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                                             Exhibits

 1-1     Data Useability Criteria to Plan Sampling, Analysis and Assessment Efforts in Baseline Risk Assessments
 1-2     Organization of the Manual
 2-1     Components and Related Data of the Risk Assessment Process
 2-2     Baseline Risk Assessment Process and Typical Sources of Uncertainty
 2-3     Range of Uncertainty of Risk Assessment
 2-4     Generic Equation for Calculating Chemical Intakes
 2-5     Roles and Responsibilities of Risk Assessment Team Members
 2-6     Example Risk Assessment Checklist for Use in Scoping
 2-7     Checklist for Reviewing the Workplan
 2-8     Checklist for Reviewing the Sampling and Analysis Plan
 3-1     Relevance of Data Quality Criteria and Planning Issues to Risk Assessment Decisions to be Made wiih
        Environmental Data
 3-2     Impact of Data Useability Criteria in Planning for the Baseline Risk Assessment
 3-3     Data Sources and Their Use in Risk Assessment
 3-4     Relative Importance of Sampling and Analysis Plan and Records in Planning for Risk Assessment
 3-5     Relative Importance of Chain-of-Custody Documentation and Activities in Planning foi Risk Assessment
 3-6     Impact of Data Quality Indicators on Sampling Considerations
 3-7     Impact of Data Quality Indicators on Analytical Considerations
 3-8     Stages of Review of Analytical Data
 3-9     Automated S>stems to Support Data Review
3-10     Data and Documentation Needed for Risk Assessment
3-11     Impact of Sampling Issues on Risk Assessment
3-12     Median Coefficient of Variation For Chemicals of Potential Concern
3-13     Sampling Variabiht> and Measurement Error
3-14     Measurement of Variation and Bias Using Field Quality Control Samples
3-15     Importance of Major Sampling Issues in Each Medium
3-16    Sources of Uncertain!) that Frequently Affect Confidence in Analytical Results
3-17     Sample Preparation Issues
3-18     Identification of Exposure Pathways Prior to Sample Design is Critical to Risk
3-19     Strengths and Weaknesses of Biased and Unbiased Sample Designs
3-20     Impact of Analytical Issues on Risk Assessment
3-21     Munition Compounds and Their Detection Limits
3-22     Summary of Most Frequently Occurring Chemicals of Potential Concern by Indu>try
3-23     Steps in the Assessment of Library Search Compounds or Tentatively Identified Compounds
3-24     Requirements for Confident Identification and Quantiuition
3-25     Comparison of Detection Limit with Concentration of Concern to Select an An.il-.tkjl Method
3-26     The Relationship of I*>irjment Calibration Cur\e and Analyte Dckxiion

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3-27     Example of Detection Limit Calculation
3-28     Sources of Uncertainty that Frequently Affect Confidence in Analytical Results
3-29     Comparison of Sample Preparation Options
3-30     Characteristics of Field and Fixed Laboratory Analyses
3-31     Strengths and Weaknesses of Field and Fixed Laboratory Analyses
 4-1     Sample Design Selection Worksheet
 4-2     Confidence Levels for the Assessment of Measurement Variability
 4-3     Factors in Determining Total Number of Samples Collected
 4-4     Relationships Between Measures of Statistical Performance and Number of Samples Required
 4-5     Automated Systems to Support Environmental Sampling
 4-6     Method Selection Worksheet
 4-7     Automated Systems to Support Method Selection
4-8a     Common Laboratory Contaminants and Interferences by Organic Analyte
4-8b     Common Laboratory Contaminants and Interferences by Inorganic Analyte
4-9a     Comparison of Analytical Options for Organic Analytes in Water
4-9b     Comparison of Analytical Options for Organic Analytes in Soil
4-9c     Comparison of Analytical Options for Inorganic Analytes in Water and Soil
4-9d     Comparison of Analytical Options for Organic and Inorganic Analytes in Air
 5-1     Data Useability Assessment Phases
 5-2     Minimum Requirements, Impact, and Corrective Actions for Data Useability Criteria
 5-3     Corrective Acuon Options When Data Do Not Meet Performance Objectives
 5-4     Data Useabilit\ Worksheet
 5-5     Using Data Qualu> Indicators to Determine Data Useability
 5-6     Steps to Assess Sampling Performance
 5-7     Recommended Minimum Statistical Performance Parameters for Risk Assessment
 5-8     Use of Quality Control Data for Risk Assessment
 5-9     Basic Model for Estimating Total Variability Across Sampling and Analysis Components
5-10     Combining Data Quality Indicators From Sampling and Analysis into a Single Assessment of Uncertainty
 6-1     Data Useability Catena Affecting Contamination Presence
 6-2     Data Useability Criteria Affecting Background Level Comparison
 6-3     Data Useability Catena Affecting Exposure Pathway Examination
 6-4     Data Useability Criteria Affecting Exposure Pathway Characterization
 6-5     Uncertainty in  Data Collection and Evaluation Decisions Affects the Certainty of the Risk Assessment

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                   ACKNOWLEDGEMENTS

This manual was developed by an EPA workgroup with membership from EPA Headquar-
ters, EPA regional officesand representatives of the contractor community. The EPA Risk
Assessment Subgroup of the Data Useability Workgroup provided valuable input regard-
ing the content, approach and organization of the manual. Technical review was provided
by professionals who included toxicologists, chemists, quality assurance specialists,
engineers, project managers, and statisticians from both EPA and contractor staff.

Leadership was provided by Data Useabiluy Workgroup Region III Co-chairpersons
Chuck Sands and Claudia Walters, and Ruth Bleyler of the Toxics Integration Branch
(TIB). Members of the Risk Assessment Subgroup include:

             Matt Charsky               Office of Waste Programs Enforcement
             Richard Brunker            US EPA Region III
             Dawn lovan                US EPA Region III
             Pat van Leeuwen            US EPA Region V
            Jon Raucher                US EPA Region VI
            Cindy Kaleri               USEPA Region VI
            Guen Hooten              USEPA Region VIII
            JimLuey                  USEPA Region VIII
            Jim LaVelle                USEPA Region VIII
            Chns Weis                 USEPA Region VIII
            Leigh Woodruff            USEPA Region X
            Ski?  Ellis                  n-T.MHIL!
            Ropm Smith                CHJM HILL
            Carla Dempsey              Weston
            Da\ e Bottrell               Viar and Company
            .Anne Babyak               Viar and Company

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                     Chapter 1 Introduction and Background
  Chapter 1
  Introduction and
  Background
   •   Presents critical data useability
]      issues.

   •   Specifies audience to be primarily
      RPMs and risk assessors.

      Defines scope and specifies
      organization of manuai.

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Chapter 1  Introduction and Background
                       ACRONYMS FOR CHAPTER ONE

                     CLP     Contract Laboratory Program
                     QA      Quality Assurance
                     QC      Quality Control
                     RAGS    Risk Assessment Guidance for Superfund
                     RI      Remedial Investigation
                     RPM     Remedial Project Manager

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                                                                Chapter 1  Introduction and Background
 1.0    INTRODUCTION AND
         BACKGROUND

 This manual was developed by the Environmental Protection
 Agency (EPA) as technical guidance primarily for Remedial
 Project Managers (RPMs) and risk assessors.  The manual is
 designed to assist RPMs in maximizing the useability of
 environmental analytical data collected in the Remedial Inves-
 tigation (RI) process for baseline risk assessments for human
 health.  Since RPMs, with assistance from technical experts,
 oversee the preparation of work plans and sampling and analy-
 sis plans (S APs) for RI data collection, it is important for them
 to understand the types, quality and quantity of data needed by
 risk assessors, and the impact that their data collection deci-
 sions have on the level of certainty of baseline risk assessments
 for human health. The manual  provides guidance on the
 following:
 •  How to design RI sampling and analytical activities that
    meet the data  quantity and data quality  needs of risk
   assessors.
 •  Procedures for assessing the quality of the data obtained in
    theRI.
 •  Options for combining environmental analytical data of
   varying levels of quality from different sources and incor-
   porating them into the risk assessment.

 «  Procedures for determining the level of confidence in the
   risk assessment based on the uncertainty in the environ-
   mental analytical data.
 •  Guidelines on the timing and execution  of the various
   activities, in order to most efficiently produce dehverables.

 Risk assessors should be an integral part of the RI planning
 process to ensure that adequate environmental analytical data
 of acceptable quality are collected during the RI. This guidance
 assists risk assessors in communicating their environmental
 analytical data needs to RPMs. Risk assessors should work
 closely with the RPM to identify  and recommend sampling
 designs and analytical methods that will maximize the quality
 of the baseline risk assessment for human health within thesite-
 related and budgetary constraints of the RI, and will produce
 consistent risk assessments useful to risk manageis.

 Although ecological data useability  is not addressed specifi-
 cally in this manual, the chemical data obtained from the site
 characterization are useable for certain elements of the ecologi-
 cal assessment.  However, in an ecological assessment, the
 chemicals of potential concern and their priorities may  be
different than those of the human health risk assessment. For
example, iron is rarely of concern in human health risk assess-
ments, but high  levels of iron  may  pose a threat to aquatic
species.
 1.1    Critical data quality issues In
        risk assessment

 The Risk Assessment Subgroup has identified five basic envi-
 ronmental data quality issues that arc frequently encountered in
 risk assessments. This manual provides procedures, m in in mm
 requirements, and other guidance to resolve or minimize the
 effect of these issues on the confidence of the risk assessment.
 The following sections describe these issues and their impact
 on data useability, and highlight the resolutions of these issues.
 The issues affect both the planning for and the assessment of
 analytical data for use in the RI risk assessments.


 1.1.1   DATA SOURCES

 Data users must select an analytical source and service appro-
 priate to the data needs  of each risk assessment.  Practical
 tradeoffs among detection limits, response time, documenta-
 tion, analytical costs, and level of uncertainty should be consid-
 ered prior to selecting sampling designs, analytical methods,
 and service providers.

 The Contract Laboratory Program (CLP) has been the principal
 source of analytical data for investigations at hazardous waste
 sites.  The CLP requires adherence to specific data acceptance
 criteria which results in data of known quality produced in a
 standardized package. Also, CLP costs are not charged directly
 to the RPM's site account.  However, in many cases, other
 analytical sources, such as field analysis or fixed laboratories
 (EPA, State, or private), can produce data of acceptable quality
 at equal or lower cost than the CLP. Accordingly, RPMs and
 mk assessors should not  use the CLP as a cbfanh opt ion N,",
 should seek the source of data that best meets the data quality
 needs of the risk assessment.  Section 4.2 of this manual
 provides guidance for selecting analytical sources.

 Field analytical data have been used primarily to aid in making
 decisions  during sampling.   However, recent  advances  in
 technology, when accompanied by sufficient and appropriate
 quality control measures,  allow field analytical data to be used
 in risk assessments with more frequency and more confidence
 than in the past. By using field analyses, RPMs can increase the
 number of sample numbers to better characterize the site and
 significantly decrease sample turnaround time to provide real-
 time decision-making in the field, as long as acceptable data
 quality is maintained. Guidance  for assessing the usability
 and applicability of field analytical data in the risk assessment
 process is also provided in Section 4.2.


 1.1.2   DETECTION LIMITS

Selecting the analytical method to meet the required detection
limits is fundamental to the useability of analytical data in risk

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 Chapter 1   Introduction and Background
assessments.  In addition, the type of detection limit such as
method detection limit (MDL), or sample quantiLation limit
(SQL), used in making data quality decisions affects the con-
fidence of the nsk  u'-'sev.ment. O'aiJaacv fo; a^km,., i.^ .c
decisions is provided in Section 4.2 of this manual.


1.1.3   QUALIFIED DATA

Laboratories  and individuals  conducting independent data
review affix coded qualifiers  to data when quality control
requirements or other evaluation criteria are not met. Qualified
data must be appropriately used in risk assessments. Data are
almost always useable in the risk assessment process, as long
as the uncertainty in the data and its impact on the level of
confidence of the risk assessment are thoroughly explained.
Section 5.6 describes procedures for incorporating qualified
data and data of various qualities into the risk assessment.


1.1.4   BACKGROUND SAMPLES

In conducting a risk assessment, it is critical to distinguish site
contamination from background levels due to anthropogenic or
naturally-occurring contamination, in order to determine the
presenceor absence of contamination. Analytical data reported
near method  detection limits, and sample results  qualified
during data review, complicate the use of background sample
data to determine site contamination. Planning for the collec-
tion of a sufficient number of background samples from repre-
sentative locations increases the confidence in decisions about
the presence or absence of site contamination. Section  4.1
discusses how statistical analysis and professional judgment
can be combined to design a samplinc program for colicuing
adequate background data.


1.1.5   CONSISTENCY IN  DATA
         COLLECTION

Data collection activities may van among parties conducting
RIs. Consistency in all Superfund acuviues  is increasingly
crucial. All parties collecting environmental analytical data for
baseline risk assessments for human health must use guidance
provided in RAGS (EPA 1989a) and this manual to ensure that
baseline risk  assessments for human health  are conducted
consistently and are protective of the public health.


1.2    Framework and Organization
        of the Manual

This guidance manual  is organi/ed  following the  usual  se-
quence to determine the useabi!it\ of cm ironmcntal analytical
data used in baseline human heakh n>k. assessments. Exhibit
l-l illustrates the conceptual framework for the manual.  Six
criteria are defined here to evaluate daia useability for baseline
risk assessments for human health:

•   Data sources.

   Documentation.

•   Analytical methods and detection limits.

•   Data quality indicators.

•   Data review.

•   Reports to risk assessor.

These criteria address the five major data quality  issues  de-
scribed in Section 1.1 and other issues that impact data useabil-
ity in the risk assessment. These data useability criteria  are
applied in RI planning to guide the design of sampling plans  and
select analytical methods for the data collection effort. The
criteria are employed again to assess the useability of  the
analytical data collected during the RI, and of data from other
studies and sources, such as site inspections (Sis). Finally, the
manual describes how to determine the degree of confidence in
the risk assessment based on the level of uncertainty of the
environmental analytical data determined using the data usea-
bility criteria.

           The primary planning objective is that
           uncertainty levels are acceptable,
           known and quantifiable, not that
           uncertainty be eliminated.


F.xhihil  1-2 summari/es uV n'irpnso of ej'h  chapter if  *J i -
manual and highlights how the chapter can best assist RPMs
and risk assessors. Worksheets, assessment tables, and other
aids are used extensively throughout the manual. Chapter
contents are summarized below:

•   Chapter 2 - The Risk Assessment Process: This chapter
   explains the purpose and objectives of a baseline  human
   health  risk assessment and describes the  four basic  ele-
   ments of a risk assessment:  1) data collection and evalu-
   ation, 2) exposure assessment, 3) toxicological assessment,
   and 4) risk characterization.  The chapter discusses  the
   uncertainties associated with the risk assessment process
   and emphasizes the  impact of analytical data  quality on
   each element. The roles and responsibilities of the RPM,
   the risk assessor, and others involved in planning  and
   conducting data  collection activities to support the  risk
   assessment are described.

