258
Dilution, 10, 58,71,72
Dipper, 106, 109-112, 116, 236-237, 313
Dispersion, 19, 22, 169, 170, 193
Displacement pump, 109, 110, 116, 206-207
Distributions, 14, 16, 17
binomial, 18
non normal, 18, 252
normal, 17-21, 67, 75, 81, 147, 148, 150, 158,
170, 244
lognormal, 17-19, 75, 149, 150, 154, 195, 244,
249-250
Distributional assumptions, 87, 145, 148, 244
Distribution heterogeneity, 91
Documentation, 86, 87, 95, 96, 122, 124-126, 139-
144, 336
DOT, 131, 133, 174
Drum thief, 108,230-231
Drums, 15, 37, 39, 72, 73, 95, 99, 100, 103, 104-105,
314, 315,316
Duplicate, 51, 74, 142, 143, 161, 162
Dynamic work plan, 161
Ease of use, 100
Effluent, 68, 94
Enforcement, 10-12, 27, 43, 63
Errors, 3, 13, 16, 88-101
analytical, 3, 69, 88, 90
components of, 88, 89
contamination, 94, 96
decision, 31, 38, 41-48, 73, 75, 76, 82, 142, 155,
160
delimitation, 94-96, 99, 100, 102, 106, 136, 137,
211,229
extraction, 94, 95, 99, 100, 102, 136, 137
fundamental, 69, 91, 92-94, 96-98, 135, 136,
197-200
preparation, 94, 95, 96
segregation and grouping, 91
Example calculations
Cohen's Adjustment, 261
confidence level when using a simple
exceedance rule, 256
locating a hot spot using composite sampling, 73
mean, 19
mean and variance using composite sampling, 71
number of samples for simple random sampling,
76
number of samples for stratified random
sampling, 79
number of samples to estimate a percentile, 82
number of samples using a "no exceedance" rule,
82
Shapiro-Wilktest, 246-247
standard deviation, 20
upper confidence limit for a normal mean, 249
upper confidence limit for a lognormal mean, 251
upper confidence limit for a percentile, 255
variance, 20
Examples of the DQO/DQA processes, 277-304
Exceedance rule method, 27-28, 255-256
Exploratory study, 74
False positive (false rejection), 42, 162
False negative (false acceptance), 42, 162
Familiarization (analytical), 50
Field QC samples, see control samples
Filliben's Statistic, 148, 244
Finite population correction, 77
Flash point, 66
Flowing or moving materials, sampling of, 15, 52, 91,
95, 96,98, 106, 309, 312, 314
Fragments, 92, 94, 99, 134, 141, 163, 192, 197
Frequency plot, 148
Fundamental error, 69, 91, 92-94, 96-98, 135, 136,
197-200
controlling, 97
definition, 163
derivation, 197-200
description, 92
Gases, 104, 114, 121, 173, 174
Geometric standard deviation (GSD), 75
Geostatistics and geostatistical methods, 15, 29, 58,
59, 80, 90, 151, 163, 192,273
Goodness-of-fit, 163, 244
Grab sample, 64, 66, 73, 80, 163, 176
Graded approach, 32, 163
Gravitational segregation, 91
338
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Index
Gray region, 41, 45-47, 49, 75, 76, 79, 81-84, 163,
281,297
Grid, 56, 57, 59, 68,80, 159, 274
Ground-water monitoring, 7, 10, 15, 28, 39, 44, 45,
114, 121, 180, 181, 185, 309, 316, 321
Grouping error, 65, 91, 93, 96, 134, 137, 138
Gy's sampling theory, 88-101
Haphazard sampling, 57
Hazardous waste:
determination, 8
regulations, 6-10, 171-189
Hazardous waste characteristics, 164-165
corrosivity, 7, 8, 13, 26, 27, 35, 40, 66, 173
ignitability, 7, 8, 13, 26, 27, 35, 40, 66, 173
reactivity, 7, 8, 13, 26, 27, 35, 40, 66, 174
