>EPA
United States
Environmental Protection
Agency
Environmental Monitoring
Systems Laboratory
P.O. Box 93478
Las Vegas NV 89193-3478
EPA/600/8-89/046
March 1989
Research and Development
Soil Sampling
Quality Assurance
User's Guide
Second Edition
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THE
SOIL SAMPLING QUALITY ASSURANCE USER'S GUIDE
Second Edition
PROJECT SUMMARY
An adequate quality assurance/quality control (QA/QC) program requires the
identification and quantification of all sources of error associated with each step of a
monitoring program so that the resulting data will be of known quality. The components of
error, or variance, include those associated with sampling, sample preparation, extraction,
analysis, and residual error. In the past, major emphasis often has been placed on QA/QC
aspects of sample analysis and closely associated operations such as sample preparation and
extraction. For monitoring a relatively inhomogeneous medium such as soil, the sampling
component of variance will usually significantly exceed the analysis component Thus, in this
case a minimum adequate QA/QC plan must include a section dealing with soil sampling. The
purpose of this document is to provide guidance in QA/QC aspects related to soil sampling.
Generally soil monitoring is undertaken to carry out the provisions and intent of
applicable environmental laws with high priority requirements associated with hazardous waste
management. The objectives of soil monitoring programs are often to obtain data on the basis
of which to answer one or more of the following questions:
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Are the concentrations of specified soil pollutants in a defined study region
significantly different from the concentrations in a control region?
Do the concentrations of specified soil pollutants in a defined region exceed
established threshold action levels?
At the measured concentrations of specified soil pollutants in a defined study
region, what is the associated risk of adverse effects to public health, welfare, or
the environment?
For each of these applications, the QA/QC methods and procedures cannot be
specified without giving careful consideration to the consequences of making an error, for
example, in a decision to require or not to require cleanup of a contaminated region. It follows
in general that to be maximally cost-effective and defensible the QA/QC objectives of a soil
monitoring program cannot be separated from the objectives of the soil monitoring program
itself.
In general, the progression of events leading to the development of an adequate Quality
Assurance Program Plan (QAPP) follows the outline shown below:
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1. State study objectives.
2. Evaluate impacts of mistakes.
3. Define data quality objectives (DQOs).
4. Design study to achieve DQOs.
5. Design QAPP to confirm achievement of DQOs.
Often it will not be possible to specify in advance what DQOs are possible to achieve. In such
cases DQO goals should be set, a QAPP prepared, and a pilot study conducted to determine the
achievability of the goals.
Present U.S. EPA guidance for development of DQOs requires that specifications for
the following factors must be addressed:
precision,
accuracy,
completeness,
representativeness, and
comparability.
A sixth factor of importance to all of the above is the detection limit of the measurement
method used. Other important factors which should be considered in specifying DQOs include:
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acceptable probability of a Type I error (judging a clean area to be
dirty);
acceptable probability of a Type II error (judging a dirty area to be
dean); and
desired minimum detectable relative difference between two different
geographical areas.
The development of DQOs involves an iterative interaction between management and
technical staff. Management identifies the needs and resources available. The technical staff
develops guidance for assisting management in making the decisions required to develop the
DQOs. The DQO process usually involves a three-stage process as outlined below.
L Identify decision types.
2. Identify data uses/needs.
3. Design data collection program.
The end result is site-specific guidance for evaluating and interpreting sampling data.
Control samples are normally as important to a sofl monitoring study as are samples
taken from the study region. The data from control samples aid in the interpretation of the
results from the study region and also help to identify sources and important transport routes
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for soil pollutants. Accordingly, the same level of effort and degree of QA/QC checks should
go into selecting and sampling a control region as goes into sampling the study region.
In the sampling of a continuous medium such as soil, it is necessary to put extra
emphasis on the definition of a sampling unit. In addition to having a specified location, each
sampling unit of soil has a certain three-dimensional volume, shape, and orientation. These
latter three characteristics, when taken together, are called the support of the sample. Changes
in support not only change the means of distribution, they also change the variances of
concentrations and the correlations of concentrations between sampling units.
It is essential that any action level for soils be defined as a concentration over a
particular support and location relative to the ground surface. In this definition of an action
level, the support is referred to in this document as the action support For example, the action
support might be defined as the top ten cm of soil over a square area of 100 m2.
The table below provides recommendations, as part of the DQO process, for confidence
levels, powers, and minimum detectable relative increases over background for different
operational situations.
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Confidence Level Power Relative Increase over Background
(1-a) (1 - ft) (100(M -/O//*n] to be Detectable
with a Probability (I - 0)
Preliminary 70-80% 90-95% 10-20%
Site Investigation
Emergency 80-90% 90-95% 10-20%
Cleanup
Planned 90-95% 90-95% 10-20%
Removal and
Remedial Response
where et = probability of a Type I error and
4» probability of a Type n error
Both Type I (false positive) and Type n (false negative) errors should be considered in
hypothesis testing. Tables and an equation are provided for use in determining the required
number of samples to achieve defined confidence levels and powers. The location of sampling
is also important Stratification of the sampling region may reduce the variance in cases where
the variance is considered to be unacceptably large. Compositing of samples is generally not
recommended since it allows no estimate of the variance among the samples being composited.
However, some compositing of samples increases the representativeness of samples and may be
justified on that basis.
Suggested types of QA/QC samples include various types of blanks, laboratory control
standards, calibration check standards, triplicate samples (splits), duplicate samples, various
kinds of audit samples, etc. How many samples of each type would be needed in a specific
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study is a question of considerable importance. The recommended approach is to determine
how each type of QA/QC sample is to be employed and then determine the number for that
type based on the use. For example, field duplicates are used to estimate the combined
variance contribution of several sources of variation. Hence, the number of field duplicates to
be obtained in a study should be dictated by how precise one wants that estimate of variance to
be.
Geostatistics (or kriging) is an application of classical statistical theory to geological
measurements that takes into account the spatial continuities of geological variables in
estimating the distribution of variables. In many ways, geostatistics is for measurements taken
in 2-, 3-, and 4-dimensional space (the three spatial dimensions and the time dimension), what
time series is for measurements taken in one-dimensional space (time). However, a principal
use of time series is in forecasting; in geostatistics the principal emphasis is on interpolation.
Nevertheless, both statistical procedures emphasize modeling the process to get an insight into
the system being investigated.
The application of classical statistical procedures to soil measurement data requires that
the samples be collected randomly (i.e^ not on systematic grids), that the data be independent
and identically distributed (with the distribution being a normal distribution), and that the
measurement error variance (particularly the between-batch error variance) be a very small
part of the total variance of the measurements in a sample survey of a region.
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In many soil sampling studies one or all of the following questions will be of primary
interest.
Are there any action supports within the study area that have pollutant
concentrations above action level?
Where are the above-action-level action supports located?
What is the spatial distribution of pollutant concentration levels among action
supports that have pollutant concentrations above action level?
Hie problem with posing soil sampling methods and objectives in terms of population
means is that the mean will depend on the size of the area chosen and the distribution of
contamination throughout that area. For example, the mean in a small area may exceed the
action level; but if the size of small area is increased by adding a substantial amount of less
contaminated soil, the mean in the larger area may not exceed the action limit. Decisions on
the need for remedial action should not be based on how one chooses the size of the area to be
sampled, but rather on whether action supports exist that are above designated action limits. A
comparison of means is reasonable in comparing pollutant concentrations at a background site
with pollutant concentrations of a site down-gradient from a suspected hazardous waste source.
Also, deanup areas may be defined so that the average concentration in those units of soil may
be compared with a standard.
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It follows from the above discussion that for most applications, geostatistica! procedures
for designing soil sampling studies and analyzing resultant data are generally preferred over
classical statistical procedures.
Once objectives have been defined for a soil monitoring study, a total study protocol,
including an appropriate QA/QC program must be prepared. Usually not enough is known
about the sources and transport properties of the soil pollutants to accomplish this in a
cost-effective manner without additional study. The suggested approach is to conduct an
exploratory study including both a literature and information search followed by selected field
measurements based on an assumed dispersion model The data resulting from this exploratory
study serve as the basis for the more definitive total study protocol If one is dealing with a
situation requiring possible emergency action to protect public health, it is necessary to
compress the planning and study design into a short time period and proceed to the definitive
study without delay. In either case, the objectives of the monitoring study constitute the driving
force for all elements of the study design, including the QA/QC aspects.
To develop the exploratory study protocol with its associated QA/QC plan, one needs
to combine into an assumed dispersion model the information obtained prior to any field
measurements. On the basis of this model, the standard deviation of the mean for soil samples
is estimated. Value judgments are used to define required precision and confidence levels
(related to acceptable levels of Type I or Type II errors). A control region is selected. The
numbers of required samples may then be calculated. Additional samples should be required to
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validate the assumed model. The locations of the sampling sites should he selected by an
appropriate combination of judgmental (use of the assumed model), systematic (to allow for the
fact that the model may be wrong), and random (to minimize bias) sampling. Sampling and
sample handling must be accomplished according to standardized procedures based on
principles designed to achieve data of both adequate quality and maximal cost-effectiveness.
Particular attention should be given to factors surrounding the disposition of non-soil materials
collected with the soil samples.
The requirements for QA/QC for the exploratory study need not be as stringent as for
the more definitive study in the sense that acceptable precisions and confidence levels may be
relaxed somewhat Allowance should be made, however, for the collection of a modest
additional number of QA/QC samples over that specified in the QA/QC plan to verify that the
QA/QC study design is adequately achieving its assigned objectives. Also, all normal analytical
QA/QC checks should be used.
If the exploratory study is conducted well, it will provide some data for achieving the
overall objectives of the total monitoring study; it will provide a check of the feasibility and
efficacy of all aspects of the monitoring design including the QA/QC plan; it will serve as a
training vehicle for all participants; it will pinpoint where additional measurements need to be
made; and it will provide a body of information and data which can be incorporated into the
final report for the total monitoring study.
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For the more definitive study, the selection of numbers of samples and sampling sites,
sample collection procedures, and sample handling methods and procedures follow and build on
the principles discussed and results obtained in the exploratory study.
Frequency of sampling is an important aspect of the more definitive study which usually
cannot be addressed in the exploratory study because of the relatively short time span over
which the exploratory study is conducted. The required frequency of sampling depends on the
objectives of the study, the sources of pollution, the pollutants of interest, transport rates, and
disappearance rates (physical, chemical, or biological transformations as well as dilution or
dispersion). Sampling frequency may be related to changes over time, season, or precipitation.
An approach that has been used successfully has been to provide intensive sampling early in the
life of the study (e.g^ monthly for the first year) and then to decrease the frequency as the
levels begin to drop. The important principle is that the sampling should be conducted often
enough that changes in the concentrations of soil pollutants important to the achievement of
the monitoring objectives are not missed.
The important questions to be answered in the analyses and interpretation of QA/QC
data are: "What is the quality of the data?" and "Could the same objective have been achieved
through an improved QA/QC design which may have required fewer resources?" It is desirable
to provide summarized tables of validated QA/QC data in the final report. This approach
allows users to verify the reported results as well as begin to build a body of QA/QC
experimental data in the literature which allow comparisons to be made among studies. Special
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emphasis should he placed on how overall levels of precision and confidence were derived from
the data. If portions of the study results are ambiguous and supportable conclusions cannot be
drawn with regard to the reliability of the data, that situation must be clearly stated.
The adequacy of all aspects of the QA/QC plan should be examined in detail with
emphasis on defining for future studies an appropriate minimum adequate plan. Some aspects
of the QA/QC plan may have been too restrictive; some may not have been restrictive enough.
Soil monitoring studies should have checks and balances built into the QA/QC plan which will
identify early in the study whether the plan is adequate and, if required, allow for corrective
action to be taken before the study continues. This is one of the major advantages of
conducting an exploratory study.
There is insufficient knowledge dealing with soil monitoring studies to state with
confidence which portions of the QA/QC plan will be generally applicable to all soil monitoring
studies and which must vary depending on site-specific factors. As experience is gained, it may
be possible to provide more adequate guidance on this subject In the meantime, it is
recommended that many important factors of QA/QC plans be considered as site-specific until
proven otherwise.
Another important aspect of QA/QC is auditing. The purpose of an audit is to insure
that all aspects of the QA/QC system planned for the project are in place and functioning well.
This includes all aspects of field, sample bank, and laboratory operations. Whenever a problem
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is identified, corrective action should be initiated and pursued until corrected. Sample
chain-of-custody procedures and raw data are checked as appropriate, and results of blind
QA/QC samples routinely inserted into the sample load are reviewed. Spot checks of sampling
methods and techniques, sampling and analysis calculations, and data transcription are
performed. Checks are made to ascertain that required documentation has been maintained
and in an orderly fashion, that each of the recorded items is properly categorized, and
cross-checking can be easily accomplished. Checks are made to insure that data recording
conforms to strict document control protocols and the program's QA/QC plan.
It is recommended that an audit of the overall QA/QC plan for sample documentation,
collection, preparation, storage, and transfer procedures be performed just before sampling
starts. This is to review critically the entire sampling operation to determine the need for any
corrective action early in the program.
The project leader of a soil monitoring project is responsible for ascertaining that all
members of his project team have adequate training and experience to carry out satisfactorily
their assigned missions and functions. This is normally accomplished through a combination of
required classroom training, briefings on the specific monitoring project about to be
implemented, and field training exercises. Special training programs should be completed by all
personnel prior to their involvement in conducting audits.
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EPA 600/8-89/046
March 1989
Soil Sampling Quality Assurance
User's Guide
Second Edition
by
Delbert S. Barth, Benjamin J. Mason,
Thomas H. Starks, and Kenneth W. Brown
Cooperative Agreement No. CR 814701
Environmental Research Center
University of Nevada-Las Vegas
Las Vegas, Nevada 89154
Kenneth W. Brown, Project Officer
Exposure Assessment Research Division
Environmental Monitoring Systems Laboratory
Office of Research and Development
Las Vegas, Nevada 89183-3478
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NOTICE
The information in this document has been funded wholly or in part by the United
States Environmental Protection Agency under Cooperative Agreement CR 814701 to the
Environmental Research Center. It has been subject to the Agency's peer and administrative
review and has been approved for publication as an EPA document.
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CONTENTS
Page
Figures v
Tables v
Preface vi
1. Introduction 1
Objectives 1
Audience 2
Approach 3
Background , 4
2. Purposes for Soil Sampling 7
3. Data Quality Objectives 16
Measurement Concepts 24
Stages for Developing DQOs 28
4. Quality Assurance Project Plans 60
5. The Concept of Support 66
6. Exploratory Study 73
Example of an Exploratory Study 78
Sample Design 79
Data Transformations 82
Quality Assurance Data , 83
7. Guidance for Specific Soil Sampling Programs 85
Objectives for Background Monitoring 90
Specific Monitoring Objectives in CERCLA and RCRA 92
Preliminary Site Investigation 104
Emergency Clean-up 105
Planned Removal and Remedial Response Studies 107
Monitoring or Research Studies 109
8. Selection of Numbers of Samples and Sampling Sites for the
Definitive Study 110
Introduction 110
Number of Sampling Sites Required 114
9. Control of Measurement-Error Variance 119
Introduction 119
Goals 122
Components of Variance 124
QA Samples , 127
Bias 137
10. Sample Design and Data Analysis 140
Sample Design 140
Role of Quality Assurance , 146
Geostatistics 149
Objectives 154
Design for Hot Spot Detection 157
Some Classical Statistical Procedures 160
11. Sample Documentation, Collection, and Preparation 170
Introduction 170
Documentation 173
Sample Collection 184
Sample Preparation 187
Quality Assurance Aspects , 199
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CONTENTS (continued)
12. Analysis and Interpretation of QA/QC Data 200
Introduction 200
Presentation of data summaries 201
Presentation of results and conclusions 203
Quality assurance aspects 204
13. System Audits and Training 206
Introduction 206
Sample bank audit 209
Field audits 211
Training 212
Glossary 213
References 222
Appendices
A. Application of Soil Monitoring Data to an Exposure and Risk
Assessment Study A-l
B. Percentiles of the t Distribution B-l
C. Data Quality Objectives Development Process C-l
IV
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LIST OF TABLES
1. Summary of Data Needs 44
2. Results from Duplicate Samples at the Palmerton NPL Site 71
3. Results from Individual Cores at the Palmerton NPL Site 72
4. Number of Samples Required in a One-sided, One-sample t-test Ill
5. Results from Splits at the Palmerton NPL Site 130
6. Type of QA/QC Samples or Procedures 132
7. Some 95% Confidence Intervals for Variance 135
8. PCB Measurements (Hypothetical Data) 167
9. Accountable Documents 177
10. Sampling Containers, Preservation Requirements, and Holding
Times for Soil Samples 194
Appendix B - Percentiles of the t Distribution , B-l
LIST OF FIGURES
1. Three Stages in the DQO Process 23
2. Elements of a Conceptual Model 39
3. Steps in Defining the Attainment Objectives 49
4. Palmerton Exploratory Sampling Design 80
5. Data Acquisition Flow for Hazardous Materials 95
6. Monitoring Data Flow 100
7. Technology Transfer Data Flow 102
Appendix A
A-l. Elements of Toxicologic Studies to Assess Adverse Effects Related to
Exposure to Environmental Pollutants A-5
A-2. Generalized Spectrum of Human Responses to an Environmental
Pollutant A-6
A-3. Possible Exposure Pathways from a source of Environmental Pollution to man A-8
A-4. General Model for Converting Environmental Pollutant Measurements in various
Media into Estimated Total Exposure to Humans A-9
A-5. Relationship of Total Human Exposure to Possible Effects and to Risk Estimation A-10
A-6. Three Hypothetical General Classes of Exposure-Response
Relationships A-ll
A-7. Exposure Monitoring Elements Requiring Quality Assurance A-13
A-8. Hypothetical Exposure Distribution A-14
A-9. Equation for Estimating Total Lifetime Dose to TCDD A-16
A-10. Estimated Daily Deposition of Soil on Human Skin by Age A-18
A-ll. Concentrations of TCDD in Soil that are Projected to Produce the
Maximum Allowable Residues in Foods A-20
A-12. Estimated Average Daily Dose Corresponding to Initial TCDD-Soil
Contamination Levels A-21
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PREFACE
Use of the first edition of the "Soil Sampling Quality Assurance User's Guide" as a text in a
series of seminars conducted at various U.S. EPA Regional Offices elicited many constructive
comments for improvements from seminar attendees. Many of these suggested improvements
have been incorporated in this second edition.
Specifically, the references have been updated, particularly through the incorporation of
recent U.S. EPA guideline documents. More attention has been given to experimental design,
specifically to procedures for developing data quality objectives. The statistical coverage has
been expanded considerably to include an introduction to applications of geostatistics and a
discussion of requirements for the definition of support in conjunction with guidance for soil
sampling.
This report is intended to be a living document providing state-of-the-art guidance.
Accordingly, from time to time revisions will be prepared to maintain harmony with
improvements in soil sampling quality assurance methodology. Future revisions will be prepared,
and authorship identified, on a chapter-by-chapter basis.
VI
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CHAPTER 1
INTRODUCTION
OBJECTIVES
This document is a user's guide presenting and explaining selected principles and
applications of methods and procedures for establishing adequate quality assurance on soil
sampling aspects of environmental monitoring programs. Soil sampling aspects treated include
sample site selection, sample collection, sample handling, sample analysis, and interpretation of
resulting data. No detailed treatment of analytical quality assurance procedures is given, since
that important aspect has been adequately treated elsewhere (U.S. EPA, 1982; U.S. EPA,
1984b). It should be noted, however, that sampling quality assurance procedures are not
separable from analytical quality assurance procedures. This is particularly true for sample
collection and handling. If an intact, timely, and representative sample of proper size and
composition is not delivered to the analytical laboratory, the analytical methods and associated
quality assurance procedures cannot yield meaningful results. Thus, the soil sampling quality
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assurance procedures presented here should be viewed as an important, integral part of the
overall quality assurance plan.
In this second edition of the Soil Sampling Quality Assurance User's Guide, the authors
have included guidance on developing Data Quality Objectives (DQOs) and have added
additional examples to aid the user in preparing an adequate soil sampling quality assurance
plan. This guide is not intended to be a generic plan for all sites; it is presented as a guidance
document only. By adhering to the principles and procedures outlined, the user should be able
to develop a quality assurance plan that will meet most soil sampling needs.
AUDIENCE
This document has been developed to serve as a user's guide for anyone designing,
implementing, or overseeing soil monitoring programs. It is especially applicable for personnel
responsible for regulatory programs where soil monitoring is an important integral element.
Special attention is given to soil sampling examples related to CERCLA, since such applications
are deemed to be high priority sampling programs. Many of the principles and procedures
discussed, however, are applicable to other situations as well.
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APPROACH
Following the discussion below of the background of quality assurance procedures used
by the U.S. Environmental Protection Agency (U.S. EPA), Chapter 2 addresses purposes for
soil sampling and Chapter 3 addresses the development of DQOs for various aspects of soil
sampling. Chapter 4 presents an outline of the objectives of quality assurance plans. Chapters
7 through 10 deal with statistical aspects of experimental design, soil support, quality assurance/
quality control (QA/QC), hypothesis testing, etc. Some attention will be focused on both
geostatistics and a technique known as the components of variance analysis. The components
of variance analysis results from the use of a statistical sampling plan designed to measure as
many of the sources of variation as can be identified and sampled in a cost-effective manner.
The analysis further identifies the amount of total sample error (or variance) that results from
each component in the sampling-analysis chain.
Discussion of the value of an exploratory study (Chapter 6) to the subsequent design of
a soil sampling quality assurance program leads logically into more detailed discussions of
sample site selection, sample collection, and sample handling (Chapter 11). These detailed
discussions will include minimal coverage of soil monitoring protocols per se, since they have
been treated in a comprehensive document (Mason, 1983). The focus of the discussions will be
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quality assurance. The goal of each discussion will be the development of design features for
sample site selection, sample collection, and sample handling to meet quality assurance
objectives of defined power and levels of confidence for each subject area.
The goal of the discussion concerning analysis and interpretation of data (Chapter 12)
will focus on quality assurance aspects. The goals of the discussion concerning analysis and
interpretation of data program audits and personnel training are treated in Chapter 13. To the
maximum extent feasible throughout this report, we will first present concepts and principles,
followed by selected examples of how these concepts and principles may be applied in realistic
situations.
BACKGROUND
Since its founding, the U.S. EPA has been aware that the environmental data needs of
the Agency require that quality assurance and quality control (QA/QC) meet predetermined
standards. U.S. EPA Order 5360.1 (U.S. EPA, 1984) establishes the responsibilities of National
Program Managers in the Agency's Mandatory Quality Assurance Program. These
responsibilities include ensuring that "data quality acceptance criteria" and QA Project Plans
are prepared for all data collection projects sponsored by the Agency. In a memorandum of
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April 17, 1984, accompanying the issuance of Order 5360.1, Deputy Administrator Aim
identified two steps that must be taken to ensure that all data collected by the U.S. EPA are
suitable for their intended use:
"...the user must first specify the quality of data he needs; then the degree of quality
control necessary to assure that the resultant data satisfy his specifications must be
determined."
The first step is accomplished through the development of Data Quality Objectives
(DQOs). Data Quality Objectives are qualitative and quantitative statements developed by
data users to specify the quality of data needed from a particular data collection activity (U.S.
EPA 1987a). DQOs must address five data characteristics: precision, accuracy,
representativeness, completeness, and comparability. A sixth data characteristic, level of
detection, should also be addressed since it is closely related to the other five. In addition,
DQOs should specify allowable probabilities of false positive (Type I) and false negative (Type
II) errors. In order to determine required numbers of samples, another important factor is the
desired minimum detectable relative difference between two data sets taken at different
locations or times. The data quality characteristics addressed are sometimes referred to as
measurement DQOs, while the probabilistic goals are termed system DQOs.
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The second step in the QA process is the preparation of quality assurance project plans
(QAPPs). The QAPP addresses the procedures to be followed to assure that the needs
expressed by the DQOs are met. The DQOs become plumblines against which the data
generated by a sampling effort can be evaluated. The whole quality assurance process is carried
out to insure that the regulator, decision maker, or researcher has reliable data of known
quality.
The chapters that follow address the various steps required to assure the quality of soil
sampling data.
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CHAPTER 2
PURPOSES FOR SOIL SAMPLING
The mission of the U.S. EPA is to control environmental pollutants to abate potential
adverse effects on man and/or the environment. Complying with this mission requires
identifying significant sources of pollutants of concern and linking these emission sources to
adverse effects upon critical receptors. This Unking is done through exposure assessment. To
carry out the intent of CERCLA, for example, concentrations of hazardous pollutants in
environmental media, including soils, should not be allowed to exceed levels established as
being adequately protective of humans and the environment. Identification of the sources of
the pollutant of concern should include not only the present emissions but also an assessment of
probable future emissions. From a soil perspective, one needs to establish the role of soils as
sources or sinks for selected air or water pollutants and how that role may change in time and
space, as well as the effect of such physical parameters as temperature, wind direction and
speed, water flow rates, and geological factors on that role. Biological factors within the soil
matrix may also be involved in the degradation or transformation of pollutants into different
chemical substances.
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Specifically, soil sampling efforts can be designed and conducted to:
determine the extent to which soils act as either sources or sinks for air or water
pollutants,
determine the risk to human health and/or the environment from soil
contamination by selected pollutants,
determine the presence and concentration of specified pollutants in comparison
to background levels,
determine the concentration of pollutants and their spatial and temporal
distribution,
measure the efficacy of control or removal actions,
obtain measurements for validation or use of soil transport and deposition
models,
determine the potential risk to flora and fauna from specific soil pollutants,
identify pollutant sources, transport mechanisms or routes, and potential
receptors,
contribute to a research technology transfer or environmental model
development study, and
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meet the provisions and intent of environmental laws such as the Resource
Conservation and Recovery Act (RCRA), the Comprehensive Environmental
Response, Compensation, and Liability Act (CERCLA), the Federal Insecticide,
Fungicide, and Rodenticide Act (FIFRA), and the Toxic Substances Control Act
(TSCA).
Soils encompass the mass (surface and subsurface) of unconsolidated mantle of
weathered rock and loose material lying above solid rock. The soil component can be defined
as all mineral and naturally occurring organic material 2 mm or less in particle size. This is the
size normally used to distinguish between soils (e. g., sands, silts, and clays) and gravels. In
addition, the 2-mm size is generally compatible with analytical laboratory methods/capabilities.
Organic matter is commonly found in many soils and must be considered as an integral part of
the soil.