•   Chapter 3 -  Criteria for Evaluating Data Useability in
   Baseline Risk Assessments: Six criteria are defined in  this
   chapter for interpreting the impact of sample collection,
   analytical  techniques, and data review  procedures on  the

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                                                            Chapter  1  Introduction  and Background
                                         EXHIBIT 1-1

    DATA USEABILITY CRITERIA TO PLAN SAMPLING, ANALYSIS AND
         ASSESSMENT EFFORTS  IN BASELINE RISK ASSESSMENTS









DEFINING
DATA USEABILITY
CRITERIA (3.1)

• Data Sources
• Documentation
• Analytical Methods
and Detection Limits

• Data Quality
Indicators
• Data Review
• Reports to Risk
Assessor


^




^


PLANNING
SAMPLING
CONSIDERATIONS
• Preliminary Sampling
Issues (3 2)
• Strategies for
Designing
Sampling Plans (4.1)

ANALYTICAL
CONSIDERATIONS
• Preliminary Analytical
Issues (3.3)
• Strategy for Selecting
Analytical Methods
(42)



^f




-+>

ASSESSING
DATA USEABILITY
CRITERIA (5 0)

• Reports to Risk
Assessor
• Documentation

• Analytical Methods
and Detection Limits
• Data Review
• Data Quality
Indicators











DETERMINING

LEVELS
OF
CERTAINTY
FOR
BASELINE
RISK
ASSESSMENT
(6.1)












 uscabiluy of analytical data in  nsk assessments.  The
 -anipling and analytical issue- :.~^i need to be addressed m
 .. -  ik div s^ ^;i; .r,a aic di-^ „- -.:  Tne chapter stresses the
 iieed to consider and plan for ~>k assessment data require-
 ments m the early design stages of the RI.
Chapter 4  - Steps for planning for the Acquisition  of
 Useable Environmental Dan  in Baseline  Risk Assess-
ment-,: This chapter provides e~ p'. .e it guidance for design-
 mi! sampling plans and select:" analytical methods based
on  tiie data quality requirer.:;-;s of baseline risk assess-
ments.  A Sample  Design  Selection  Worksheet and a
Method Selection Workshee: are provided  as part of the
step-by-step guidance for making data collection decisions
for  individual sites.

Chapter 5-Assessment of En\ _"?n:nental Data for Uscabil-
it>  in  Baseline Risk  Asscssme-L? This chapter explains
how to assess the iiscabihtv  of sac-specific data for risk
assessments after data collector and describes options
available to risk assessors for incorporating analytical data
from different sources and of > anous levels of quality into
the  baseline risk assessment  l?aia evaluation is presented
in assessment phases to address data useability by the six
criteria defined in Chapter 3  For each assessment phase.
the chapter defines minimum da'a requirements jmi ex-
plains how to identify performance objectives, determine
actual performance compared to objectives, and execute
appropriate corrective actions for data critical to the risk
assessment.
Chapter 6 - Application of Data to Risk Assessments  This
chapter details procedures for determining the overall level
of uncertainty associated \\ith the risk assessment  "1 he
discussion addresses characten/ation  of exposure path-
ways, determining the presence or absence of chemicals, of
potential concern, and distinguishing site contamination
from background levels.
Appendices - The appendices provide analytical and sam-
pling technical reference materials including: descriptions
of generic organic and inorganic data review packages;
listings of common pollutants, analytical methods, detec-
tion or quantitation limits, and common  laboratory con-
taminants; calculation formulas for statistical evaluation,
and information on analytical data qualifiers.

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Chapter 1   Introduction and Background
                                                   EXHIBIT 1-2

                                    ORGANIZATION OF THE MANUAL
           Chapter 1
           Introduction and Background

              Presents critical data useability issues.
              Specifies audience to be primarily RPMs and
              risk assessors.
           •   Defines scope and specifies organization of manual.
              Chapter 2
              The Risk Assessment Process
                   Explains the elements of a risk assessment and the impact of
                   analytical data quality on each element.
                   Defines the uncertainties in the risk assessment process.
                   Describes the roles of the risk assessor, RPM and others
                   involved with the risk assessment planning and assessment
                   process.	
                      Chapter 3
                      Criteria for Evaluating Data Useability in Baseline
                      Risk Assessments
                         Defines six criteria for assessing data useability.
                         Applies criteria to sampling and analytical issues.
                           Chapter 4
                           Steps for Planning for the Acquisition of Useable
                           Environmental Data in Baseline Risk Assessments

                              Provides guidelines for designing sampling plans and selecting
                              analytical methods.
                           •   Provides worksheets to support sampling plan design and
                              analytical method selection.
                                   Chapter 5
                                   Assessment of Environmental Data for
                                   Useability in Baseline Risk Assessments

                                   •  Describes minimum requirements for useable data
                                     Explains how to determine actual performance compared to
                                     objectives.
                                     Recommends corrective actions for critical data not meeting
                                     objectives.
                                     Describes options for combining data from different
                                     sources and of varying quality into the risk assessment.
                                          Chapter 6
                                          Application of Data to Risk Assessments

                                          •  Provides procedures to determine the uncertainty of the
                                             analytical data.
                                          •  Explains how to distinguish site from background levels of
                                             contamination and determine presence (absence) of
                                             contamination.
                                             Discusses how to characterize exposure pathways
                                              APPENDICES
                                                  Provide technical reference tables for sampling and analysis
                                                  Describe data review packages and meanings of selected
                                                  data qualifiers

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                               Chapter 2 The Risk Assessment Process
Chapter 1
Introduction and Background
              Chapter 2
              The Risk Assessment
              Process
              •  Explains the elements of a risk
                 assessment and the impact of
                 analytical data quality on each
                 element.

                 Defines the uncertainties in the risk
                 assessment process.

                 Describes the roles of tiie risk
                 assessor, RPM and others involved
                 with the risk assessment planning
                 and assessment process.

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Chapter 2 The Risk Assessment Process
                           ACRONYMS FOR CHAPTER TWO
                         ATSDR   Agency for Toxic Study and Disease Registry
                         CERCLA  Comprehensive Environmental Response, Com-
                                  pensation, and Liability Act
                         FS       Feasibility Study
                         IRIS      Integrated Risk Information System
                         LOAEL   Lowest-observable-adverse-effects-level
                         LSI      Listing Site Inspection
                         NOAEL   No-observable-adverse-effects-level
                         NPL      National Priorities List
                         RAGS    Risk Assessment Guidance for Superfund
                         RfD      Reference Dose
                         RI       Remedial Investigation
                         RME     Reasonable Maximum Exposure
                         RPM     Remedial Project Manager
                         QA      Quality Assurance
                         QC      Quality Control
                         S -Y?      Sampling ana Ana!) sis Plan
                         UCL      Upper Confidence Limit

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                                                           Chapter 2  The Risk Assessment Process
2.0    THE RISK ASSESSMENT
        PROCESS

This chapter presents an overview oi the dam euilecuuii and
evaluation issues that affect the quality and useabihty of
baseline human health risk assessments. The discussion fo-
cuses on the influence of the quality of environmental analyti-
cal data on the level of certainty of the risk assessment, and the
importance of an analysis of data limitations in characterizing
risks to human health.

The chapter assists Remedial Project Managers (RPMs) and
other professionals in planning and conducting RI data collec-
tion efforts supportive of the risk assessment process by en-
hancing their understanding of the data quality needs of risk
assessors.

Two main sections comprise this chapter:
•  Section 2.1 provides an overview of the baseline human
    health risk assessment, and discusses the significance of
    uncertainty in each stage of the risk assessment process.
•  Section 2.2 is a summary of the roles and responsibilities of
    key participants in the risk assessment process.


2.1    Overview of  Baseline  Human
        Health  Risk Assessment and
        the Evaluation of Uncertainty

The approach to baseline human health risk assessments of
exposure to chemicals of potential concern is well established.
The National Research Council (7s RQ prepared a comprehen-
sive overview of the structure of this assessment (NRC 1983)
that has become the foundation for subsequent EPA guidance
(EPA 1986b; EPA 1989a,b). The Risk Assessment Guidance
for Superfund (RAGS): Human Health Evaluation Manual Part
A (EPA 1989a) provides a detaileJ presentation of the human
health baseline risk assessment for the Superfund Program.

The risk assessment process has lour components:
•  Data collection and evaluation.
«  Exposure assessment.
•  Toxicity assessment.
•  Risk characterization.

Exhibit 2-1 lists information sought in each component of the
baseline risk assessment.

Uncertainty analysis is often \ ie\- ed as the last step i.i the risk
characten/ation process. HO'A e\ er, uncertainty analysis is a
fundamental clement of each of ir.e components of risk assess-
ment, and the results for each component must be presented
with an explicit statement of Ac degree of confidence. These
                     EXHIBIT 2-1

         COMPONENTS AND RELATED DATA OF
           THE RISK ASSESSMENT PROCESS
RlSKASSESSMENf
COMPONENT
Data Collection and
Evaluation
Exposure Assessment
Toxcity Assessment
Risk Characterization
DATA
Background moni'onng da'a for all aMecMd media
Environmental data 'or all rolgvant media
List of chemicals o! potential concern
Shape of the distribution of sampling data
Confidence limits surrounding estimates of
representative values.
Release rates.
Physical, chemcal and biological parameters to
evaluate transport and transformation of site-
related chemicals.
Parameters to characterize receptor groups
including actrviiy/behavior/sensrtivrty
Estimates of exposure concentrations for all
chemicals, environmental media and receptors at r i .c
issues determining the level of confidence in each component
of risk assessment.

           The risk assessor must be involved
          from the start of the RI process to
           maximize the useahility of environ-
           mental analytical data for risk assess-
           ments.
The importance of obtaining analytical data that fulfill the
needs of risk assessment cannot be overstated.  The risk
assessor must be involved from the start of the risk assessment
process to help establish the scope of the investigation and ihe
design of the sampling and analysis program.

All analytical data that result from risk assessment operations
must be evaluated for their useability in baseline risk a-^ess-
ments. The procedures used to evaluate the adequacy of the
data should be documented along with the resulting csunuies
of the levels of confidence Although limitations in the aiul.' i
cal data introduce additional uncertainties and restrict the ,lcpth

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Chapter 2 The Risk Assessment Process
                                                   EXHIBIT 2-2

                           BASELINE RISK ASSESSMENT PROCESS AND
                                TYPICAL SOURCES OF UNCERTAINTY
      Exposure Assessment
      Assumptions regarding intake factors,
      population characteristics, and
      exposure patterns may not adequately
      characterize exposure and may result in
      underestimates or overestimates of risk.

      Assumption of 100 percent
      bioavailabiltiy of chemicals in
      environmental media (soil in particular)
      may result in overestimates of risk.

      Assumption that chemicals of potential
      concern do not degrade or transform in
      the environment may result in
      underestimates or overestimates
      of risk.

      Incremental risks associated with
      exposure to site-related chemicais 01
      potential concern cannot be fully
      characterized. May result in
      underestimates of risk.

      Methods used to estimate inhalation
      exposure to suspended partieulates or
      dust may overestimate intake and nsk.

     1 Very few percutaneous absorptcn
      factors  are available for chemicaJs of
      potential concern. Exposure from
      dermal  contact may be overestimated
      using conservative default values
      Source: RAGS (EPA 1989a>
   Data Collection
   and Evaluation

Use of inappropriate method
detection limits may result in
underestimates of risk.

When insufficient number of
samples are taken, methods
used to estimate contaminant
concentrations may result in
overestimates of risk.

Contaminant loss during
sampling may result in
underestimates of risk.
        Risk
Characterization
Risk/dosa astimates are
assumed to be additive in the
absence of information on
synergism and antagonism.
May result in overestimates
or underestimates of risk.

Toxicity measures are not
available for all chemicals of
potential concern. Risks
cannot be quantitatively
characterized for these
compounds. May result in
underestimates of risk.

In the absence of toxicity
measures for polynudear
aromatic hydrocarbons
(PAHs).  the cancer slope
factor for benzo(a)pyrene
and the RfDs for napthalene
are commonly adopted. This
approach may overestimate
risks
Toxicity Assessment

Critical toxicity measures are
derived from animal studies
using high dose levels.
Exposures in humans occur at
low dose levels. Assumption of
linearity at low dose may result
in overestimates or
underestimates of risk.

Extrapolation of results of
toxicity studies from animals to
humans may introduce error and
uncertainty, inadequate
consideration of differences in
absorption, pharmacokinetics,
and target organ systems, and
variability in population
sensitivity.

There is considerable
uncertainty in estimates of
toxicity measures. Critical
toxicity measures are subject to
change as new evidence
becomes available.  May result
in overestimates or
underestimates of risk

Use of upper 95th percent
confidence limit on cancer slope
factor may result in
overestimates of lifetime cancer
risk.

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                                                               Chapter 2  The  Risk Assessment Process
and breadth of the evaluation, they are not the only source of
uncertainty in risk assessment.  Exhibit 2-2 identifies some
typical sources of uncertainty inherent in each component of
the risk assessment.

Risk assessment procedures cover a continuum from simpli-
fied, screening-level assessments, to more comprehensive
evaluations requiring a robust data set (one designed to support
statistical analyses) with a substantial  investment of time and
resources.  Exhibit 2-3 presents this  continuum concept of
baseline  risk as-
sessment   and
summarizes  the
influence of the
level of certainty
in the  analytical
datasetontherisk
assessment. Two
columns   are
shown. The first
column  defines
the range of the
analysis from low
to high degree of
uncertainty. The
second column
                                          lifetime cancer risk and the calculation of hazard indices.
                                          However, the level of analytical uncertainty surrounding
                                          these measures is not quantitated and may be large. Given
                                          the limitations of the analytical data, only a qualitative
                                          evaluation of the analytical uncertainty is  generally tea-
                                          sible.  Most baseline risk assessments fall  within tins
                                          category.

                                          The third level of the continuum is a qualitative assessment
                                          of risk. The assessment is qualitative because no numeric
                                      EXHIBIT 2-3
              RANGE OF UNCERTAINTY OF RISK ASSESSMENT
describes the as-
sociated   data
useability  and
limitations in the
risk analysis.

•  The first level
   of analysis in
   Exhibit 2-3 is
   a quantitative
   risk  assess-
   ment  based
   upon a sam-
   pling   pro-
   gram that can
   be   statisti-
   cally   ana-
    RANGE OF ANALYSES
Quantitative Assessment of Risk:

Uncertainty minimized, quantified, and
explicitly stated. Uncertainty is low.
Quantitative Assessment of Risk:

Magnitude of uncertainty unknown.  No
explicit quantitative estimates provided.
Qualitative, tabular summary of factors
influencing risk estimates may be
provided.
Qualitative Assessment of Risk:

No uncertainty estimate possible.
Uncertainty is nigh.
           DESCRIPTION/LIMITATIONS
Risk assessment conducted using well designed, robust
data set.  Sampling program, based on geostatistical or
random design, will support statistical analysis of results.
Statistical analysis used to characterize monitoring data.
Confidence limits or probability distributions may be
developed for all key input variables.
Risk assessment conducted using data set of limited quality
and size. Data set limited by cost, resource and time
constraints. No meaningful statistical analysis can be
conducted.  Results of risk assessment may be quantified
but uncertainty surrounding these measures cannot be
quantified. Only a qualitative statement is possible.
The majority of baseline risk assessments typically fall
within this category.
Risks cannot be quantified due to insufficient monitoring or
modeling data. Qualitative statement of risks based on
historical information or circumstantial evidence of
contaminantion is provided. This evaluation must be
considered a preliminary, screening level assessment.
   lyzed.  The assessment explicitly bounds and quantitates
   the uncertainty in all estimates. This analysis represents an
   ideal that the risk assessment team strives to attain. The
   assessment is considered "quantitative" in that numeric
   estimates are derived for the potential for both adverse non-
   carcinogenic and carcinogeni; effects, and the level of
   certainty can be quantitated.

   The mid-range of the continuum shown in Exhibit 2-3 is a
   quantitative  assessment based on a limited number of
   samples oron data that cannot be fully quantitated. The risk
   characterization may include - jmeric estimates of excess
                                          measures can be derived to indicate the potential for ad-
                                          verse effects, and the level of certainty cannot be asses>>cd.
                                          The risk to human health may be considered only in general
                                          terms.  The assessment may be based upon limited sources
                                          of historical information such as disposal records, circum-
                                          stantial evidence  of contamination, or preliminan.  site
                                          assessment data.