toxicity, 7, 8, 13, 26, 27, 35, 40, 66, 73, 120, 173
Health and safety, 38, 50, 84, 97, 130
Heterogeneity, 4, 26, 52, 53, 66, 68, 69, 88, 90-91,
93, 106, 137, 138, 163, 191-196
large-scale, 91, 191,192
periodic, 91
short-range, 68, 91, 93, 191
Heterogeneous waste, 4, 57, 58, 94, 107, 191-196
Histogram, 17, 18, 147, 148,255
Holding time, 66, 74, 122, 123-124, 131, 141, 143,
163
Homogenization, 4, 23, 66, 69, 91, 92, 102, 134, 320
stationary processes, 134
dynamic processes, 134
Homogeneity, 164, 192
Homogeneous, 92, 93, 97, 98, 134, 136
Hot spots, 38, 39, 53, 57, 59, 65, 67, 71-73, 164, 274
Hypothesis, 40, 41
alternative, 43, 157
null, 41-47, 49, 76, 79, 82, 150, 152-155, 157
Hypothesis testing versus statistical intervals, 25
Increments, 61, 65, 91, 93, 94, 96, 134, 135, 138,
158, 164, 194
Independence or independent samples, 69, 71
International Air Transport Association (IATA), 131,
133
Interpolation, 261
Ignitability, 7, 8, 13, 26, 27, 35, 40, 66, 173
Investigation derived waste (IDW), 118, 129-130
Jackknife, 152,250,252
Judgment sampling, 48, 51, 55, 63-64
Kemmerer depth sampler, 100, 108, 109, 117,210-
211
Labels, sample, 96, 124, 125, 131, 141, 310, 314
Land Disposal Restrictions (LDRs), 7, 8, 9-10, 13, 26,
27, 35, 40, 44,66, 82, 113, 160, 171, 176, 177
Landfill, 28, 34, 52, 82, 104, 106
Land treatment, 8, 28, 33, 37, 41, 52, 121, 183
Large-scale heterogeneity, 91, 191,192
Less-than values, see nondetects
Liquid grab sampler, 109-111, 237
Liquids, 90, 98, 100, 109, 110, 120, 136
Logbook, 124, 140, 143, 146
Lognormal distribution, 17-19, 75, 149, 150, 154, 195,
244, 249-250
Maps, 29, 33, 37, 58, 59, 124, 141
Margin of error, 13
Mass of a sample, 4, 23, 36, 92, 96-97, 136, 137,
197-200
Mean, 14, 17, 18-19, 40, 165
Mean square error, 89, 165
Measurement: 15-16
bias, 23
random variability, 23-24
Median, 17, 19, 39, 40, 88, 155, 165, 249, 252
Miniature core sampler, 111-113, 117,222-223
Modified syringe sampler, 111-113, 117,224
Multi-phase mixtures, 98
Nondetects, 146, 147, 150, 154, 257-258
Nonparametric methods, 18, 83, 150, 153, 165, 252,
255, 256
Nonprobability sampling, 51, 55, 63, 193
Normal distribution, 17-18, 20, 21, 67, 75, 147, 148,
150, 244
Normal probability plot, 18, 147, 148, 290-291
Nuggets, 92
Number of samples
composite sampling, 80
mean, normal distribution, using simple random
sampling or systematic sampling, 73, 80
mean, normal distribution, using stratified random
sampling, 77
mean, lognormal distribution, 75
percentile or proportion, 81
using an exceedance rule, 83
Optimal design, 50, 78, 96
Outliers, 145, 147, 148-149, 165, 250, 322
OSHA, 130
Packaging and shipping, 131
sample packaging, 131
sample shipping, 133
Parameter (statistical), 21, 23, 24, 25, 27, 39-40, 166
Particle size distribution, 16, 94-95
Particle size reduction, 69, 91, 93, 96, 97, 98, 136,
137, 138, 192, 198,200
Particulate, 90, 95, 97, 134, 137, 317
Pass or fail data, 18, 28, 35, 40, 81, 153
Percentile, 20, 21, 26-27, 39-40, 45, 81, 151, 153,
166, 253
Performance-based measurement system (PBMS),
86
Peristaltic pump, 109-111, 118, 202, 204-205
pH, 66, 173, 174