The non-soil fraction (e.g., automobile fluff, wood chips, various absorbents and
mineral/organic material greater than 2 mm) must also be addressed during the sampling
effort. This component may contain a greater amount of contaminant(s) than the associated
soil. At sites in which this occurs reporting contaminant levels only in the soil fraction will
ultimately lead to inappropriate and incorrect decision making. Decision makers must be
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aware that a number of problems are normally encountered in obtaining and using data from
non-soil components. For example, questions arise concerning the validity of data obtained
from the analysis of materials that do not meet the size and volume requirements in which the
analytical processes were validated. Also, standard reference and audit materials are not
available to substantiate and validate analytical results. The current recommended procedures
are to identify and record the type and volume of non-soil material for each sample collected.
A minimum of 10 percent of these non-soil samples should be submitted for analysis. Proper
data assessment and conclusions made from these results are paramount to the success of a soil
sampling program.
The behavior of pollutants in the soil environment is a function of the pollutant's and
soil's physical and chemical properties. Soil sorption (the retention of substances by adsorption
or absorption) is related to properties of the pollutants (e.g., solubilities, heats of solution,
viscosity, and vapor pressure) and to properties of soils (e.g., clay content, organic content,
texture, permeability, pH, particle size, specific surface area, ion exchange capacity, water
content, and temperature). The soil components that are most associated with sorption are clay
content and organic matter. The soil particle surface characteristics thought to be most
important in adsorption are surface area and cation exchange capacity (CEC).
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The extreme complexity and variability of soil necessitates a multitude of sampling/
monitoring approaches. The investigator must select methods and approaches that will satisfy
the stated program objectives while accommodating specific site needs.
Both field and laboratory tests are necessary to understand the presence and behavior
of pollutants in soil. Field tests primarily supply definitive information for soil classification
and its relation to on-site environmental conditions. Laboratory tests supply analytical data
beyond the capabilities of most field measurements, such as the type and quantity of a
pollutant.
Soil containment measurements may be source-, transport-, or receptor-oriented, or
some combination. For example, if the major concern is possible risk to human receptors, it
may be wise to take early measurements in the immediate vicinity of the receptors to obtain the
best estimates of exposures resulting from soil contamination. If exposures are deemed to be
insignificant or acceptable, no further measurements may be required. If, however, the
exposures are deemed to be unacceptable, additional measurements will be required to identify
both important pollutant sources and important exposure pathways. Information on these
matters will be necessary to devise cost-effective control strategies.
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Determination of risk to human health and the environment from contaminated soils
involves several steps. Required are exposure and dose distributions to the most sensitive
populations or receptors of concern via all significant exposure pathways. This will involve
possible soil-related exposure from other media such as air or water, exposure from the soils
themselves either through ingestion, inhalation, or skin absorption, as well as exposure through
ingestion of foods contaminated directly or indirectly from the soils. An additional parameter
is the biological availability of the pollutant(s) of concern. Thus, it is important to measure or
estimate the extent to which the soils act as sources (through contacting air or waters) for the
pollutant(s) of concern. Knowing the concentration of pollutants in air or water originating
from contaminated soils is not sufficient for estimating exposure. An additional parameter
required is the biological availability of the pollutant(s) of concern. For example, if soil
pollutants are not incorporated into the edible parts of crops or animal products, even large
concentrations in the soil might not lead to significant human exposure through ingestion of
food stuffs. In such an instance, however, inhalation of vapors from the soil or ingestion of
drinking water might constitute an important exposure pathway.
Once desired exposure or dose distributions have been constructed, comparison to
established exposure or dose-response relationships enables a determination of whether or not
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the existing risk is acceptable. Underestimating exposures or doses might lead to accepting an
unacceptable risk, whereas overestimating might lead to unnecessary, and possibly costly,
control actions. A detailed case study showing how an action level for dioxin in soil was derived
is presented in Appendix A.
If significant quantities of pollutant(s) become permanently attached to soil and remain
biologically unavailable, the soils may constitute a sink. Pollutant control needs in these cases
may be reduced by the amounts by which the soils reduce the pollutant availability.
Underestimating the ability of soils to act as a sink might lead to source control requirements
more stringent than necessary, whereas overestimating might lead to less stringent control
requirements than necessary.
If significant quantities of selected pollutants are found to be associated with soils
initially and then released slowly over relatively long periods of time, the soils, in essence, act as
pollutant sources. Underestimating the extent to which soils act as sources will lead to
inappropriate and insufficient controls of other additional sources, whereas overestimating may
lead to expensive soil removal to a greater degree than necessary. Soil removal as a cleanup
measure is a complicated proposition. It involves extensive testing of the soils and evaluation of
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proposed disposal options to determine which option will have the least environmental impact
with due regard for cost.
Soil sampling to measure the efficacy of control or removal actions must be preceded by
the establishment of unacceptable concentrations of pollutants of concern in soil. Once
unacceptable concentrations, or action levels, have been identified, it is then possible to devise
sampling plans with defined probabilities of Type I or Type H errors. A critical consideration in
this instance will be the depth and surface areal extent of the soil sample on the basis of which
the soil concentration will be calculated. This is addressed in greater detail in a subsequent
chapter dealing with the concept of sample support and "action support" (Chapter 5).
Soil sampling for validation or use of soil transport and deposition models will not
normally lead to control actions. Positive or negative errors are unlikely to lead to
corresponding over- or underestimates of control needs. However, errors of unknown direction
and size, if sufficiently large, might seem to validate an erroneous model or fail to validate an
acceptable model. The consequences of such errors cannot be evaluated without knowing the
purposes for which the model might be used and what actions might be taken on the basis of
conclusions drawn from the model.
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Prior to undertaking any soil sampling program to achieve defined objectives, it is
necessary to establish appropriate measurement and system DQOs. These should be
established after due consideration of the consequences of taking actions which might
subsequently be shown not to be justified on the basis of the available data.
Once appropriate DQOs have been established, an operational protocol should be
prepared, setting forth what is to be done for what purpose and how, when, where, and how
many samples will be collected. Also, the protocol should indicate how the samples will be
preserved, prepared for analysis, and then analyzed for what substances, and how the resulting
data will be validated, analyzed, and interpreted. As part of this protocol, a complete QA/QC
plan must be included covering all aspects of the experimental program with special attention to
sampling aspects. Quality assurance is defined as the system of activities required to provide a
quality product, whereas quality control is the system of activities required to provide
information as to whether the quality assurance system is performing adequately. It cannot be
overemphasized that an adequate QA/QC program cannot be tailored for a study until a clear
statement of monitoring objectives has been provided, together with allowable errors.
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CHAPTERS
DATA QUALITY OBJECTIVES
Studies conducted in the past were often controlled by data quality that was considered
to be the "best data possible" (U.S. EPA, 1986-b). It was not uncommon to expend considerable
resources on a sampling and analysis program, only to find that the samples were not collected
in a manner that would allow valid conclusions to be drawn from the resulting data. The "best
data possible" approach provided useful data in some cases but frequently lacked the scientific
rigor required for the regulatory arena. The development of Data Quality Objectives (DQOs)
is an attempt to provide the rigor required to meet the data needs of the U.S. EPA.
"Data Quality Objectives are qualitative and quantitative statements of the quality of
data needed to support specific decisions or regulatory actions" (U.S. EPA, 1986a). The
important starting point for the detailed design of a data collection effort, DQOs are the basis
for specifying the quality assurance and quality control activities and requirements associated
with the data collection process. During the detailed planning and preparation of technical
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guidance for data collectors, DQOs are used as the key for developing explicit, quantitative
statements of the type of errors that will be controlled, the level to which those errors will be
controlled, and the information that will be collected in order to characterize all of the known
sources of error. These quantitative statements are known as data quality indicators. Data
quality indicators are needed to select appropriate methods for sample collection, laboratory
analysis, and statistical data analysis. They also form the basis for selecting QA and QC
procedures (U.S. EPA, 1987a).
The DQO process is a dynamic process that has not yet been implemented uniformly in
all regions; therefore, the information presented in this document is for guidance only. The
three-stage process envisioned in guidance documents (U.S. EPA, 1986a, 1987a) includes
requirements for the following factors to be addressed:
precision,
accuracy,
completeness,
representativeness, and
comparability.
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A sixth factor, "detection limit," has been added by the authors as a critical factor which should
be considered in specifying the other five factors.
Statistical rigor, combined with managerial and budgetary guidance, should be used to
develop the specific objectives required to develop the specifications for the above factors.
Statistical sampling is a mechanism by which the QA/QC program can determine the sampling
precision and provide a measure of the reliability of the entire sampling effort.
It is essential that the reliability of the data be reported. Buffington (1978) quotes
Congressman George E. Brown, Jr., as saying "no number is significant, and subsequently
worthy of being recorded, without an estimate of its uncertainty." This statement should be
considered when designing the QA/QC plan for a soil sampling effort because soil is by its very
nature extremely variable. Superimposed on this natural variability are other sources of
variation or error that can be introduced into the final result by the sampling and analytical
efforts. These sources of variation can lead a manager to conclude that an area needs no
remedial action when, in fact, it does need such action (called a Type n error) or, alternatively,
conclude an action is needed when, in fact, no actions should be taken (called a Type I error).
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To establish an adequate, cost-effective QA/QC plan for a soil monitoring program, it
is necessary, after careful analysis of the consequences, for a decision-making official to specify
probabilities of Type I and Type n errors that will be allowed in making decisions based on
sample data.
The acceptable probability for each type of error must be established in relation to the
consequences of making such errors and depends upon the specific objectives of the soil
monitoring program. The Type I error is the error most often considered in the literature. In
environmental monitoring, however, a Type n error may be more important than a Type I
error. The clean-up of a highly toxic spill would be an example where a false negative could
create major problems for the project manager. The Type n error would lead the manager to
conclude that a clean-up of some areas is not necessary when, in fact, the action levels are being
exceeded and clean-up is necessary. The probabilities of Type I and n errors for the QA/QC
effort should equal the probability levels chosen for the overall sampling effort itself. This
acceptable probability of error in different cases may, for example, range from 20 percent to 1
percent or less. In some circumstances, the level selected by value judgment may simply be a
statement of a probability of error not to be exceeded in the final data. The authors are
proposing that these two probabilities be included as system DQOs in conjunction with the
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measurement DQOs outlined earlier, i.e., precision, accuracy, representativeness,
completeness, comparability (PARCC), and detection limit.
There may be a temptation in many cases to avoid making the necessary value
judgments concerning acceptable probabilities of making different kinds of error. The course
of action often substituted for the difficult value judgment is to adopt as a guiding principle the
concept that one should always strive to achieve the highest power and level of confidence (or
lowest probability of error) possible with existing available resources. The resulting data are
then used as the basis for making decisions with the assumption that this guiding principle gives
the best possible result. Obviously, such an approach will rarely, if ever, be cost effective. Two
types of errors are possible. The data may be much better than required, which indicates
resources have been wasted, or the data may not be of adequate quality, thereby resulting in
decisions of doubtful validity. This point may be summarized by stating that resource
availability is an important factor for consideration in the establishment of quality assurance
programs, but resource availability should not be accepted as the sole determinant of required
quality assurance methods and procedures. Maximal cost effectiveness should be the overall
goal. This generally means that a minimum adequate quality assurance plan must be defined
and then implemented. The DQO process has been designed to incorporate both cost and
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reliability of the data as a first principle, and thus precludes the tendency to use cost as the
single guiding principle for implementing a particular sampling design.
Once system and measurement data quality objectives (DQOs) have been set on the
basis of acceptable risks of making mistakes in any resulting decisions, an experimental design
can be adopted to achieve the required DQOs. The purpose of the Quality Assurance Project
Plan (QAPP) then becomes the collection and analysis of adequate QA/QC samples to confirm
the achievement of the DQOs. Another way of stating this is that the objective of the QAPP is
to define the procedures used to achieve the desired quality of the data and thus insure that it is
adequate to the degree required for its intended end use. (These matters will be covered more
extensively in subsequent chapters).
Guidance on the DQO process (U.S. EPA, 1987a) identifies three stages for arriving at
the quality of data to be used:
1) Identify decision types,
2) Identify data uses/needs, and
3) Design data collection program.
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Figure 1 identifies each stage along with the tasks covered in each stage, Thus, the DQO
process becomes an iterative interaction between management and the technical staff.
Management identifies the needs and resources available. The technical staff develops
guidance for assisting management in making the decisions required to develop the DQOs.
The end result is site-specific guidance for evaluating and interpreting sampling data.
Agency guidance (U.S. EPA, 1987a) provides the basis for developing the DQOs.
Statistical designs should be used so that the quality assurance procedures can verify that the
DQOs are being met. Where possible, numerical values or limits should be placed on precision,
accuracy, representativeness, completeness, comparability, and detection limits. Once these
numerical measures are defined, the preparation of statistical designs, sampling protocols and
quality assurance plans can be initiated. The result of such a process meets the needs of the
Agency for quality data, addresses the requirements of the user, and aids the technical staff in
providing the quality of services requested.
Chapters 7 through 10 discuss in detail the statistical aspects of sample design and data
evaluation.
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Stage 1
Identify Decision Types
Identify and involve data users
Evaluate available data
Develop conceptual model
Specify RI/FS objectives
1
Stage 2
Identify Data Uses/Needs
Identify data uses
Identify data types
Evaluate sampling/analysis options
Identify data quantity needs
Identify data quality needs
Specify PARCC goals
Stage 3
Design Data Collection Program
Design program
Develop data collection documentation
Figure 1: Three Stages of the DQO Process (U.S. EPA, 1987a)
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The soil sampling requirements of both CERCLA and RCRA are site-specific. A
history of the site, including the sources of the pollutant and a conceptual model of the routes
of exposure should be developed before the sampling plan is finalized. It may be necessary to
conduct an exploratory study before this conceptual model can be confirmed or a different
model defined. (Chapter 6 outlines guidance for conducting the exploratory study.) This study
should provide insight into the types of pollutants present, the population at risk, and the
magnitude of the risk. These factors can then be combined to design the final sampling plan
and to specify the size of sampling unit or support (Chapter 5) addressed by each sample or set
of samples.
MEASUREMENT CONCEPTS
The following three sections discuss some basic concepts that must be kept in mind
when developing data quality objectives for soil sampling. These concepts may have application
in other types of sampling, but they are considered to be particularly pertinent to soil sampling
efforts.
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Support
As presented in Chapter 5, the support for a sample is the unit of soil that the sampling
effort measures. The support has a specific size, shape, and orientation. In the vernacular of
the soil scientist, this unit of soil is not too unlike the pedon in terms of its dimensions and
definition (Soil Survey Staff, 1975). The choice of support can significantly affect the precision
of estimates obtained from the survey data (Starks, 1986).
Risk and exposure assessment data can be used to define an action level for a particular
chemical. This action level must be defined as a concentration over a particular support. Starks
(1986) identifies this particular support as an "action support."
The action support should be defined prior to establishing study objectives or,
alternately, as part of the DQO system. The support must be kept in mind when characterizing
acceptable levels of probability of Type I and Type n errors, when defining precision, when
evaluating representativeness and comparability, and when defining detection limits for the
analytical method used.
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The Measurement Process
Data users often look at a concentration obtained from a laboratory as being "the
concentration" in the soil without realizing that the number generated by the laboratory is the
end point of an entire process extending from design of the sampling through collecting,
handling, processing, analysis, quality evaluation, and reporting. Variation or error can occur at
any of the steps in the process. The final number reported represents the actual concentration
found in the soil plus a number of components of variation.
A regulator or researcher would like to have an analytical result that has no error in the
reported concentration, but this is not possible to attain with a medium such as soil. Since it is
not possible to eliminate the natural error in the measurement process, the investigator would
like to know the variation so that he can use this information in making a decision or in
controlling the quality of the data. A components of variance analysis provides a means for
determining the source of the variation in the data and estimating its magnitude.
Examination of the results of a components of variance analysis performed on soils data
from an NPL site sampled for PCBs indicates that 92% of the total variation came from the
location of the sample, while only 8% was introduced after the sample was taken. Less than 1%
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of the total could be attributed to the analytical process itself; yet, this latter area is where a
majority of the QA resources are normally focused. These relative values are probably a
reasonable pattern for many different soil studies. Properly specified data quality objectives
should bring a balance into the QA/QC process.
Generally, a decision must be made as to whether a site is contaminated enough to
cause an environmental problem, and, following this decision, within the site an area-by-area
decision must be made as to which must be cleaned or remedied and to what extent.
No valid decision can be made about the data or the site under investigation without
some knowledge of the magnitude and sources of error in the data. This aspect becomes very
important when concentrations of pollutants in a support approach an action level.
Concentrations that exceed the action level by orders of magnitude require only limited QA as
do those areas that contain no pollutant. The area where sampling intensity and increased
quality assurance becomes important are those areas where it is not possible to make a clear
decision as to the need for and extent of action.
The design of a sampling effort and its associated quality assurance plan must
accomplish three things:
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Determine the variability in the entire measurement process along with the
sources and magnitude of the variation in the results generated;
Provide a means of determining whether a sampling program meets the DQO's
provided; and
Identify areas of contamination where action is needed.
STAGES FOR DEVELOPING DQOS
Figure 1 shows the three stages for developing the data quality objectives for a study or
decision process. Each stage is discussed below.
Stage 1: Identity Decision Types
Stage 1 of the DQO process provides the foundation for Stages 2 and 3. In
Stage 1, all available information on the site is compiled and analyzed. Based on the
available information, a conceptual model or models (related to different categories of
pollutants present) of the site are developed. These models describe suspected sources,
contaminant pathways, and potential receptors. The models will assist in identifying
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decisions which must be made as well as deficiencies in the existing information. Stage
1 is undertaken to define the types of decisions which will be made during the remedial
investigation/feasibility study (RI/FS) and involves defining program objectives and
identifying and involving end-users of the data. The decision maker and all potential
data users should be involved in this and all subsequent DQO stages. Stage 1 results in
the specification of the decision-making process and justification for the collection of
any new data (U.S. EPA, 1987a).
Identify and Involve Data Users: The person with primary interest in the DQO process
is the ultimate decision maker. It is not likely that this individual will be directly involved with
the process of developing the DQOs, but a representative will be designated by him to help
make the necessary detailed decisions. Individuals likely to become involved are:
Regional Administrator or representative
The Remedial Project Manager
DOJ, EPA, and State Attorneys
Chemists
Quality Assurance Officer
Statistician
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Risk specialist
Other technical specialists needed to design and review the DQOs
Consultants and contractors
Other interested parties
The Remedial Project Manager (RPM) manages remedial activites and is accountable
for the technical quality, schedule, and cost of the work. Therefore, he is the primary person
responsible for insuring that the DQOs meet program needs for a particular site. The RPM is
not expected to and should not develop the DQOs alone but must include the necessary
engineers, hydrogeologists, soil scientists, chemists, statisticians, risk specialists, and
toxicologists in the design of the DQOs.
In time, portions of the DQO development process may become standardized. Generic
levels of measurement and system DQOs may be developed. Until such standard DQOs are
established, the entire DQO development process will have to be followed in each case with all
parties being involved. However, Regions may desire to establish a means for developing and
reviewing a generic set of DQOs for use in emergency situations. This would avoid hasty
decisions on the quality of data needed for clean-up and reduce the amount of time needed to
field an emergency team.
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There are occasions where the Potentially Responsible Parties (PRPs) may be asked to
review the DQOs. This is usually addressed through the attorneys representing the PRPs and
will be responded to by a consultant or a technical member of each PRP's staff. In cases where
there is considerable public interest, representatives from interested parties also may be asked
to review and comment on the DQOs. These reviews can greatly reduce the likelihood of
conflict at later stages of the study.
The RPM must be familiar with the site to properly identify the potential data users. A
site visit combined with the results of an exploratory study can provide a basis for selecting the
individuals and disciplines to identify DQO requirements. The process of conducting the
RI/FS is an ongoing, iterative process. As data become available, it may be necessary to
redefine the team required to evaluate the data and provide guidance on the overall planning
and execution of the sampling effort. Refinement of the data collection process also may
require that additional technical staff be added to improve the review and evaluation of the
data.
Chemists and statisticians should be a part of the planning for any soil sampling effort.
The analytical chemist can provide insight into the types of analyses needed and the levels of
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detection that are required to meet the objectives of the study. Soil scientists or geochemists
should be included to aid in evaluating the interactions of the chemicals with the soil. Scientists
and statisticians trained in geostatistics can provide guidance in the use of the various
geostatistical tools to evaluate the spatial distribution of the chemicals.
The RPM may desire to have all of the various data users represented during the initial
planning meetings. The choice of users and their involvement can be defined better after the
first few meetings. Even though a user may not be directly involved in the development of the
DQOs, all potential users should be given an opportunity to review and comment on the final
study objectives, protocols, QA/QC plans, and reports.
Assemble and Evaluate Background Data:
Background data: Many sources of soil related data are available for use in planning a
study. Mason (1983) outlines a number of sources of published and public domain soils data. A
detailed list of other sources of background information is provided in an Agency document
(U.S. EPA, 1985). Results of any preliminary or exploratory studies provide an excellent source
of information for use in developing final DQOs. Data quality objectives for studies conducted
in similar settings also provide an excellent resource for use in specifying DQOs.
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The information that is being accumulated about a site for the first time will generally
be more fragmentary and incomplete. The quality of the data at the outset may be insufficient
to support required decisions. As the RI/FS efforts continue, the data should improve in
reliability and become more useful in guiding further sampling efforts.
Site Visit: A site visit provides the basis for identifying the types of data that may prove
to be useful to the team. When feasible, Agency team members as well as involved contractors
should take part in the site visit. This visit may also provide information on any potential health
and safety risks. Site visits are used to:
Inventory other possible off-site sources of contamination;
Identify any exposed populations;
Confirm existing information;
Record observational data about the site;
Determine existing site conditions;
Determine access, possible sampling points, obstructions, and site
configurational limitations;
Determine the possible presence of volatile chemicals, explosive hazards,
etc.;
Determine restrictions or Limitations for particular RI activities;
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Delineate areas of waste storage or contamination and their contents;
Determine the security of the site and identify where this needs to be
repaired or improved; and
Identify and document any monitoring, industrial or potable water wells
on or near the site.
A topographic map at a scale large enough for recording changes to the site and other
pertinent information such as locations of underground pipes or wires should be available
during the site visit. Photocopies of portions of USGS, 7Vi-minute Quad sheets can be
prepared. The graphic scale of the map should also be enlarged and copied at the same time as
the map. This provides a means of plotting any objects or conditions observed.
A tool that can be quite useful for the site visit is a scaled aerial photograph of the site
and adjacent environs. Information on obtaining and interpreting aerial photographs can be
obtained at U.S. EPA's Environmental Monitoring Systems Laboratory, Las Vegas, NV.
Dated photographs and video movies of the site are also useful for preparing study
protocols. These should be well documented so that they can be used as evidence at a later
date if this becomes necessary.
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All of these pieces of information become part of the official record to be assembled for
the site.
Evaluate Existing Data: Data that have been assembled from all available sources
should be evaluated as to their relevancy and accuracy. Adequate time should be spent in
examining the data set that has been assembled. Often information that is not classed as valid
because of QA restrictions can be used in establishing a hypothesis about how the pollutants on
the site have behaved over time. These data cannot be used in making the final decisions about
the need for clean-up, but they can be used to help develop a conceptual model for the site.
Factors that must be considered in evaluating the data for their usefulness are:
the age of the data sets and their comparability,
the precision and accuracy of the data,
the sampling design used to collect the samples,
the methods used to collect, preserve, handle, and transport the samples,
the analytical methods used to measure the pollutant,
the detection limits for the methods, and
the quality control measures used by the laboratory and field team.
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Older data sets were often acquired by methods that are no longer considered to be
valid. For example, soil samples for volatile chemicals were originally collected in a large
container with headspace. Current methods call for use of a 125 ml wide mouth glass bottle
filled with no headspace. Reported values for volatiles from the former method probably
indicate only the qualitative presence or absence of volatile chemicals. The quantitative
concentrations reported should be seriously questioned.
Similar to sample collection and handling methods, analytical methods have also
improved over time. This should also be taken into consideration when evaluating previously
collected data. Any uncertainty associated with the data should be a major consideration in
evaluating the useability of the data. Keep in mind the discussion on sampling and analytical
errors outlined in the section above on the Measurement Process. If a components of variance
test was carried out or can be carried out on the data, the test results can be used to determine
the usefulness of the data. One of the major factors that the investigator or RPM attempts to
determine is an estimate of the probable precision and accuracy of the available data. Any
available components of variance data can be used as a guide for designing the sampling effort
to meet the DQO requirements.
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The primary objective of the data evaluation process is to determine if the data are a
valid representation of the site at the time the samples were collected. A valid representation
of the site provides a means of determining if there have been changes in the conditions at the
site over time. Some chemicals may have been degraded, volatilized or leached from the site.
Other chemicals may have been deposited on the site, moved through or onto the site from
surrounding areas, or formed at the site through various physical and biological interactions.
This aspect of the evaluation becomes very important when litigation is anticipated.
Develop a Conceptual Model of the Site:
A guidance report (U.S. EPA, 1985) outlines procedures for developing a conceptual
model or models. Essentially models are graphic and narrative descriptions of the site, the
pollutants on the site, and the behavior of the pollutants over time. The models should describe
all potential routes of exposure that may be important during the operation of the site and
following deposition of the pollutants at the site. The graphic depiction of routes of exposure
helps the RPM and the other decision makers to visualize where problems may exist. The
models essentially become hypotheses that are to be tested by the sampling effort. A properly
designed sampling plan will address all of the routes of exposure and the populations that may
have been exposed. For example, vaporization, contact, and leaching are the major
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mechanisms whereby soil pollutants may be transported from contaminated soil to receptors.
The vapors may be inhaled; soil particles may come in contact with skin, be inhaled, or be
ingested; and leachates may become sources of surface and groundwater pollution. Figure 2,
taken from U.S. EPA, 1987a, outlines the important elements of a conceptual model.
Investigators must be cognizant that soil is only a part of the total system that should be
considered at a site. A model of the soil compartment includes the following components:
soil cover,
soil elevation contours,
soil matrix,
particle sizes,
soil solution,
soil vapor, and
associated debris.
As collected data identify specific areas where the model is not valid, the model should
be modified to reflect the new information. An important model input is the presence of
contaminated non-soil debris in the soil mass which is usually one of the first things identified
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during soil sampling. This must be considered in the evaluation of the hazard posed by the site.
Most soil sampling and analytical efforts attempt to remove large pieces of debris such as wood,
etc., from the sample. In some cases, the debris rather than the soil may be the source of the
pollution, since it is common to use wood chips or shredded wood as an absorbent for liquid
wastes. Screening non-soil debris out of the soil material and excluding it from the analysis may
bias the results against obtaining a valid assessment of the risk posed by the site.