                                                  All data can be used in the baseline
                                                  risk assessment as loni; as their
                                                  uncertainties art- rlearl\  described

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 Chapter 2 The Risk Assessment Process
Risk assessments sometimes must be conducted using data of
limited quantity and various quality. When RPMs and other
technical experts involved in the RI understand the data and the
quality of data required in risk assessments, they are better able
to design data collection programs which meet these require-
ments.


2.1.1   DATA COLLECTION AND
         EVALUATION

Overview of methods for data collection
and evaluation

Data collection begins with a statement of the risk assessment
purpose and an understanding of the problems to be addressed
for the site under investigation. This involves a review of all
available historical data and the development of a conceptual
site model (EPA 1989a). Once the conceptual model has been
developed and information has been disseminated to project
staff, data gaps and requirements  for the baseline risk assess-
ment can be identified during scoping.   Several key  issues
should be addressed at this time (Neptune et al. 1990):
•   Identify the types of data needed.

•   Specify how the data will be used.
•   Establish the desired level of certainty for conclusions to be
   derived from the analytical data.

Carefully designed sampling and analysis programs will mini-
mize the subsequent need to qualify the environmental data
during the data assessment phase. The objective of the data
collection effort is to produce data that can be used to assess
risks to human health with a known  degree of confidence.

A complete list of chemicals of potential concern is produced
when the analytical data have been collected and evaluated.
This list of analytes is the focus of the risk assessment EPA no
longer advocates the selection of "indicator compounds" be-
cause this  may not accurately the reflect the total risk from
exposure to multiple site chemicals  of potential concern, nor
improve the quality or accuracy of the risk assessment.

Uncertainty in data collection and
evaluation

Four principal  decisions must be  made in the data collection
and evaluation phase of the risk assessment:

•   Determining the presence and le% e Is of contamination at the
   site under evaluation.

•   Determining if the levels of site contamination differ sig-
   nificantly from background concentrations.
•  Evaluating whether the analytical data are  adequate to
   identify and examine exposure pathways.
•  Evaluating whether the analytical data are adequate to fully
   characterize exposure pathways.

These decisions are examined in detail in subsequent chapters
in this manual. The discussion in this section introduces (he
basic concepts.

Determining what contamination is present and at
what level

Once the site is suspected to be contaminated and chemicals of
potential concern have been identified, the levels of chemical
contamination in the affected environmental media must be
quantitated in order to derive exposure and intake estimates.
Estimates of the site contamination must be produced with
explicit descriptions of the degree of confidence associated
with the concentration values.

Variability in observed concentration levels is  due to both
sampling design and  laboratory analysis.  The key issue in
optimizing the useability  of data is to understand, minimize,
and quantify these uncertainties in the risk assessment

EPA's objective is to  protect human health and  the environ-
ment.  Therefore, the design of remedial investigation pro-
grams is intended to minimize two potential errors:

•  Not detecting site  contamination that is actually present
   (i.e., false negative values).

•  Deriving site concentrations that do not accurately charac-
   terize the magnitude of contamination.

Determining if site concentrations differ
significantly from background concentrations

A fundamental issue in conducting baseline risk assessments is
determining if there is an increased risk to human health and the
environment The ability to answer this question depends on
the degree of confidence  in determining that the background
concentrations are significantly different from the concentra-
tions of the chemicals of potential concern at the sue. Gener-
ally, this question can be confidently answered only if the
design of the sampling program accommodates the collection
of both site and background samples and if the selection of
analytical methods is appropriate.

Evaluation of the differences between site and background
concentrations is conducted by comparing observed levels of
chemicals of potential concern at the site with measured back-
ground concentrations of the same chemicals  in the same
environmental media. Statistically,  this is a test of the null
hypothesis, that the mean concentration of a chemical at the
study area is not significantly different from the mean value at
the background (or remote uncontaminalcd) location.
                                                      12

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                                                              Chapter 2  The Risk Assessment Process
 If data from background samples arc, clearly different from the
 results of site monitoring (e.g., chemical concentrations differ
 consistently by two orders of magnitude), statistical analysis of
 liie data  n>a> not be necc.ssai).  L.ulji ou^li ^i.^umsLi.ijj>,
 RAGS (EPA 1989a) indicates that the primary issue is estab-
 lishing a reliable representation of the extent of the contami-
 nated area.

 The null hypothesis is always evaluated and accepted or re-
 jected with a specified level of certainty . This level of certainty
 is defined by the significance or confidence level. Type I error
 is the probability that the null hypothesis is rejected when in f ac t
 it is true (i.e., false positives).  Type II error is the probability
 that the null hypothesis is accepted when it is false (i.e., false
 negatives). The effects of sampling and analysis design on the
 likelihood of these two types of errors are described in Chap-
 ter 4.

 Evaluating whether analytical data are adequate to
 identify and examine exposure pathways

 Identifying and delineating exposure pathways is important in
 identifying potentially exposed populations and in developing
 intake estimates. In the baseline risk  assessment, the risk
 assessor combines data on the extent of contamination with
 information on human activity patterns to identify exposure
 pathways and to examine the exposure area. The ability to
 accomplish this depends on the adequacy of available analyti-
 cal data.

 Sampling should be designed to provide representative data for
 exposure areas at a site, to address hot spots, to  evaluate the
 transport of site-related chemicals of potential concern, and to
 facilitate the identification. of _!'. c ••• :v > .j-e patl: .=. :r. -= A -.'ell-
 designed sampling and analysis program will result in data of
 known quality, quantification of spaual and temporal variabil-
 ity, and specify an approach for interpreting the magnitude of
 observed values (e.g., comparison -A iih background levels or
 some other benchmark).  The de\ elopment of a three-dimen-
 sional characterization of the extent of contamination at the site
 is a critical element of this
Evaluating whether analytical data are adequate to
fully characterize exposure pathways
Heterogeneity should be conside
medium under evaluation. Hotspo
characterized.  Neptune et al  \Nc::
posed the concept of an "exposure ~
receptors are projected to i me era:;
establishes a basis for summan/ir;
and transport modeling. The samr
must be designed to enable the risk .
charactcri/.ation of exposure ru'j".
temporally identify the critical arc^
red in  the environmental
is need to be identified and
-leetal. 1990) have pro-
~it"as the area over u hich
:  exposure  This concept
: ihe results of monitoring
ling and analysis program
L-^OsSor to refine the initial
-3\s and 10 spatially and
.^ o>'exposure.
 2.1.2   EXPOSURE ASSESSMENT

 Overview of methods for exposure
 assessment

 The objectives of the exposure assessment are to:
 •   Define exposure pathways.
 •   Identify potentially exposed populations.
 •   Measure or estimate the magnitude, duration, and  fre-
    quency of exposure for each receptor (or receptor group).

 Analysis of the first two objectives is particularly important in
 identifying data needs. Quantitation of the exposure concentra-
 tion of each chemical of potential concern in each environ-
 mental medium, and an assessment of the transport and trans-
 formation of the subject compounds are critical to the exposure
 assessment.  These analyses provide essential information on
 the nature and extent of contamination needed for risk assess-
 ment. Typically, the exposure assessment uses both monitor-
 ing data and environmental transport models.

 Actions at hazardous waste sites are based on an estimate of the
 reasonable maximum exposure (RME) expected to occur under
 both current and future conditions of land use (EPA  1989a).
 EPA defines the RME as the highest exposure that is rcasonabl y
 expected to occur at a site over time. RMEs are  estimated for
 individual pathways, and combined across exposure pathways
 if appropriate. Once potentially exposed populations are iden-
 tified, environmental concentrations at points of exposure must
 be determined or projected. Intake estimates (in mg/kg-da\)
 are then developed for each chemical of potential concern using
 a conservative, protective estimate of the concentra'aon _\ r-
 tacted by the receptors over the  exposure period   The 45
 percent upper confidence limit (UCL) on the arithmetic mean
 environmental concentration  is used in developing the RME
 scenario (EPA 1989a).

 In the  risk assessment report, estimates of intake must be
 accompanied by a full description of the assumptions made n
 their development.  In addition, the risk assessor should indi-
 cate the range of possible values for each input parameter in the
 assessment.  This information ma> be used subsequently to
 conduct sensitivity and uncertainty analysis in the risk charac-
 tcri/ation.

 Uncertainty analysis  in exposure
 assessment

 Exposure assessments can introduce large uncertainty into the
 baseline risk  assessment process.  The  largest measure of
uncertainty will be associatcd with charactcn/mg transport and
transformation of chemicals m the environment, establishing
exposure settings, and deriving estimates of sub-, hromc  an.)
                                                      n

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 Chapter 2  The Risk Assessment Process
chronic intake.  The ultimate effect of uncertainty in the
exposure assessment is an uncertain estimate of intake.

The following two parts of this sub-section discuss the signifi-
cance of uncertainty in  the analytical data set  on  selected
aspects of exposure assessment For a complete discussion of
the exposure assessment process, the reader is referred to the
Superfund Exposure Assessment Manual (SEAM) (EPA 1988c)
and RAGS  (EPA 1989a).

Characterizing environmental fate,  exposure
pathways and identifying receptors at risk

An evaluation of the transport and transformation of chemicals
in the environment is conducted for several reasons:
•   to understand the behavior of site-related chemicals of
    potential concern.
•   to project the ultimate disposition of these chemicals.
•   to identify exposure pathways and receptors at risk.
•   to characterize environmental concentrations at the point of
    exposure.

These evaluations cannot be accomplished with any degree of
confidence if the analytical data are inadequate.

Environmental fate transport assessment often uses models to
estimate concentrations in environmental media at points dis-
tant from the source of release. These transport models are
commonly selected for their utility in describing or interpreting
a set of monitoring data. Chemical transport models must be
carefully selected for their ability to meaningfully characterize
the behavior of compounds in the  environmental  medium
under investigation.   Models that are inappropriate for the
geophysical conditions at the site will result in errors in the
exposure assessment. For example, the model may be designed
to predict contaminant movement through clay while soils at
the site are primarily made up of sand.  Additionally, if the
analytical data  set is severely limited in sire,  or does not
accurately characterize the nature of contamination at the site,
a transport model cannot be properK selected or calibrated
accurately. This will introduce additional  uncertainty. Uncer-
tainty in the analytical data, compounded by uncertainty caused
by the selection of the transport models may yield results that
are meaningless or uninterpretable.

Monitoring data are most appropriately used to estimate cur-
rent or existing  exposure when direct contact with contami-
nated environmental media is the primary concern. Transport
modeling is required, however, in order to evaluate the poten-
tial for future exposure, or exposure at a distance from the
source of release. In both cases, success in identifying poten-
tially exposed populations depends  on the adequacy of the
analytical data.
Estimating chemical intake

Uncertainties in all elements of the exposure assessment arc
brought together and compounded in the estimate of intake. It
is here that the professional judgement of the n.sk assessor
becomes particularly important. The risk assessor must exam-
ine and interpret a diversity of informntion:

•  The nature, extent and magnitude of contamination.

•  Results of environmental transport modeling.

•  Identification of exposure pathways.

•  Identification of receptor groups currently at risk and poten-
   tially at risk in the future.
•  Activity patterns and sensitivities of receptors and receptor
   groups.

Based on this information, the risk assessor must characterize
the exposure setting and quantify all parameters needed in the
equations to estimate intake (EPA 1989a).  Chemical intake is
a function of the concentration of the chemical at the point of
contact, the amount of contaminated medium contact per unit
time or event,  the exposure  frequency and duration,  body
weight, and the average period during which exposure occurs.
Exhibit 2-4 is the generic form of the intake equation used in
exposure assessment.

                       EXHIBIT 24
 GENERIC EQUATION FOR CALCULATING CHEMICAL INTAKES
                     / CRXEFD \    J
                     V ~""BW	 /  *  A
                            weight-da,,
          Chemical-related variable
               C -   chemical concentration, me average
                    concentration contacted sver the exposure
                    pecod (e g . mg'liter wa'e *

          Variables thai describe the exposed DOOL, a* on

               CR -  contact rate l*^ amour* :* contarn-ia'ec
                    -medium contacted per ^- • :* (e g
                    liters/day)

               EFO - exposure frequency and duraton, describes
                    how long and how often etposure occurs
                    Often calculated using two terms (EF and ED)

                      EF  -   exposure frequency (days/year)

                      ED  -   exposure duMton (years)

               BW -  body weight, the average body weight over the
                    exposure period (kg)


          Assessment -determined variable

               AT -  averaging time, period ov*r wh«ch exposure is
                    averaged (days)
  Sourc* RAGS {EPA 1989a)
                                                         14

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                                                              Chapter 2 The  Risk Assessment Process
 The specific form of the intake equation will vary depending
 upon the exposure pathway under consideration, e.g., inges-
 tion, inhalation, dermal contact (EPA 1989a). For the purpose
 ui'quantiLiuug intake, each of the variables in these equations,
 including chemical concentration, are commonly character-
 ised as point estimates. However, each intake variable in the
 equation has a range of possible values.  Site-specific charac-
 teristics determine the selection of the most appropriate values.
 EPA is currently establishing standardized exposure parame-
 ters to decrease the variability in the exposure assessment.
 These standard factors may be used when site-specific data are
 unavailable.  Intake values should be  selected so that the
 combination of all values result in an estimate of reasonable
 maximum exposure for that pathway (EPA 1989a).

 The recommended approach to uncertainty analysis in expo-
 sure assessment is to explicitly present the range of observed
 values for chemicals in the environment and the factors used in
 developing intake estimates (EPA  1989a).  A summary of
 major assumptions and the anticipated effects on final exposure
 estimates is then developed.  This provides a qualitative but
 helpful characterization of the level of confidence in the intake
 estimates.
 2.1.3   TOXICITY ASSESSMENT

 Overview of methods for toxicity
 assessment

 The objectives of the toxicity assessment are to evaluate the
 inherent toxicity of the compounds under investigation, and to
 identify and select lexicological measures for use in evaluating
 the significance of the exposure.  Toxicity assessments con-
 ducted under the Superfund program rely on scientific data
 available in the literature on adverse effects in humans and
 nonhuman species used to identify toxicity measures for use in
 risk assessment.

 Several important measures of toxicity are needed in conduct-
 ing an assessment of risk to human health. Reference doses
 (RfDs) are  used for oral and inhalation exposure to evaluate
 non-carcinogenic and developmental effects and cancer slope
 factors are  used for the oral and inhalation  pathways  for
 carcinogens.

 RfDs are measures verified by EPA to evaluate the potential for
 non-carcinogenic effects.  The RfD is defined as an estimate
 (with uncertainty spanning an order of magnitude or more) of
 a daily exposure level for human populations, including sensi-
 tive sub-populations, that is likeh to be without an appreciable
 risk of ad verse effect over the pencxl of exposure (EPA 1989a).
These measures are derived from no-observable-adverse-ef-
 fccts levels  (NOAEL^) or louest-ohservable-advcrse-effccts
 levels (LO AELs) and the application of uncertainty and modi-
 fying factors (EPA 1989a). Uncertainty factors are used to
 account for the variation in sensitivity of human sub-popula-
 tions and the uncertainty inherent in extrapolation of the results
 of animal studies to humans. Modifying factors account for
 additional uncertainties in the studies used to derive the NO AEL
 or LOAEL.

 Cancer slope factors are defined as plausible, upper-bound
 estimates of the probability of cancer response in an exposed
 individual, per unit intake over a lifetime exposure period (EPA
 1989a). For carcinogenic compounds, EPA assigns a weight-
 of-evidence rating that reflects the likelihood that the toxicant
 is a human carcinogen. EPA commonly develops slope factors
 for carcinogens with weight-of-evidence classifications (EPA
 1989a).