Photoionization detector, 60
339
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Index
Piles:
elongated, 52, 138
staging, 37, 120
waste, 16, 37, 104, 106, 168, 178, 187, 317
Pilot study, 43, 50, 74, 80, 93, 315
Pipes, 37, 52, 60, 94, 95, 98, 104, 105, 106, 109-112,
120, 196,312
Plunger type sampler, 109-111, 118,232-234
Point estimate, 21, 27, 252
Point of (waste) generation, 6, 15, 33, 37, 39, 52, 73,
76, 82, 104, 106, 171, 193, 255, 295, 299, 300
Point source discharge, 106, 182, 236, 238
Ponar dredge, 111, 118, 207-209, 308, 309
Populations, 13, 14-15, 16, 17, 24, 28, 194, 250
Pore water, 15, 42, 182
Precision, 11, 14, 22-24, 25, 26, 52, 58, 64, 65, 69,
70,74, 80, 125, 134, 166, 194
Preliminary study, see pilot study
Preparation error, 94, 95, 96
Preservation, 92, 94, 96, 123-124, 131, 180, 308, 309
Probability plot, 18, 21, 147-149, 245, 255, 257
Process knowledge or knowledge of the waste, 1, 9,
10, 13, 27, 28, 34, 40, 43, 64, 175, 293
Proving the negative, 11-12, 13, 295
Proving the positive, 11-12, 13, 63
Quality assurance project plan (QAPP), 1, 3, 4, 30,
33, 34, 48, 50, 51, 84-87, 139-142, 144, 146, 166
Quality control, 1, 11, 24, 30, 51, 87, 96, 122, 124-
125, 167, 313
Quick Safety Rule (Pitard's), 97, 198
Random number, 57
Random variability, 3, 24, 26, 88-89, 322
Randomization, 51
Range, 17,41,43,45,75, 167
Ranked set sampling: 54
description, 60
procedure, 61
RCRA:
summary of regulatory citations, 171-189
Reactivity, 7, 8, 13, 26, 27, 35, 40, 66, 174
Regulatory threshold, 11, 26, 27, 35, 63, 72, 82, 124
Relative standard deviation, 97, 156, 167
Relative variance, 97, 197, 279
Remediation, 31, 33, 37, 44, 167, 179
Repeatability, see precision
Representative sample, 7, 9, 13, 16, 17, 168, 173-
175, 178, 179, 180, 191
Riffle splitter, 134-135
Rinsate, 96, 168, 286
Risk assessment, 29, 139
Roll-off bin or container, 15, 37, 39, 52, 82, 95, 96, 99,
104, 106, 113,255
Rotating coring device, 113, 118, 225, 227-228
Rosner's Test, 149
Sample:
biased, 55, 64
correct, 96
discrete, 26, 64, 66, 100
duplicate, 51,74, 142, 161
grab, 64, 66, 73, 80, 163, 176
individual, 47, 64
random, 19, 57-60, 67, 77, 79, 80, 243
representative, 7, 9, 13, 16, 17, 168, 173-175,
178, 179, 180, 191
split, 72, 95, 123, 125, 135, 168
statistical, 14, 16, 19,21,27, 169
Sample collection design, see sampling design
Sampling design, 51
authoritative, 62
biased, 64
judgmental, 63
probabilistic, 51
ranked set, 60-61
simple random, 57
stratified, 57-58
systematic, 59-60
Sampling in space and time, 52
Sampling devices, 109-114
limitations, 102
selecting, 95
Scientific method, 160, 168
Scoop, 98, 100, 107, 111-113, 118, 135, 137, 239-
240, 315, 319
Sediment, 104, 105, 114, 121, 133
Segregation error, 91
Sequential sampling, 54, 61-62
Settleable solids profiler, 109-111, 118, 233-234
Shapiro-Wilktest, 147, 148, 244-246
Sheet mixing, 134
Shelby tube, 100
Shipping samples, 133
Short range heterogeneity, 68, 91, 93, 191
Shovel, 99, 100, 111-113, 119, 239-241
Significance level, 47
Simple random sampling, 57
Slurry, 52, 106, 111, 120, 312
Software:
ASSESS, 275
DataQUEST, 275
DEFT, 31,45,73, 84,273
DQOPro, 274