Mathematical models or computer codes are often used to estimate the extent of the
exposure. The conceptual model developed during the RI/FS can become the basis of the
computer models used to evaluate the risk to exposed individuals. Modelers should become a
valuable part of the team defining the DQOs for a particular site.
Specify the RI/FS Objectives:
The remedial investigation (RI) addresses data collection and site characterization to
identify and assess threats or potential threats to human health and the environment posed by a
site. The feasibility study (FS) identifies and evaluates remedial alternatives using appropriate
environmental, engineering, and economic factors (U.S. EPA, 1987b).
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Some of the questions that should be addressed before or during the RI/FS study
include:
Is the area contaminated with hazardous chemicals?
What is the distribution of the chemicals over the site?
Are there any areas that create a threat of an immediate life-threatening
exposure?
What are the dominant routes of soil exposure at the site?
At the concentrations seen, what is the minimum size area to be considered as
posing a risk to the environment or the surrounding population?
Which areas must be treated in order to reduce the risk from exposure to an
acceptable level?
Which remedies can be applied at this site in order to clean up the soil?
What is the volume of material that must be treated by the remedy?
What is the source of the pollutants?
Are there other sources from which the chemicals could have migrated onto the
site from other outside areas?
The two components, RI and FS, are conducted as interdependent phases so that the
data collection and assessment requirements of the RI complement and support the
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recommendations of the FS. The resulting report identifies possible remedial actions and
makes specific recommendations.
A graphic illustration showing the relationship of the DQO process to the phased RI/FS
approach is presented in U.S. EPA 1987b. Investigators must incorporate and apply DQO
process requirements during the RI/FS scoping effort and after each RI/FS data collection
activity.
Stage 2: Identify Data Uses and Needs
Stage 2 results in the stipulation of the criteria for determining data adequacy. This
stage involves specifying the level of data certainty sufficient to meet the objectives
specified in Stage 1. In Stage 2, the needs and goals of the remedial investigation will
be determined and all decisions to be based on information gathered during the RI
specified This stage also provides for the evaluation and selection of the sampling
approaches and the analytical options and evaluation of the use of a multiple-option
approach to effect a more timely or cost effective RI/FS (U.S. EPA, 1987a).
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Identify Data Uses and Needs: Stage 2 starts after the evaluation of the existing data
and the determination of how well the data fit the conceptual model that was designed. In rare
cases after concluding Stage 1, there may be adequate data to make a decision without
additional sampling. In most cases, the type, quantity, and quality of required data will be
defined in Stage 2. One should then attempt to identify all of the expected uses of the data.
The amount of detail will depend upon the level of the effort. In cases where soil sampling is
only a minor part of a RI, the table would be very abbreviated. Where soil is the major
component of exposure and a soil remedy is anticipated, the table could be extensive. The
specification of the data types must be in adequate detail to address the objectives of the
RI/FS.
A number of the new remedies such as fixation and soil cleaning that are being used
require that components of the soil mass be segregated, screened, or processed in some manner
during the remedy. It is impossible to determine the feasibility of implementing these remedies
without physical data such as unit density, percent debris, percent moisture, etc. Also a number
of non-standard chemical analyses may be required. These should be included in the Summary
of Data Needs Table (Table 1) even though guidance for developing the DQOs (U.S. EPA,
1987a) calls for rather broad generic data uses.
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Once the data needs and types have been identified, priorities must be set so that
resources can be properly allocated to the sampling effort. This prioritization should be closely
linked to the allowable level of Type I and Type II errors. The required data quality for the
highest priority data use will control the planning and implementation of the sampling effort.
Since soil sampling is expensive, there is a tendency to attempt to acquire as much data
as possible from a single study. It may be more cost effective to conduct the sampling effort in
stages or increments (i.e., exploratory study followed by a more definitive effort). Data from an
exploratory study can provide considerable guidance in identifying the types of samples needed,
the analyses required, and the quality of data that can be expected for a particular sampling
method. The example presented below shows how the results of the exploratory study can be
used to identify the analytical needs of a study.
Example: A transformer repair yard located in a small Florida town was
sampled during an exploratory study. Soil samples were collected at three depths
from twenty-five grid points over the site. Priority pollutant analyses were carried
out on these samples. The only pollutants found were PCBs (reported as Aroclor
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1260), trichlorobenzene and tetrachlorobenzene. No breakdown products of the
PCBs or the chlorobenzenes were noted.
In this case, extensive use of priority pollutant analyses on any additional samples would
be wasteful of resources. Analyses should be focused on the three chemicals identified.
Verification of the findings of the exploratory study may be substantiated by submitting a
limited number of new samples for priority pollutant analyses.
Evaluate Various Sampling and Analysis Options: Each component of the sampling
process (i.e., type of sample and its associated analyses) must be identified and carefully
evaluated to determine if the particular sample type and analysis will provide the necessary
information to meet the use or data need.
An example would be an emergency response situation such as a spill site where
exposure to the population is critical. The high concentrations found at these sites often can be
detected by use of some Level I field instruments. Screening of the soil with a photoionization
or a flame ionization detector, for example, could provide the necessary results for the
immediate clean-up of the spill Samples collected in and around the area identified by the
Level I instruments may then be submitted to a field laboratory for Level n analysis. The areas
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identified as requiring clean-up could then be addressed for the emergency response action. A
limited number of samples should be submitted for Level in or IV analysis. This phasing of the
analysis can provide the rapid turnaround needed by the emergency situation and still provide
the necessary high quality data needed for verification and possible litigation. For information
concerning analytical levels appropriate for selected data uses, see U.S. EPA 1987a.
The use of various levels of analyses should be considered when allocating resources for
the RI/FS. The cost of field methods (i.e., Level 1 and 2) time-to-data availability are usually
considerably less than the cost and time of a Level IV analysis. By coordinating Levels I and n
with the laboratory methods (i.e., Level HI), a higher quality data set can be developed in less
time and for less cost.
Acceptance Criteria: Westat (1988) outlines a process for determining when a
particular environmental goal has been attained. This process can be used to assist in planning
the system DQOs for a particular sampling effort. Figure 3, taken from Westat's report,
outlines appropriate attainment objectives that should be developed by the DQO Team in
conjunction with the decision makers. The decision criteria that are developed must take into
consideration the action level and the acceptable risks associated with that level. The
acceptable risk of a false negative, for example, is defined as the probability that an
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unremediated area will in fact exceed the action level.
There are few regulatory guidelines or standards for soils; therefore, it may be
necessary to determine a reasonable action level for identifying potential areas in need of
cleanup. Where multiple chemicals are present, one can prioritize chemicals according to their
toxicity, mobility, persistence, and concentrations. An Agency publication (U.S. EPA, 1984) can
be a resource in helping to select indicator chemicals.
It may not be possible to precisely define the cleanup area at a particular site at the
outset. It may be possible to arrive at an estimated size for this area based on the conceptual
model prior to the sampling. This can become the basis for defining the monitoring design
approach.
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c
Start
Define the sample areas.
I
Specify the sample
collection procedures.
Specify the chemicals
to be tested.
i
Specify the parameter to be
compared to the cleanup standard.
Specify the probability of mistakenly
declaring the sample area clean.
I
Review all elements of the
attainment objectives.
Yes
Are any
changes in the
attainment objectives
required?
Figure 3. Steps in defining the attainment objectives.
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Data Quantity and Quality Needs: Guidance for determining the number of samples
that must be taken and the type of analyses that must be carried out is provided in Mason
(1983) and in later chapters of this document. There must be a balancing between the number
of samples and the resources available to meet the sampling/monitoring needs. A properly
designed exploratory study will provide data needed for determining the final number of
samples to be taken. In those cases where an exploratory study cannot be carried out, a phased
sampling plan may be used. This allows the collection of an initial set of data that is used to
design the next phase of collection. Addressing the questions presented below will be helpful in
selecting sampling locations and numbers.
Are there visible sources of pollutant on the surface of the soil?
Is there soil erosion or recent cuts or fills on the site?
What is the surface water flow pattern?
Are there sensitive ecosystems or residences located down gradient from the
site?
Are there known hotspots on the site?
Are there confining layers or porous layers in the soil horizon?
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It helps to evaluate the form of the chemical pollutant, the media that transport it, the reactions
that it may undergo, the routes that it may follow, and any sinks or other restraints that it may
encounter as it moves from source to receptor. Those locations in the soil system where the
pollutant is likely to be found should be sampled.
The required quality of the data can vary depending upon the use of the data. The
evaluation of the existing data that was carried out in Stage 1 will indicate if the data are of
adequate quality for the needs of the data users. The actual data quality that can be assigned to
a particular data set can only be determined after the results are evaluated and interpreted. If
the data do not meet the specified DQOs, additional data must be collected to bring the final
data set up to the level of quality required to make the final decision.
Specify Precision, Accuracy, Representativeness, Completeness, Comparability, and
Detection Limit Goals: The key phase of Stage 2 is the setting of the required levels of
precision, accuracy, representativeness, completeness, and comparability along with the
detection limits needed to meet the data quality objectives. All data uses do not require the
same quality of data. "What is required, however, is that all data collected be of known and
documented quality" (U.S. EPA, 1987a).
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Detection Limits: Appropriate detection limits should be selected for the intended
purposes of the sampling effort. Sampling to identify strata to be used in a later, more
definitive sampling effort may be carried out with Level I or n techniques in the field. The
minimum detection limit of these instruments may be quite high compared to laboratory
methods. For example, measurements of background levels would not normally be done with
these field instruments because of their high detection limits. Field instruments with a higher
detection limit may be appropriate for use in areas that have high pollutant levels. If a 10 ppm
clean-up level has been identified, it is not cost effective to use an analytical procedure with a
parts-per-billion detection limit. A field gas chromatograph can provide the reliability for the
clean-up work. A set of samples can be submitted to a laboratory to verify that the field
analyses meet the desired standards.
Field audit samples with a low concentration can be used to determine the minimum
detection limits for the measurement process being used. The variability data from the analyses
of the field audit samples will provide a means for determining at what level analytical results
should be reported without qualifiers.
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The detection limit selected should reflect the risks associated with exposure to a
particular pollutant. Detection limits should not be chosen a priori on the basis of the methods
available for analysis, but should result from a review of the needs of the decision maker and
the data users. A rule of thumb might be to use whenever possible a detection limit one order
of magnitude lower than any level of concern. This allows values close to the concentration
levels of concern to be reported and evaluated.
Precision: The precision required for a particular study will depend upon the difference
between background levels and the action level Measurements of chemicals that have a very
low action level such as 2,3,7,8-tetrachlorodibenzodioxin (TCDD) will require much greater
precision than would measurements of a chemical with an action level in the parts per million
range. The amount of preparation that samples undergo prior to analysis will also greatly
influence the precision of the measurement process. Soil samples that have been taken for
metals analysis are often dried, sieved, and mixed, and then carefully subsampled. These
subsamples provide a much more precise measure of the average concentration in the sample
than would be expected from a sample that could not be prepared in the same manner. For
example, samples collected for volatile organic analysis cannot be dried, ground, or mixed if
they are to reflect the concentrations found in the soil.
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Laboratory precision is only one part of the total precision of the measurement process
leading from sample collection through data reporting. Selection of an acceptable precision
level should not be based solely on what is attainable in the laboratory. Once the sample is
submitted to the laboratory much of the sample-to-sample variation has already been
introduced into the sample by activities in the field.
A key factor to remember when making a decision on the desired level of precision is
that the selection should be made on the basis of risk of exposure if protection of public health
and the environment is the principle matter of concern. The detection limits, the sampling
methods, and the sample handling procedures must then be specified on the basis of the levels
of pollutants judged harmful by the risk assessment. Where litigation is a key factor and costs
are high, the choice of techniques for assuring adequate precision become important.
Normally precision is measured by the standard deviation of the data set; however, the
range can also be used (Bauer, 1971). Replicate quality control samples are submitted from the
field to provide a means of determining the precision of the measurement process. Two types
of samples should be used for this purpose. Routine samples should be submitted as either
splits or co-located samples. In addition to the routine samples, field audit samples also should
be submitted on a regular basis.
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Accuracy: Accuracy is controlled primarily by the laboratory and is reported as bias.
Standards, spiked samples, referee samples, and field audit samples are all used to assess and
control the accuracy of the results as well as the comparability of the results.
Representativeness: Representativeness is the degree to which the samples collected
reflect the conditions at a particular site. For example, a sample of soil screened from a rubble
pile does not represent the conditions at that site, but only provides a measure of a small
fraction of material that happens to fall within the screened particle size range.
Sampling techniques and the monitoring design selected determine what is actually
being measured. The rationale for selecting a particular technique or design (e.g., when, when,
and how to sample) should be carefully defined and documented as to its applicability in
defining site conditions. Samples that are biased toward hotspots should be identified.
Sampling only suspected hotspots is often used in the initial stages of an investigation and
insures that some potential problems will be identified quickly, but it generally provides only a
limited indication of the magnitude of the total problem.
Completeness: Completeness is a measure of the amount of validated data that is
obtained from a particular sampling scheme. It is calculated by dividing the number of
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validated data points by the total number of samples collected. The design of a particular
sampling effort provides a minimum number of samples that is needed to yield a desired level
of precision for the final results. The probabilities of false positive and false negative answers
are specified at the outset. Obviously any loss from the required number of samples will impact
the final results. The U.S. Department of Energy has set a completeness objective for the
Environmental Survey Program at 90% for both field sampling and laboratory analyses (U.S.
DOE, 1987).
The planning stages of any study must take into consideration the fact that not all
samples will make it intact through the entire measurement process. Sample containers will be
broken, instruments will fall out of control, data will be lost, sample tags will be lost, storage
conditions will be violated, etc. There are many factors that can lead to a sample result being
invalidated. This can be compensated for by oversampling or by using a phased sampling effort
that allows areas where samples were lost to be resampled in subsequent phases. This latter
approach insures that the desired number of samples will be collected.
The completeness goal must be realistic and must assure that adequate data will be
available for meeting the objectives of the sampling program.
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Comparability: The comparability objective provides the needed control over the total
measurement processes to insure that different studies can be compared. Comparability
provides a basis for comparing trends over time or space, for evaluating the relationship
between sampling programs, and for insuring that phased sampling efforts produce data of a
consistent quality. The quality control procedures used in the laboratory provide a portion of
the control over comparability, but the field audit sample provides the best basis for insuring
that data sets are comparable. A field audit sample from a previous study should be included in
the first few batches of samples submitted from a new site. This allows comparisons to be made
through the two sets of field audit samples.
When sampling is to occur over an extended period of time or when the investigator
desires to compare several sites, it is necessary to insure that the samples be collected in a
comparable manner, from comparable fractions of the soil mass, and with comparable methods.
For example, one should not attempt to compare samples collected by coring with bucket auger
samples.
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Stage 3: Design of the Data Collection Program
During Stage 3, the methods used to obtain and analyze data, as well
as the quality and quantity of data required to achieve the objectives
outlined in Stage 2 will be specified. This information is provided in
documents such as the work plan or quality assurance project plan.
Stage 3 results in the specification of the methods by which data of
acceptable quality and quantity will be obtained (U.S. EPA, 1987a).
During Stage 2, specific guidelines have been developed for sample collection, chemical
analyses, and data evaluation. The data quality objectives that were defined are used to design
the procedures that will be used to acquire the quality of data that is needed to meet the
demands of the decision maker and the data users. Stage 3 compiles information and merges it
with the data quality objectives to arrive at a data collection program. The output of Stage 3
should be a set of well-defined and documented plans for acquiring the data and insuring that
the quality of the data meets the DQOs. A U.S. EPA publication (U.S. EPA, 1987b) provides
an example of DQOs for soil sampling that can be used as a guide.
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Mason (1983) has prepared a manual for use in preparing soil sampling protocols. This
document outlines sample design considerations and sampling techniques that can be used to
prepare the documentation needed for a soil sampling effort.
A detailed protocol or work plan that spells out detailed instructions for every aspect of
the soil sampling program should be prepared. Documentation should include instructions for
acquisition and preparation of sampling equipment, sampling/monitoring design, health and
safety, quality assurance, decontamination, disposal of wastes, sample and document control,
analytical procedures and data validation and analysis. These aspects may be combined into
one large document, but most likely it will be a series of documents addressing specific aspects
of the entire measurement process.
To provide additional information and assistance to those responsible for designing and
implementing sampling/monitoring programs, a Data Quality Objectives Development Process
is presented in Appendix C. This process involves a four-stage interactive approach. The
accompanying checklists and critical elements of a quality assurance plan are used by U.S. EPA
Region 10 to address and identify site-specific Data Quality Objective requirements.
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CHAPTER 4
QUALITY ASSURANCE PROJECT PLANS
The U.S. Environmental Protection Agency quality assurance policy requires that every
environmental monitoring and measurement project must have a written and approved Quality
Assurance Project Plan (QAPP) (U.S. EPA, 1980, 1986, 1987d). These plans are based on the
data quality objectives that have been developed for the RI/FS. The QAPP becomes the
primary instrument for directing the quality assurance effort for the project and for insuring
that the DQOs are met. U.S. EPA policy requires that the QAPP contain the sixteen elements
listed below.
1. Title page with provision for approval signatures.
2. Table of Contents. (This must include a serial listing of each of the 16 QAPP
components.)
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3. Project Description. (A general description of the project should be provided
together with the intended end use of the acquired data. This should be closely
tied to the objectives identified during the DQO stages.)
4. Project organization and responsibility. (List the key individuals, including the
QA Officer, who are responsible for ensuring that the collection of valid
measurement data and the routine assessment of measurement systems have
met the DQOs.)
5. QA objectives for measurement data in terms of precision, accuracy, complete-
ness, representativeness, and comparability. (For each major measurement
parameter, list the DQOs for precision, accuracy, and completeness, detection
limits, along with the probability of committing a Type I or Type n error that
was used in defining the objectives. All measurements must be made so that the
results are representative of the media and conditions being measured.
6. Sampling procedures. (For each major measurement parameter, including all
pollutant measurement systems, provide a description of the sampling
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procedures to be used. Any later changes to these procedures should be
documented by attachment of approved amendments to these procedures.)
7. Sample custody. (Where samples may be needed for legal purposes, chain-of-
custody procedures will be used. Most RI/FS data falls into this category. It will
be necessary to define a detailed set of procedures to be followed by the field
teams and the laboratories to insure that the chain-of-custody is followed.)
8. Calibration procedures and frequency. (Information should be provided on the
calibration standards to be used and their sources.)
9. Analytical procedures. (Describe the analytical procedures to be used for each
major measurement parameter along with the associated detection limits.)
10. Data analysis, validation, and reporting. (This section will include the principal
criteria that will be used to validate data integrity during collection and
reporting of data as well as methods used to treat outliers. This section should
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also detail the procedures that will be used to insure that the DQOs have been
met.)
11. Internal quality control checks. (Examples of items to be considered include
replicates, spike samples, split samples, field audit samples, control charts,
blanks, internal standards, span gases, quality control samples, surrogate
samples, calibration standards and devices, and reagent checks.)
12. Performance and systems audits. (Each QAPP must describe the internal and
external performance and systems audits that will be required to monitor the
capability and performance of the total measurement system. The use of field
audit samples should be outlined in this section.)
13. Preventive maintenance. (This section should include a schedule of important
preventive maintenance tasks as well as inspection activities.)
14. Specific routine procedures used to assess data precision, accuracy, and
completeness. (These procedures should include the equations used to calculate
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precision, accuracy, and completeness, and the methods used to gather data for
the precision and accuracy calculations. The types of control charts to be used
along with the equations used to calculate control limits should also be included
in this section.)
15. Corrective action. (This section must include the predetermined limits for data
acceptability beyond which corrective action is required as well as specific
procedures for corrective action.)
16. Quality assurance reports to management. (These reports should include
periodic assessment of measurement data accuracy, precision, and completeness
as well as an identification of significant QA problems and recommended
solutions. The interim reports should specifically outline any problems that will
cause failure to meet the DQOs. The final report should address how well the
data quality objectives for the study have been achieved, along with any reasons
for failure to meet these objectives.)
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Since the original design of the QAPP, the use of data quality objectives has been
implemented. The data quality objectives define the standards that must be met in order to
insure that the quality of the data meets the needs of both the decision makers and the data
users. Properly implemented, these objectives become a powerful tool in the overall RI/FS
process. The QAPP becomes the roadmap for confirming achievement of the objectives
outlined during the DQO process. To be maximally effective, QAPPs should be designed in
such a way that out-of-control situations are detected at the earliest possible time so that
corrective actions may be taken quickly to avoid wasting valuable resources.
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CHAPTERS
THE CONCEPT OF SUPPORT
The better known texts on sampling theory and methods (e.g., Hansen et al., 1953;
Cochran, 1977) deal primarily with the sampling of people, housing units, or businesses where
sampling units are discrete and well defined. In the sampling of a continuous medium such as
soil, it is necessary to emphasize the definition of a sampling unit. In addition to a specified
location, each soil sampling unit has a certain three-dimensional volume, shape, and
orientation. These latter three characteristics when taken together are called the support of the
sample. The concept of support is somewhat analogous to the concept of a pedon used in soil
classification work (Soil Survey Staff, 1975).
The choice of support will affect the characteristics of the distribution of the pollutant
concentrations of the population of possible sampling units in the region being sampled. For
example, if the sampling unit is a soil core and pollutant concentration decreases with depth,
then the longer the core, the smaller is the mean concentration of pollutant in the sampling
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unit. However, changes in support not only change the means of the distributions, they also
change the variances of concentrations and the correlations of concentrations between sampling
units. Large variances of pollutant concentrations between sampling units often necessitate
taking large numbers of samples or cause a compromise that results in larger than desired
variances of sample estimates. In many cases, a change in the support of the sampling unit will
substantially reduce the variance between sampling units and thereby reduce the variances of
sample estimates.
Since one of the objectives of QA is to estimate the precision of measurements and
sample estimates, it is essential that the support of all sampling units within a study be the
same. However, it is possible to change support between an exploratory study and the
definitive study and still be able to use the data from both studies in making estimates. This is
permissible when the changes in support have not altered the expected values of the
concentrations in the sampling units, and the data from the two studies are weighted to reflect
the differences in variances of the measurements.
Soil is a very heterogeneous material. Samples with very small support volume (say
1 mm3) may vary from zero to very high concentrations, regardless of the concentrations in
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samples of larger volume from the same location. With no prior information, it is difficult to
choose an appropriate support. With information as to the spatial continuity of the pollutant
concentration in the soil, one can follow a systematic procedure to determine the appropriate
support for a soil sample. (See Starks, 1986.) In a later paragraph, it is shown how quality
assurance samples can also give information on the appropriateness of the chosen support.
Often one finds action levels expressed as a certain number of parts per million or parts
per billion, but for soils, such statements of action level have little meaning. As mentioned
above, for sampling units with very small volumes, it is almost certain that some samples will be
above action level For example, suppose that all the pollutant were concentrated in particles of
size 1 mm3 and that each of these particles has 10 times the action level concentration. If these
particles were uniformly distributed in the top ten cm of the soil in the area of interest, and the
total volume of all such particles were only 0.1 percent of the total volume of the soil in the top
ten cm, one would conclude that no remedial action is necessary. However, If the particles were
all concentrated in the soil so that they formed a "hot spot" of perhaps the top ten cm of one
acre in a 1,000-acre region, it would be important to locate the hot spot so that remedial action
could be taken. Hence, it is essential that the action level be defined as a concentration over a
particular support and location rektive to the ground surface.
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In this definition of action levels, the support will be referred to as the action support.
For example, one might state as an action level that if there is a soil volume that has a
concentration of pollutant X exceeding 10 ppm in the top ten cm over a square area of at least
100 m2, then remedial action should be taken on that volume. In this example, the action
support is the top 10 cm over a square area of 100 m2.
At the Palmerton, Pennsylvania, Superfund site, cadmium was one of the soil pollutants
of interest (Starks et al., 1987). The support of soil samples taken in an exploratory study was a
set of four (2 cm diameter) cores taken to a depth of 15 cm with one core from each of the four
cardinal compass points on a 6 m diameter circle. The four cores were composited at all but 10
of the 211 sample locations. At ten locations, measurements were obtained from each of the
four cores. At another 10 sample locations, the sampling team took a second (duplicate)
sample of four composited cores within 0.5 m of each of the original four cores. (See Table 2.)
The variance between measurements on the duplicate pairs was 31 percent of the total
variance between samples (after correcting for changes in expected values over locations). The
variation between measurements on individual cores was found to be quite large. (See Table
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3.) Other QA sample results indicated that the large variances found between duplicates and
individual core measurements were not caused by subsampling or chemical analysis errors.
To reduce the large component of total variance caused by the placement of the sample
cores, it was decided that nine cores should be taken at each sampling location in the second
(definitive) study. The nine cores came from four points at the cardinal compass points of a 6
m circle, from the four minor compass points of a concentric 4.25 m circle, and one point at the
center of the circles. This increase in the support of the samples, plus the increased experience
of the sampling teams, brought the variance between duplicates down to less than 10 percent of
the total variance in the definitive study.
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TABLE 2. RESULTS FROM DUPLICATE SAMPLES AT THE
PALMERTON SITE
(mg/kg)
POINT
AK26
A030
BP30
BQ33
BQ34
BT32
BT46
BY29
DL78
DP34
Median:
CADMIUM D.3
8.85
22.30
86.30
58.20
100.4
39.50
172.0
43.70
2.76
68.10
s
7.35
8.46
63.10
40.30
77.10
37.90
144.0
34.20
2.71
61.70
2 = lLj2/20
1.50
13.84
23.20
17.90
23.30
2.40
28.00
9.50
0.05
6.40
11.67
» 0.0691
Lb
0.186
0.970
0.313
0.368
0.264
0.041
0.178
0.245
0.018
0.098
0.216
5 D - absolute pair difference
b L » absolute pair difference of log-transformed data
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TABLE 3. RESULTS FROM INDIVIDUAL CORES
AT THE PALMERTON NPL SITE
(Cadmium, mg/kg)
* Sample variances on ln(Cd) over 4 cores at each site.
Pooled sample variance based on first nine sample points: 0.3659.
Pooled sample variance based on all ten sample points: 0.8453.
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SAMPLE
POINT
BO29
BR33
BR38
BS33
BU33
BU38
BX30
CF46
CI34
CJ50
CORES
S
30.7
56.7
104.0
42.9
29.4
64.0
56.3
51.5
112.0
0.75
W
5.0
66.8
147.0
21.2
24.1
33.1
35.9
97.1
167.0
47.4
\I
66.3
25.8
124.0
30.5
31.0
82.3
46.2
46.7
158.0
115.0
E
29.0
104.3
100.6
45.5
48.6
127.0
111.0
79.0
196.0
52.0
RANGE
61.3
78.5
46.4
24.3
24.5
93.9
75.1
50.4
84.0
114.25
V*
1.195
0.339
0.031
0.124
0.877
0.316
0.234
0.121
0.055
5.159
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CHAPTER 6
EXPLORATORY STUDY
Once objectives which involve the need for soil sampling have been defined, the next
step is to develop a total study protocol including DQOs as well as an appropriate QA/QC
program. Answers to the following questions should be available or estimates made in order to
develop the protocols.