 To reduce  variability  in toxicological values used for risk
 assessment, a standardized hierarchy of available toxicological
 data is specified for Superfund. The primary source of informa-
 tion for these data is the Integrated Risk Information S ystem
 (IRIS) database. IRIS consists of verified RfDs  and cancer
 slope factors, and health risk and EPA regulatory information.
 Data in IRIS are regularly reviewed and updated. If toxicity
 measures are not available in IRIS, the  EPA Health Effects
 Assessment Summary  Tables (HEAST) (EPA 1990c) are used
 as a secondary current source of information.   Additional
 sources of toxicity information are provided in RAGS (EPA
 1989a).

 Toxicity evaluations commonly consider lethal and sublethal
 effects following short-term or long-term exposure.  The tox-
 icity measures typically used include the  following (EPA
 1989b):

 •    LD50 or LC50 - the administered dose or environmental
   concentration resulting in mortality in 50 percent of the
   exposed organisms
 •  ED50 or EC50 - the administered dose or environmental
   concentration resulting in nonlethal physiological or be-
   havioral effects in 50 percent of the exposed organisms
 •  NOEL/NOAEL - threshold levels below which  an effect is
   not observed
 •  LOEL/LOAEL - the lowest recorded dose at which effects
   were observed.

The  toxicity  assessment is conducted in parallel with the
exposure assessment,  but may begin as early as  the data
collection and evaluation phase.  As chemicals of potential
concern are identified at the site, the project toxicologist begins
to identify the toxicity  measures. A well designed sampling
and analysis program  will  facilitate timely identification of
those chemicals that will be the focus of the risk assessment

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 Chapter 2  The Risk Assessment Process
Uncertainty analysis and toxicity
assessment
risk assessment. Limitations in the analytical data from envi-
ronmental samples affect the results of the toxicity assessment,
but not to the extent that they affect other components of the
Supcrfund risk assessment process. Their principal influence
is on the ability to comprehensively identify, select, and de-
velop toxicity measures needed in risk characterization. The
selection of appropriate toxicity measures is influenced by
monitoring data from environmental samples to the extent that
this information assists in identifying chemicals of potential
concern, exposure pathways, and the time periods over which
exposure may occur. Based on this information, the project
toxicologist will identify sub-chronic or chronic RfDs  and
cancer slope factors for oral and inhalation pathways.

In preparing a list of toxicity measures for risk assessment, i t is
important to provide an indication of the degree of confidence
associated with these values. Weight-of-evidence classifica-
tions  should be included  in the discussion of cancer slope
factors.  Uncertainty and modifying factors used in deriving
RfDs from NOAELs or LOAELs should be included in the
discussion of non-carcinogenic effects.


2.1.4   RISK CHARACTERIZATION

Overview of methods for risk
characterization

The last step in the baseline risk assessment is risk characteri-
zation.  This is the  process of integrating the  results of the
exposure and toxicity assessments, i.e., comparing estimates
of intake with appropriate lexicological measures to determine
the likelihood of adverse effects in potentially exposed popu-
lations.   Risk characterization is considered separately for
carcinogenic and non-carcinogenic effects because organisms
typically respond differently following exposure to carcino-
genic and non-carcinogenic agents   For non-carcinogenic
effects, toxicologists recognize the existence of a threshold of
exposure below which there is likely to be no appreciable risk
of adverse health impacts in an exposed individual. It is the
c urrent EPA position that expos ure to any level of care inogcmc
compounds is considered to cam a risk of adverse effect, and
that exposure is not characterized b> the existence of a thresh-
old.

The procedure for calculating  nsk from exposure to carcino-
genic compounds has been established by EPA (EPA 1986b,
EPA 1989a,b). A non-threshold, dose-response model is used
to calculate a cancer slope factor i mathematically, the slope of
the dose-response curve) for each Chemical. The cancer slope
I actor is used in conjunction v. ith -Jio chronic daily intake  to
derive a probabilistic estimate of excess lifetime cancer risk to
the individual.

A  linean/ed muiiisUiL'c dose-response model is most  com-
monly used by hPA miierivmg the cancer slope estimates. I he
mathematical relationship used in  cancer risk estimates as-
sumes that the dose-response relationship is linear in the low-
dose portion  of the multistage model dose-response curve
(EPA 1989a).  Given this assumption, the slope factor is a
constant, and risk is directly proportional to intake.

The recommended practice for evaluating the potential for non-
carcinogenic  effects is to compare the RfD to  the intake
experienced by the potentially exposed population (EPA 1989a).
This ratio (intake/RfD) is termed the "hazard quotient." It is not
a probabilistic estimate of risk, but simply a measure of con-
cern, or an indicator of the potential for adverse effects. A m ore
detailed discussion of risk characterization  is presented  in
RAGS (EPA 1989a). For further discussion of methods for risk
characterization of environmental or ecological effects, refer to
the references (ORNL 1982, 1986; EPA 1988f, 1989b,f).

Uncertainty analysis  in risk
characterization

No risk assessment is certain. Risk assessment is a process that
provides a best estimate of potential (present and future) risk
along with the limitations or uncertainties associated wilh the
estimates.   The most obvious effect of limitations  in the
analytical data on risk characterization is the ability to accu-
rately project the potential for adverse effects in potentially
exposed individuals. This includes estimates of excess lifetime
cancer risk and the potential for adverse non-carcinogenic
eficcis in numans. Qeariy, a me available monitoring daia  Jo
not facilitate a meaningful determination of reasonable  maxi-
mum exposure values, the nsk estimates will directly reflect
this uncertainty.

            Uncertainties in toxtcolngical meas-
            ures and exposure assessment are
            greater than uncertainties in environ-
            mental analytical data and usually
            have a more significant effect on the
            uncertainty of the risk assessment.
Baseline risk assessments may be conducted using data of
widely varying quality  Resource and time constraints often
limit the opportunity to develop a well-designed and compre-
hensive data set.  Nonetheless, risk assessments must be eon-
ducted using the available information even when there is no
opportunity to improve the data set.   However, the  results
should be presented with an explicit statement regarding limi-
tations and unrerlamiv.

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                                                                    Chapter 2  The Risk Assessment Process
If possible, a sensitivity analysis should be
conducted to bound the results of risk assess-
ments.  A simple approach might consist of
establishing the range of potential values
(e.g., minimum, most likely, and maximum)
for key input variables and discussing the
influence on the resultant level of uncertainty
in the risk estimates. The key variables can
then be ranked with respect to the magnitude
of potential effect on the risk estimates. More
quantitative approaches to uncertainty analy-
sis are desirable if they can be supported by
the available information. Combining proba-
bility distributions using Monte Carlo tech-
niques is one commonly-cited example (EPA
1988c, 1989a,Finkel 1990). An overview of
recommended methods  for assessment of
uncertainty in risk characterization is pre-
sented in the Superfund Exposure Assess-
ment Manual  (SEAM)  (EPA  1988c)  and
RAGS (EPA 1989a). A more detailed con-
sideration  of uncertainty  analysis in  risk
assessment may be found in Methodology
for Characterization of Uncertainty in Expo-
sure Assessment (EPA 1985) and Confront-
ing  Uncertainty in  Risk Management: A
Guide for Decision-Makers (Finkel 1990).


2.2    Roles and

         Responsibilities of
         Key Risk

         Assessment
         Personnel

The risk assessor generally conduc is the risk
assessment with participation from individu-
als with specific skills and technical exper-
tise. The quality and utility of the baseline
risk assessment will ultimately depend on the
planning and interaction of these technical
professionals.  Key participants include the
remedial project manager (RPM) and the risk
assessor, who are primarily responsible for
ensuring that data collected during the RI are
useable for risk assessment activines.  Other
participants include hydrogeologists, chem-
ists, statisticians, quality assurance staff, and
other technical  support personnel involved
in planning and conducting the RI  Exhibit 2-
5 summarizes the roles and responsibilities
of the risk assessment participant
                            EXHIBIT 2-5
                ROLES AND RESPONSIBILITIES OF
               RISK ASSESSMENT TEAM MEMBERS
      INDIVIDUAL
Remedial Project Manager
Risk Assessor
 Hydrogeologist, chemist and
 other technical support
Quality Assurance Specialist
                                      ROLE/RESPONSIBILrTY
                             Directs, coordinates and monitors all activities

                             Establishes network with other data users
                             Including federal, state and local agencies.

                             Gathers existing site data.

                             Organizes scoping meeting.

                             Controls budget and schedule

                             Guides preparation of QA documents

                             Ensures that risk assessor receives preliminary
                             analytical data.

                             Contributes to data assessment.

                             Resolves problems affecting RI objectives
                             including risk assessment issues (e g ,
                             re-sampling, re-analysis).
                           •  Reviews all relevant existing site data.

                           •  Develops preliminary list of chemicals of potential concern
                             and conceptual site model.

                           •  Recommends sample design, analytical
                             requirements including chemicals of potential
                             concern, detection limits and quality control needs
                             dunng project scoping

                           •  Communicates frequently w.th RPM,
                             hydrogeologist and chemis: to ensure that data
                             collected meet needs.

                           •  Reviews and contributes to SAP and QA
                             documents.

                           •  Assesses preliminary aa:a as soon as
                             available to verify conce^'-a1 s *e ~odel and
                             assess data quality

                           •  Recommends corrective action (e g . resampling.
                             reanalysis)

                           •  Assesses reviewed data to' ^seasility m risk
                             assessment.

                           •  Prepares risk assessmen-
Provides technical input 'o scos rg

Prepares/provides input :o SAP aTd QA documents
in support ol risk assess.-er ca;a needs.

Communicates frequently with risk assessor on
status of data collection and issues affecting data

Provides preliminary data 'or rev ew by risk
assessor

Supports fate and transpo"; —oaelmg for the
exposure assessment.

Implements corrective ac ons :o improve data
useability.
Supports preparation ol OA documents

Provides QAdata or reco'—'e-Oations for appropnate 3C

Reviews data tor quality
                                                            17

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Chapter 2  The Risk Assessment Process
2.2.1    PROJECT COORDINATION

All data collection activities that support the risk assessment
arc coordinated by theRPM. The RPM's responsibilities hecin
upon site listing and continue through deletion of the site from
the National Priority List (NPL).  A network of technical
experts, including representatives of other agencies involved in
human health or environmental assessments or related issues,
is  established  at the start of  the RI. This ensures  that the
potential for adverse effects to human health and the environ-
ment is adequately assessed during the RI. To successfully plan
and direct the  sampling and analysis effort, the RPM must
facilitate the interaction of the key participants.


2.2.2   GATHERING EXISTING SITE DATA
         AND DEVELOPING THE
         CONCEPTUAL MODEL

The RPM should ensure that a broad spectrum analysis was
conducted at the site for all media and should review industry-
specific records to minimize the potential for false negatives. A
preliminary list of the chemicals of potential concern is pre-
pared to assist in scoping and developing the conceptual model
of the  site.  The evaluation of the  existing site data is  an
important element in planning the scope of the risk assessment.
The RPM is responsible for gathering historical site data for use

                      EXHIBIT 2-6

            EXAMPLE RISK ASSESSMENT
          CHECKLIST FOR USE IN SCOPING
       Has all historical information bee" ;='~e'ed and characterized
       and is it ready  to be used9

       What sample matrices should be -.^5'gated9

       What analytical methods shou c w _~ec9

       Are the methods appropriate fo' • =• assessment9

       Will ary special quality co^tro  -ec_ 'r—e"ts be necessary9

       Who will conduct the analysis9

       What analytical data sources sno-- c-oe-used (fixed laboratory
       and/or field analysis)?

       What sampling designs are apD'or^ate9

       How will the data review be acco—i' s~ec9

       What types of deliverables will be •=•:- '«' -nsu""^ en"19

Have analytical methods been selected that have detection limits adequate to quantify
compounds at tne concentration of concern7

Have SOPs been prepared for sampling, analysis and data review9

Will the sampling and analysis program result in the data needed for me baseline risk assessment to

   address each medium, exposure pathway and chemical of potential concern.
   evaluate background concentrations.
   provide detail on sample locations, sampling  frequency, statist.ca cesign and analys.s.
   evaluate temporal as well as spatial variation, and
   support evaluation of current as well as future resource uses?
                  to balance cost with sampling needs and time constraints. The
                  advantages and disadvantages of field analyses and fixed labo-
                  ratory analyses should be considered, as described in Chapters
                  3 and 4 of this manual. The risk assessment participants can
                  assist in the development of field sampling  plans and the
                  selection of appropriate analytical methods that  will provide
                  the risk assessor with a set of uscable data, within the budgeting
                  and scheduling constraints of the RPM
                                                           19

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 Chapter 2 The Risk Assessment Process
2.2.6   ITERATIVE COMMUNICATION

Continuing, open, and frequent communication among the
ivniicipants is critical to the success of the remedial investiga-
tion and baseline risk assessment. A single meeting or discus-
sion  is rarely adequate to ensure that all relevant issues have
been addressed. The development of the risk assessment within
the remedial investigation report  is an iterative process of
action, feedback and correction or adjustment.

After review of the workplan, the SAP and the QAPjP, the RPM
should monitor  the flow of information.  The risk assessor
should assist the RPM to ensure that the data produced are in
compliance with the requirements  of the workplan and SAP.
Key questions to be considered once the data become available
are:

•  Have correct sampling protocols been followed?

•  Have all critical samples been collected?

•  Have the samples been analyzed as requested?

•  Are data arriving in a timely fashion?

•  Has quality assurance been addressed as stated in the SAP
   and Q APjP?

•  Have the data been reviewed as stated in the SAP?

•  Is the quality of  the analuical data acceptable for their
   intended use?

Based upon these considerations, the RPM and risk assessor
must jointly determine if any corrective actions are needed,
such  as requesting additional sampling, using alternative ana-
lytical methods or reanalyzing sample?.


2.2.7   DATA ASSESSMENT

The RPM and risk assessor should work with other participants
to identify a list of chemicals of potential concern and decide on
data review procedures. This information should be developed
during project scoping and incorporated into the workplan and
SAP. The RPM, risk assessor, and project chemist should be in
agreement regarding the type and levels of data review re-
quired. Typically, the RPM will assess the data reviewed by the
chemist unless other arrangements have  been established and
explicitly stated in the SAP.
The risk assessor may request prelim inary data, or results that
have received only a partial review, in order to expedite the risk
assessment to save time and resources. However, preliminary
d:ii;» s!ir\n!'1 not he  used in calculating risk  Once the full
analytical  data set  is obtained, the RPM and risk assessor
should consult with the project chemist and statistician  to
assess the utility of all available information


2.2.8   ASSESSMENT AND
         PRESENTATION OF
         ENVIRONMENTAL ANALYTICAL
         DATA

Once the environmental data are evaluated in the data review
process, the risk assessor develops a final data set for use in the
baseline risk assessment. All chemicals of potential concern
should now be identified. The risk assessor should prepare
summary tables containing the following information:

•  Site name and sample locations.

•  Number of samples per medium.

•  Analyte-specific sample quantitation limits.

•  Number of values above the quantitation limit.

•  Measures of central tendency: 95 percent upper confidence
   limit on the arithmetic mean of the environmental concen-
   tration.

•  Specifications for the treatment of detection or quantitation
   limits and treatment of qualified data.
•  Ranges of concentrations.

All assumptions, qualifications, and  limitations should be
explicitly stated on the tables. The final data summary tables
should be provided for review to the RPM, project hydrogeolo-
gist, project chemist and other appropriate project staff. These
are the data that will be used in the baseline risk assessment to
determine  the potential risk to human health. It is essential,
therefore, that this information consists of the best data avail-
able and reflects the collective review of the key participant^ in
the risk assessment.
                                                      20

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           Chapters Criteriafor Evaluating Data Useability in Baseline Risk Assessments
Introduction and Background
   Chapter 2
   The Risk Assessment Process
                Chapter 3
                Criteria for Evaluating Data
                Useability in Baseline Risk
                Assessments

                •  Defines six criteria for
                   assessing data useabiiity.