ELIPGRID-PC, 274
GeoEAS, 29, 273
MTCASfaf, 275
UnCensor, 257
Visual Sample Plan (VSP), 274
Soil:
background concentrations, 28, 33, 37, 41
volatiles in soil, 101
Soil gas, 104, 114, 121, 310, 312, 313, 314
Solid waste, 1, 8-9, 13, 15, 16, 26, 173, 174, 178
Solid waste management unit (SWMU), 15, 33, 37,
44, 52,67,79, 113, 185,277
Spatial correlation, 29, 68, 68, 80, 163
Spatula, 137, 138, 239
340
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Split barrel sampler, 104, 112, 113, 119,216-217,306
Splitting of samples, 135
Standard deviation:
definition, 19-20, 169
for composite sampling, 70
for simple random or systematic sampling, 19-20,
242
for stratified random sampling, 243
Standard error of the mean, 21, 242
description, 21
for composite sampling, 71
for simple random or systematic sampling, 21,
242
for stratified random sampling, 77, 243
Standard operating procedures (SOPs), 51, 86, 87,
124, 135, 136, 140, 142, 169
Statistical intervals, 25
Statistical methods, 241-261
Statistical tables, 263-272
Statistical software, 273-275
Stratification, 194, 196
by component, 58
Stratified random sampling, 53, 57-58
Stratum, 57, 58, 59, 77-79, 169, 194, 195, 243
Student's t distribution, 248-250, 263
Subsampling, 135
liquids, 136
mixtures of liquids and solids, 136
soils and solid media, 136
Superfund, 2, 15, 38, 94
Support, 16
decision, see decision unit
sample, 94-95
Swing jar sampler, 109-111, 119, 238
Syringe sampler, 109-113, 119,211-212
Systematic sampling, 53, 59-60
Tank(s), 7, 37, 52, 104, 105, 106, 109-111, 115, 117,
120, 121, 129, 182
Target population, 36, 37, 53, 57, 58
t distribution, see Student's t distribution
Thief, 100, 108-113, 116, 117, 217-219, 230-231
Thin-walled tube, 112, 113, 119,219-221
Time (sampling over), 52
Tolerance limit, 27
Transformations of data, 150, 249
Trends, 29, 53, 57, 59, 60, 91, 150
Trier, 100, 111-113, 119, 218-219, 314
Trowel, 99, 100, 111-113, 119, 239-240
Two-sample tests, 28, 151
Type I error, 42, 43, 44, 47, 75, 76, 79, 83, 162, 170
Type II error, 42, 43, 44, 47, 75, 76, 78, 83, 155, 162,
170
Universal treatment standards (UTS), 33, 151, 177,
256
Upper confidence limit (UCL), see confidence limit
Used oil, 7, 8, 120, 172, 189
Index
Vadosezone, 107, 114, 121, 170,217,221,226, 310,
313, 315
Valved drum sampler, 109, 110, 119,231-232
Variance, 19-20, 23
additivity of variances, 89
for composite samples, 70
simple random or systematic sampling, 242
stratified random sampling, 243
Verification and validation, 2, 87, 139-144
Volatiles, sampling, 101
Volume or mass of a sample, 94, 96-97, 108
Walsh's Test, 149
Waste:
debris, 10, 58, 94, 97, 104, 106, 107, 113, 121,
160, 191-196
investigation derived, 118, 129-130
hazardous, 6-10, 171-189
heterogeneous, 4, 57, 58, 94, 107, 191-196
multi-phase, 98
nonhazardous, 13, 34, 38, 58, 82, 129, 194, 255
one-dimensional, 52, 56, 95, 96, 98, 102, 138
three-dimensional, 95, 96, 99
two-dimensional, 56, 59, 95, 99, 102
Waste analysis plan (WAP), 1, 3, 4, 10, 30, 50, 84,
85, 139
Weighting factor, 58, 77-79, 243
X-ray fluorescence, 60
341
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