What are the probable sources of the pollutants of concern?
How have the emissions from these sources varied in the past compared to their
present levels?
What are the important transport routes which contribute to soil
contamination?
What is the geographic extent of the contamination?
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What average concentrations of the pollutants exist at different locations, and
how do these vary as a function of space and time?
Do localized areas of high concentrations exist, and if so, where are they and
what are the concentrations?
Is it possible to stratify the sampling region to reduce the spatial variations
within strata?
What are the soil characteristics, hydrological features, meteorological or
climatic factors, land use patterns, and agricultural practices affecting the
transport and distribution of the pollutants of concern in the soil?
What is an appropriate background or control region to use for the study?
What are the acceptable levels of precision, minimum relative levels of
detectability, and probabilities of both Type I and Type n errors for this study?
If answers to all of these questions are not available, an exploratory study (this also can
be called a pilot study or a preliminary study) should be carried out. To be designed after a site
visit, this study should address the components of a conceptual dispersion model. Clearly not
all the above questions can be answered in detail by a single exploratory study; however, as
many as possible should be attempted.
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The authors recommend developing the conceptual model after a compilation of
literature and all existing data have been completed in Stage 1 of the DQO process. Much of
the information pertinent to the above questions may already be available in the published
literature, in the files of governmental agencies or industrial corporations, in ongoing or
completed research at local universities, or in the knowledge of local citizens. A carefully
planned and organized effort should be mounted to accumulate what relevant information is
available. Only after this information has been collected, collated, and evaluated should any
field measurements be made. It is good policy to adhere to a reasonable fixed period of time
for the collection and analysis of available information, otherwise this process could drag on
interminably. Only at the end of the fixed time period, and based on whatever information is
available at that time, should the design and implementation of the field measurements portion
of the exploratory study be undertaken.
In those cases where there is not enough data available for designing the soil sampling
study, an exploratory study becomes an essential element of the planning process. Properly
designed, the exploratory study is simply phase one of a multiphased sampling effort.
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The primary intent of the exploratory study is to determine if the site poses a potentially
unacceptable risk to the local human population or the environment. The study also assembles
and collects data needed to prepare revised Data Quality Objectives, work plans, QAPPs, and
sampling protocols.
The designs used to acquire the data during the exploratory study should have a
statistical basis that provides some measure of the precision that can be expected for the
particular soil-pollutant combinations that exist on the site. The authors recommend that a
coarse grid pattern be used in order to provide some information on the distribution of the
chemicals over the site. The investigators may decide that a combination of judgmental
sampling and systematic sampling may provide more useful information for a particular
situation. Those samples that are selected on a judgmental basis are biased toward finding
pollution. Because of this bias, the samples should be identified in such a way that the
statistician can take the bias into consideration when providing a statistical analysis of the data.
One possible alternate approach is to stratify the study area on judgmental evidence and then
take random samples within each stratified area.
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Aerial photographs of the site and its vicinity along with appropriate maps should be
assembled before the exploratory study. This provides a basis for evaluating terrain, surface
water flow, erosional patterns, land use patterns, and other similar factors that may influence
the distribution and dispersal of the pollutants over the site. Careful attention should be paid
to the identification of the primary sources of the pollution and to the primary routes of
migration. These should be noted on the maps and incorporated into the conceptual model of
the site.
The conceptual model of the site should be used as the basis for designing the sampling
effort. Those areas deemed to be of primary concern should be sampled in such a manner that
the model can in fact be verified or amended as needed. This involves incorporating into the
sampling design adequate replication on several scales to determine the expected spatial
variation over the site. Assumptions may have to be made about the expected variation that
one would expect to encounter based upon similar situations at other sites. These would then
be adjusted in the final sampling effort to incorporate the information gained in the exploratory
study or preliminary site investigation. The Palmerton NPL Site example discussed below is an
excellent example of the use of the preliminary study to guide the final sample design. The
short-range variation (Le., variation between samples 0.5 m apart) among results from
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neighboring grid points was considered to be too large based on the preliminary study. This
was corrected by altering grid spacing to effectively reduce the influence of the short-range
variation on the final results.
Guidance for the confidence level (1-ot) and the power (!-/>) of the data set that should
be used for the exploratory study are given in Chapter 7 along with the relative increase over
background that should be used in designing any random statistical sampling plans. This
chapter also recommends the number of quality assurance and quality control samples that
should be taken during the exploratory study.
EXAMPLE OF AN EXPLORATORY STUDY
The Palmerton NPL Site is used as an example of the use of an exploratory study to
develop the guidance that is needed to develop the main phases of a definitive soil sampling
program. In the narrative that follows the reasoning for the sample design, reasoning for
transformation of data, and the types of quality of assurance data are discussed. (For more
details than can be presented here about the study, the reader is referred to Starks et al., 1986;
Starks et a/., 1987; and U.S. EPA, 1989.)
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The purpose of the entire study was to determine the spatial distribution of certain
metals (principal interest was in the concentrations of cadmium and lead) in the soil in
residential and farming regions near two zinc smelters. The two smelters are located on the
southwest and the southeast edges of Palmerton, Pennsylvania. (See Figure 4.) This
information was needed to plan remediation procedures for the area. The site RPM requested
a geostatistical analysis of the data to obtain kriging estimates of the spatial distribution of the
metals in the soil. The purpose of the exploratory study was to estimate the extent and the
spatial structure of soil contamination by cadmium, copper, lead, and zinc.
Sample Design
As stated above, the principal objective of the exploratory study was to obtain
information for estimating spatial structure and extent of the pollution. Estimation of spatial
structure requires information on how concentrations vary with location and how differences in
concentrations vary with distances between sampling units. A square grid of sampling locations
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. Jr,ast Slag Pile
/ Plant .
4000ft.
Aquashicola
Creek
Figure 4. Palmerton Exploratory Sampling Design.
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is good for this purpose in that each interior point can be associated with several other points a
given multiple of the unit grid distance from it. The unit grid distance is of importance in that
in that if it is too large, no spatial correlation will be detected; and if it is too small, more
information than necessary will be obtained, and either a very small area will be sampled or a
very large number of samples will be required.
In planning this study, the designers used results from a study of lead pollution in soil in
Dallas, Texas, which indicated a range of influence (a distance beyond which there was no
spatial correlation) of about 1,200 feet. Based on this information, a unit grid spacing of 400
feet was selected. This provided pairs of points at four distances (400, 800, 40072 and 800/2
feet) at which positive spatial covariances might be expected for use in estimating the
covariance function. To obtain a reasonable number of such pairs, a rotated square grid of 85
sample points was formed over a diamond-shaped area. (See Figure 4.) To obtain information
on the extent of metal pollution in the soil, additional sample points were selected along eight
transects originating at the center of the diamond and extending through its sides and vertices.
This grid of sample points was centered in the Town of Palmerton and oriented in such
a way that three of the transects conformed to the valley system in which the town lies. One
transect was bent to follow the Lehigh River. These transects also followed the principal
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windrose directions. The lengths of the transects were based on windrose data and land use
data. The number of samples to be collected and analyzed had to be kept small, so that there
would be resources to carry out the definitive study. Thus, spacing between points along the
transect was generally greater than within the square grid. By centering the design in
Palmerton, it was unnecessary to wait for the definitive study to obtain kriging estimates of
metal concentrations for the area where most of the people in the study region lived.
Data Transformations
The need to transform data arises from the need to stabilize variances. If variance
changes from location to location, there is no one variance to estimate. In addition, the kriging
estimation process in geostatistics is variance-based and, therefore, requires a stable variance.
The data in Tables 2 and 3 indicate that the variability in concentration measurements between
duplicates and between cores from the same site tends to increase as the average concentration
increases, and the same occurs between splits from the same sample. These phenomena were
also observed in the concentration measurements for the other three metals. This indicates the
need for a data transformation. Several methods for making a choice of transformation are
given by Box and Cox (1964) and Hoaglin et al. (1983). However, estimates of proper
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transformations based on the small amount of data in the above mentioned tables are rather
unreliable. In this case, it was known that the logarithmic transformation had been applied to
these types of measurements in the past, and it was found to do a good job of stabilizing
variance on a larger set of duplicate lead measurements from the Dallas study. Hence, the
simple logarithmic transformation (Y=lnX) was employed.
Graphical plots of means versus standard deviations for the log-transformed metal
concentrations data showed no indication of a relationship between the two, and the variances
of duplicates for the four metals were very close to being the same, even though the mean
concentrations were quite different. This is as it should be, since the process of accumulation in
the soil should be the same for all of the metals. Similar inter-metal confirmations of the
logarithmic transformation were found in the data from split samples.
Assurance Data
Several types of quality assurance data were collected in the Palmerton exploratory
study. These included duplicates, splits, and individual cores. In addition, decontamination
blanks and QC samples were analyzed. A decontamination blank was collected at one sample
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location out of every 20. This blank was obtained by bringing the sample corer into contact with
distilled deionized (DDI) (ASTM Type n quality) water (final rinse) prior to its use in taking
the soil sample. The blanks were prepared in the field at the sample locations, then shipped
with the field samples through the sample bank to the soil chemistry laboratory to determine
whether the sample collection instruments were contaminated prior to taking a soil sample.
Additional decontamination blanks were prepared in the sample bank by bringing DDI water
into contact with the soil sieve and mixing equipment. One such blank was prepared after 40
samples were passed through the equipment to determine whether it was being properly
cleaned between samples. Four QC samples consisting of DDI water with known quantities of
Cd, Cu, Pb, and Zn were prepared and sent through the sample bank to the laboratory for
accuracy checks.
If anything, this exploratory study was a bit short on QC samples. At least 20 duplicate
samples should have been taken to allow better estimation of both the measurement error
variance and the appropriate data transformation. In addition, no field audit samples
(performance evaluation soils) were employed to check the precision and accuracy of the
methods by using a soil matrix.
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CHAPTER?
GUIDANCE FOR SPECIFIC SOIL SAMPLING PROGRAMS
Chapter 2 discussed some of the functional objectives for soil sampling. The material
that follows presents guidance for determining the confidence level, the power, and the
detectable relative difference between different data sets that should be anticipated for
different types of soil sampling programs. Most of the information relates to the provisions and
intent of RCRA and CERCLA. Operational situations in which soil sampling may be involved
include:
background monitoring,
preliminary site investigation,
emergency cleanup operations,
planned removal operations,
remedial response operations,
monitoring, and
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research or technology transfer studies.
With the possible exception of research or technology transfer studies, all of the
operational situations listed have a potential for litigation. For this reason, a statistical
experimental design incorporating appropriate QA/QC measures including National
Enforcement Investigation Center (NEIC) "chain-of-custody" procedures should be
incorporated into the overall sampling program. The total QA/QC plan should be designed to
insure that the data quality objectives are met.
Background Sampling
Sampling to determine the background levels of the various chemicals found in the
environment should be carried out as part of any routine sampling programs. Background
levels are usually found in those areas where the levels are below the minimum detection limits.
However, certain of the trace metals may be present at levels that are detectable and still be
background levels. In order to determine if a specific area is contaminated above background,
it may be necessary to carry out studies with this specific objective in mind.
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Background areas should be areas outside industrial complexes that may be contributing
to the overall pollution burden and should be upwind and upstream from them. These areas
should be in similar topographical settings and have the same or very similar soil types. The
parent material for the soil should be the same if at all possible. When the same soil type
cannot be found, care should be taken to insure that the amount of organic matter and clay is
similar in the soils chosen for the background areas.
These factors become especially important when chemicals normally found in the soil
are the pollutant of concern. Sensitive analytical methods with low detection limits will detect
many of the metals in the soil When these are detected in the soil near a site, care must be
taken in interpreting this as an indication of pollution.
Preliminary Site Investigation
The purpose of a preliminary site investigation or exploratory study is to provide
information about a specific site that can be used in making initial management decisions and,
should further work be necessary, for designing a more detailed and comprehensive sampling
investigation. Since the data collected during the preliminary study will be used to make
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important decisions about the site, it is essential that the reliability of the data be demonstrated
through incorporation and implementation of adequate QA/QC. For example, the preliminary
results may indicate that an emergency response should be initiated. Making an erroneous
decision based upon data of unknown quality could lead to serious and costly consequences.
Emergency Cleanup Operations
The purpose of an emergency cleanup operation is to remove enough of the pollutants
as quickly as possible to achieve a level that is considered an acceptable risk to human health or
the environment. The principal role of the QA/QC plan in this situation is to provide a reliable
demonstration that cleanup operations have been adequate. An emergency cleanup operation
often leads to a requirement for either a planned removal or a remedial response operation.
Thus, any soil sampling undertaken during the emergency phase should have adequate QA/QC
measures incorporated into the study to ensure that the resulting data may be used as a
foundation for any subsequent investigations.
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Planned Removal and Remedial Response Operations
The purpose of planned removal or remedial response operations (they differ
principally with regard to time scale) is to provide a more permanent solution to the problem.
These operations and the associated RI/FS may involve extensive sampling and data analysis
programs. Adequate QA/QC measures are essential, since litigation to recover the costs of the
operations is a probable sequel Consequently, all data collected may be closely examined in
court.
Monitoring
Monitoring, or sequential measurements over time, may take place before, during, or
after any of the operational situations listed above. Whatever trends are measured must be
demonstrated to be reliable in order to serve as a basis for making decisions that hold up to
challenges.
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Research or Technology Transfer Studies
The purposes of research or technology transfer studies vary widely. In any event, the
incorporation of adequate QA/QC plans into these studies is mandatory in order for the results
of the studies to withstand the normal peer review processes required for publication and/or
application of the findings.
In summary, an adequate QA/QC plan should be part of any soil sampling program
relevant to any of the operational situations listed. The only remaining question pertains to the
definition of the word "adequate." The sections that follow discuss the above study types in
more detail.
OBJECTIVES FOR BACKGROUND MONITORING
Generally, the design of soil monitoring programs requires that the levels of defined
hazardous or potentially hazardous substances and their spatial and temporal trends be
measured for some specific purpose. Often it is critical not only to quantify levels and trends,
but also to link the existing levels to sources. This is necessary to enable adequate control
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actions to be taken whenever a situation that is hazardous to human health, welfare, or the
environment is identified. The situation often is complicated by the fact that multiple sources
contribute to the measured levels. The situation is further complicated by the presence of
pollutants of recent origin mixed with pollutants of past origin. This mixing becomes especially
important when the investigator attempts to trace the migration from source to receptor and
also in predicting future levels after various proposed control measures are implemented.
Identification of spatial and temporal trends, along with linkage of observed measurements to
sources, requires that adequate background, reference, or control samples be taken.
In the absence of such background samples, interpretation of the resulting data may
become extremely difficult, if not impossible. The burden of proof that background samples are
not necessary for a particular soil monitoring study rests with the principal Investigator. In the
absence of such proof, a prudent investigator will ensure that an adequate number of
background samples be included in the monitoring study design.
Since measured levels in presumably higher concentration areas will be compared to
background levels, QA/QC procedures are just as critical for the background measurements as
they are for the study area measurements. Thus, for background sampling, a QA/QC
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procedural umbrella must cover the selection of appropriate geographical areas; the selection
of sampling sites within the geographical areas; sampling, sample storage and/or preparation;
sample analysis, data reduction, and interpretation of study results.
Under most circumstances, background data will not be available for a given monitoring
location. These data must be acquired either before or during the exploratory or preliminary
investigation phase. The intensity of the background sampling that is undertaken depends upon
the pollutants being measured, the soil characteristics and variability, the levels of pollutant
likely to be found in the study area, and the purpose of the study.
SPECIFIC OBJECTIVES FOR MONITORING IN SUPPORT OF CERCLA AND RCRA
The principal sampling media now being measured to carry out the provisions and
intent of CERCLA and RCRA are soil and groundwater. Hazardous constituents from a
hazardous waste facility may enter soils through transport of the constituents from the waste
site to soils via organic solvent, surface water, or groundwater flow. Air transport followed by
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dry precipitation, rainout, or washout will generally be less important than other transport
routes.
Suppose a situation exists in which hazardous waste constituents have been leaving a
site for a relatively long period of time and nearby soils have built up considerable levels of
pollutants. Further, suppose that the soils now constitute a source of the hazardous
constituents. At this time, removal of the hazardous wastes from their original disposal site
may still leave a significant unsolved problem in the form of the contaminated soils, which may
cause human exposure through skin contact or through ingestion or inhalation of soil particles.
Also human foods, contaminated directly or indirectly through contact with soils, may be unfit
for human consumption. Furthermore, as the hazardous constituents move through different
trophic levels, substantial biomagnification of contaminants may take place, thereby increasing
the risk to humans consuming foods from higher trophic levels. Thus, it is conceivable that
situations may exist in which concentrations of hazardous constituents in soils may represent a
major risk to human health or the environment. To identify such situations, data from soil
sampling is an important link in the chain of required evidence.
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The specific QA/QC precision and confidence level objectives for any sampling study
are controlled in part by the goals of the particular study. Three situations where soil sampling
would probably be undertaken are:
hazardous materials investigations for areas such as abandoned landfills or
chemical spills,
monitoring studies, and
technology transfer.
The data flow that can occur in each of these situations is outlined in Figures 5,6, and 7.
The data generated in each category can provide input into the development of plans and
specifications for the other situations. Data that have been subjected to a good QA/QC plan
can be relied upon as a resource for the development of new data.
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End
End
Preliminary Site
Investigation
Emergency
Clean-up
Yes
Clean-up
Complete
" 9
Figure 5. Data acquisition flow for hazardous materials.
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Remedial
Response
Litigation
Sampling
Monitoring
Effort
Figure 5. (Continued)
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Corrective
Action
Design Corrective
Measures
Figures. (Continued)
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Remedial
Response
Clean-up
Complete
Is
Litigation
Planned
Planned
Removal
Planned
Removal
FigureS. (Continued)
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Is
Research
Needed
Research
Studies
FigureS. (Continued)
99
-------
Design
Monitoring Study
1
Acquire
Monitoring Data
Exceed
Standards
Initiate
Corrective Action
No
Figured. Monitoring data flow.
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Litigation
Anticipated
Litigation
Studies
Figure 6. (Continued)
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Technology
Transfer
Studies
Figure 7. Technology transfer data flow.
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The main area where the magnitude of the soil sampling can be controlled is in the
precision required by the sampling designs. The accuracy of the sampling is unknown because
the true average is not available. Repeated sampling for high precision nevertheless, must rely
on the analytical accuracy obtained in the laboratory to insure that the methods used measure
what is present in the soil sample. Thus, the accuracy for analysis applies only to the samples,
while the accuracy for the study depends on the degree to which the samples are representative
of the area. The balancing of resources with data reliability is a primary goal of the DQO
process.
The specific goals for each type of study will determine the allowable probabilities of
Type I and Type n errors and the minimum relative difference between sampled population
mean and either background mean, or designated action level that is considered important to
detect. Suggested guidelines are presented below for the operational situations listed
previously.
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PRELIMINARY SITE INVESTIGATION
The preliminary investigation is the foundation upon which other studies in hazardous
waste site assessments should be based. As part of this study, it is essential to determine
whether or not soils are the sample media of importance to the total assessment. The total
assessment must provide data which will enable decision makers to decide whether the soil
contaminants pose an imminent and substantial endangerment to human health requiring
emergency action, and whether there is an unacceptable long-term risk to man or the
environment. If soils are determined to be unimportant in the preliminary study, it is likely that
no further attention will be directed to them. In view of this, a Type n error is considered to be
of greater importance than a Type I error. Presented below are suggested guidelines for use in
developing DQOs that may be used initially.
Confidence Level Power Relative Increase over Background
(1-a) (1 - ft) [lOO^-Mo)//^ to be Detectable
with a Probability (1-4)
70 - 80% 90 - 95% 10 - 30%
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If resources limit the number of samples that can be taken, the investigator should
determine, for the number of samples that can be collected, value judgment-based optimum
values for confidence level, power, and detectable relative difference. If these values are
deemed adequate, the study may proceed.
Using five-percent duplicate samples may provide adequate QA/QC for measuring
variance between samples (Plumb, 1981). However, there should be a minimum of two sets of
duplicates in each strata sampled. As data become available, these assumptions should be
checked. This is usually accomplished by collecting and analyzing more duplicates initially and
then checking to determine the minimum number required for the sites being sampled and the
pollutants being measured.
EMERGENCY CLEAN-UP
Emergency sampling is designed to identify those areas in which soils are contaminated
to such a degree as to threaten imminent and substantial endangerment to human health. The
threat may be due to the soils acting as a source of hazardous constituents to drinking water,
air, or human foods. The emergency action in these cases may be nothing more than staying
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indoors on windy days, using dust suppression, switching to bottled water for drinking and/or
taking certain locally produced human foods off the market rather than a full-scale soil removal
program. Soil removal may well be implemented at a later date as part of a planned removal or
a remedial response operation. Of course, any long-term solution to the problem would also
have to address the removal of the primary source of hazardous substances to the soils.
For an emergency response operation involving soils, a Type n error is considered of
greater importance than a Type I error. Presented below are suggested guidelines for
developing DQOs to be used for emergency response operations.
Confidence Level Power Relative Increase over Background
(1 - a) (1-0) or an Action Level to be Detectable
with Probability (1-0)
80 - 90% 90 - 95% 10 - 20%
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PLANNED REMOVAL AND REMEDIAL RESPONSE STUDIES
Planned removal and remedial response studies are sometimes continuations of those
initiated during emergency clean-up studies. They should be designed to provide specific
information needed to resolve control option issues. The areas to be surveyed should be
stratified and sampled according to a design that can be used to determine spatial variability. A
suitable statistical design should be formulated so that components of variance for the study
situation may be identified and evaluated. Appropriate QA/QC procedures must be
formulated and implemented.
If the sampling during exploratory or emergency response investigations has been done
properly, there will be a sound basis for determining the sample size and sampling site
distributions. The design will have to incorporate information on the vertical distribution as
well as the horizontal distributions. Measurements of concentration trends with time may be of
critical importance, particularly if soil concentrations are changing appreciably with time. For
example, the concentrations of pollutants in soils may decrease with time once the primary
source of contamination is removed. This reduction in concentration may be due to a
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combination of biotic degradation of the contaminants, chemical degradation, volatilization,
removal of contaminants by leaching, etc.
For a planned removal or a remedial response operation involving soils, it is considered
that a "type I and a Type n error are of about equal significance. Furthermore, an attempt at
cost recovery which might lead to litigation is a likely successor to these studies. Accordingly, it
is important to achieve the highest order of precision and accuracy feasible. Presented below
are suggested guidelines for developing DQOs that may be used for planned removal and
remedial response studies.
Confidence Level
(1-or)
Power Relative Increase over Background
(1-0) or an Action Level to be Detectable
with Probability (1-0)
90-95%
90-95%
10 - 20%
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MONITORING OR RESEARCH STUDIES
The guidelines for confidence levels, power, and detectable relative differences for
monitoring or research studies should be set on the basis of the objectives of each study. As
actions which may be taken on the basis of resulting data become more and more significant
and costly, greater effort should be placed on achieving an increased level of reliability for the
data. Publication of the results in a peer-reviewed journal will usually require some
demonstration that an adequate QA/QC plan has been incorporated into the experimental
protocol.
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CHAPTERS
SELECTION OF NUMBERS OF SAMPLES AND SAMPLING SITES FOR THE
DEFINITIVE STUDY
INTRODUCTION
The QA/QC plan must be designed to allow for estimation of errors in the
determination of, as a minimum, mean concentrations and standard deviations of the means.
In some cases the primary interest may be in the determination of a reasonable mean of
extreme values (the stratum having the highest mean concentration) which must be compared
to an acceptable action level In the latter case corrective actions will generally be required if
the acceptable action level is deemed to be exceeded. For this case, the QA/QC plan must
provide data on the basis of which one may state with what reliability the action level is, or is
not, exceeded. Both Type I and Type n errors must be taken into consideration. These errors
can be controlled only by choosing an appropriate number of samples. (See Table 4.)
On the basis of data from the exploratory study, the following minimum amount of
information wifl be available.
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TABLE 4. NUMBER OF SAMPLES REQUIRED IN A ONE-SIDED ONE-SAMPLE
t-TEST TO ACHIEVE A MINIMUM DETECTABLE RELATIVE
DIFFERENCE AT CONFIDENCE LEVEL (1-a) AND POWER OF (1-0).
Coefficient Power Confidence
of Level
Variation
(%) (%) (%)
10 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
15 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
20 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
Minimum Detectable
Relative Difference
(%)
I
66
45
36
26
55
36
28
19
43
27
19
12
145
99
78
57
120
79
60
41
94
58
42
26
256
175
138
100
211
139
107
73
164
101
73
46
10
19
13
10
7
16
10
8
5
13
8
6
4
39
26
21
15
32
21
16
11
26
16
11
7
66
45
36
26
55
36
28
19
43
27
19
12
20
7
5
3
2
6
4
3
2
6
3
2
2
12
8
6
4
11
7
5
3
9
5
4
2
19
13
10
7
16
10
8
5
13
8
6
4
30
5
3
2
2
5
3
2
1
4
3
2
1
7
5
3
2
6
4
3
2
6
3
2
2
10
9
5
4
9
6
4
3
8
5
3
2
I
40
4
3
2
1
4
2
2
1
4
2
2
1
5
3
3
2
5
3
2
1
5
3
2
1
7
5
3
2
6
4
3
2
6
3
2
2
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TABLE 4. CONTINUED
Coefficient
of
Variation
Power Confidence
Level
Minimum Detectable
Relative Difference
{70) {70 ) {70)
25 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
30 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
35 95 99
95
90
80
90 99
95
90
80
80 99
95
90
80
i
5
397
272
216
155
329
272
166
114
254
156
114
72
571
391
310
223
472
310
238
163
364
224
164
103
532
421
304
641
421
323
222
495
305
222
140
10
102
69
55
40
85
70
42
29
66
41
30
19
145
99
78
57
120
79
61
41
84
58
42
26
196
134
106
77
163
107
82
56
126
78
57
36
20
28
19
15
11
24
19
12
8
19
12
8
5
39
26
21
15
32
21
16
11
26
16
11
7
42
35
26
20
43
28
21
15
34
21
15
10
30
14
9
7
5
12
9
6
4
10
6
4
3
19
13
10
7
16
10
8
5
13
8
6
4
25
17
13
9
21
14
10
7
17
10
7
5
40
9
6
5
3
8
6
4
3
7
4
3
2
12
8
6
4
11
7
5
3
9
5
4
2
15
10
8
6
13
8
6
4
11
7
5
3
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Mean concentrations and standard deviations of the means for stratified regions
(assuming it was deemed necessary to stratify the study region).