                •  Applies criteria to
                   sampling and analytical
                   issues.
                            21

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Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
                          ACRONYMS FOR CHAPTER THREE

                   AA       Atomic Absorption Spcclroscopy
                   ARAR    Applicable or Relevant and Appropriate Requirement
                   CLP      Contract Laboratory Program
                   DL       Detection Limit
                   DQO     Data Quality Objective
                   ESD      Environmental Services Division
                   GC       GasChroraatography
                   HRS      Hazard Ranking System
                   ICP       Inductively Coupled Plasma Atomic Emission Spectroscopy
                   CRDL    Contract Required Detection Limit
                   CRQL    Contract Required Quantitation Limit
                   CV       Coefficient of Variation
                   BCD     Electron Capture Detector
                   ID       Instrument Detection Limit
                   LOL     Limit of Linearity
                   LOQ     Limit of Quamitation
                   LSC      Library Search Compound
                   LSI       Listing Site Inspection
                   MDL     Method Detection Limit
                   MS       Mass Spectrometry
                   NPL     National Priorities List
                   PA/SI     Preliminary Assessment/Site Inspection
                   PAH     Polychlorinated Aromatic Hydrocarbon
                   PCS     Polychlori nared B iphenyl
                   PRP      Potentially Responsible Party
                   QC       Quality Control
                   RAGS    Risk Assessment Guidance for Superfunci
                   RI       Remedial Investigation
                   RME     Reasonable Maximum Exposure
                   RPM     Remedial Project Manager
                   SAP     Sampling and Analysis Plan
                   SOP      Standard Operating Procedure
                   SQL     Sample Quanu'tation Limit
                   TIC       Tentatively Identified Compound
                   TRIS     Toxic Release Inventory System
                                                 22

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                         Chapters Criteria for Evaluating Data Useability in Baseline Risk Assessments
3.0    CRITERIA FOR EVALUATING
        DATA USEABILITY IN
        BASELINE RISK
        ASSESSMENTS
This chapter assists RPMs and risk assessors in applying data
uscability criteria to plan data collection efforts to maximize
the useability of environmental analytical data in baseline risk
assessments, and addresses preliminary issues in planning
sampling and analysis programs.  The chapter provides the
background information  necessary to conduct
strategic planning for the acquisition of environ-
mental data for baseline risk assessments.  The
quality of a risk assessment is  intimately tied to
the adequacy of the sampling and analysis plan
(SAP) developed during the remedial investiga-
tion.

           Planning improves the useability
           of environmental analytical data
           in the final risk assessment.


Data needs for baseline risk assessments may
differ from those for identifying the natuie and
extentofcontaminationataSuperfund vtc. Data
collected from planning that is solely aimed at
this second purpose may be inadequate for the
risk assessment and an additional round of sarn
pimg U) support the risk a.v>e:>smcni iiia\ bo >..•-
.',.;rcJ  Accordin T!   t'ic nck a-'^.r-'^ " • ^n-jW N"1
i>n active  member  of  the team  n'jnning iho
Remedial Investigation (RI) and must be con-
Milted from the sun of tlie plajimric; process.

Four fundamental divisions for r.A .Lv-e^snient
arc to bo made with the data acqu.rcu oaring the
RI, as discussed in Chapter 2   Exhibit 3-1 pres-
ents tliesc decisions, lists the enters or pl&irn-!.:
issues affecting them, and refers 10 .\^; ; aiconcem
   have been identified, the ^\ord
            volves background levels of contamination. Arc site con-
            centrations sufficiently elevated from background levels to
            support an evaluation of increased risk for human health
            • Iii». u> .site L')ntaiiii;iatio;i'.'
            All exposure pathways must bo  identified and exposure
            pathways examined.  The two decisions concerning expo-
            sure  pathways primarily involve the identification and
            sampling of media of concern.
            The final decision involves the characterization of exposure
            pathways. Sampling must be representative and satisfy

                    EXHIBIT 3-1
RELEVANCE OF DATA QUALITY CRITERIA AND
  PLANNING ISSUES TO RISK ASSESSMENT
         DECISIONS TO BE MADE WITH
             ENVIRONMENTAL DATA
Decisions to be
Made with
Environmental
Data
What
Contamination is
Present and at
Wli-M 1 evels7













Are Sue
Co;irenva::ons
Sut' t pnTly
Dif f^i .'}O' From



Criteria or Planning Issue(s)
• Data Review
• Chemicals of Potential Concern
•Library Search Compounds (LSCs)
•Identification and Quantitation
•Detection Limits
• Media Variability
•Sample Preparation
• Fixed Laboratory vs. Field Methods
•Specifying Contaminant
Characteristics
• Measurement Error
•Strategy for Selecting Analytical
Methods
•Completing the Method Selection
Worksheet
• Evaluating Routine Methods
•Selecting Analytical Laboratories
•Acceptable Limits for Confidence.
Power, and Minimum Detectab'e
Relative Difference
-Determining Number of Background
j Ra. Kcjr-ju^d9 ! Samples
Are A!i Fxpo^ure
n lthw.ji ys
.J.-tMilin^ and
.


Are All Exposure
Pathways Fully
f'1, l, ,< i, ri/.id"










• Data Sources
Section
3.1.5

3.2.1, 3.3.1
3 3.2
3.3.3
334
3 3.5
3 3.6
337

4 1 Step
4.1 Step

4 2

^ C> i
422
424


4 1 Step

4 i Step
3 1 1
•Documentation i 312
• Muciia Variability 323
•Identification of Exposure Pathways
•Comparability with Previous Sample
Designs
•Analytical Methods and Detection
Limits
• Data Quality Indicators
-Sampling Variability V5 Measure -ion
Error
•Sample Preparation and Sample
Preservation
•Use of Judgmental or Purposive
Sample Design
•Strategies for Designing Sampling
Plans
•Identifying Sample Design
Alternatives
•Selecting Performance Mo.T.i>r-''>


- iJotormif iing Hot Spots
•Resource Issues
325

4.1 Step

3 1 3
3 1.4

322

324

326

4 1

4 1 Stop
4 1 Stop
4 ' S!up
4 1 Stop







II
III D








III A-C

IV C





V B













II
V A
V C
                                                     23

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Chapters Criteria for Evaluating Data Useability in Baseline Risk Assessments
   performance objectives determined during the planning
   process.  However, analysis also must be representative,
   and a broad spectrum analysis must be available to charac-
   terise the pathway

A principal objective of the RI is to collect useable environ-
mental analytical data to enable the risk assessor to make these
                                          decisions. RI planning and implementation of RI plans affect
                                          the  certainty of  chemical  identification  and  quantitation.
                                          Therefore, these four decisions should be kept in mind through-
                                          The chapter is organi/cd into three sections which define the
                                          data uscability criteria and explains how to apply the criteria to
                                                  EXHIBIT 3-2
                           IMPACT OF DATA USEABILITY CRITERIA IN
                     PLANNING  FOR THE BASELINE  RISK ASSESSMENT
         DATA
     USEABILITY
     CRITERION
                   IMPORTANCE
        SUGGESTED ACTION
     Data Sources
     (31 1)
Data sources must be comparable if data are combined for
quantitative use in risk assessment. Plans can be made in
the RI for use of most appropriate data sources so that
issues of data compatibility are not encountered
Data from different data sources can be used
together to balance turnaround time, quality
of data, and cost. Consultation with a
chemist or statistician may be needed to
ensure compatibility of data sets
     Documentation
     (31.2)
Deviations from the sampling and analysis plan (SAP) and
standard operating procedures (SOPs) must be documented
so that the risk assessor will be aware of limitations in the
data. The risk assessor may need additional documentation,
such as field records on weather conditions, physical
parameters and site-specific geology  Field records will
impact the useability of some data.  Data useable for risk
assessment must be identified with a specific location
Review the workplan and SAP and, if
appropriate, SOPs. As the data arrive,
check for adherence to the SAP so that
corrective action such as re-sampling may be
taken and still adhere to the project timetable

Stress importance of chain-ol-custody for
sample point  identification in RI planning
meetings
     Analytical
     Methods/
     Detection
     Limits
     (31 3)
The method chosen must assay for the chemical of potential
concern The choice of method involves planning for a
detection limit that will meet the concentration levels of
concern If the detection limit is not low enough to confirm
the presence and amount of contamination, samples will
have to be re-analyzed at a lower detection limit if possible
Participate in selecting methods with
appropriate detection limits during RI
planning   Consultation with a chemist is
rcooired whpn a method's detection limits are
near or above concentration levels of
concern
     Data Quality
     Indicators
     (31 4)

     Completeness
     Comparability
     Representa-
     tiveness
Completeness for critical samples must be 100%
Unforeseen prDb'e~"s during sample collection and analysis
can affect data cor^p'eteness  If a sample data set for risk
assessment is not complete, more samples may have to be
analyzed, affecting RI time and resource constraints

The risk levels generated in the quantitative risk
assessment will be questionable if incompatible data sets
are used together
 Sample data must accurately reflect the site characteristics
 to effectively represent the site's risk to human health and the
 environment  Hot spots and exposure pathway media must
 have representanve data
Completeness should be defined in the
s^rrole design for both the number of samples
and quantity of useable data needed to meet
performance objectives Critical samples
need to be identified during scoping.

Planning for use of comparable methods,
sufficient quality control, and common units
of measure for different data sets that will be
used together will facilitate data
comparability  Consultation with a chemist is
needed to ensure compatibility of data sets

 Plans for collection of sufficient number ot
 samples, a sample design that accounts for
 exposure pathway media, and an adequate
 number of samples for risk assessment should
 be discussed during scoping and documented
 in the SAP
                                                           24

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                           Chapters CriteriaforEvaluatingDataUseability in Baseline RiskAssessments
plan data collection efforts that maximize the useability of the
data in baseline risk assessments:

•  Section 3.1-Data Useability Criteria
•  Section 3.2-Prehminary Sampling Issues
•  Section 3.3-Preliminary Analytical Issues

Appendices contain additional tables and reference materials
for use in the planning process.


3.1    Data Useability Criteria

Exhibit 3-2 lists the six Data Useability Criteria involved in
planning for the risk assessment, summarizes the importance of
each criterion to risk assessment, and lists suggested actions
that can be taken during the planning process to improve the
useability of the analytical data. The following sections define
each criterion and describe its effect on risk assessment.
                                        3.1.1    DATA SOURCES

                                        The data sources selected during the RI planning process arc
                                        dependent upon the type of data required and their intended use
                                        Data collected prior to the RI are con -idcre-J h^ionca!. \ita
                                        collected during the RI are considered current and are usually
                                        specified in the RI planning process. Data may be anal) aval or
                                        non-analytical.  Examples of historical non-analyucal data are
                                        site records, manifests, and disposal records. Historical ana-
                                        lytical data that are often available from the Preliminary As-
                                        sessment/Site Investigation (PA/SI) include reports on the
                                        physical testing, screening, and analysis of samples. The same
                                        analytical data requirements apply, whether the da ta are c urren t
                                        or historical. The minimum criteria for analytical data are
                                        discussed in Chapter 5.

                                        Exhibit 3-3 identifies available data sources and their primary
                                        use(s) in the risk assessment process. Historical (pre-RI) and
                                        RI (current) analytical data sources are briefly discussed.
                                               EXHIBIT 3-2
                        IMPACT OF DATA USEABILITY CRITERIA IN
                  PLANNING FOR THE BASELINE RISK ASSESSMENT
                                                (continued)
      DATA
  USEABILITY
  CRITERION
                 IMPORTANCE
     SUGGESTED ACTION
   Precision
If the reported result is near the concentration of concern,
it is necessary to be as precise as possible in order to
minimize false negatives.
Plan for the use of quality control samples
(duplicates replicates ar'd'or col'cor ^
samples)  Assess confidence limits from the
quality control sample data
   Accuracy
Quantitative accuracy information is critical when results are
reported near the level of concern.  Contamination in the field,
shipping or laboratory may skew the analytical results.
Instruments rha: are not calibrated or tuned properly  may also
bias results  The use of data that is biased affects the
interpretation of risk levels
Plan and assess quality control data (blanks,
spikes,  performance evaluation samples) to
measure bias in sampling and analysis  A
chemist should be consulted to interpret
qualified data reported n
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Chapters CriteriaforEvaluatingDataUseability in Baseline RiskAssessments
                    EXHIBIT 3-3

        DATA SOURCES AND THEIR USE IN
                RISK ASSESSMENT
Available Data
Sources
PA/SI Data
Hazard Ranking
System (MRS)
Documentation
Listing Site Inspection
(LSI)
Site Records on
Removal and Disposal
Toxic Release
Inventory System
(TRIS)
Industry-Specific
Site, Source and
Media Characteristics
Field Screening
Field Analytical
Fixed Laboratory -
CLP, Non-CLP (EPA,
State, PRP, Private)
Data Type
Analytical
Site records,
Manifests,
PA/SI,
Analytical
She records,
PA/SI.
Analytical
Administrative
Chemical
discharge
Physical
parameters
(e g , meteor-
ological,
geological)
Analytical
Primary Use(s)
- Quantitative Risk Assessment
- Trends
- Quantitative Risk Assessment
- Trends
- Planning (chemicals present)
- Quantitative Risk Assessment
- Trends
- Planning (chemicals present)
- Planning (chemicals present)
- Planning (chemicals present)
- Fate and Transport
- Define Exposure Pathways
- Preliminary Assessment
• Site Characterization
Analytical , - Quantitative Risk Assessment
| - Site Characterization
Analytical
- Quantitative Risk Assessment
- Reference
- 3road Screen
- Confirmatory
- Ste Characterization
Mobile laboratories often have available the same mstrumentatbn
as fixed laboratories with the exception of ICP or MS
Data sources prior to remedial
investigation

           Historical analytical data or a broad
           spectrum analysis should be used to
           initially identify the chemicals of
           potential concern or exposure areas.


Historical data sources may be used to determine sampling
location and analytical approaches in the RI. Access to histori-
cal site data is critical to the identification of exposure pathways
prior to determining the sample design. Historical analytical
data are often available from the PA/SI. Early site inspections
may locate industrial process information that suggests chemi-
cals of potential concern.  These background data  indicate
industry-specific analytes and general levels of contamination
 that are useful in the development of the sample design and the
 selection of analytical methods. Sources of historical analyti-
 cal data for  baseline risk assessment include:  the PA/SI,
 Ha/.ard Ranking System (HRS) documentation, the listing site
 inspection (LSI), site records on removal and disposal, and
 industry-specific  systems for chemical  discharge  permits.
 Results from analyses by State or local governments may also
 indicate chemicals of potential concern.

 The quality of historical data mustbe determined prior to its use
 in the RI. The difficulty in using historical analytical data in the
 RI is that methods and detection limits may not be documented.
 Also, whether data review was performed may not be known.
 This information is required if analytical data are to be used in
 the quantitative components of the risk assessment.

 Historical analytical data of unknown quality  may be used in
 developing the conceptual model or as a basis for scoping, but
 not in the determination of exposure concentrations. Analyti-
 cal data from the PA/SI that meet minimum  data useability
 requirements can be combined with data from the RI to estim ate
 exposure  concentrations. Similarly, historical data of lower
 quality may be used if the concentrations are confirmed by
 subsequent RI analyses.