Mean concentrations and standard deviation of the mean for the control region.
Results of tests at specified confidence levels to determine whether or not the
mean concentrations in all strata are significantly different from the control region
mean concentration.
Results of tests at specified confidence levels to determine whether or not peak
or maximum measured concentrations exceed any established action levels.
Some measure, through analysis of variance tests, of the distribution of observed
variances among various elements of the sampling process such as sample collection,
sample handling, and sample analysis.
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An evaluation must be made during Stage 2 of the DQO process to determine which
elements of the exploratory study provide sufficient information to meet program objectives,
and where additional measurements will be necessary. Generally, since the exploratory study
was designed to provide only a limited sample of the desired study population, it will be
necessary to obtain additional measurements to improve the levels of precision and confidence,
to confirm the results, and to expand the measurements to cover regions not previously
sampled.
NUMBER OF SAMPLING SITES REQUIRED
The minimum number of samples, n, required to achieve a specified precision and
confidence level at a defined minimum detectable relative difference may be estimated by the
use of Table 4 or one of the following equations:
n * [(Za +
for a one-sided, one-sample t-test, and
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for a one-sided, two-sample t-test
where: Za is a percentile of the standard normal distribution such that P(Z > Zft ) = ^ Za is
similarly defined, and D » (minimum relative detectable difference/CV. CV * coefficient of
variation. For a two-sided t-test, the values for Za should be changed to Za/2.
As an example of application of the first equation above, assume CV » 30%,
Confidence Level = 80%, Power = 95%, and Minimum Detectable Relative Difference =
20%. From Appendix B for infinite degrees of freedom (t distribution becomes a normal one)
Za = 0.842 and Z^ - 1.645. From the data assumed, D » 20%/30%. Therefore
n * [(0.842 + 1.645)/(20/30)]2 » 0.5 (0.842)2
n * 13.917 + 0.354 - 14.269
n = 15 (always round up) which agrees with the value given in Table 4.
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In case multiple pollutants are present, the particular pollutant requiring the greatest
number of samples to achieve the assigned DQOs would be the controlling factor. In this
instance, however, all samples collected may not have to be analyzed for all pollutants.
In general, a suitable soil sample from a number of possible sampling designs may be
selected on the basis of random, stratified random, judgmental, or systematic sampling. The
authors recommend the use of geostatistical techniques as the most appropriate methods of
handling spatial data. The tools of geostatistics are easier to apply and the utilization of
resources is better if one of the systematic designs is used.
The optimum approach appears to be a combination of systematic and judgmental
sampling. Assuming that appropriate information has been obtained in the information-
gathering phase of the exploratory study, a conceptual model may be hypothesized describing
the spatial distribution of soil contamination, as well as identifying a likely background or
control area. Judgmental samples can be taken for any purpose; however, these purposes must
be documented and explained Randomization of a systematic grid can be difficult because,
once the spacing is selected, the starting point identified and the orientation chosen, there are
no degrees of freedom to use for randomizing the sample location.
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The major axis of orientation should be selected along the most likely route of
migration. It is likely that there will be no indication of the most likely direction of migration;
however, in such cases, the direction used as the major axis of the sampling grid can be chosen
at random.
The starting point of the grid may be chosen on the basis of either a random start or the
center of the pollution source. A smelter stack would be a possible point source for the
pollution, and could be the starting point for the grid. The major axis would be the direction of
the prevailing winds. Sites with no known major concentration(s) or where the source is quite
large in area require a different starting point. This can be handled by placing a grid over a
map of the area. A random number table can then be used to select a grid point on this map,
which becomes the starting point for the sampling grid. Once the sampling grid is properly
transferred to the map, a convenient numbering scheme can be set up to allow identification of
the samples.
Samples would then be collected from each of the grid nodes or at some subset of these
nodes, depending upon the intensity of the sampling expected. The Palmerton NPL Site study,
discussed previously, provides a good example of this approach. Samples were collected
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intensely in the areas near the smelter sites, but only selected nodes were sampled in areas
distant from the sources. The starting point for the grid was the center of the Town of
Palmerton.
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CHAPTER 9
CONTROL OF MEASUREMENT-ERROR VARIANCE
INTRODUCTION
The quality assurance plan should address two types of variation in soil sample data.
One is the population variation, the variation between true sample values, which is a function of
the spatial variation in the pollutant concentrations. Treatment of this type of variation is
discussed in Chapter 10. The other variation, measurement-error variation in the data, is
induced by differences between true sample values and reported values.
The distribution of the true values of the pollutant concentrations in the population of
sampling units will typically be multimodal and nothing like the probability distributions dealt
with in statistics textbooks. The modes of the distribution will probably correspond to
background values, and concentrations of various types of materials that have found their way
to the site being sampled. It is the distribution of measurement errors and of deviations of true
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values from expected values (see Geostatistics, Chapter 10) that are of principal concern in the
development of the quality assurance plan and in the evaluation of the quality assurance data.
Fortunately, these latter distributions generally are similar to distributions discussed in statistics
texts.
Plans for the taking of samples, analysis of samples, and analysis of resulting data are
based on assumptions concerning the probability distributions of the measurement errors or of
the deviations of true values from expected values. These assumptions should be consistent
with results from past surveys taken under similar conditions, and, in particular, with the results
of an exploratory study.
The variability in measurement errors is a function of the variable being measured, the
sample collection method and handling procedures, the analytical procedure, and the data
transcription procedures. If the distribution of the measurement errors is normal, it is
symmmetric about its expected value (center of gravity of the probability distribution), and its
variability is completely characterized by its variance (moment of inertia of the probability
distribution about its center of gravity when probability is treated as mass). The symmetry
makes the expected value a reasonable measure of location, whereas in non-symmetric
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distributions other measures of location may be preferred (e.g., the median). Also, the
statistician has means of dividing variance into components representing various sources of
variation.
With most non-normal probability distributions, the variability is only partially described
by the variance. Hence, these properties of symmetry, and variance representing variation, are
two prime reasons for transforming variables so that the new variables will have approximately
normal probability distributions. Procedures for such transformations are given in Box and Cox
(1964) and in Hoaglin et al. (1983). A discussion of the importance of the normality assumption
and data transformations appears in Scheffe (1959). In what follows, we shall assume that the
data have been transformed so that the measurement errors are nearly normal in distribution.
Additional information about the distribution of measurement errors for various types of
pollutants and measurement procedures may be obtained from the U.S. EPA's Regional Offices
and Laboratories and its National Enforcement Investigation Center in Denver, Colorado.
If the variable of interest has a count measurement, such as with radioactivity or the
presence or absence of a pollutant, other statistical methods are required. These methods are
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usually denoted as qualitative or discrete statistical methods. Bishop et al. (1975) is a good
reference to such procedures. The methods of this chapter should not be applied to count data.
As stated above, the measurement error variance is the variance of the differences
between the true concentrations for the sampling units and the reported concentrations. The
variance of the differences between the true and reported concentrations is typically the sum of
variances of many random errors that are made in sample taking, sample handling, sample
analysis, and data transcription.
GOALS
There are two commonly encountered rules of thumb in restricting the
measurement-error variance that may be viewed as goals in quality assurance. They are quite
similar and equally reasonable. One rule says keep the measurement error variance to less
than one-tenth the total variance between measurements; the other says keep the measurement
error standard deviation to less than one-fourth the total between-measurement standard
deviation.
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The reason for these rules is that if they are achieved in the absence of measurement
bias, the measurement error on the average is so small relative to the differences between
measurements that it can effectively be ignored when analyzing the data. If one has not
accomplished this goal in a survey, there are many almost insoluble difficulties in the final
analysis of the data. A major problem is that measurement errors are typically correlated (e.g.,
a calibration error that causes one measurement to be too large will cause the other
measurements to be too large until the next calibration). The levels of correlation in these
measurements are difficult to estimate, but are needed to calculate estimation variances if
measurement error variance is not small relative to total variance. Once either goal has been
reached, it is hard to justify any additional effort to reduce measurement error variance, since
that reduction will affect such a negligibly small change in the total variance and in the
variances of the sample estimates.
It is necessary to search out the major sources of measurement error variance and
develop QA procedures to ensure that these sources are controlled. It is also necessary that
QA data be obtained to monitor the sources of error and to provide an estimate of their
contributions to total error variance in the final QA data analysis.
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COMPONENTS OF VARIANCE
The measurement error in a soil sampling survey is usually the sum of several errors
from independent sources. The total measurement error variance can be represented by a sum
of the variances of the errors arising from these independent sources. A procedure called
components of variance analysis (Scheffe, 1959; Snedecor and Cochran, 1982) provides
estimates of the portion of the total variance coming from each of the sources in the
measurement process. Basic assumptions of this procedure are that the measurement errors
are normal in distribution, independent, and for each independent source have constant
variance.
Example: Consider the hypothetical data from a stratified random sample design
that has four strata, three random samples per stratum, two subsamples per
sample, and one analysis per subsample. The stratum effects are assumed to be
fixed unknown constants. The random sources of variation in the data are
between samples within strata and between subsamples within samples (combined
with analytical error). In the table of data below, a period in place of a letter in
the subscript means that the data have been summed over that letter (e.g., 2^ X.{..
- V
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Computations:
I. C=X2/(abn) = (82.30)2/24 = 282.2204
H. Total: Z^ X2k - C - (3.172 + ...+ 4.632) - C * 29.8656
m. Strata: ^ X2../bn - C - (14.752 + ...+ 26.812)/6 - C = 23.7517
IV. Samples: 1^ X2 /n - C = (5.812+...+ 20.422)/2 - C = 26.3109
V. Samples within Strata: IV - m - 26.3109 - 23.7517 » 2.5592
VI. Subsamples within Samples: H-IV » 29.8656 - 26.3109 - 3.5547
These computations are now organized within the table below.
Analysis of Variance
Source of
Variation
Strata
Samples/
Strata
Subsamples/
Samples
Total
Degrees of
Freedom
a-1-3
a(b-l)-8
ab(n-l)-12
23
Sum of
Squares
23.7517
2.5592
3.5547
29.8656
Mean
Square
7.9172
0.3199
0.2962
Expected
Mean Square
0^+nff2 + bnM/3
"l*1^
"1
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From the analysis of variance table, one obtains the variance estimates:
SA = 0.2962, which estimates c£, variance owing to subsampling and analysis;
s2B = (0.3199 - 0.2962)/2 = 0.0118, which estimates o£, the variance owing to
sampling within strata.
The symbol M in the above table stands for the sum of squared deviations of
stratum means about their grand mean.
The results of this analysis indicate that the experimenter should either have
made a greater effort to reduce subsampling and analytical errors or taken many
more subsamples, since the error variance estimated by s^ (» 0.2962) is much larger
than the estimated variance s| (= 0.0118), between samples within strata, which is
just the opposite of the goal suggested earlier in this chapter.
While the above example illustrates a classic components of variance analysis for a
situation in which the data have a hierarchical structure (i.e., strata, samples within strata, etc.),
there are many instances in environmental monitoring where this hierarchical structure is
lacking and other methods of separating the variance components are called for. Such an
example involving quality assurance samples is given in the next section.
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QA SAMPLES
In quality assurance, procedures are specified for the survey in an attempt to keep
measurement errors, measurement bias, and measurement error variance small. It is essential
that the sampling project include the means to determine whether the procedures are being
followed and the necessary data at the end of the survey to show how successful the quality
assurance procedures were in controlling measurement-error variance. To obtain the needed
data, it is necessary to introduce QA/QC samples into the measurement process.
The principal independent sources of random error must be specified. If the
independent sources of random error are listed as sources A, B, ..., then the total measurement
error variance, aT2 can be written as
where a^is the variance of the random errors associated with source A, etc. A partial list of
such sources might include failing to sample at specified sampling locations, mistakes in taking
the sample, errors in processing the sample, subsampling errors, analytical errors, and
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transcription errors. Measurements on quality assurance samples can be used to estimate the
variances from one source or the combined variance of several sources of random
measurement error. In addition, quality control samples can be used to determine whether the
measurement systems are in control during the survey. For example, laboratory audit samples
are run along with field samples. If the errors in the measurements of these audit samples are
too large, they will indicate that the laboratory analytical process is out of control and that
corrective action is required prior to analysis of additional field samples.
Example: This example again considers the exploratory study performed at the
Palmerton NPL Site (Starks et al., 1987) mentioned earlier. The duplicate samples
(Table 2) taken at each of ten sampling locations were QC samples. The individual
cores (Table 3) that were taken at ten sites but not composited were QC samples. In
this study, the four composited cores taken from each site were mixed and sieved. A
subsample was then taken and sent to the laboratory for analysis of concentrations of
- four metals (Cd, Pb, Cu, and Zn). For the soil from 10 sites, an additional subsample
(called a split) was taken after the mixing and sent to the laboratory with no
identification to associate it with the first subsample taken from the soil sample.
These splits were also QC samples. The results from the splits are given in Table 5.
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This example shows how these three types of QC samples were used in evaluating
the QA procedure employed in the exploratory study.
The variance estimate s2 - 0.0032 in Table 5 gives an estimate of the
measurement error variance coming from subsampling, analysis, and data recording.
The variance estimate s2 = 0.0691 obtained from duplicate samples in Table 2 is an
estimate of the total of the variance coming from short range (0.5 m) spatial
variation, sample taking and handling, sifting and mixing, and also the subsampling,
analysis, and data recording. Hence, the difference, s2, » 0.0659, between these two
variance estimates is an estimate of the total of the measurement error variances
coming from short range (0.5 m) spatial variation, sample taking and handling, sifting
and mixing. If the variance s2 were primarily from errors in sampling handling,
sifting and mixing, one would expect a variance between individual cores (s2 =
0.3659, Table 3) similar to that between duplicates. This was not the case, so one is
led to the conclusion that the combination of short-range spatial variation and
variation in sample taking is the major contributor to total measurement-error
variance. For this reason, the support of the sampling units was increased from four
to nine cores in the second (definitive) study.
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TABLE 5. RESULTS FROM SPLITS AT THE PALMERTON NPL SITE
SITE
DD70
BV35
CB34
BT70
AY34
BT35
DP82
CB42
BR34
CD24
CADMIUM*
4.17 4.13
116 106
73.8 63.1
7.34 7.32
88.0 81.9
95.1 83.1
2.50 2.43
280 279
68.7 63.3
9.1 8.99
s2 - XLj2/20 »
D"
0.04
10.00
10.70
0.02
6.10
12.00
0.07
1.00
5.40
0.11
0.0032
Lb
0.010
.090
.157
.003
.072
.135
.028
.004
.082
.012
"units are mg/kg
bD * absolute pair difference, and L = absolute pair difference of log-transformed data
Table 6 lists typical QA/QC samples and how measurements of these samples are used
in the control of the measurement process and in the evaluation of the quality assurance
procedures employed by the project. To obtain an unbiased measure of the internal consistency
of the samples and their analyses, the individual QA/QC samples should be labeled with a code
number so that the chemist (and preferably also the laboratory) does not know the relationship
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between the samples he is analyzing. This reduces the chances of conscious or unconscious
efforts to improve the apparent consistency of the analyses.
Samples can be split to:
provide samples for both parties in a litigation or potential litigation
situation;
provide a measure of the within-sample variability;
provide materials for spiking in order to test recovery; and
provide a measure of the analytical and extraction errors.
The location of the sample splitting determines the components of variance that are measured
by the split. A split made in the sample bank (i.e., facility to which samples are sent from the
field) measures error introduced from that level onward. A split made in the field includes
errors associated with field handling. A split or series of subsamples made in the laboratory for
extraction purposes measures the extraction error and subsequent analytical errors.
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Table 6. Type of QA/QC Samples or Procedures
Procedure Description
1. Field Blank A sample container filled with distilled, deionized (DDI)
water, exposed during sampling and then analyzed to detect
accidental or incidental contamination.
2. Sample Bank Rinsate A sample (last rinse of DDI water) of DDI water, passed
over the sample preparation apparatus, after cleaning, to
check for residual contamination.
3. Field Rinsate A sample (last rinse of DDI water) of DDI water, passed
over the sampling apparatus after cleaning, to check for
residual contamination.
4. Reagent Blank A DDI water sample analyzed as a routine sample to check
for reagent contamination.
5. Calibration Check Standard A standard material to check instrument calibration.
6. Spiked Extract A separate aliquot of extract to which a known amount of
anatyte is added to check for extract matrix effects on the
recovery of added analyte.
7. Spiked Sample A separate aliquot of the soil sample haying an appropriate
standard reference material added to check for soU and
extract matrix effects on recovery.
8. Total Recoverable A second aliquot of the sample which is analyzed by a more
rigorous method to check the efficacy of the protocol
method.
9. Laboratory Control Standard A sample of a soil standard carried through the analytical
procedure to determine overall method bias.
10. Re-extraction A re-extraction of the residue from the first extraction to
determine extraction efficiency.
(Continued)
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Table 6. Continued
Procedure
Description
11. Split Extract
12. Triplicate Samples (Splits)
13. Duplicate Sample
14. Field Audit
15. External Laboratory Audit
16. Internal Laboratory Audit
An additional aliquot of the extract which is analyzed to
check injection and instrument reproducibility.
The prepared sample is split into three portions to provide
blind duplicates for the analytical laboratory and a third
replicate for the referee laboratory to determine
interlaboratory precision.
An additional sample taken near the field sample to
determine total within-batch measurement error.
A sample of well-characterized soil that is taken into the
field with the sampling crew, sent through the sample bank
to the laboratory with the field samples to detect bias in the
entire measurement process and to determine batch to batch
variability.
A sample of well-characterized soil sent directly to the
laboratory for analysis. The analyte concentrations are
unknown to the laboratory. This type of sample is used to
estimate laboratory bias and batch-to-batch variability. It
may also be used for external quality control of the
laboratory.
A sample of well-characterized soil, whose analyte
concentrations are known to the laboratory, to be used for
internal laboratory quality control.
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Spiked samples are prepared by adding a known amount of reference chemical to one of a
pair of split samples. Comparing the results of the analysis of a spiked member to that of the
non-spiked member of the split measures spike recovery and provides a measure of the
analytical bias. Spiked samples are difficult to prepare with soil material itself. Frequently the
spike solution is added to the extract of the soil sample. This avoids the problem of mixing, but
does not provide a measure of the interaction of the chemicals in the soil with the spike, neither
does it provide an evaluation of the extraction efficiency.
Blanks and rinsates provide a measure of various cross-contamination sources, background
levels in the reagents, decontamination efficiency, and other potential error that can be
introduced from sources other than the sample. For example, a field blank measures input
from contaminated dust or air into the sample. A rinsate sample measures any chemical that
may have been on the sampling tools after the decontamination process is completed.
A question that frequently arises is how many QA/QC samples of each type are needed in
a study. One often sees rules of thumb such as one for every 20 field samples. However, such
rules of thumb are oversimplifications and should be treated with great caution. A better
approach is to determine how each type of QA/QC sample is to be employed and then
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determine the number for that type based on the use. For example, field duplicates are used to
estimate the combined variance contribution of several sources of variation. Hence, the
number of field duplicates to be obtained in the study should be dictated by how precise one
wants that estimate of the variance to be. The precision of an estimate of the variance depends
on the degrees of freedom (i.e., number of duplicate pairs) of the estimate. Table 7 gives the
95% confidence intervals for various numbers of degrees of freedom, based on an assumption
that the data are, or have been transformed to, normally distributed data. Methods for
obtaining such confidence intervals for any number of degrees of freedom are given in most
elementary statistics texts.
Table 7. Some 95 Percent Confidence Intervals for Variance
Degrees of Freedom Confidence Interval
2 0.27s2* a2* 39.21s2
5 0.39s2 * 02* 6.02s2
10 0.49s2 * 02* 3.08s2
20 0.58s2* a2* 2.08s2
50 0.70s2* a2* 1.61s2
100 0.77s2 * a2 $ 1.35s2
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If it is decided that 20 degrees of freedom gives satisfactory precision for the estimate of
the variance, one might equally space the duplicate samples among the field samples so as to
have 20 duplicates by the end of the survey. Alternatively, one might take duplicate samples at
a fairly high frequency at the start of the survey until 10 duplicate pairs are obtained and then
obtain the remaining ten duplicate pairs at a reduced rate over the remainder of the survey.
This second procedure would allow an early estimate of the variance based on 10 degrees of
freedom to determine whether the QA plan is resulting in error variances in the range
expected, and the remaining ten pairs would allow the after-survey variance estimate to take the
entire survey into account.
Some types of samples, such as the calibration check standards, are used to provide a
quality control function. That is, if measurements of these check standards differ by too much
from their reference values, the instrument is declared out of control and will have to be
adjusted. Then it will be necessary to go back and re-analyze all samples between the last
in-control reading and the out-of-control reading. The frequency of use of samples of this
quality control type should be based on costs of the analyses of these samples versus the costs of
reanalyzing field samples in out-of-control situations. This frequency of use will also be a
function of the probability of obtaining an out-of-control situation in the laboratory. Of course
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the objective is to minimize expenditures of both time and money while obtaining data of
adequate quality.
The percentage of the total monitoring effort allocated to QA/QC will depend on many
factors including the size of the project, the available knowledge concerning sampling and
analytical procedures, the relationship of environmental risk to pollutant concentration, and the
nearness of action levels to method detection limits. Typically the smaller the project, the
larger will be the proportion of cost allocated to QA/QC. New, untried procedures will
typically require pilot study runs and additional training for personnel. If the action level is
near the method detection limit, there will be little room for error in the measurements, and
the QA/QC effort may have to be large to assure that measurement errors are kept small. One
should not specify a certain percentage of a project's costs to QA/QC without considering the
above factors.
BIAS
Bias identifies a systematic component of error that causes the mean value of the sample
data to be either consistently higher or consistently lower than the true mean value. Bias may
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be caused by faults in sampling design, sampling procedure, handling procedure, or analytical
procedure. An example of a bias would be the error in analytical results introduced by an
instrument's being out of calibration during a portion of the analysis. Laboratories usually
introduce reference and audit samples into their sample load to detect possible changes. Bias
in soil sampling is difficult to detect. The presence of a bias can be proven by the technique
described as standard additions or by using audit samples. On the other hand, it is difficult to
prove that bias is not present because an apparent lack of bias may be the result of an inability
to measure it rather than its actual absence.
A procedure called standard additions is commonly used to detect bias in a sampling effort.
In this procedure, known amounts of standard solutions are added to aliquots of soil samples.
It is recommended that this be done in the field or in a field laboratory. The main problem
encountered is that mixing soils to obtain homogeneity is difficult in a laboratory, and even
more so in the field. Several known quantities of the standard are added to the aliquots of the
soil samples. The analytical results should follow a straight line:
y » a + bx,
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where x is the increase in concentration caused by the addition and y is the value obtained by
the laboratory. Bias is indicated if the data do not follow a straight line, or if a < 0. If the units
of x and y are the same, the value of b should be near one, and a significant deviation from one
would indicate a proportional bias.
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CHAPTER 10
SAMPLE DESIGN AND DATA ANALYSIS
Data obtained from soil sampling is used to estimate characteristics of the sampled
population, such as pollutant concentrations on action supports at various locations on the study
site and mean concentrations of background regions. There are three basic approaches for
increasing the precision of statistical estimates and the power of statistical tests to be based on
survey results. They are: (1) to use more efficient statistical estimators and tests; (2) to
improve the sampling design; and (3) to increase the sample size (i.e., to increase the sampling
density). This chapter deals with the influence of sample design and estimation techniques on
the variance of estimates and the power of tests, with determination of required sampling
density, and with statistical analysis of survey data.
SAMPLE DESIGN
Given a site to be sampled, several decisions must be made as to how the soil will be
sampled. First, the support for the sampling unit must be specified, then decisions concerning
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type of sampling design and sampling density must be made. The prime objectives of a
statistical sampling design are either to provide the most complete information possible about a
question of interest for a fixed survey cost or to minimize survey cost for a fixed amount of
information. A common measure of the amount of information provided by a survey about an
estimated parameter is the inverse of the variance of the estimate. Secondary concerns in
sampling design are simplicity of resulting data analysis and simplicity of field operations in
performing the survey.
A common type of design given in many elementary texts is the simple random sample
design in which the sampling units are determined by random selection without replacement.
This plan for site investigation simplifies the statistical analysis; however, it is typically very
wasteful of resources and is, therefore, very difficult to justify.
The stratified random design is another common type of design. With this design the
region to be sampled is partitioned into subregions (strata) on the basis of suspected
differences in level of pollutant, on cost of sampling, on the basis of equal strata areas, or on
some combination of the above. A simple random sample is taken from each stratum. For
example, one may have sufficient information to divide the site into strata where the level of
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pollutant concentrations is either far above action level, near action level, or far below action
level. In this case, it would seem reasonable to expend most of the sampling effort (i.e., high
sample density) on the strata that is near action level, so that one can decide with a high level of
accuracy which parcels of land need remediation and which do not. Stratification ensures that
all subregions of the site will be sampled, which may not be the case with a simple random
sample of the site.
Stratification can use scientific or historical knowledge that the pollutant concentrations
are quite different in identifiable segments of the area being sampled to improve the
subsequent estimate of the mean concentration over the entire site. Another criterion that may
be useful in stratification for environmental soil sampling is distance from known point sources.
Both stratified random sample and simple random sample procedures were developed
for sampling of discrete sampling units and do not adequately take into account the spatial
continuity and spatial correlation of soil properties. Samples taken at locations that are close
together tend to give redundant information and are therefore wasteful of resources. For this
reason, some type of sample selection grid (systematic design) is often used to assure that
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sample locations will not be close to one another. The grid may be radial, triangular,
rectangular, hexagonal, etc.
Systematic grid designs provide many of the advantages of stratification plus the
avoidance of redundant samples. They thereby improve precision and power. Investigations of
the efficiencies of the grid designs show that the hexagonal grid is the most efficient given
certain assumptions about the spatial distribution of the pollutant, but the square or rectangular
grid is easier to use in practice. The difference in efficiency is not great. The radial grid has
some advantages in investigating the distribution of a pollutant near a point source.
A grid will typically be oriented in the direction of flow of the pollutant, which may
relate to site topography or a wind rose. Once the sampling density (grid spacing) and the
orientation of the grid has been determined, a selection of one sample location will completely
determine the locations of all sample locations.