 Data sources for the remedial
 investigation

 The planning process for theRI identifies gaps in the analytical
 data and determines additional data collection requirements.
 Three types of analytical data sources can be used during the RI
• to acquire analytical data for a risk assessment. These include
 field  screening, field analyses, and fixed laboratory analyses.
 •  Field screening can be performed using chemical field test
    kits, ion-specific probes, and other monitoring equipment
    but should be confirmed by other techniques.
 •  Field analyses can  be performed  using instruments and
    procedures equivalent to fixed laboratory analyses and
    produce legally defensible data if quality control proce-
    dures are implemented.
 •  Fixed laboratory analyses are particularly useful for broad
    spectrum and confirmation analyses and generally provide
    more information over a wider range of analytes than field
    analyses.

 A discussion of issues related to field and fixed laboratory
 analyses is presented in Section 3.3.7.

 Analytical services are expensive and are a significant budget
 item within Superfund. Although, as a service, CLP costs do
 not appear on the Rl/FS project budget, CLP resources should
 be conserved when possible.  Excessive CLP tests for one sue
 may  preclude analysis needed at another.  Analytc-specific
 methods may be used for previously identified chemicals anil
 may provide more accurate results but should follow a broad
                                                      26
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                           Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
spectrum analysis. Site samples analyzed by CLP services take
an average of 35 days to produce results and data review may
add significantly to this.  A well-run mobile laboratory can
quickly produce good "first look" results, and useful results can
be followed up immediately while on site. Mobile laboratory
services could replace some CLP services if analytical capa-
bilities are determined to be adequate and minimum quality
control requirements are met.
                                                                                 EXHIBIT 3-4
3.1.2    DOCUMENTATION

In order to provide a basis for assessing a data collection
activity and sample analysis, it is essential to document certain
information. During the scoping of the RI, plans should be
made for documenting the data collection activities.  This RI
documentation can be used later to evaluate completeness,
comparability, representativeness, accuracy, and precision of
the analytical data sets. Four major types of documentation are
produced during an RI:
•  Sampling and Analysis Plan (SAP), including a Quality
   Assurance Project Plan (QAPjP).
•  Standard operating procedures (SOPs).
•  Field and analytical records.
•  Chain-of-custody records.

A list of all the specific information required in these docu-
ments is normally contained in the SAP (EPA 1989a).

The end use requirements for the data must be clearly estab-
lished as part of the planning objectives of the scoping meeting
and documented in the SAP. The quality objectives for assess-
ing data against the stated performance objectives should also
be determined during the planning process and documented in
the SAP.

Standard operating procedures (SOPs)
and field/analytical records

Data collection and analysis procedures must be accurately
documented in order to substantiate the analysis of the sample,
conclusions derived from the data,  and the reliability of the
reported analytical data. Therefore, SOPs for field or analytical
methods must be written for all field and laboratory processes.
SOPs provide consistency in how samples are collected and
analyzed, and determine the level of systematic errpr associ-
ated  with data collection and analysis.  Exhibit 3-4  lists the
types of SOPs, field records, and analytical records that arc usu-
ally associated with RI data collection and analyses, and relates
the importance of each to the risk assessment.

All deviations from the referenced SOPs should be prc-ap-
provcd by the RPM and documented.   Samples that arc not
 RELATIVE IMPORTANCE OF SAMPLING AND ANALYSIS
     PLAN AND RECORDS IN PLANNING FOR RISK
                     ASSESSMENT
            DOCUMENTATION
 Sampling and Analysis Plan
     Selection and Identification of Sampling Points

  •   Sample Collection SOP

     Analytical Procedures or Proiocols

     Data Reporting and Review SOP

  •   QA Project Plan

     Method-Specific QC Procedures

  •   QA/QC Procedures

     Documented Procedures for Corrective Action

     Instrument Monitoring SOP Including
     Corrective Action and Maintenance

     Sample Preservation and Shipping SOP

     Sample Receipt, Custody, Tracking and
     Storage SOP

     Installation and Monitoring o( Equipment SOP
                                           IMPORTANCE
Critical

High

High

High

High

Medium

Medium

Medium

Medium


Medium

Low

Low
 Field and Analytical Records
  •  Field Log Records                             H>gh

    Field Information Describing Weather Conditions,    j     H>gr
    Physical Parameters or Site-Specific Geology

    Documentation for Deviations from SAP and SOPs        High

    Field Decisions/Documentation

    Data from Analysis - raw data such as instrument outou ,    MeO",
    S^TVR. -Vo^otograrrr; arc1 laboratory narra"ve

    Internal Laboratory Records                       Low
  KEY   Critical  -  Essential to the useability of data tor risk assessment
       Hqh   -  Should be addressed in planning for risk assessment
       Medium  -  Primarily impacts how data are qualified in risk assessment
       Low    -  Has little effect on useabilily of data for risk assessment
collected or analyzed in accordance with established SOPs ma\
be of limited use because their quality cannot be determined

Chain-of-custody

The technical team must decide during scoping what data may
be used for cost recovery actions, and plan accordingly for ihc
use of full scale chain-of-custody or less formal  cham-of-
custody procedures. Full scalcchain-of-custody is required for
cost recovery and enforcement actions, but is not a  minimum
requirement for risk assessment. Full scale chain-of-cusiody
includes  sample labels and formal documentation that prove
the sample was not tampered with or lost in the data collection
and analysis process.  For risk assessment, limited  cham-of-
custody can be applied, as long as all data can be clearh  iJcn-
-------
 Chapters Criteria for Evaluating Data Useability in Baseline RiskAssessments
tified with dates
and sampling
points. Sample
identity is veri-
fi'ib!1'' from the
collector's
notebook and
laboratory data
sheets, as well
as from a for-
mal chain-of-
custody.
Exhibit 3-5 lists
the procedures
associated with
chain-of-cus-
tody documen-
tation, includ-
ing the mecha-
nism to prove
chain-of-cus-
tody and the
data collection
processes in-
volved, and re-
lates the impor-
tance of each
item to the risk as
EXHIBIT 3-5
RELATIVE IMPORTANCE OF
CHAIN-OF-CUSTODY DOCUMENTATION
AND ACTIVITIES IN PLANNING FOR
RISK ASSESSMENT
CHAIN-OF-CUSTODY
DOCUMENTATION
Mechanism to Prove Cham-of-
Custody
Documentaton Linking Data
to Sample Point
• Sampling Data
• Sample Tags
• Custody S»a)s
Laboratory Racopt and Tracking
Activity
Sampling
Field Analysis
* Shipping
• Internal Laboratory
IMPORTANCE
Critical
Critical
Medium
Low
Low
Medium
Medium
Medium
Low
KEY
CntKftl - EtMnfial to tfw UMtbtlity of data for nsk M*«csm»nt
High - Should tw •ddr*M«d in planning for ntk MMMrrwnt
Medium - Pnmanly imped* now data ar» qualified m n«k •M*Mm*nt.
Low - Hi. lit»« «««cl on usability of data for nik aw*um*nt
sessment.
• Completeness
• Comparability
• Representativeness
• Precision
• Accuracy
The data and accompanying documentation are used to evalu-
ate these indicators. The risk assessor may need to communi-
cate with a chemist or statistician after the data collection
process has been completed to evaluate data quality indicators.
Therefore, the SAP, field and analytical records, and SOPs
should be accessible. Exhibits 3-6 and 3-7 summarize the im-
portance of data quality indicators for sampling and analysis
(respectively) to risk assessment and list suggested planning
actions.
The definition of each of the data qua] ity indicators is presented
in the following paragraphs. Note that the specific use of the
indicators to measure data useability is different for sampling
and analysis. For example, completeness as applied to sam-
pling refers to the number of samples to be collected. Com-
pleteness as applied to analytical performance primarily ap-
plies to the number of data points that indicate an analytical
result for each chemical of interest (e.g., 10 samples analyzed
for 25 chemicals will produce 250 data points total, (10 data
points for each chemical).
EXHIBIT 3-6
3.1.3   ANALYTICAL METHODS
        AND DETECTION LIMITS

The choice of analytical methods is a crucial step
in RI planning.  Appropriate analytical methods
have detection limits that meet risk assessment
requirements for chemicals of potential concern
and have sufficient quality control measures to
ensure confident target compound identification
andquantitation. The detection lunitof the method
directly  affects the  useability of data because
chemicals reported near the detection limit have a
greaterpossibility of false negatives and positives.
The risk assessor or RPM must consult a chemist
for assistance when using methods that have detec-
tion limits near or above the required action level.


3.1.4   DATA QUALITY
        INDICATORS

This section discusses each of five data quality
indicators as they  relate to  the assessment of
sampling and analysis.
                                                      IMPACT OF DATA QUALITY INDICATORS
                                                         ON SAMPLING CONSIDERATIONS
DATA QUALITY
INDICATOR
Completeness
Camaarab lily
~?"25--'..d. v jiess
Precision
A::-j-acy
IMPORTANCE
fay jecrease samo'e
reo-esentaliveness fV
!de"t,rication of false negatives
ana e5- mate sf ave^a~e
co^centrat.on
AD' :/ 13 comD'ne ana yL'cai
-es. '.s across sar-iD TTJ esisoles
a^-d -. me oeriods
A>, ; ca^ce ?r fa'be •••egat ve^ a^
ra'se 205it.ive5 (fie - sanding
ccriamnation)
Potential bias of concentration
estimates
Increased uncertainty of risk
assessment because or increasing
vanatHily in concentration
es: -iates
r^ay -esult in Bias ?,
"d, '"•JSu'L in fa"jf; msi LI w_"j and
'd, : .'"at'.' O'jl ' idt "- / ' " "''.' rfti jn
SUGGESTED
ACTION
Stipulate SODs fc-- saT.^ls
cottectioi and Ka-idii",9 "i
5AP to e'-s^e coms'ete
an J s a1 d j3^>2 ^;
Lse f'e S3~ie ^^o a^e^ sari- •;
design across eo-sojes a-d
t me ae' c:s
'_*j^ a ^~z 35e: 5d~i^ e d°= ;~
Collect 3'1 lona sa-i^'es as
reauired

Use asDr^Dna:.? sarrole desi^":
Use SOPs !"or sanDle ;o"e^'-'?"
and ha- i ing
Use quality control results
f^r ni-jn^to' i^g
                                                  28
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                          Chapters Criteria forEvaluating Data Useability in Baseline Risk Assessments
Completeness

Completeness is a measure of the amount
of unable data resulting from a data col-
lection activity.  The required level of
completeness should be defined for the
number of samples required in the sample
design and for the quantity of useable data
for chemical-specific data points needed
to meet performance objectives.  All de-
fined data items must be obtained for
critical samples and chemicals.

Problems  that occur during data collec-
tion and analysis affect the completeness
of a data  set.  Fewer samples may  be
collected and analyzed than originally
planned because of site access problems.
Laboratory performance may be affected
if capacity is exceeded, causing data to be
rejected. Some samples may not be ana-
lyzed due  to matrix problems. Samples
that are invalid due to holding time viola-
tions may  have to be re-collected or the
data set may be determined as useable
only to a limited extent  Therefore, ad-
vance  planning  in identifying  critical
samples and the use of alternative sam-
pling procedures are both necessary to
ensure completeness of a data set for the
baseline risk assessment.

Comparability
                       EXHIBIT 3-7

IMPACT OF DATA QUALITY ON ANALYTICAL CONSIDERATIONS
DATA
QUALfTY
INDICATOR
Completeness
Comparability
Representativeness
Precision
Accuracy
i
ASSOCIATED
ISSUES SUGGESTED ACTION
Poor da la quality or lost samptes reflux
data set and decrease confidence m
supporting information
Ability to combing analytical results
acquired from various sources using
different methods for samples taken over
the period of investigation
Potential for false negatives
N on homogeneity of sample
Potential for false positives
Potential for change m sample before
analysis
Monrto ring
Confidence in distinction between srte and
background levels of contamination
Primary importance when action limit
approaches detection limit
Confidence m distinction between sr(e and
background levels of contamination As
concentration of concern approaches the
detection limit, the differentiation includes
confidence in the determination of
presence or absence of chemical of
potential concern
y'«pare SODs lo sujjpoi* sanply tracing and analytical
procedures, review, and rgporting aspects of laboratory
operates
Reference analyte-specilic method performance
characteristics
Reference applicable fate and transport documentation
Anticipate f! samples are eollccied
             over a specified time span  I lie S A I* should describe sampling
             techniques and the rationale used to select sampling locations
                                                       29
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Chapters Criteria for Evaluating DataUseability in Baseline RiskAssessments
Precision

Precision is a quantitative measure of the variability of a s
measurements compared to the mean and is usuallv rcportc
a coefficient of variation or a standard devia-
tion of the arithmetic mean. Results of quality
control samples arc used to calculate the pre-
cision of the measurement process. Measure-
ment error is a mixture of sample collection
and analytical factors.   Collection  of  field
duplicate samples will optimize the ability of
sample results to distinguish sampling from
analytical variability. Analytical variability
can be measured through the analysis of labo-
ratory duplicates or through multiple analyses
of performance evaluation samples. If ana-
lytical results are reported near a concentra-
tion of  concern, the  standard deviation or
coefficient of variation can be used to deter-
mine the confidence level of the reported data.
A statistician or a chem ist should be consulted
if this determination is difficult.
                                                  •das
                                                         risk assessor, and the project chemist to reach a concensus on
                                                         the level and depth of data review to be performed. Exhibit 3-
                                                         8 provides a summary of the characteristics and uses of diffcr-
                                                                     EXHIBIT 3-8

                                                      STAGES OF REVIEW OF ANALYTICAL DATA
Accuracy
L»v«l of
Review
None
Full
Partial
Automated
Sample*
Initial
Initial samples analyzec
for broad spectrum
components
Analyte*
All
All
Critical samples for all analytes
or
all samples for critical analytes
All
All
Parameter*
Analytical results
All analytical results. QC.
and raw data
Selected analytical
results, QC, or raw data
Parameters available to
the automated system
No raw data are
evaluated
Potential Dies
Qualitatively identify risk
assessment analytes
Modify SAP.
Quantitatively confirm risk
assessment. Modify SAP
Modify review process
Improve timeliness,
overall efficiency,
save resourses
Improve timeliness,
consistency, cost
effectiveness. May
electronically transfe' to a
database Eliminate
transcription errors
Accuracy is a measure of the closeness of a
reported concentration to the true value.  This measure is
usually expressed as bias (high or low) and determined by
calculating percent recovery from spiked samples. The risk as-
sessor should know the required level of certainty for the end
use of the data when reviewing accuracy information. When
results are  reported at or near a concentration of concern,
accuracy information is critical

Contamination in the field, during shipping, or at the laboratory
that may skew the analytical results is determined by the results
of blank analyses. Field and trip blanks should be used during
the RI to identify contamination and the associated bias related
to sample  collection or shipment. Method blanks should be
used to monitor laboratory contamination.


3.1.5   DATA REVIEW

This section discusses the importance of alternative levels of
data review to the risk assessment. The two major effects on
data useability from the data re\ iew are'
•  The timeliness of the data re% icv.
•  The level and depth of review  
-------
                          Chapters Criteriafor Evaluating DataUseabilityin Baseline Risk Assessments
requirements. A full data review will minimize false positives,
false negatives, calculation errors, and transcription errors.
Partial review should be utilized only after broad spectrum
analysis results have undergone full review. Time and resource
constraints can he balanced with flexible use of data review
alternatives.

Depth of data review refers  to the selection  of  evaluation
criteria, ranging from generalized criteria that may affect an
entire data set (e.g., holding time) to analyte specific criteria
that may affect only a portion of results from one sample (e.g.,
recovery of a surrogate spike for organics or analyte spike
recovery for inorganics). The RPM must decide the depth of re-
view for each data source to provide a balance between usea-
bility of data and resource constraints. Data used quantitatively
must be reviewed on an analyte-specific basis.