A possible shortcoming of the grid design is the possibility of a periodicity in the
pollutant concentrations, with the grid spacing a multiple of the period. This is an extremely
unlikely situation in pollution studies, but one way to guard against this possibility is to
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superimpose a small stratified random sample over the grid design. In this case the strata
would be subregions of approximately equal area. In practice, even when a grid (systematic)
design is employed, many of the actual sample locations will not be at the grid locations because
of the presence of obstructions such as roads, houses, rocks, and trees. Also, a soil sample
should not be taken at a specified grid point if it is evident that fill has recently been added or if
there has been a recent grade cut at the location. When the field crew cannot sample at a
specified location, they should have instructions to take a sample at the nearest point in a
prespecified direction from the original point where a sample can be obtained, provided that
the location is within a specified distance (usually less than half the grid spacing) of the original
point.
Simple random sampling and stratified random sampling designs are among a class of
designs originally developed for the sampling of units that are discrete objects such as people,
houses, and retail stores. The statistical analysis techniques associated with these designs are
primarily associated with the estimation of population means. The basic designs and statistical
procedures associated with surveys of discrete objects are given in a text by Hansen et al.,
(1953).
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The systematic grid designs are more closely related to the sampling of continuous
media such as soil, air, and sediment. In the sampling of continuous media, the sampling units
must be defined in terms of support (Chapter 5). Gy (1982) gives an extensive description of
techniques for the sampling of the continuous media of participate materials. The statistical
analyses associated with the results of these surveys of continuous media are typically aimed at
estimating the spatial distribution of a property of the media such as a pollutant concentration
or in finding "hot spots" within the region or site being sampled.
Many of the statistical techniques used in the analysis of data from surveys of
continuous media fall into a category called geostatistics. For sample surveys involving random
selection of sampling units, the statistical procedures are usually formed on a probability base
provided by the randomization, while in geostatistics, the statistical inferences are based on
what is known as a random field model A good discussion of the nature and differences of
these two approaches is given in a paper by Bergman and Quimby (1988).
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ROLE OF QUALITY ASSURANCE IN SAMPLE DESIGN
The Quality Assurance Officer should be involved in reviewing the sampling design
proposed by the investigator. He or she should require that the information obtained provides
measures of the components of variance that are identified in the field. An additional quality
check that should be undertaken as part of the QA program is the review of the design by
qualified soil scientists and other peers who are in a position to provide the necessary oversight
of the sampling effort.
Broms (1980) makes the following statement: There should be a balance between the
soil investigation method, the quality of the soil samples, and the care and skill spent on the
preparation and testing of the samples. There is no point in spending time and money on
careful sample preparation and testing if the quality of the samples is poor." The QA program
must address the total flow of information from the design to the reporting of results. The
sampling design is the foundation of the whole study; therefore, it must be given careful
consideration if the purposes and data quality objectives of the sampling effort are to be met.
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Compositing of Samples
From the point of view of geostatistics, it is desirable to have a sample support that has
a fixed depth and a square horizontal cross-section, because such supports can be grouped
together to form rectangular blocks and action supports, and potential clean-up areas (e.g., city
lots) are typically rectangular. However, to sample such a square support of sufficient
cross-sectional area to make its short-range variance small requires the taking of a very large
quantity of soil It is difficult to handle large quantities of soil and make them homogeneous
prior to subsampling. One way to avoid this problem is to take a uniform array of soil cores
within the square in sufficient number so that the error variance associated with the true
differences between the average pollutant concentration of the squares and that of the
associated composites of the cores is quite small relative to the short-range variance of
pollutant concentrations of the square supports. This procedure is explained in detail in Starks
(1986).
While compositing of cores at individual sampling sites can be quite advantageous in
terms of handling costs and measurement errors, the compositing of samples from different
sampling locations should be done with great caution if at all. The compositing of samples
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technique is often performed to reduce sample handling and analytical costs. This procedure is
used extensively by agricultural workers to determine fertilizer requirements for farm fields. It
is also done in medical studies to screen blood samples for relatively rare antibodies. Peterson
and Calvin (1965) make the following statement about the technique:
"It should be pointed out that the composite samples provide only an estimate of the
mean of the population from which the samples forming the composite are drawn.
No estimate of the variance of the mean, and hence, the precision with which the
mean is estimated can be obtained from a composite of the samples. It is not
sufficient to analyze two or more subsamples from the same composite to obtain an
estimate of the variation within the population. Such a procedure would permit the
estimation of the variation among subsamples within the composite, but not the
variation among samples in the field. Similarly, if composites are formed from
samples within different parts of a population, the variability among the parts, but
not the variability within the parts, can be estimated. If an estimate of the variability
among sampled units within the population is required, two or more samples taken
at random within the population must be analyzed separately."
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Youden and Steiner (1975) caution against the use of the composite sample for many of
the same reasons as those outlined above. Since the prime purpose of QA/QC is to assess and
assure acceptable values for the bias and the precision of the data and of estimates obtained
from the data, it is essential to be able to gauge the precision of the data. Therefore, the
compositing of samples cannot, in general, be recommended unless it is for a stated specific
purpose and unless a justification is provided.
Some work on determining the precision of estimates of the mean from composite
samples has been published. Such estimates of precision usually require strong assumptions
about variance components and/or the stochastic nature of the composited samples. (See
Duncan, 1962, and Elder et al., 1980.)
GEOSTATISTICS
Geostatistics is an application of classical statistical theory to geological measurements
that takes into account the spatial continuities of geological variables in estimating the
distribution of variables. In many ways, geostatistics is for measurements taken in 2-, 3-, and
4-dimensional space (the three spatial dimensions and the time dimension), what time series is
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for measurements taken in one-dimensional space time. However, a principal use of time
series is in forecasting; in geostatistics, the principal emphasis is on interpolation. Nevertheless,
both statistical procedures emphasize modeling the process to get an insight into the system
being investigated.
For purposes of discussion, consider sampling units that have a support that is a volume
with a square horizontal cross-section (s x s) and a fixed depth d for a total volume of ds2. It
will be assumed that the correlation (and covariance) between a measurement of a sampling
unit and that of any other sampling unit is strictly a function of the distance between the units.
(It is important to remember that the population of all the sampling units is the volume of soil
of interest in the site under investigation and that the soil samples taken in the survey form a
proper subset of the population of sampling units.)
A geostatistical estimation procedure called block kriging (named after a South African
mining engineer named D.G. Krige) is employed to estimate the mean pollutant concentration
in a rectangular block of sampling units. The estimate of the mean concentration is a linear
combination 2a^, of the concentration measurements z. obtained at sample locations on or near
the block. The coefficients a. are chosen to minimize, subject to certain constraints, the
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estimation variance V(M* - M) where M* is the estimator of the mean block concentration and
M is the mean block concentration.
V(M* - M) = 22ajajCov(Zi,ZJ) + ZZCov(Ch,Ck) - 2ai2Cov(Zi,Ch)
where Cov stands for covariance, Z; is the random variable corresponding to measurement z;,
and Ch is the concentration of pollutant in sampling unit h in the block. The calculation of the
coefficients a; that minimize the estimation variance is a simple mathematical procedure
involving the solution of a system of linear equations subject to a set of linear constraints on the
ar Once the a{ are calculated, the estimate of the mean concentration and the variance for that
estimator are found directly by substitution in the above equations. The constraints imposed on
the coefficients a: depend on how the expected value of Z{ is related to the location of the
sampling unit L The relationship between location and expected value is called drift in the
geostatistical literature. If the expected value of Z. is independent of the location of the
sampling unit, one says that there is no drift. The nature of the drift and of the covariance
function taken together are sometimes referred to as the spatial structure of the phenomenon
being measured Typically, the spatial structure will have to be estimated from the data, but the
nature of the phenomenon being measured will usually provide basic information as to which
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spatial structures are reasonable and which are not. For example, in measuring the pollution
emanating from a point source, it would seem reasonable to expect drift to be present; that is,
one would expect to find higher concentrations of the pollutant close to the source than at a
greater distance.
One of the major problems in using these geostatistical procedures is in the estimation
of the covariance as a function of distance. It must be estimated from the data. The several
ways of doing this, unfortunately, will typically give quite different answers. It is essential that
the assumptions about drift used in obtaining an estimate of the covariance function be
consistent with the realities of the phenomenon being measured and that the estimated
covariance function be checked against the data for goodness of fit. This check against the data
is done by a process called cross-validation. Cross-validation in this instance consists of kriging
to obtain an estimate of the concentration at each sample location based on data from
neighboring sample locations. The observed measurement at that location is then subtracted,
and this difference is divided by the square-root of the estimation variance to obtain a
standardized score. This is done for all sample locations, and the sample variance of the
standardized scores is obtained. This sample variance should be close to one. (Starks and Fang
[1982] also conjectured that the standardized scores should have an approximately normal
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distribution.) One of the problems faced by users of geostatistics is that there are a large
number of software packages on the market that will take the data and do the kriging without
any consideration of whether the assumptions implicit in the package procedure are correct and
without any cross-validation of the results.
It should be pointed out that because kriging obtains estimates by assigning larger
weights to nearby sample location measurements and smaller weights to those more distant, the
estimations of pollutant concentrations are quite similar over a wide range of covariance
functions that might be employed. However, in quality assurance, one is also interested in
estimating the precision of the concentration estimates, and here is where the trouble lies. Bad
estimates of covariance functions will usually lead to bad estimates of the precision of the
kriging estimates. Bad estimates of precision in the exploratory study can, in turn, lead to
inaccurate estimates of the number of samples needed in the definitive study.
Once an acceptable estimate of the covariance function has been found from the
exploratory study, an acceptable spacing for sampling locations on a square grid can be
determined by use of the kriging procedure. The estimation variance in block kriging is
determined solely by the covariance function, the spacing of the sample locations, and the
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position of the block relative to the sample points. A block in the center of the square formed
by four points in the sample location grid will have a larger estimation variance associated with
it than will any other block of equal size located within the array of sampling locations. Hence,
one can pick a grid spacing distance, arbitrarily assign values to the sample locations, perform
the block kriging on a block at the center of a square, and find the maximum estimation
variance for that block size. By trial and error, one can quickly find a grid spacing that gives a
maximum estimation variance that is sufficiently small to be in accord with precision
requirements of the quality assurance plan (i.e., satisfies data quality objectives).
WARNING: Kriging is a good procedure for interpolation, but a bad procedure for
extrapolation. Do not give credence to block kriging estimates for locations that are beyond the
range of the sample locations.
OBJECTIVES
In all the operational situations listed in Chapter 7, preliminary site investigations,
emergency cleanup operations, planned removal operations, remedial response operations,
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monitoring, and research or technology transfer studies, one or all of the following questions
will be of primary interest.
Are there any action supports (see Chapter 5) within the study area that have
pollutant concentrations above action level concentration?
Where are the above-action-level action supports located?
What is the spatial distribution of pollutant concentration levels among action
supports that have pollutant concentrations above action level?
In many situations, the answer to the first question is known from previous studies. But if it is
not known, one needs to plan the sample survey in such a way as to be reasonably sure that
there are no action supports with pollutant concentrations above action level if none of the
samples in the survey has a measured concentration above action level The statistical
procedures for "hot spot" detection that are used in such planning are discussed in a subsequent
paragraph. The procedures for answering the other two questions were discussed earlier in the
section on geostatistics. No elaborate (or simple) tests of hypotheses are required. If no
samples show concentrations above action level, no remedial action is called for. If, however, a
sample with proper support, which is considered reliable because of an excellent QA/QC
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program being in place, is obtained that has a pollutant concentration above the action level,
then remedial action is called for in the neighborhood of that sample.
The problem with posing soil sampling methods and objectives in terms of population
means is that the mean will depend on the area chosen. If one chooses a small area near a
point source, the mean may exceed the action level; but if one increases the area so that it
contains a region that is not contaminated or that is only lightly contaminated by the point
source, the mean may not exceed the action limit. Decisions on the need for remedial action
should not be based on how one chooses the size of the area to be sampled, but rather on whether
action supports exist that are above designated action limits. About the only place where a
comparison of means seems reasonable is in comparing the pollutant concentrations at a
background (up-gradient) site with the pollutant concentrations of a site down-gradient from a
suspected point source. Also, clean-up areas may be defined so that the average concentration
in those units of soil must be compared with a standard.
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DESIGN FOR HOT SPOT DETECTION
As stated earlier in this chapter, one of the primary questions in many environmental
monitoring situations is whether there are any action supports in the study area in which the
pollutant concentration exceeds the action level concentration. (We shall call an action support
in which the pollutant concentration exceeds the action level a hot spot.) If this is a primary
question in a study, then subsequent questions in the planning of the sampling design are:
What is the probability that a sample will detect a hot spot? and
What is the probability that a hot spot exists when no hot spot is found in the
sampling?
The procedures for addressing these design problems are discussed in more detail by Gilbert
(1987).
The assumptions that will be made in this discussion are the following:
(1) the hot spot is circular in horizontal cross-section;
(2) samples are taken on a square grid;
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(3) the distance between grid points is much larger than the sample support
diameter;
(4) there are no measurement misclassification errors (i.e., if a sample comes from
a hot spot, the measured pollutant concentration in the sample will exceed
action level; and, if the sample is not of a hot spot, the measured pollutant
concentration will be below action level); and
(5) either the hot spot or an initial point in defining the sampling grid is randomly
located within the site.
(Gilbert [1987] allows elliptical hot spots and rectangular or triangular grids In his discussion.)
Let R represent the radius of a hot spot and D be the distance between adjacent grid
points where samples will be collected. The probability that a grid point will fall on a hot spot is
easily obtained from a geometrical argument since at least one grid point must fall in any
square of area D2 centered at the center of the hot spot. From this concept, it follows that the
probability of sampling a hot spot is given by
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P(H) = (wR2) / D2 ifR^D/2
= {R2[* 2 arc cos (D/(2R))] + (D/4),/(4R2 - D2)}/!)2 if D/2 < R < DJ2/2
= 1 if R £ DJ2/2
where the angle whose cosine is D/(2R) is expressed in radian measure. If the grid spacing is
taken to be D = 2R, the probability of a hit is ff/4 = 0.785, which implies that the probability
that this grid spacing would not hit a hot spot if it exists is 0.215.
The second question concerning the probability that no hot spot exists (given that none
was found) requires the use of a subjective probability, P(E), based on historical and perhaps
geophysical evidence of the existence of a hot spot on the site. Then, if E is the event that there
are no hot spots at the study site and if R is the event that no hot spot is sampled in the survey,
Bayes formula gives
P(E | H) - P(R | E) P(E) / [ P(H | E)P(E) + P(R |B)P(E)]
- P(H| E) P(E) / [P(H| E)P(E) + P(E)].
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For the case where D = 2R, it was found that P(H | E) = 0.215, so if one is given that the
chance P(E) of a hot spot is thought to be 0.25 prior to the investigation, the probability of a hot
spot existing if the study does not find a hot spot is
P(E | no hit) « 0.215 (0.25) / [0.215 (0.25) + 0.75] = 0.067.
Hence, the probability that no hot spot exists is (1 - 0.067) = 0.933.
SOME CLASSICAL STATISTICAL PROCEDURES
In this section, classical tests of hypotheses, confidence intervals, and prediction
intervals based on the Student's t-distribution will be discussed. While these procedures do not
apply to the three primary questions listed above concerning the existence and location of
action supports above action level, they may be useful in comparing pollutant concentrations in
regions up-gradient and down-gradient from a possible point source. It should also be pointed
out that these procedures are only applicable to random samples (i.e., not to systematic grid
samples), and great care is required in using them for anything other than simple random
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samples. The basic assumptions of these procedures are that the data in a sample are
independent and identically distributed (with the distribution being a normal distribution) and
that the measurement error variance (particularly the between-batch error variance) is a very
small part of the total variance of the measurements in a sample survey of a region.
Confidence Intervals
Often one wishes to estimate the concentration of measured pollutant over an action
support or over a larger subregion of a study area and to indicate the precision of the estimated
concentration. The precision may be indicated by a variance, standard deviation, coefficient of
variation, or confidence interval for the expected value (mean) H of the concentration. Where
statistical designs involving randomization in the selection of all sample points are employed,
the analysis of variance table (see example in Chapter 9) often provides needed information for
the calculation of these quantities and intervals.
The confidence interval is bounded by confidence limits which represent the bounds of
the uncertainty caused by the variability of the data in the study. A two-tailed confidence
interval for /i based on the assumptions stated above is of the form
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x-ts/ymi /i
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is (
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3.16 * M <, 3.70.
Prediction Intervals
Prediction intervals (see Hahn, 1969, or Guttman et aL, 1982) are similar to confidence
intervals in appearance but are used to give an interval estimate of one future randomly chosen
sample value. If that one additional sample is to be taken from stratum i, the defining
two-tailed interval for the one future value, xtf (say), is
X; - ts/[(l/n)+(l/bn)]* x.rf * x, + ts7[(l/n)+(l/bn)],
where Xj is the sample mean for stratum L Hence, one can say for the above example that if one
more sample were randomly taken from stratum 1 (which had sample mean 2.46), one would be
95 percent confident that the mean of the analyses of the two subsamples of that sample would
give a value xtf such that
2.46-(2.306)(y0.3199)y[(l/2) + (1/6)] i x.,, i 2.46 + (2.306)(70.3199)y[(l/2) + (1/6)],
which is
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One-tailed confidence and prediction intervals can be obtained using the same methods,
only leaving off the bound on one side and using the "one-tailed" heading on the t-table in
Appendix B.
Tests of Hypotheses
Probably the most commonly used test of hypotheses for comparison between two
population means or the comparison of a population mean with some standard value (e.g.,
action level) is a t-test. To compare two means, jij and /I2, using data from simple random
samples of the two populations, the following test statistic is employed:
t, = £
where the pooled standard deviation,
»p « yKOV1)'!2 + (n2-l)s22}/(n1+n2-2)]
and x j, s?, and n. are the sample mean, sample variance, and sample size of sample i (i= 1,2).
This two-sample t-test requires one additional assumption to the ones mentioned earlier;
namely, it is assumed that the population variance is the same for both populations sampled.
The test is of the null (no difference) hypothesis H: Mj = H2 versus either the two-tailed
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alternative A: /it ^ Hy or a one-tailed alternative such as A: ^ > ny For the two-tailed
alternative, one accepts the alternative hypothesis only if | t§ | > t, where t is the value found in
the table of Appendix B and listed in the (1-cr) column, for two-tailed alternatives, and in the
(nj+nj-2) degrees of freedom (df) row. For the one-tail alternative, one accepts the alternative
hypothesis only if ts > t, where the value t is again found as before, only now use the (1-or)
column for one-tailed tests.
The need to use a one-sample t-test which compares a population mean against a
standard value may arise in determining whether the mean concentration of a pollutant in a
study area or clean-up unit of a study area exceeds a specified action level. The test statistic for
this test is
tc»(x-L)(yn)/s,
where L is the action level, and n is the sample size. One- and two-tailed tests of H: M = L
versus A: p + L or A: n > L, are performed in the same way as described for the two-sample
tests, except now the degrees of freedom are (n-1) for the one-sample tests.
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Example: A preliminary study is done in an area suspected of being
contaminated with polychlorinated biphenyls (PCBs). Sixteen soil samples
were collected from both the study area and from a background area through
the use of simple random sampling. It was decided before sampling that a
t-test of H: /is » /ig versus A: ps > /ig will be performed on the data and that
the probability of making a Type I error (i.e., accepting A when H is true) will
be limited to 1 percent (a - 0.01). Table 8 lists the concentration
measurements.
TABLES. PCB MEASUREMENTS (HYPOTHETICALDATA)
Background Area (ppb)
35.8 38.5
45.5 36.0
35.5 40.5
32.0 35.5
50.0 45.5
39.0 37.0
37.0 36.0
47.0 53.0
XB- 40.23 4s36-8825
*s - 52.61 s* - 60.2598
Study Area (ppb)
47.0 50.0
62.0 49.6
47.0 53.5
59.5 68.0
40.0 60.0
57.5 45.0
48.5 42.5
53.0 58.7
nB - 16 CVB -
n, - 16 CVS -
15.1%
14.8%
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The test statistic is calculated as follows:
sp - /[15(36.8825 + 60.2598)/(16 + 16-2)] = 6.97
tg - [52.61 - 40.23]/[6.9?y(2/16)] = 5.02
The critical value for t for an a * 0.01, one- tailed t-test with 30 degrees of
freedom is found in Appendix B to be 2.457. The observed value of the test
statistic 5.02 is larger than the critical value, so it would be concluded that the
mean concentration for PCB is larger in the study area than in the
background area. A one-sided 99 percent confidence interval for /J$ - /Jg is
"s "B > *s *B ' MO/IS) + (1/nJ] - 6.28 ppb.
One might also wish to test whether the mean concentration of the
study site is above an action level of 50 ppb. Now one uses a one-tailed, one
sample t-test of H: Hs <, 50 versus A: Hs > 50. Here the maximum possible
probability of making a Type I error is set at 5 percent (a = 0-05) for
illustrative purposes. The test statistic takes the value
tc - (52.61 - 50.00)(/16)//60.2598 = 1.34.
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The critical value found from the table in Appendix B for a one-tailed test
with or = 0.05 and 15 degrees of freedom is 1.753. Since the value of the test
statistic is less than the critical value, one does not accept the alternative
hypothesis. Here one must worry whether the alternative hypothesis might
have been accepted if more samples had been taken. That is, has a Type n
error been committed in this case for lack of sufficient information?
In the use of one-tailed t-tests and confidence intervals such as those illustrated in the
above example, one needs to worry about the assumption of normal distribution for the data
and the equality of population variances. While two-tailed tests are relatively robust with
respect to these assumptions, one-tailed tests are not. Unfortunately, as has been pointed out
before, the underlying distribution of the population of pollutant concentrations can be quite
nonnormal and also difficult to transform to normality. Further, there is no reason to expect
the population variances to be equal in two different regions. To avoid this problem in
one-tailed procedures, one may prefer to employ rank tests (see Lehmann, 1975) that do not
require distribution assumptions.
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CHAPTER 11
SAMPLE DOCUMENTATION, COLLECTION AND PREPARATION
INTRODUCTION
An important segment of a study's QA/QC plan deals with sample documentation,
collection, and preparation methods. These aspects of the definitive study must be identified
and appropriately applied if the specific objectives of the sampling/monitoring effort are to be
met. Improperly collected and documented samples can void the entire study. As such, the
final protocol must provide guidance and identify sample collection and handling methods,
equipment requirements, sampling locations, documentation requirements, sample compositing
requirements and methods, and the depth or depths that will be sampled.
The authors recommend that the RPM or investigators be able to estimate the
components of variance or error associated with each element of the sample collection and
preparation methods and procedures used from the data generated by the study. Evidence
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from the exploratory study pertinent to this estimation process should be taken into
consideration. It is recommended that a minimum adequate documentation and sample
methodology approach be selected consistent with the objectives of the study, the resources
available and the designated levels of precision and confidence. It is important that criteria or
procedures for determining, during and after the fact, whether or not the sample
collection/preparation elements of the protocol were satisfactorily achieved. Guidance for
selecting, incorporating, assessing, and interpreting sampling QA/QC data is presented in
Chapter 12.
The recommendations and guidance presented in this chapter are general in nature.
However, recommended detailed procedures and methods addressing and identifying
documentation, soil sample collection methods, and soil sample preparation methods are
presented in a number of reports including the following working protocols and guidance
documents:
Documentation of EMSL-LV Contribution to Dallas Lead Study
U.S. EPA EPA-600/4-84-012 1984. Las Vegas, Nevada
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Sampling for Hazardous Materials U.S. EPA OERR, Environmental Response
Team. Washington, D.C.
The Environmental Survey Manual U.S. DOE Volumes 1-4 DOE/EH-0053 1989.
Washington, D.C.
Refuge Contaminant Monitoring Operations Manual. Soil Sampling Reference
Field Methods U.S. FWS. 1988. Prepared by USDOE/INEL/EG&G, Idaho Falls,
Idaho.
Preparation of Soil Sampling Protocol: Techniques and Strategies. U.S. EPA
EPA-600/4-83-020 1983. Las Vegas, Nevada
National Enforcement Investigations Center Policies and Procedures. U.S. EPA
NEIC EPA-330/9-78-001-R 1986. Denver, Colorado
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Refuge Contaminant Monitoring Operations Manual. Documentation Guidance
Standard Operating Procedure. U.S. FWS 1989. Prepared by USDOE/
INEL/EG&G, Idaho Falls, Idaho.
DOCUMENTATION
Documentation establishes procedures and identifies written records that must be
incorporated into the operating procedures for sampling/monitoring efforts. Document control
procedures are required for the following three reasons:
Enhances and facilitates sample tracking and the interpretation of sampling and
analytical data.
Standardizes data entries for input into data management systems for efficient
retrieval and data manipulation.
Identifies and establishes the authenticity of data collected for possible remedial
measures.
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The first parameter addresses the need to view sampling and analysis results as a
function of data quality and data application. Knowledge of the circumstances under which the
samples were collected, handled, preserved, transported, and analyzed will play an important
role in how analytical data are used and interpreted.
The second parameter addresses the need for uniformity in data recording, as a number
of sampling teams may be involved in sample collecting and data gathering. As such, a
consistent, standardized documentation program is essential for developing an effective and
efficient data management system. The third parameter addresses the potential for the
adjudication of sampling and analysis results and the associated role that evidentiary
proceedings may play in remedial measures.
Contaminant monitoring from an enforcement remedial perspective will involve
information gathering procedures that are more restrictive on personnel, materials, and
methods than procedures used for many ecological research and/or environmental surveys. As
a result, some protocols previously used for collecting, handling, documenting, and shipping
samples may fail to meet the demands required for contaminant sampling situations.
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For EPA's contaminant sampling/monitoring efforts, record-keeping and documenting
field activities are essential elements of a thorough investigation. A written record of all field
data, samples, observations, and events provide the following:
Ensures that all essential and required information is consistently acquired and
preserved for current use and future reference.
Assures timely, correct, and complete analysis for all parameters being
requested.
Satisfies quality assurance requirements.
Establishes a chain-of-custody record for samples.
Provides evidence in court proceedings.
Provides solid basis for further sampling activities.
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Maintaining standardized records enhances the usability of data necessary for decision
making. Using standard forms also ensures that the same types of information will be recorded
consistently. These records will document and support decisions regarding the existence and
abatement of contaminant problems.
Document Control
Document control is a systematic procedure for ensuring that all sampling/monitoring
program documents are property identified and accounted for during program implementation
and after program completion. Document control encompasses the following:
serialized documents,
document inventory and assignment record, and
document file repository.
Presented in Table 9 are the program documents that are accountable and must be
identified and included in the document control procedure. Also identified are those
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documents that are commonly serialized. For detailed guidance in the selection and use of
appropriate documents, see U.S. EPA (1986), U.S. DOE (1987), and U.S. FWS (1988) (1989).
Because of the complexity and importance of proper document control it may be
advisable to select an individual to oversee and coordinate all document control responsibilities.
The size and magnitude of the sampling effort will be a determining factor in the selection of a
document coordinator. This decision must be made on a case-by-case basis.
TABLE 9. ACCOUNTABLE DOCUMENTS
Document Control Identifiers and Headers Serialized
Sampling plan Sample identification documents
Quality assurance plan Tags
Analytical forms Chain-of-custody documents
Logbooks
Field data records and forms
Shipping forms
Correspondence
Photographs, maps, drawings, etc.
Check-Out logs
Litigation documents
Final report
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A number of documents identified in Table 9 require only the proper program identifier
title and header (e.g., photographs, correspondence). Others, such as project logbooks, chain-
of-custody forms, field data forms, and sample identification documents, require detailed
entries. The header, consisting of the program identifier (title or code), Section, Revision,
Date, and page of should be placed on the upper right-hand corner of each page of
documents such as QA/QC plans and sampling/analytical protocols.
Document inventory will provide document accountability to the appropriate data users
and to those who will use the data results to make decisions. For example, decisions and
actions taken concerning any changes in samples/monitoring methodology or any remedial
measures will be available. All documents should be cataloged, categorized, and have a unique
program identifier that identifies the region, specific site and year sampling/monitoring activity
was conducted.
After the sampling/monitoring program has been completed, all documents generated
should be assembled and stored in a program file or repository. The RPM or his/her designee
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is responsible for ensuring that the collection, assembly, and inventory of all program
documents are completed. This document file repository should have an index identifying all
included program documents and a system that identifies the disposition of and location of all
original and copied documents. This file is considered accountable; therefore, any documents
leaving the repository must be signed out.
The following general guidance will apply to all documents for contaminant
sampling/monitoring program:
Entries made in logbooks, field records and forms, sample labels and tags, and
chain-of-custody documents should be made only with waterproof ink and/or
grease pencils. If lead pencils or other writing instruments are used, note the
reason in the logbook.
Correct errors by drawing a single line through the error and enter the correct
information.
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Initial and date all corrections. (A list of names and initials should be part of
the written record.)
Enter in the logbook the location and disposition of voided documents by
recording their serial number (serialized documents) and/or program header
and identifier information.
Place pre-numbered (serialized), and voided documents in the program file for
accountability.
Use only bound logbooks.
Logbooks
Logbooks are maintained to (1) record, identify and describe all pertinent
sampling/monitoring activities and (2) to record quantitative information for each sample
collected. Included with a contents page for easy reference, the field logbook should also
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address and describe team activities (e.g., activity log) sampling site descriptions and sample
descriptions, including field measurement data (e.g., sample log).
Field Data Records and Forms
A number of sampling/monitoring situations may require the collection of field data
that necessitates the use of specialized field data forms such as profile descriptions, core logs,
and field measurements (e.g., pH, temperature). When specialized forms are used, they must
be included in the Field Logbook or, if more convenient, bound in a separate Field Data
Records and Forms Logbook. If a Field Data Records and Forms Logbook is required, it must
have the appropriate identifier and header and be categorized by sample matrix with an
appropriate table of contents page. This logbook becomes a part of the program file and must
be filled out and handled as previously identified for all program documents.
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Sample Labels and Tags
Sample labels and tags are required for properly identifying samples and evidence. The
data obtained from samples collected for a sampling/monitoring activity may be used for
remedial measures. All samples must be properly labeled and tagged.
It is recommended that physical samples be identified with a label and a tag. Both
sample labels and sample tags must accompany physical samples to the analytical laboratory.
However, while the sample label will be disposed of with the sample, the sample tag must be
kept as a permanent record in the program files. The sample tag should be returned to the
originator and/or the custodian of the program files as physical evidence of sample receipt and
analysis, and may later be introduced as evidence in litigation proceedings.
Chain-of-Custody
Chain-of-Custody (COC) is mandatory in all cases that involve litigation.
Chain-of-Custody records perform three functions: (a) records who has custody of a sample,
(b) identifies who takes possession of a sample when it is transferred, and (c) verifies that a
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sample was constantly under custody between sample collection and laboratory analysis.
According to the U.S. EPA's National Enforcement Investigation Center's (NEIC)
Policies and Procedures (1986), in-situ measurements can be considered and will constitute
evidence. A sample collected from a site for the determination of contaminants can be
considered as physical evidence.
A sample is under custody if:
it is in your possession,
it is in your view, after being in your possession,
it was in your possession and you locked it up, and
it is in a designated secure area.
To establish the integrity of samples, it is necessary to demonstrate that the samples
were maintained under custody from the time they were collected in the field to the time they
were analyzed in the laboratory.
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The Chain-of-Custody Record form must list all transfers in the possession of samples.
(See U.S. EPA (1986), U.S. EPA (1984), U.S. DOE (1987), and U.S. FWS (1989) for guidance.)
Properly used, this piece of documentary evidence will attest that the sample was constantly
under custody between sample collection and laboratory analysis.
While being shipped from the field to the laboratory, samples pass through the hands of
postal clerks, couriers, and others who are unidentified. The samples, however, are effectively
in a secure area. NEIC procedures require that a custody seal be affixed to the shipping
container in such a way that, if the shipping container is properly secured and arrives at the
laboratory with the custody seal intact and with adequate documentation, the integrity of the
samples can be demonstrated.
SAMPLE COLLECTION
Devices for collecting samples must successfully operate in conditions such as sand, silt
and clays in rocky, dry, and wet environments, surface area sampling requirements, depth
requirements, and must be able to collect the required volume. In addition, the sample
collection device should provide the most cost-efficient sample over the total sampling effort.
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U.S. EPA (1983), U.S. EPA (1983a), U.S. EPA (1988) and US. FWS (1988) provide
information on available soil sampling devices and their operational requirements. Soils are
extremely complex and will provide investigators with a multitude of sampling situations. As a
result, no single sampling method can be recommended. Sampling personnel will have to select
the method that will best accommodate their sampling needs and that will satisfy the stated
program objectives. Sampling devices must be carefully cleaned prior to and between each
sample to avoid cross contamination. Suggested cleaning or decontamination procedures are
presented in U.S. DOE (1987), U.S. FWS (1989a) and in U.S. EPA (1982) and (1984).
Frequency of Sampling
Frequency of sampling depends on program objectives, sources of pollution, pollutants
of interest, transport rates, and disappearance rates (physical, chemical or biological
transformations, as well as dilution or the determination of dispersion). Sampling frequency
may be related to changes over time, season, or precipitation. Normally little information will
be obtained on sampling frequency from the exploratory study, but in those cases where
temporal changes are expected, the final study should address sampling frequency in the design
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and in the selection of sampling devices. It is not uncommon for many definitive studies to be
conducted over a period of one year or more or through cycles of wet and dry environments.
Rapid changes expected in the concentration of pollutants in soil are normally
associated with precipitation. Precipitation may influence the movement of chemical pollutants
downward and aids in decomposition. Sampling frequency associated with either major rainfall
events or with accumulated amounts of rainfall can often provide valuable information on
changes that are occurring.
Monitoring studies are often designed to measure the effects of some remedial measure
on the site. Trends are important in these cases. The frequency of sampling should be
designed to measure changes, e.g., efficiency of remedial measures. One approach used
successfully has been to provide intensive initial sampling early, then decrease sampling
frequency as the levels begin to drop. One recommended procedure would be to sample
monthly for the first year, quarterly for the second year, semiannually for the next two to three
years, then annually thereafter.
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Evaluation of the trend of the data should allow the RPM to determine when the
sampling frequency can be reduced or halted completely. Monthly sampling may provide the
needed data for performing statistical tests and for determining the yearly variation.
Samples collected for evaluating trends can usually be obtained on some subset of the
initial year's sampling. The major focus is mainly on the highly contaminated and on the
immediately adjacent areas. The investigator is primarily interested in detecting changes in
these adjacent areas in order to provide early warning of the efficacy of remedial measures.
SAMPLE PREPARATION
Sample preparation encompasses all physical handling of sample(s) following the actual
collection. This includes, but is not limited to the following:
transfer from the collecting device,
sieving/mixing procedures,
drying methods and procedures,
selecting and using containers,
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preservation,
archiving (storage), and
transportation and shipment.
It is inappropriate to initiate a sampling effort without first becoming familiar with
sample preparation requirements. For example, it is not recommended to dry and sieve
samples that are collected for the determination of volatile contaminants. Collecting samples
that cannot be suitably analyzed will not yield high quality decision-making data, thereby
compromising achievement of the sampling/monitoring objectives.
In addition to the protocols and guidance documents previously identified,
recommended soil sample preparation methods for different contaminant analyses are
presented in U.S. EPA (1986a), U.S. EPA (1989), OSU (1971), and by Peterson and Calvin
(1965).
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Sieving/Mixing
Note: Sieving and mixing can only be carried out on soils containing pollutants with
little or no tendency to vaporize. This requires that the techniques discussed below not be used
for volatile pollutants.
Analytical methods that are used by the U.S. EPA to analyze soils have been validated
with a prescribed sample volume and a specified particle size. As such, for the best analytical
results, the analyst must be provided with a sample that is commensurate with the analytical
requirements. The responsibility for providing the appropriate sample for analysis lies with
sampling personnel. This responsibility also includes the requirement to prepare (e.g., mix and
sieve) soil samples in a prescribed manner to provide a representative sample from the total
soil material collected. For example, when single- or multiple-sample cores are collected for
compositing, it is recommended that the samples be prepared before they are shipped to the
analytical laboratory.
Soil sieving/mixing sample preparation methods must satisfy the following
requirements:
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provide the specified amount of material,
provide a representative aliquot of the total sample collected, and
provide an adequate and appropriate sample to enable analyses for the required
contaminants (e.g., volatiles, semivolatiles, metals).
It is extremely rare to collect soil that does not contain non-soil components (e.g., rocks,
non-mineral material). Also, in many soils organic matter is commonly found and is an integral
part of the soil matrix. Both the non-soil components and the organic matter may play an
important role in the interpretation of the analytical data (e.g., the non-soil components may be
a source of contamination to the soil matrix).
The potential for errors being introduced in the sample sieving and mixing procedures is
high, especially involving discarded non-soil or non-sieved material, as well as possible physical
and/or chemical losses during any grinding or drying operation. Decisions concerning the
non-soil fraction may be made on the basis of data obtained from an exploratory study.
Available data may indicate that significant contamination is in the discarded portion. If so, it
is recommended that the discarded portion from ten percent of the samples collected from
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areas having the highest concentrations be analyzed. An estimate can then be made of the total
amount of contamination being discarded by multiplying the measured concentration in the
discarded material by the total amount of the discarded material. Assuming that this amount is
uniformly distributed through the soil sample remaining after non-soil and non-sieved materials
have been discarded, one can then calculate an estimated value for the potential soil sample
total concentration, if none of the contamination had been discarded. Comparison of this
potential concentration to the actual measured concentration will enable an estimate of the
possible error related to discarded contamination.
If the error estimated by this process exceeds acceptable limits specified in the QA/QC
plan, it might be necessary to modify sample preparation procedures for the definitive study.
One might consider a sample sieving and mixing procedure in which the entire collected sample
(soil and non-soil materials) is extracted in the analytical laboratory. The analytical results
could then be reported as amounts of contaminant per gram of mixed material. At present
there is no acceptable method for proceeding in cases such as these. One problem is the lack of
standard reference materials for determining and measuring errors in extraction efficiency.
One solution may be to try different methods of extraction and compare the results. The final
interpretation of the data must then take into consideration these estimated errors.
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If the error estimated by this process exceeds acceptable limits specified in the QA/QC
plan, it might be necessary to modify sample preparation procedures for the definitive study.
One might consider a sample sieving and mixing procedure in which the entire collected sample
(soil and non-soil materials) is extracted in the analytical laboratory. The analytical results
could then be reported as amounts of contaminant per gram of mixed material. At present
there is no acceptable method for proceeding in cases such as these. One problem is the lack of
standard reference materials for determining and measuring errors in extraction efficiency.
One solution may be to try different methods of extraction and compare the results. The final
interpretation of the data must then take into consideration these estimated errors.
Sample Containers
The current EPA recommended container, preservation and holding time requirements
for specific contaminants is shown in Table 10. Recommended sample volumes are presented
in U.S. EPA (1983a), U.S. DOE (1987), and U.S. FWS (1989).
It is recommended that sample containers be obtained from a commercial source that
provides containers cleaned to EPA-approved specifications. The cleaning procedures used
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should be EPA approved. Also, sampling personnel should check current container and sample
volume recommendations as improvements in containers, materials used in their construction,
holding time requirements, preservation procedures, and analytical protocols are consistently
being updated and improved.
Archiving
A number of sampling/monitoring circumstances may require the archiving or storing
of collected samples or portions of collected samples that have been submitted for analysis. For
example, the design of a monitoring program may require that a large number of samples be
collected. If there is uncertainty as to the definitive identity of a contaminant(s) or cost of
analysis is a concern, an alternative for analyzing all of the samples collected is to select only a
small number of them for analysis. Following the analysis and data assessment of these initial
samples, a decision to analyze additional samples can be made. Additional reasons for
archiving samples is to provide a "back-up" if a sample is lost or spilled, and/or when additional
analysis is necessary for validating an unexpected or unusual (exceedingly high or low) result.
When samples are being archived, the samples should be stored in containers and under the
preservation requirements presented on Table 10. If samples are stored for a period longer
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TABLE 10. SAMPLING CONTAINERS, PRESERVATION REQUIREMENTS, AND
HOLDING TIMES FOR SOIL SAMPLES
Contaminant
Acidity
Alkalinity
Ammonia
Sulfate
Sulfide
Sulfite
Nitrate
Nitrate-Nitrite
Nitrite
Oil and Grease
Organic Carbon
Metals
Chromium VI
Mercury
Metals except above
Cyanide
Organic Compounds
Extractives
(including phthalates,
nitrosamines organo-
chlorine pesticides,
PCB's nitroaromatics,
isophorone, polynuclear
aromatic hydrocarbons,
haloethers, chlorinated
hydrocarbons and TCDD)
Extractables (phenols)
Purgables (halocarbons
and aromatics)
Container
P,G
P,G
P,G
P,G
P,G
P,G
P,G
P,G
P,G
G
P,G
P,G
P,G
P,G
P,G
G, teflon-lined
cap
G, teflon-lined
cap
G, teflon-lined
septum
Preservation
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool,4eC
Cool,40C
Cool,40C
Cool, 4°C
Cool, 4°C
Cool,40C
Cool, 4°C
Holding Time
14 days
14 days
28 days
28 days
28 days
48 hours
48 hours
28 days
48 hours
28 days
28 days
48 hours
28 days
6 months
28 days
7 days (until
extraction)
30 days (after
extraction)
7 days (until
extraction)
30 days (after
extraction)
14 days
(continued)
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TABLE 10. (Continued)
Contaminant
Container
Preservation Holding Time
Purgables (acrolein and
acrylonitrate
Orthophosphate
Pesticides
Phenols
Phosphorus
Phosphorus, total
Chlorinated organic
compounds
Polyethylene (P)
or Glass (G)
G, teflon-lined
septum
P,G
G, teflon-lined
cap
G
G
P,G
G, teflon-lined
cap
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
Cool, 4°C
3 days
48 hours
7 days (until
extraction)
30 days (after
extraction)
28 days
48 hours
28 days
7 days (until
extraction)
30 days (after
extraction)
P » polyethylene
G » glass
Sample preservation should be performed immediately upon sample collection. For composite
samples, each aliquot should be preserved at the time of collection. When impossible to
preserve each aliquot, then samples may be preserved by maintaining at 4°C until compositing
and sample splitting is completed.
Samples should be analyzed as soon as possible after collection. The times listed are the
maximum times that samples may be held before analysis and still considered valid. Samples
may be held for longer periods only if the analytical laboratory has data on file to show that the
specific types of samples under study are stable for the longer time.
For additional information see U.S. EPA (1983a).
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than the stated holding time, a decision concerning the utility of the data obtained from their
analysis must be made. This decision would have to be made by the data user on a case-by-case
basis as a function of the intended use of that particular data and should be documented as
necessary.
Sample Bank
For sampling studies that require a large number of samples and/or extensive
pre-analytical sample preparation, a sample bank may be advantageous. The sample bank is
the element that operates between the field sampling effort and the analytical laboratory. It is
established to handle the distribution and preparation of samples for large sampling efforts
(U.S. EPA, 1980). However, for smaller studies the sample bank's responsibilities are often
incorporated into the responsibilities of the field sampling team or the analytical laboratory.
The following sample bank responsibilities and procedures have been used successfully
on a number of soil monitoring studies (U.S. EPA 1982,1984,1989).
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A. Issuing Supplies:
(1) The sample bank issues, as required, sample containers, sample
collection tags, chain-of-custody forms, and site description forms to the
sampling teams. Sample collection tags and chain-of-custody forms are
normally accountable documents; the sample bank will log the forms by
numerical lot identifying the team and/or the individual responsible for
the temporary custody of these documents.
(2) The sample bank may be required to store sampling equipment in a
suitable environment. If sampling equipment is stored at the sample
bank, issuing this equipment to the sampling teams as required will be
necessary.
B. Record Keeping
(1) Custodian for all records pertaining to the sampling, sample preparation
as required, and shipment of soil samples to analytical laboratories.
(2) Responsibility for record filing and storing, for storing and preparation
of soil samples, and for dispensing containers, sampling equipment and
all custody documents such as chain-of-custody forms and sample
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collection and analytical tags, as required.
(3) Responsibility for updating and maintaining the project's master log
book, auditing the records as required, generating QC samples (e.g.,
sample bank blanks, splits, etc.), accepting QA/QC samples for inclusion
into the analytical scheme, and for scheduling the collection of field
sample blanks.
(4) Responsibility for completing, as required, analysis data reporting forms
and for assuring that all chain-of custody requirements pertaining to all
field sampling, shipping and sample bank operations are adhered to.
(5) All unused accountable documents as shown in Table 10 must be
returned to the sample bank on a daily basis. However, depending upon
circumstances, such as a sampling team's schedule and route,
accountable documents may be retained by the sampling team leader.
The sample bank supervisor, however, must be aware of the situation.
Preparation of soil samples for analysis normally requires sample bank personnel to dry,
sieve, mix and aliquot samples appropriately. The preparation procedures selected must be
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identified in the protocol and adequately address the contaminant(s) to be measured and the
analytical requirements.
QUALITY ASSURANCE ASPECTS
QA/QC procedures of the sample documentation/collection effort must identify and
determine the magnitude of errors associated with characterizing soil contamination introduced
through the sample collection effort. Audits (Chapter 13) are an effective tool for insuring that
sampling is being done as specified. Factors most likely to influence the magnitude of the
sample collection error are collection and preparation methods, and frequency of sampling.
Perhaps the most important of these are preparation methods and frequency of sampling.
The tools and equipment used for collecting and preparing soil samples themselves are
not likely to be sources of error. Errors will most likely occur in the inconsistent use of these
devices. Proper replication, decontamination and appropriate QC sample selection, analysis,
and assessment will insure that the precision of the procedure(s) meets the QA/QC objectives
and thence the DQOs.
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CHAPTER 12
ANALYSIS AND INTERPRETATION OF QA/QC DATA
INTRODUCTION
One goal in the analysis and interpretation of data is to show how all aspects of QA/QC
for a soil monitoring study combine to give an overall level of precision and confidence for the
data resulting from the study. Another goal may be to determine whether all QA/QC
procedures used were necessary and adequate and should definitely be incorporated into future
studies of the same type. This entire evaluation must be closely linked to the objectives, and
specifically, to the data quality objectives of the study. In summary, the important questions to
be answered are: "What is the quality of the data (maximum accuracy attainable)?" and "Could
the same objective have been achieved through an improved QA/QC design which may have
required fewer resources?"
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PRESENTATION OF DATA SUMMARIES
It is desirable to provide summarized tables of validated QA/QC data in the final
report. For example, QA/QC data validation procedures used in a number of soil sampling
studies reported by Brown and Black (1983) included validation of sample data sets by checking
and assessing the accompanying QA/QC data. The criteria for QA/QC samples and
procedures used to validate all data included:
Samples and Procedures Example Criteria
1. Reagent Blanks* Concentrations had to be less than
0.25
2. Calibration Check* Recovery must be between 95% and 105%
Standards of the known value for either the first
analysis or the first re-check analysis.
3. Laboratory Control* Recovery must be between 90% and 110%
Standards of the known value for either the first
analysis or the first re-check analysis.
* Applies to analysis of soils for lead
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One of the studies discussed by Brown and Black (1983) involved lead-contaminated
soils. The results of the QC analyses for this soil monitoring study were presented as follows:
QC Sample
Calibration Check Standard
Laboratory Control Standard
Field Blank (/ig ml'1)
Sample Bank Blank (/ig ml"1)
Reagent Blank (jig ml"1)
Re-extraction Analysis
Total Recoverable
SpUt Extract (CV)*
Spiked Extract
Spiked Sample
Duplicate Aliquot (CV)
Duplicate Sample (CV)
Triplicate Analysis (CV)
No.
150
147
76
77
148
17
144
147
147
147
134
129
220
Mean
101.5%
101.2%
<0.25
<0.2S
<0.25
1.7%
99.8%
0.0089
99.4%
100.4%
0.053
0.189
0.144
s
2.6%
4.1%
1.4%
8.0%
0.0079
5.0%
5.1%
0.047
0.168
0.128
CV
Mean
From data summarized in this fashion, it is possible to determine the adequacy of the
QAPP in insuring the achievement of the assigned DQOs.
It is required that the QA/QC plan document and insure that all data collected, whether
used for research or for monitoring purposes, be scientifically valid, defensible and of known
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precision and accuracy. The described presentation of QC data, though designed for analysis of
lead in soil, can be used as a guide for other sampling and data analysis protocols and/or
QA/QC plans.
Presentation of QA/QC data allows readers to verify conclusions drawn as to the
reliability of the data. Such an approach also contributes to the building of a body of QA/QC
and monitoring experimental data in the literature which allow comparisons to be made
between and among studies. Procedures used to validate the individual data points should be
presented, and where some points are discarded, arguments should be presented to support
these decisions.
PRESENTATION OF RESULTS AND CONCLUSIONS
Special emphasis should be placed on how overall levels of precision and confidence
were derived from the data. Great care must be exercised to insure that, in determining results
and conclusions, assumptions are not made which were not part of the study design and which
cannot be tested by data derived from the study. If portions of the study results are ambiguous
and supportable conclusions cannot be drawn with regard to the total reliability of the data, that
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situation must be clearly stated. In that event it is desirable to include recommendations for
conducting an improved study in such a way as to clarify the observed ambiguities.
QUALITY ASSURANCE ASPECTS
The adequacy of all aspects of the QA/QC plan should be examined in detail with
emphasis on defining an appropriate minimum adequate plan for future studies. Some aspects
of the plan actually used may have been too restrictive, while others may not have been
restrictive enough. Appropriate analyses and interpretation of the data should identify the
actual situation.
Future soil monitoring studies should have checks and balances built into the QA/QC
plan which will identify early in the study whether the plan is adequate and, if necessary, allow
for corrective action to be taken before the study continues. This is one of the major
advantages of conducting an exploratory study along the lines outlined in this report. If there
are problems with the QA/QC plan, they will often be identified in the exploratory study and be
corrected before major resources are expended.
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There is insufficient knowledge dealing with soil monitoring studies to state with
confidence which components of the QA/QC plan will be generally applicable to all soil
monitoring studies and which components should vary depending on site-specific factors. As
experience is gained, it may be possible to provide more adequate guidance on this subject. In
the meantime, it is recommended that the best approach is to assume that important factors of
QA/QC plans are site-specific, and to conduct an appropriate exploratory study at each new
study site to verify that various aspects of the QA/QC plan are adequate to meet program
objectives prior to proceeding with the final definitive study.
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CHAPTER 13
SYSTEM AUDITS AND TRAINING
INTRODUCTION
An adequate soil sampling quality assurance program ensures that the quality of the
final product meets required standards. Audits are an integral part of the quality assurance
process and are vital for assuring that program procedures are being implemented. They are
performed to document the implementation of the quality assurance program plan, quality
assurance project plan and/or associated operational protocols.
Three types of audits are commonly used to determine adequacy of the analytical
measurement system, adequacy of the data collection system, completeness of the
documentation of data collection activities, and document if required data collection and data
quality objectives are being met. These audits are commonly referred to as System Audits,
Performance Audits, and Data Quality Audits.
System Audits are qualitative on-site field audits that evaluate the technical
aspects of field operations (e.g., sampling methods) against the requirements of
approved QA plans and protocols. System audit reports note problems and
recommend or allow corrective actions to be taken to protect the validity of
collected data.
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Data Quality Audits are evaluations of the documentation associated with data
quality indicators of measurement data to verify that the generated data are of
known and documented quality. This is an important part of the validation of
data packages showing that the methods and Standard Operating Procedures
(SOPs) designated in the QA plans were followed and that the resulting data set
is a functional part of satisfying the established DQOs. The results are vital to
decisions regarding the legal defensibility of the data should it be challenged in
litigation.
Performance Audits are generally based on Performance Evaluation (PE)
samples. Samples having known concentrations may be tested as unknowns in
the laboratory or a sample may be analyzed for the presence of certain
compounds. Performance audits are used to determine objectively whether an
analytical measurement system is operating within established control limits at
the time of the audit. The performance of personnel and instrumentation are
tested by the degree of accuracy obtained.
Standard Operating Procedures to assist auditors in addressing critical program
elements and preparing for on-site audits are presented in U.S. EPA (1985). The
recommended initial phase for conducting an audit is the preparation of a program specific
checklist. Examples of audit checklists and laboratory evaluations are presented in U.S. EPA
(1984a, b, 1989), and for numerous sampling effects conducted for the environmental Survey
Program (U.S. DOE 1987). A discussion with the RPM concerning the current status of the
project and the identity of any problems encountered is suggested before conducting on-site
field audits.
Audits, in most part, are conducted by appropriate elements of agencies or
organizations having cognizance over a monitoring project. However, audits can be conducted
by independent or third party organizations. The frequency of auditing should be determined
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by the RPM or project officer. Juran et al., (1979), states that, "the activities subject to audit
should include any that affect quality regardless of the internal organizational location." For
illustrative purposes, important factors that are addressed in a systems audit will be discussed.
Definitive procedures for conducting audits of analytical measurement systems are presented in
(U.S. EPA, 1984a, 1989).