Automated data review systems

Automated data review systems  can be used to assess all
samples and analytes for which there are computer-readable
data in the format required by the automated system. The depth
of review will depend on both the data and the assessment
system. The primary advantagesof automated data review sys-
tems for the risk assessor are timeliness, the elimination of tran-
scription errors that can be introduced during manual review
processes, and computer-readable output, which usually in-
cludes results and qualifiers.  This information can be trans-
ferred  to computer-assisted  risk  assessment  and exposure
modeling systems.  Exhibit 3-9 provides a list of software that
aid data review and evaluation.
                    EXHIBIT 3-9
  AUTOMATED SYSTEMS TO SUPPORT DATA REVIEW
SYSTEM
[
CADRE
Computer Assissed Data
Review and Evaluation
eDATA
Electronic Data Transfer
and Validation System
EPA CONTACT
Jonn Nooermc
Quality Asswa-c* IS
USE=>A EMSW -V
(702; 796-2"C
William Coak*y
USE PA
Emergency Resocrs*
Team
(201)906-692'
1 All systems will run on any IBM compattote PC A~ **
A fixed drsk K recommended
DESCRIPTION
An automated evaluation system
That accepts fttes trom CLP lormat
otsK denvery or mainframe transfer
and assesses data based on
USEPA Functional Guidelines for
Data Validation (default crneria)
System accepts manual entry of
o"~ef data sets, and rutes lor
evaluation can t>« usef-OefinwO to
re'iect specific information needs
[inorganic system is in
development }
An automated review system
developed to assist in rapid
evaluation of data in emergency
response May be applicable tor
botn CLP and non-CLP data
System comomes DOOs,
ore-established site specifications
OC catena, and sample collection
data with Laboratory results to
determine useability
n &40K RAM (minimum)
3.1.6   REPORTS FROM SAMPLING AND
         ANALYSIS TO RISK ASSESSOR

Preliminary data reports assist the risk assessor in identifying
sampling or analytical problems early enough so that corrective
actions can be taken during data collection to increase the
quality of a quantitative risk assessment. The request for
preliminary data should be made during RI  planning and
formalized in the SAP. The useof such information may reduce
the overall time required for the  risk assessment.

Exhibit 3-10 lists the final data and documentation needed to
support risk assessment, and rates the importance of each item.
Data are most useable when reported in a readable format and
accompanied by additional, clarifying information. Report
structures are usually defined by Regional policy and include
specification of the format for manual summaries and machine-
readable data (where required) and for summary tables from
data review. The RPM can ask the data reviewers to provide a
data summary table listing sample results, sample quantitation
limits, and  qualifiers on  diskette for downloading  into
Risk*Assistant, spreadsheets, or other software programs that
the risk assessor may  use.  An  example of a recommended
tabular results report format appears in Appendix I.

The data reviewer should provide a narrative summary, that is
comprehensible to a nonchemist, describing specific sampling
or analytical problems, data qualification flags, detection limit
definitions, and interpretation of quality control data.  This
summary must always be followed and supported by a detailed
commentary that explicitly addresses each item from the nar-
rative on a technical basis. The commentary facilitates data use
by providing a technical justification for data qualification. If
a nontechnical narrative is unavailable, the risk assessor  must
(ata minimum) be provided with explanations of qualification
flags, detection limits, and interpretation of quality control data
(see Appendices I, V, VI and VII for examples).  A chemist
familiar with the site can be requested to interpret the anal\ tical
review with site-specific information, such as physical sue
conditions that affect sample results.


3.2    Preliminary sampling issues

The design and assessment of environmental sampling proce-
dures is a complex subject and well beyond the scope of this
guidance manual, but involves criteria which need to be ad-
dressed. Statistical and non-statistical sampling considerations
provide the risk assessor with a  basis for identifying sample
design and data collection problems, interpreting the signifi-
cance of analytical error, and selecting methods based on the
expected contribution of sampling and analytical components
to total measurement error.  A comprehensive discussion of
environmental sampling procedures is given in Principles of
Environmental Sampling (Keith  1987,1990a)and Methods for
                                                      31
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Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
Evaluating the Attainment of Cleanup Standards
(EPA 19891).

This section provides a framework, key issues, and
tools and guidance for use by non-statisticians to
determine the potential impact of sampling proce-
dures on data useability for risk assessment and to
identify situations that require statistical support.
The primary purpose of this guidance is to increase
the statistical sophistication and understanding of
RPMs and risk assessors. Major sampling con-
cerns are  discussed as issues and reminders  or
identified  as questions to be resolved. Sampling
design and assessment tools used in this section
include reference tables, statistical formulas and
checklists. Guidelines are presented in the form of
rules of thumb. A Sample Design Selection Work-
sheet with step-by-step instructions is presented in
Chapter 4.

Several assumptions concerning sampling and as-
sociated statistical procedures have been made to
simplify the discussion in this section. They are:
•  The RPM and risk assessor are familiar with
    basic environmental sampling and statistical
    terms and logic.
•  Statisticians will be consulted for any signifi-
    cant problems or situations violating these as-
    sumptions or not covered in the guidance.
•  Detailed  discussions  of statistical sampling
    issues  by medium and sample design are cur-
    rently  available (Keith 1987 and 1990a, EPA
    19891).
•  Variations of stratified random or systematic random sam-
   pling (grid) have been used S > stematic sampling requires
   special variance calculations in order to estimate statistical
   performance parameters such  as power and confidence
    level; these are not addressed m this manual.
•  Chemical distributions are log  normal  in form or exist in
   some form which approxima^s normal when transformed
   (e.g., logarithmic). Measures of variability are  given in
   transformed units. Parametric statistical tests are utilized in
   the examples.  Such tests in\olve distributional assump-
   tions but have high power \>. ith few samples.
•  Sampling quality  assurance procedures are not  separate
   from analytical quality assurance procedures, especially in
   theareaofsamplecollection and handling,even though the
   discussion will treat them separately.

This section also discusses sampling concepts and information
on issues  known to significant!)  affect sample design and
assessment.  Exhibit 3-11 summ^n/.Oi the importance of each
                EXHIBIT 3-10

DATA AND DOCUMENTATION NEEDED FOR
            RISK ASSESSMENT
DATA AND DOCUMENTATION
• Site description with a detailed map indicating site location, showng
the site relative to surrounding structures, terrain features, population or
receptors, indicating air and water (tow. and describing the operative .ndustnal
process if appropriate.
• Site map with sample locations identified.
• Description of sampling design and procedures including rationale
• Description of analytical method used and detection limits including
sample-specif ic quantitation limits (SOU) and detection
limits for non-dated data.
• ResuKs given on a per-sample basis, qualified for analytical limitations
and error, and accompanied by SQLs
• Field conditions and physical parameter data as appropriate for the media
involved in the exposure assessment
• Narrative explanation of qualified data on an analyte and sample bas.s,
indicating direction of bias.
• QC data results such as blanks, replicates and spikes for field and laboratory
• Estimated quantities of compoundsAentatively identified compounds
• Definitions and descriptions of flagged data
• Results given in summary tables in alphabetical order
• Raw data (instrument output, chromatograms, spectra)
• Definitions of technical jargon used in narratives
IMPORTANCE
Critical



Critical
Critical
Critical


Critical

Critical

High

High
High
High
Medium
Medium
Low
KEY Critical • Essential to the useability of data tor risk assessment
High - Should b« addressed in planning for risk assessment
Medium - Primarily impacts how data are qualified in risk assessment
Low - Has little effect on useabtlrty of data for risk assessment
  of the preliminary planning issues to the risk assessment and
  lists actions to be considered during the planning process to
  reduce or eliminate their effect on data useability.

  3.2.1    CHEMICALS OF POTENTIAL
           CONCERN

  Chemicals of potential  concern  ore chemicals that may be
  hazardous to human health and are identified at the site, initially
  from historical sources.  Chemicals previously identified at
  Superfund sites have varying rates of occurrence, average
  concentrations, and coefficients of variation.  These differ-
  ences are a function of fate and transport properties, occurrence
  in different media, and interactions with other chemicals, in
  addition to  use and disposal practices. Information on fre-
  quency and coefficient of variation determines the number of
  samples required to adequately characterize exposure path-
  ways and is essential in designing sampling plans. Low fre-
  quencies of occurrence and high coefficients of variation mean
  that more samples will be required to charactcri/.e the exposure
                                                      32
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      Chapters Criteria for Evaluating Data Useability in Baseline RiskAssessments
                             EXHIBIT 3-11




            IMPACT OF SAMPLING ISSUES ON RISK ASSESSMENT
ISSUE
Chemeals of Potential
Concern
(32.1)
Sampling Variability vs
Measurement Error
(3 2.2)
Madia Variability
(3.2.3)
Sample Preparation
and Sample
Preservation
(3.2.4)
Identification of
Exposure Pathways
(3.2 5)
Use of Judgmental or
Purposive Sample
Design
(326)
IMPORTANCE
Chemicals have different rales of occurrence
and coefficients of variation This impacts the
probability of false negatives and reduces
confidence limits for estimates of
concentration
Sampling variability can exceed
measurement error by a factor of three to four
(EPA 1989c)
Sampling variability increases uncertainty or
variability; measurement error increases bias
Sampling problem* vary widely by media as
do variability and bias.
Contamination can bo Introduced during
sample preparation, producing false positives.
Filtering may remove contaminants sorbed on
particles.
Not all samples taken in a site charactereatioi
are useful for risk assessment. Often only a
tew samples have been taken in the area of
interest.
Statistical sample designs may be costly and
do not take advantage of known areas of
contamination.
|_
SUGGESTED
ACTION
Increase the number ol samples for
chemicals with low occurrence and/or
high coefficients of variability
Reduce sampling variability by taking
more samples (using less expensive
methods). This allows more samples
to be analyzed.
Use OC samples to estimate and
control bias.
Design media-specific sampling
approaches.
Use blanks at sources of potential
contamination. Collect filtered and
unfdtered samples.
Sample designs should specifically
address exposure pathways. Risk
assessors should partcpata in
scoping meeting.
Use judgmental sampling to identify
contaminated areas, then use an
unbiased method to characterize
exposure.
                           EXHIBIT 3-12



MEDIAN COEFFICIENT OF VARIATION FOR CHEMICALS OF POTENTIAL CONCERN

Chemical of
Potential Concern
Chtoromethane
Tr ic hloromethane/Ch torero' ~
Tetrachtoromefhans/'Carx:- 'e"a
1 ,2-dichloroethane
Tetrachloroethane
Vinyl chloride
Tetrachtoroethene
Dichloropropane
Isophorone
Bo (2-chloroethyl) ethar
1 ,4-dlchloroben2ene
Sis (2-ethythexyf) phthaiale
Benzo(a) pyrene
Styrene
N-nitrosodiphenyamm«
DDE
DDT
Dieldnn
Heptaohtor
Gamma-BHC (kndane)
PCS 1260
Arsenic
Beryllium
Cadmium
Chromium
Mercury
Lead (Pta)

Soil/Sediment
Medl«n%CV 2
167
539
LT^.orOa 154
176
17.0
11 0
245
19.0
0.7
05
08
0.7
0.5
16.9
0.5
4.5
2.9
4.4
46
63
021
403
271 3
1346
11 9
1032.3
109
List of chermcals of poce~r.,a concern is derived from health
Statistical Database Sj-oe
Median percent coe"c«^ y
3 Novembef 1 988 to p-a*»-.
Number of sites
at which chemical
wu detected
61
392
38
64
56
55
392
29
74
10
120
1197
1058
117
142
328
521
274
249
142
251
1098
1091
1096
1098
1098
1098

Water
MedlinNCV 2
500
452
93
247
174
157
333
13.3
184
20 1
173
295
10.8
333
305
8130
5882
33
351 9
454 1
41 7
580
1000
33 7
230
5000
973
based levels and frequency of occurance at Supertund sitf*
Number of sites
at which chemical
w» detected 3
134
519
',~:
158
101
197
367
79
72
34
119
782
76
69
96
40
125
101
151
134
23
940
931
945
948
948
939
s listed in the CLP
r of sites for which data exist totals 8.900 )
»anatton of analyle concentrations




                                33
-------
Chapters Criteriafor Evaluating Data Useability in Baseline Risk Assessments
pathways of interest.  Situations potentially involving false
negatives occur as variability increases and occurrence rates
decrease.

           1 he prevention of false negatives in
           sampling and analysis is of more
           concern to risk assessment than the
           prevention of false positives in order to
           be protective of human health.

Data for aqueous and soil/sediment matrices on frequently oc-
curring chemicals of potential concern, and releases from in-
dustries known to produce waste commonly found at Super-
fund sites are available for volatiles, extractable organics, pes-
ticides/PCBs, tentatively identified organic compounds, and
metals (See Appendix II).  Data are also available  for the
calculation of site-specific coefficients of variation. Exhibit 3-
12 indicates the occurrence rates and coefficients of variation
for selected chemicals of potential concern to risk assessors.

3.2.2   SAMPLING VARIABILITY VERSUS
         MEASUREMENT ERROR
                               EXHIBIT 3-13

                      SAMPLING VARIABILITY AND
                         MEASUREMENT ERROR
Sampling  variability
and measurement er-
ror are two key con-
cepts  involved  in
planning and assess-
ing sample collection
efforts.  Each is dis-
cussed in the context
of evaluating strate-
gies for the collection
of both site and back-
ground   samples.
Exhibit 3-13 defines
and illustrates sources
of sampling variabil-
ity and measurement
error.

Most sampling plans
are a necessary com-
promise between cost
and confidence level.
Basically, two types of decisions must be made:
•  Determine the statistical performance necessary to produce
   the quality of data appropriate to meet the risk assessor's
   sampling variability performance objectives.
   Determine the types and numbers of quality control samples
   required to detect and estimate measurement error.
                      Sampling variability  the variation
                      between true sample values that is a
                      function ot the spatial vacation in the
                      poiiuiani concentrations

                      Measurement-error variation, the
                      variation induced by differences
                      between true sample values and
                      reported values  Error is a (unction of
                      the following

                         • Sample collection variation
                         • Sarr.piepreparation/handling
                          preset anon/storage variation
                         • Analyucal variation
                         • Daia processing variation
          I Large variation of a contaminant in a
          \ medium indicates that the number of
           samples should be increased or that
           the medium should be stratified to
           reduce variability


Sampling plans attempt to minimize and estimate both sam-
pling variability and measurement error.  Sampling variability
affects the degree of confidence and power the risk assessor can
expect.  Confidence is the ability to detect a false positive
hypothesis, and power is the ability to detect a false negative.
Power is more important for risk assessment.  An estimate of
the sampling variability that is a function of the spatial variation
in the concentrations of chemicals of potential concern is ob-
tained by  calculating  the coefficient of variation for each
chemical.  When the coefficient of variation is less than 20%
and a substantial quantity of data are available, the effect of
spatial and temporal variation on concentrations of chemicals
of potential concern is minimal, and the power and certainty of
statistical tests is high (EPA 1989c).

Measurement error is estimated using the results of the quality
control samples and represents the difference between the true
sample value and the reported value.  This difference has five
basic sources: the contaminant being measured, sample collec-
tion procedures, sample handling procedures, analytical proce-

                       EXHIBIT3-14
          MEASUREMENT OF VARIATION AND BIAS
          USING FIELD QUALITY CONTROL SAMPLES
Quality Control
FwkJ duplicate
FtekJ evaluation sample
(in pairs)

F»W Wank
Field nnsate
Tnp blank
V* nation or &«• U*a>ur*d
Provides data required to estimate the sum of
•utwamptmg and analytical variances
Provides data obtained from l
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                           Chapters CriteriaforEvaluating DataUseabilityin Baseline RiskAssessments
dures, and data production procedures. Measurementerrbrdue
to analytical procedures is discussed in Section 3.3 under
analytical considerations. Measurement error due to sampling
is estimated by examining  the precision of results from field
duplicates.  The minimum recommended number of field
duplicates is one for every 20 environmental samples (or a fre-
quency of 5%). A minimum of one set of duplicates should be
taken per medium sampled unless many strata are involved;
five sets are recommended.  Exhibit 3 -14 summarizes the types
and uses of quality control  samples in defining variation and
bias.