Specifically system audits:
verify that sampling methodology is being performed in accordance with
program requirements,
check on the use of appropriate field QA/QC measures,
check methods of sample handling, i.e., packaging, labeling, preserving,
transporting, and archiving in accordance with program requirements,
check program documentation, i.e., records (site description, chain-of-custody
collection and analytical tags, field and sample bank log books and field work
sheets),
recommend corrective action if a problem is identified,
assess personnel experience and qualifications if required,
follow-up on any corrective action previously mandated,
provide on-site debriefings for sampling team and sample bank personnel, and
provide a written evaluation of the sampling and sample bank program.
Components of a systems audit may include sample bank operations and field
operations.
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SAMPLE BANK AUDIT
The primary objective is to determine the status of all Sample Bank documentation and
archived samples. Emphasis is placed on:
verifying that the documentation is in order and sufficient to establish the
disposition of any sample collected,
determining any discrepancies that currently exist and initiating corrective
action as appropriate,
verifying that the recording and documentation of QA/QC measures (blanks,
duplicate spikes, blinds) is in accordance with the QA/QC plan, and
establishing procedures for final disposition and mechanics of transfer of all
Sample Bank holdings upon termination of the operation.
An initial step is to inventory the Sample Bank records and archived samples. The records that
must be inspected are:
Chain-of-custody forms, including
Field forms and
Analysis forms;
Sample tags, includin
Field tags am
Analysis tags;
Analysis forms, including
Individual samples and
Batch sheets;
Shipment forms;
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Logbooks, including
Soils and
Daily log.
The operational procedures inspected include:
Preparation Procedures (sample bank or analytical laboratory),
Preservation,
Drying (if used),
Sieving,
Mixing,
Packaging, and
Shipping.
Housekeeping,
Safety,
Decontamination, and
Evaluation of Swipe Samples;
Security,
Forms (documents),
Samples; and
Storage,
Sampling equipment, and
Archived samples (when appropriate).
Check that required documentation has been maintained in an orderly fashion, that
each of the recorded items is properly categorized, and cross-checking can be easily performed.
In addition, ensure that data recording conforms to approved documentation procedures.
Check archived samples. Verify that appropriate samples exist for each entry in the
logbook. Review sample bank logbooks for complete sample information. In addition, checks
for the identification and documentation of split and duplicate samples, and field and Sample
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Bank rinsate samples must be performed. Detailed sample bank procedures are presented in
U.S. EPA (1982,1984, and 1989).
FIELD AUDITS
The primary objective is to determine the status of sampling operations. Emphasis is
placed on:
verifying that operational aspects and procedures are in accordance with the
protocols and QA/QC plan,
verifying the collection of all samples including duplicates, rinsates, and blanks,
verifying that documentation is in order and sufficient to establish the collection
location of any sample collected,
determining discrepancies that exist and initiating corrective action as
appropriate, and
collecting independent samples.
Records inspected include:
a. chain-of-custody forms,
b. sample tags,
c. site description forms, and
d. log books.
The operational procedures inspected include:
sampling procedures,
equipment,
techniques,
decontamination,
collection of duplicate and field blank samples,
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security,
sample storage and transportation,
containers
contaminated waste storage and disposal, and
site Description Form entries.
TRAINING
The project officer is responsible for determining that all members of his team have
adequate training and experience to cany out satisfactorily their assigned missions and
functions. Until a field sampling team has worked together long enough for the project leader
to have verified this, it is good practice, in addition to any classroom training or experience, to
conduct comprehensive briefing sessions for all involved parties. During these sessions, all
aspects of the sampling protocol, including the QA/QC plan, are presented and discussed in
detail. Sufficient field training exercises should follow the briefing sessions until each team
member can demonstrate successfully that he can perform his job well and without delay.
In summary, the sampling effort must include classroom and field training programs
that provide detailed instruction and practical experience to personnel in sample collection
techniques and procedures, labeling, preservation, documentation, transport, and sample bank
operational procedures. Also, any specialized training, such as field measurement procedures
and documentation, should be completed by all personnel prior to their involvement in the
conduction of any audits.
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GLOSSARY
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Absorption
The penetration of a substance into or through another.
Accuracy
Measures the bias in a measurement system; it is difficult to measure for
the entire data collection activity. Sources of error are the sampling
process, field contamination, preservation, handling, sample matrix,
sample preparation, and analysis techniques. Sampling accuracy may be
assessed by evaluating the results of field/trip blanks, analytical accuracy
may be assessed through use of known and unknown QC samples and
matrix spikes.
Anion
A negatively charged ion.
Background Level Amount of pollutants present in the ambient soil due to natural sources.
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Bulk density The mass of dry soil per unit bulk volume, determined before drying to a
constant weight at 105°C.
Calibration Check
Standard A standard material to check instrument calibration.
Cation
A positively charged ion.
Cation-exchange
The total of exchangeable cations that a soil can absorb, expressed either
in milliequivalents per gram or in milliequivalents per 100 grams of soil.
Comparability
A qualitative parameter expressing the confidence with which one data
set can be compared with another. Sample data should be comparable
with other measurement data for similar samples and sample conditions.
This goal is achieved through using standard techniques to collect and
analyze representative samples and reporting analytical results in
appropriate units. Comparability is limited to the other PARCC
parameters because only when precision and accuracy are known can
data sets be compared with confidence.
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Completeness Defined as the percentage of measurements made which are judged to
be valid measurements. The completeness goal is essentially the same
for all data uses: that a sufficient amount of valid data be generated. It
is important that critical samples are identified and plans made to
achieve valid data for them.
Data Quality
Objectives (DQOs) Qualitative and quantitative statements which specify the quality of the
data required to support Agency decisions during remedial response
activities. DQOs are determined based on the end use of the data to be
collected.
Duplicate Sample An additional sample taken near the field sample co-located to
determine total within-batch measurement error variance.
External Laboratory
Audit Sample A sample of well-characterized soil that is sent directly to the laboratory
for analysis. The analyte concentrations are unknown to the laboratory.
This type of sample is used to estimate laboratory bias and laboratory
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batch to batch variability. It may also be used for external quality control
of the laboratory.
Field Audit Sample A sample of well-characterized soil that is taken into the field with the
sampling crew, sent through the sample bank to the laboratory with the
field samples to detect bias in the entire measurement process and to
determine batch-to-batch variability.
Field Blank
A sample container filled with distilled, deionized water, exposed during
sampling and then analyzed to detect accidental or incidental
contamination.
Field Rinsate
A blank (last rinse using distilled deionized water) passed over the
sampling apparatus after cleaning, to check for residual contamination.
Heavy Metals
Metals having a specific gravity of 5.0 or over.
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Internal Laboratory
Audit Sample A sample of well-characterized soil whose analyte concentrations are
known to the laboratory to be used for internal laboratory quality
control.
Laboratory Control
Standard A sample of a soil standard carried through the analytical procedure to
determine overall method bias.
Matrix
The predominant material of which the sample to be analyzed is
composed.
PARCC
Precision, accuracy, representativeness, completeness, and comparability
parameters.
Precision
Measures the reproducibility of measurements under a given set of
conditions. Specifically, it is a quantitative measure of the variability of a
group of measurements compared to their average value. Precision is
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usually stated in terms of standard deviation, but other estimates such as
the coefficient of variation (relative standard deviation), range
(maximum value minus minimum value), and relative range are
common.
Reagent Blank
A DDI water sample analyzed as a routine check for reagent
contamination.
Re-extraction
A re-extraction of the residue from the first extraction to determine
extraction efficiency.
Remedial Project
Manager (RPM) Manages remedial activities at assigned regional sites. Accountable for
the technical quality, schedule, and cost of work.
Representativess
Expresses the degree to which sample data accurately and precisely
represent a characteristic of a population, parameter variations at a
sampling point, or an environmental condition. Representativeness is a
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qualitative parameter which is most concerned with the proper design of
the sampling program. The representativeness criterion is best satisfied
by making certain that sampling locations are selected properly and a
sufficient number of samples are collected.
Sample Bank Rinsate A sample (last rinse using distilled, deionized water) passed through the
sample preparation apparatus, after cleaning, to check for residual
contamination.
Semivolatiles
A group of organic compounds consisting of base/neutrals, acids, and
pesticides that are identified in and analyzed by Method 625 in 40 CRF
Part 136.
Soil classification The systematic arrangement of soils into groups or categories on the
basis of their characteristics.
Soil Profile
A vertical section of the soil from the surface through all its horizons,
including C horizons.
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Spiked Extract
A separate aliquot of extract that is spiked to check for extract matrix
effects on the recovery of known added analytes.
Spiked Sample
A separate aliquot of a soil sample spiked with an appropriate standard
reference material to check for soil and extract matrix effects on
recovery.
Split Extract
An additional aliquot of the extract which is analyzed to check injection
and instrument reproducibility.
Total Recoverable A second aliquot of a sample digested by a more rigorous method to
check the efficacy of the protocol method.
Triplicate Samples
(Splits)
The prepared sample is split into three portions to provide blind
duplicates for the analytical laboratory and a third replicate for the
referee laboratory to determine interlaboratory precision.
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Volatile Solids or liquids which are relatively unstable at standard temperature
and pressure and undergo spontaneous phase change to a gaseous state.
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References
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REFERENCES
1. Bauer, E.L. A Statistical Manual for Chemists. Academic Press. New York, NY. 193
pp. 1971.
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Chemical Rubber Co. Cleveland, OH. 1968.
3. Borgman, L.E., and W.F. Quimby. Sampling for Tests of Hypothesis When Data are
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5. Broms, Bengt B. Soil Sampling in Europe: State-of-the-Art, Journal of the
Geotechnical Engineering Div., 106:65-98,1980.
5. Brown, K.W., and S.C. Black. Quality Assurance and Quality Control Data Validation
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7. BufGngton, J.P. Developing Recommendations to Improve Quality Assurance for
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Assurance Environmental Measurements. Denver, CO. 1978.
8. Cochran, W.G. Sampling Techniques (3rd. Ed.). John Wiley & Sons. New York, NY.
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9. Davis, J.C. Statistics and Data Analysis in Geology. John Wiley A Sons. New York,
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10. Gilbert, R.O. Statistical Methods for Environmental Pollution Monitoring. Van
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11. Gulezian, R.C. Statistics for Decision Making. W.B. Saunders Co. Philadelphia, PA.
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15. Hoaglin, D.C., F. Mosteller, and J.W. Tukey. Understanding Robust and Exploratory
Data Analysis. John Wiley & Sons. New York, NY. 447pp. 1983.
16. Juran, J.M., P.M. Gryna, Jr. and R.S. Bingham, Jr., eds. Quality Control Handbook,
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17. Lehmann, E1L. Nonparametrics: Statistical Methods Based on Ranks. Holden-Day
Inc. San Francisco, CA. 457 pp. 1975.
18. Mason, BJ. Preparation of Soil Sampling Protocols: Techniques and Strategies.
EPA-600/4-83-020. Environmental Monitoring Systems Laboratory. U.S.
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19. Natrella, M.G. Experimental Statistics. NBS Handbook 91. National Bureau of
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20. Oregon State University. Methods of Soil Analysis Used in Soil Testing Laboratory at
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21. Peterson, R.G. and L.D. Calvin. Sampling. In: Methods of Soil Analysis. Part 1,
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34. U.S. Environmental Protection Agency. Characterization of Hazardous Waste Sites-A
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225
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Appendix A
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APPENDIX A
APPLICATION OF SOIL MONITORING DATA
TO AN EXPOSURE AND RISK ASSESSMENT STUDY
One of the possible purposes for soil monitoring is to provide data for input into
exposure and risk assessment studies. The risk assessment study conducted by the Centers for
Disease Control (CDC) to estimate an allowable concentration of 2,3,7,8-tetrachlorodibenzo-
dioxin (TCDD) in residential soil provides an instructive example. Prior to presenting the
example, however, a brief introduction to the general subject of risk assessment will be given.
Risk assessment as defined by the World Health Organization (WHO) is composed of
three different elements:
A-l
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risk identification,
risk estimation, and
risk evaluation or management.
Risk identification involves the accumulation of sufficient evidence to warrant identifying the
presence of a specific pollutant in the environment, at a defined concentration and averaging
time, as possibly being an unacceptable risk to man or the environment. A formal risk
identification on the basis of a qualitative value judgement requires further study to determine
whether the risk is or is not acceptable.
Risk estimation is the process whereby a risk which has been identified is quantified in
terms of developing estimates of the numbers of people, for example, who would suffer adverse
health effects as a result of exposure to defined levels of the pollutant(s) of concern. Risk
estimation requires the availability of both applicable exposure-response relationships for the
adverse effect of concern in the exposed population and existing exposure distributions in the
appropriate population(s). By comparing exposure distributions to exposure-response
relationships, it a possible to predict the expected number of adverse effects in the exposed
population(s).
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Risk evaluation (or management) is the process whereby responsible public officials
come to a value judgment decision as to what risk is acceptable. Social, economic, political, and
health considerations generally are involved in this important decision. If the present risk as
estimated in the previous step is deemed unacceptable, it is imperative that prompt action be
initiated to reduce the risk to acceptable levels.
The risk assessment process is often easier to define than it is to perform. For example,
the risk estimation process assumes the availability of applicable exposure-response
relationships. Let us briefly examine how such relationships are developed. Figure A-l depicts
the elements of toxicologic studies designed to assess adverse effects related to exposure to
environmental pollutants. Such studies are usually the basis for exposure-response
relationships in humans. Note that there are many different possible testing systems, there are
several possible exposure routes, the form and levels of pollutant may vary over wide limits, and
there is almost an unending list of possible adverse effect end points.
Studies where different combinations of the toxicologic study elements have been
examined comprehensively exist for only a very small number of substances. The situation is
additionally complicated by the fact that results from experimental animals, usually at high
A-3
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exposures, must be extrapolated to humans, usually at much lower exposures. Also,
combination effects resulting from the presence of more than one pollutant at a time in the real
world are usually not assessed.
Due to variations in sensitivity, not all humans respond the same way to the same
exposures. Figure A-2 shows a generalized spectrum of human responses to an environmental
pollutant. Generally in the United States, an increased body burden and physiological or
biochemical changes of uncertain significance are not considered to be adverse health effects.
Figure A-3 shows the possible routes of entry of pollutants to man from the generating
source(s). In the development of exposure distributions to man, significant exposures via all
routes of entry must be assessed. When exposures via more than one route are important, it is
necessary to estimate the total exposure by appropriately summing all contributions.
Figure A-4 presents a total exposure model showing how media measurements (shown
on the top line in the slide) may be converted to exposures. At the present time, the ability to
quantify human exposures via skin absorption for many pollutants is not considered adequate.
More research is needed here.
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Appendix A
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Figure A-5 shows the relationship of exposure to risk estimation. Note that it may be
possible to infer some information about total exposure from the effects of increased body
burden, or physiological and biochemical changes of uncertain significance.
Figure A-6 gives three hypothetical general classes of exposure-response relationships.
Curve I shows the situation where there is no threshold of exposure which must be exceeded
prior to observation of some effects. Many feel that this is the appropriate class to use for
cancer-causing pollutants. Curve n shows that a threshold of exposure must be exceeded prior
to observation of effects. Curve HI depicts a situation where there are some effects at zero
exposure. This shows that the pollutant of concern is not the only cause or contributor to the
adverse effect being measured. Generally, experimental points used to define exposure-
response relationships are for high exposures, and the shape and location of the curve near zero
exposure is unknown. Note that if the curve is really of Class n, but is assumed to be of Class I,
extrapolation of an experimental point at B through zero would seriously overestimate the
effects of low exposures.
A-7
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Figure A-3. Possible exposure pathways from a source of environmental pollution to man.
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Appendix A
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Figure A-7 depicts exposure monitoring elements requiring quality assurance (QA).
Note that one must have a QA plan for many more factors than analytical techniques.
Figure A-8 shows a hypothetical example of an exposure distribution. In using an
exposure distribution together with an exposure-response relationship to derive a quantitative
risk estimate, one must decide to what population the exposure distribution should be
applicable. Also whether the important exposure is the mean or one of the top percentiles must
be decided.
ASSESSING DIOXIN EXPOSURE
In the early 1970s, a waste oil dealer in Missouri disposed of waste materials containing
TCDD by mixing them with salvage oil and spraying the mixture on dirt roads and riding
arenas. Measurements in soil gave values ranging from less than 1 to greater than 1,000 parts
per billion (ppb) of TCDD. The Centers for Disease Control (CDC) was assigned the task of
assessing possible health implications and, if feasible, recommending a soil concentration value
for TCDD which should not be exceeded. The reference describing the results of CDC's
deliberations is given below.
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Appendix A
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Kimbrough, R.D., H. Falk, P. Stehr, and G. Fries. Health Implications of
2,3,7,8-Tetrachlorodibenzodioxin (TCDD) Contamination of Residential Soil. Jour, of Toxicol.
and Environ, fflth, 1984.
The following were identified as the most important factors influencing human exposure
(dose):
concentrations of environmental contamination,
location of and access to contaminated areas,
type of activities in contaminated areas,
duration of exposure, and
specific exposure mechanisms.
Figure A-9 shows the mathematical equation derived to calculate the total lifetime dose
to TCDD. Note that dose is used rather than exposure. Exposure via a specific route may be
converted to dose via the same route by multiplying the exposure by the percent absorbed. The
use of exposure is more conservative since it is implicitly assumed that the absorption percent is
100. As an example, exposure to skin is the amount of pollutant in contact with the skin,
whereas dose is the amount which is absorbed through the skin.
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A-16
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Appendix A
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Note that only three exposure routes were considered: dermal absorption through direct
contact with the soil, ingestion of soil, and the inhalation of dust to which TCDD was attached.
Possible exposures via inhalation of vapors or ingestion of food or water containing TCDD
were not included. Probably the most serious omission is the food exposure route.
Some additional assumptions made on the basis of the limited data available are listed
below:
The environmental half-life of TCDD in soil is 12 years.
TCDD levels in airborne dust are the same as those in soil
Indoor TCDD levels in dust are the same as outdoor levels.
Fifteen m3 of air is exchanged per person per day.
The GI absorption rate of TCDD in soil is 30%.
Exposures would take place only 6 months of the year because of seasonal
influences and varying activity patterns.
The dermal absorption rate of TCDD in soil is 1%.
Figure A-10 gives the estimated dairy deposition of soil on skin by age. The same
amounts were assumed to be ingested each day. These values are based on work done studying
lead uptake from contaminated soils.
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Appendix A
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The authors make the point that their analysis applies only to residential areas and
suggest that a lower safe value may be more appropriate for range or dairy farm areas, whereas
a higher value may be adequate for commercial areas. Figure A-11 shows some results derived
by the Food and Drug Administration (FDA) by analogy to polybrominated biphenyl (PBB)
data. A maximum allowable intake of 100 picograms/day was assumed. Note that the value in
soil which would produce the maximum allowable residue in milk is 6.2 pg/g or 6.2 parts per
trillion (ppt).
Based on direct extrapolation of rodent data to humans and extrapolation to low doses
by the linear derived multistage model, a dose of 28 fg/kg body weight/day is calculated as the
virtually safe dose (an added cancer risk of 1/106). For a 70 kg man, this is equivalent to 1.96
pg/person/day. A uniform concentration of 1 ppb in soil by the model used would lead to 44
pg/person/day.
Figure A-12 gives the estimated average daily dose corresponding to initial TCDD-soil
contamination levels. It also shows the uncertainty ranges for both 10"6 and 10*5 excess lifetime
cancer risks. On the basis of these results and the assumption that not 100% of contaminated
areas would be at the peak level, the authors conclude that 1 ppb is a soil level of TCDD which
should not be exceeded in residential areas.
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APPENDIX B.
PERCENTILES OF THE t DISTRIBUTION
Confidence Level (%): 1-ot/, for two-tailed test
20 30 60 SO 90
Confidence Level (%): 1-cr for one-tailed test
60 70 80 90 95
Appendix B
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Page 1 of 1
.325
.289
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6 .265
7 .263
8 .262
9 .261
10 .260
11 .260
12 .259
13 .259
14 .258
15 .258
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18 .257
19 .257
20 .257
21 .257
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1.782
1.771
1.761
1.753
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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
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2.571
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2.074
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2.064
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1.960
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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
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9.925
5.641
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
B-l
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APPENDIX C
DATA QUALITY OBJECTIVES DEVELOPMENT PROCESS
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Appendix C
Revision 1
03/01/89
Page 2 of 13
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DATA QUALITY OBJECTIVES (DQOs) DEVELOPMENT CHECKLIST
FOR STAGE I (DECISION MAKER)
'AUTHORITIES"
L. Initial perception of what decision must be made.
Comment:
2. What information is needed?
Comment:
3. Why and When needed?
Comment:
4. How will information/data be used?
Comment:
5. What are consequences of inadequate/incomplete data?
Comment:
6. Estimates of Time and Resources available?
Comment:
7. Establishment of Priority for project?
Comment:
8. Record Decision.
Comment:
9. Close Project or Phase of Project.
Comment:
Complete? a
Complete? a
Complele? a
Complete? a
Complete? a
Complete? a
Complete? o
Complete? a
Complete? a
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DATA QUALITY OBJECTIVES (DQOs) DEVELOPMENT CHECKLIST
FOR STAGE II (SENIOR PROGRAM STAFF)
"MANAGEMENT"
I. Examine Stage I Results Complete? ~
a. Interaction with Decision MakerKs). -
b. Internal Discussion and Work Groups. 2
c. Section/Office Tasking. z>
Comment:__ __
2. Generate specific guidance for data collection project. Complete? 2
a. Interaction with decision makerts). a
b. Internal Discussion and Work groups. -
c. Section/Office Tasking -
Comment:^
3. Refine and Define DQOs: Complete? a
a. Proposed and final statements of type and quality
of environmental data Required. -z
b. Technical constraints; define logistic and
resource limits on Data Collection Project. a
Comment:
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DATA QUALITY OBJECTIVES (DQOs) DEVELOPMENT CHECKLIST
FOR STAGE III (TECHNICAL STAFF)
"PLANNING"
1. Develop QA Project Plan and Work Plan:
a. Draft addressing all elements in guidance.
b. Internal Review.
c. External Review.
d. Finalize Draft QAPjP.
e. Documentation oi* all operations.
f. Sample acquisition and analyses.
g. Data reduction and validation
Comment:
2. Develop Acceptance Criteria for evaluation of Project.
Comment:
Complete? c
G
a
Complete? c
3. Oversight of Project Team activities.
Comment:
Complete? a
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DATA QUALITY OBJECTIVES (DQOs) DEVELOPMENT CHECKLIST
I'OR STAGE IV (PROJECT TEAM)
"SUPPORT"
I. Execute Work Plan field operations adhering
to QAPjP criteria for procedures and documentation.
Comment:
Complete? a
2. Analyze samples, reduce and validate data.
Comment:
Complete?
3. Make and correlate all necessary observations
for professional opinion statements.
Comment:
Complete? a
4. Preliminary Report
Comment:
Complete? 3
a. Validated data package
b. Operational documentation
c. Technical Deliverables as called for in QAPjP
(includes but not limited to, Professional Opinions,
Model or Trend analyses results, Photograph, etc.)
o. Execute Corrective actions if needed
Comment:
Complete? a
6. Close out site operations unless otherwise
directed for anticipated subsequent phases.
Comment:
Complete? a
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Appendix C
Revision 1
03/01/89
Page 7 of 13
EXAMPLE FORMAT AND CRITICAL ELEMENTS OF
QUALITY ASSURANCE PLAN
Project name:.
Project code:
Address:
Responsible organization:
Approvals:
Project Officer: Date.
QA Off1cer:_ Date.
ESD Peer Review: Oate_
Regional Sample Control Center (RSCC): Date.
Super vi sor: Date.
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ORGANIZATION AND RESPONSIBILITY
The following is a list of key project personnel =na their
'esccnsi bi 1 1 ties :
Organization Manager _
Project Officer _
QA Officer
Appendix C
Revision 1
03/01/89
Page 8 of 13
Field Operation
Laboratory Operation
Data Quality Review
System/Performance Audit
PROJECT CODES AND SAMPLE NUMBERS (to be completed by RSCC)
Project NO.:
Laboratory Designated:
Sample Numbers assigned: from_
Account NO..
EPA
CLP
"to "
Private
PROJECT DESCRIPTION
1. Objective and Scope:
2. Schedule of Tasks and Milestones:
Activities
Dates
3. Data Usage:.
4. Monitoring network/sample collection design ard
aticnale:
-2- C-8
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PROJECT DESCRIPTION - continued
Appendix C
Revision 1
03/01/89
Page 9 of 13
# of
Samples
Sample
Matrix
Col lec-
tion
Fre-
quency
Anal yt-
icai
Parame-
ter
Type Of
Sample
Contai ner
Samoie
Preserva-
tion
Holdi nq
Time
Analyti cal
Detection
Limi t
Qual i tyj
Control
Samples
DATA QUALITY OBJECTIVES
1. Precision and Accuracy protocols/1imlts:
2. Data Representativeness:
3. Data Comparabi1ity:
4. Data Completeness:
SAMPLING PROCEDURES (including QC checks):
-3-
09
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Appendix C
Revision 1
SAMPLE CUSTODY PROCEDURES: 03/01/89
Page LO of L3
CALIBRATION PROCEDURES AND PREVENTIVE MAINTENANCE:
ANALYTICAL METHODS (including QC checks):
DOCUMENTATION, DATA REDUCTION AND REPORTING
1. Documentation:
2. Data Reduction and Reporting:
DATA ASSESSMENT:
PERFORMANCE/SYSTEM AUDITS:
-4-
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Appendix C
Revision 1
CORRECTIVE ACTION: 03/01/89
Page 11 of 13
REPORTS:
-5-
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Appendix C
SAMPLE ALTERATION CHECKLIST Revision 1
03/01/89
Page 12 of 13
Project Name and Number:
Material to be sampled:
Measurement Parameter:
Standard Procedure for Field collection & Laboratory Analysis (cite references):
Reason for change in Field Procedure or Analytical Variation:
Variation from Field or Analytical Procedure:
Special Equipment, Materials, or Personnel Required:
Initiators Name: Date:
Project Approval: Date:
Laboratory Approval: Date:
<^A Officer/Reviewer: Date:
Sample Control Center: Dato:
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CORRECTIVE ACTION CHECKLIST
Project Name and Number:
Appendix C
Revision 1
03/01/89
Page 13 of 13
Sample Dates Involved:
Measurement Parameters):
Acceptable Data Range:
Problem Areas Requiring Corrective Action:
Measures Required to Correct Problems:
Means of Detecting Problems and Verifying Correction:
Initiators Name:
Project Approval:
Laboratory Approval: _
QA Officer/Reviewer:
Sample Control Center:
-6-
Date:
Date:
Date:
Date:
Date:
(2-11)5/86
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