            The contribution of sampling variabil-
            ity to total error is typically much
            greater than the analytical error.


In summarizing the discussion of sampling and measurement
error, one finding puts the concepts  in perspective:  "An
analysis of the components of total error from soils data from
an NPL site sampled for PCBs indicated that 92% of the total
variation came from the location of the sample and 8% from the
measurement process" (EPA 1989c). Of the 8%, less than 1%
could be attributed to the analytical process.  The rest is
attributable to sample collection, sample handling, data proc-
essing and pollutant characteristics. Sampling variability is
often three to four times that in troduc ed by measurement error.
Exceptions to this observation on the components of variation
or sources of error occur in instances of poor method perform-
ance for specific analytes.
MAJOR
SAMPLING
ISSUES
Contaminant
Migration

Temporal
Variation

Spacial
Variation

Topographic/
Geological
Properties

Hot Spots

Sample
Collection

Sample
Preparation/
Handling

Sample
Storage

Sample
Preservation
                      EXHIBrT3-15

            IMPORTANCE OF MAJOR SAMPLING
                 ISSUES IN EACH MEDIUM
        PROBLEM LIKELIHOOD BY MEDIUM

      GROUND  SURFACE             HAZARDOUS
SOIL   WATER   WATER   AIR  BIOTA    WASTE
Key     ++ • Likely source of significant sampling problem

       + - Potential source of sampling problem
Source   Keith 1990b
3.2.3    MEDIA VARIABILITY

Appropriate samples must be collected from each medium of
concern and, for heterogeneous media, from designated strata.
Media to  be sampled should include diose currently uncon-
taminated but of concern, as well as those currently contami-
nated. For media of a heterogeneous nature (e.g., soil, surface
water, and hazardous waste), strata should be established and
samples specified by stratum to reduce variability, the coeffi-
cient of variation and the required number of samples.

Sampling considerations vary according to media.  The sam-
pling concern may involve contaminant occurrence, temporal
variation, spatial variation, sample collection, or sample pres-
ervation.  Exhibit 3-15 indicates potential sampling problem
areas for each medium.  Problem areas are classified relative
to other media.  RPMs can use this exhibit to identify possible
sampling  problems in each medium and plan for them in the
data collection design. Sample designs must be structured to
identify and address hot spots. Exhibit 3-16shows the sources
of uncertainty across media. Note: In formation needed for fate
and transport modeling should be obtained during a site sam-
pling investigation. This mformauon also differs by the me-
dium of concern (EPA 1989a).
                                                                                  EXHIBIT 3-16
      SOURCES OF UNCERTAINTY THAT FREQUENTLY
      AFFECT CONFIDENCE IN ANALYTICAL RESULTS
 SAMPLING
SOURCE OF
UNCERTAINTY
      DEGREE OF SIGNIFICANCE BY MEDIUM


      SOIL    WATER    AIR   BIOTA
HAZARDOUS
  WASTE
    Design
    Contamination
    Collection
    Preparation
    Storage
    Preservation
 LA8OFIATOHY

    Storage
    Preparation
    Analysis
    Reporting

 ANALYTE-SPECIFIC
    Volatility
    Pholodegradation
    Chemical Degradation
    Microbial Degradation
    Contamination
 KEY
   +» - Likely source ol significant error or uncena.nl/
   * •• Potentalfy significant source of error or uncertainly
   /+ . Magnitude of effect determined by exammaton of data
                                                          35
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 Chapters Criteria for Evaluating Data Useability in Baseline Risk Assessments
3.2.4   SAMPLE PREPARATION AND
         SAMPLE PRESERVATION

Some samples require preparation in the field in order to ensure
that the results of analyses reflect the true characteristics of the
sample. The use of sample filtration and compositing proce-
dures is discussed in this section.
If the risk assessor needs to discriminate between theamountof
analyte present in true solution in a sample and that amount
sorbed to solid particles, then the samplemustbe filtered. Some
samples, such as tap water, are never filtered because there is
no paniculate content. This filtration should be performed in
the field as soon as possible after the sample has been taken and
before any preservative has been added to the sample. Filtra-
tion often does not proceed smoothly.  It is common practice
only to filter a small proportion of all  samples taken, and to
perform analyses for the total content of the analyte in the
majority of samples. Exhibit 3-17 depicts the effects of filtered
versus unfiltered samples and other sample preparation issues.
Reducing the number of samples by compositing is also a form
of sample preparation.  Compositing  may be performed to
reduce analytical costs, or in situations where the risk assessor
has determined that an average value will best characterize an
exposure pathway. Compositing cannot be used for the iden-
tification of hot spots, but can be  effective when averaging
across the exposure area for the RME trespasser scenario.
Caution should be exercised when compositing since low level
detects can be averaged out and become non-detects.
Sample character-
istics  can be dis-
turbed by  post-
sampling biologi-
cal activity or by
irreversible sorp-
tion of analytes of
concern onto the
walls of the sample
container. A vari-
ety of acids and bi-
ocides used  for
preservation  are
discussed in stan-
dard works such as
"Standard  Meth-
ods for the Exami-
nation of  Water
and Wastewater"
(Clescerietal.Eds.
1989).
          EXHIBIT 3-17

SAMPLE PREPARATION ISSUES
Issue
Sample
Integrity
Source ol
Analyle
Media
Analyte
Speciation
Number ol
Samples to
be Analyzed
Action
Preservation — Acids. Biocides
Unnrtered Samples - Measure
to:al analytes
FJtered Samples - Discriminate
sorbed and unsorbed analytes
Choice of sample preparation
protocols affects analyte
soeaalon
Composite Samples
(However, this raises the
e"ec!ive detection limit in
prccortion to the number of
samples composited )
                                      3.2.5   IDENTIFICATION OF EXPOSURE
                                              PATHWAYS

                                      Current and future exposure pathv/ays are often  limited to
                                      particular areas of a site. If sampling activity can be concen-
                                      trated in these areas, the precision and accuracy of the data
                                      supporting risk assessments can be improved.

                                      Exposure pathways should be designated prior to the design of
                                      the sampling procedures. For the risk assessment, at least one
                                      broad spectrum analytical sample is required and two or three
                                      are recommended for each medium in an exposure pathway. If
                                      the site sample design fails to consider all exposure pathways
                                      and media, additional samples will be required.

                                      The risk assessment requires the characterization of each
                                      exposure area for the site. Samples not falling within the areas
                                      of potential concern are not used in the identification of
                                      chemicals of potential concern nor in the calculation of reason-
                                      able maximum exposure concentration. Depending on expo-
                                      sure pathways, the risk assessor may utilize  only a small
                                      number of samples that were collected  at a site.  Exhibit 3-18
                                      shows why the identification of exposure pathways is critical to
                                      the sample design in order to maximize the number of samples
                                      that are useable in the risk assessment.

                                                           EXHIBIT 3-13
                                         IDENTIFICATION OF EXPOSURE PATHWAYS PRIOR TO SAMPLE
                                               DESIGN IS CRITICAL TO RISK ASSESSMENT
E
ar
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                         Chapters CriteriaforEvaluatingDataUseability in Baseline RiskAssessments
3.2.6    USE OF JUDGMENTAL OR
         PURPOSIVE SAMPLE DESIGN

The use of judgmental or purposive designs that may specify
sampling points based on existing site knowledge may be
appropriate for the initial phase of site sampling or in situations
where the risk assessment is performed using few samples. In
such instances, non-statistical approaches may be more effec-
tive in accomplishing the purpose of the risk assessment for
human health, than statistical designs with unacceptably large
sampling variability.

Judgmental  samples can be incorporated into a statistical
design if the sampling procedures designate the area of sus-
pected contamination as an exposure  area or stratum.  The
judgmental samples are then selected randomly within the area
of known contamination.  This procedure allows the judg-
mental samples to be used later if a statistical design is devel-
oped as a result of the initial sampling. Under the procedures
described, the initial judgmental samples are not considered
biased for the exposure area.  Exhibit 3-19 summarizes the
strengths and weaknesses of biased and unbiased sample de-
signs.

Resource constraints sometimes reduce the number
of samples upon which the risk assessment will be
conducted and therefore potentially  increase the
variability associated with the  results. When the
number of samples that can be taken  is restricted,
judgmental  sampling may be able to identify the
chemicals of potential concern even though the design will not
enable an estimation of the uncertainty in quantitation of the
amount of chemicals of potential concern present at the site.
However, the RME or UCL cannot be calculated from results
of ;iju:\
of false ne^at'ie
Weikrwum
inability to calculate
uncertainty

inability to
determine RME

Decreases
'epresentativeness
increases
probability of false
negatives
Resource intensive

May require
statistician

" melmess



ANALYTICAL
ISSUE
Chemicals of
Potential Concern
(331)
Lkrary Search,
Tentatively identified
Compounds
(332)
Identification and
Quant rtaton
(3 3.3)
Detection Limns
(3 3.4)
Media Variability
(3 3.5)
Sample Preparation
(3.3 6)
Fixed Laboratory
vs FiekJ Analyses
(3 3.7)
Laboratory Performance
Problems
(338)
IMPORTANCE
Chemicals of potential
toxcological significance may be
omitted
Identification and q^an'ia' on 20
not have high confidence
False negalves may occur when
analyies are p/esent near the
method detection limit
Risk levels "iay be at
concentrations tower than
measureable
Variability and bias may be
introduced to analytical
measurements
Variability and bias may be
Introduced to analytical
measurements
Tradeoffs required wrth regard to
speed, precrsion, accuracy.
personnel, identification,
quantrtation and detection limits
Quality of dala may be
compromised
SUGGESTED ACTION
Examine existing data and srte history
for industry -specific wastes to
determine anaJytes for measurement
3e^orm broad spectrum anays s
6$ p'e^fa3 to reques* 'u^ie'
analyses if potentially toxc
compounds are discovered during
screening Compare results from
mult pie samplings or historical data
Use technique with definitive
•Centificatron (e g . GC-MS)
A'lernaiively, use technique wti
ce'.nitive identification tirsi, followed
Dy another technique (e g , GC) to
acr
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 Chapters CriteriaforEvaluating Data Useability in Baseline Risk Assessments
•  The RPM and the risk assessor arc familiar with
   standard analytical chemical procedures.
•  Chemists arc available and will  be consulted for
   any significant problems or situations not covered
   in the manual.
•  Reference books on environmental issues in ana-
   lytical chemistry  are currenUy available (ASTM
   1979, Manahan 1975, Dragun 1988, Muntauetal,
   Eds.  1990, Taylor 1987).
•  Analytical quality assurance procedures are used
   in conjunction with and affect sampling quality as-
   surance procedures, even though the discussion
   treats these procedures separately.

Exhibit 3-20 summarizes the importance of each ana-
lytical issue to risk assessment and lists suggested
actions during the planning process.  The issues are
addressed  individually in  Section  3.3, including a
statement of the issue, its effect on data quality for risk
assessment, and how to anticipate and plan for poten-
tial problems.


3.3.1    CHEMICALS OF  POTENTIAL
          CONCERN

            The risk assessor should use prelimi-
            nary data to identify  chemicals of
            potential concern and determine any
            need to modify the sampling or
            analytical design.
                       EXHIBIT 3-21
    Mlu-1 floN COMPOUNDS AND THEIR DETECTION LIMITS
                                                 Detection Limit
                           nd Name
     TNB
     DN3
     Tetryl
     TNT
     2,4 DNT
     TAX
     SEX
     2.6 DNT
     2.4,5 TNT
     2 Am ONT
     4 Am DNT
     2,4DAmNT
     2,6 OAmNT
     DIMP
     TNG


     OMMP
     NG
Octahydfo-1l3.5,7-totranitro-1.3,5,7-tetra.:ocine
Haxahydro-1 3 5 tnnitro t 3,5 tnazine
Nitrobenzene
1 3.5-Tnnitro6enzene
l,3-Dinrtroberuen»
Methyl-2.4,6-tnnitrophenylnrlramine
2 4,6-Trinitrotoluene
2 4-Dmitrotoluene
H«xahydro-1 -(N)-ac«tyt-3.5-dinitro-1,3,5-tnazin«
Octahydro-1-(N)-acetyl-3.5.7-trinitro-1,3,5,7-tetrazocine
2,6-Dinrtrotoluen*
2.4,5-Trinitrotoluene
2-Amino-4,6-dinitrotoluen«
4-Amino-2,6-dinitrotolu«n«
2,4- Diamino-6-nitrotoluen«
2,6-Diamino-4-mtrotoluene
Disopropyl-methylphosphonate
Trinitrogylcerol (Nitroglycerin)
Nitrocellulose
Dimethyl meth/lphosphonate
Nitroguariadine
 Health advisory complete
 Health advisory in preparation (1 990}
5 1
4 2
6 4
59
9 1
44
63
23
Depending upon ma'nx and instrument conditions, these compounds may be chroTiatographab'e
and 'entatrVQ y c*3r'i'i6d as indicators o' ;r«s presence of munitions ciu' ng GC-VS iD'ary searcn
procedures

Detection limns are provided where available Specific compounds with complete heath advisories
are designated as target analytes with defined detection limits specrtied in a hign perfo"nance hq'j'd
chromatographic method developed and provided bytheUS Army To xic and Hazardous
Materials Agency
Chemicals of potential concern are uiose chemicals
with potential toxicological significance that may be present at
the site.  These chemicals ma> ha\e defined toxicological
thresholds for oral, inhalation, and dermal exposure pathways,
may have target concentration le\el< set by the  regulatory
process, or may be of environment! eoncern.  Many  other
chemicals may be present without ar'fecurig the level of i isk i> >
the ex posed population. The need for r.
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                         Chapters Criteria for Evaluating Data Useability in Baseline RiskAssessments
                   EXHIBIT 3-22

   SUMMARY OF MOST FREQUENTLY OCCURRING
CHEMICALS OF POTENTIAL CONCERN BY INDUSTRY
COMPOUND
Acetone
Aluminium
Ammonia
Ammonium Nitrate
Ammonium Sulfale
Anthracene
Arsenic
Benzene
Biphenyl
Chlorine
Chlorobenzane
Chromium
Copper
Cydohexane
Dibenzoturan
Dichloromethane
Fomaldehyde
Freon
Glycol Etna's
Hydrochloric Acid
Lead
Manganese
Methaioi
Mainy.' £ -r Karor,e
Naphthalene
Nickel
Nitric Acid
Pentachlo'oonenol
Propylene
Sodium Su"a;e
Sodium htyC'ox
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Chapters Criteria for Evaluating Data Useability in Baseline Risk Assessments
assessor should discuss the data requirements with a chemist to
determine the appropriate analytical method.

Many compounds that appear as tentatively identified com-
pounds during broad spectrum analyses belong to compound
hsdrecarbons, and polycyclic aromatic hydrocarbons (PAHs).
1 he risk assessor may be able to make a preliminary judgement
>it UKICII) at the  compound class level without a  definitive
identification of each compound present.  For example, in a
sample contaminated by gasoline, organics analysis would
indicate a series of TICs as aliphatic hydrocarbons of increas-
ing size. These may not be carcinogenic, and more precise
identification may not be required. If a similar sample were
contaminated with coal tar, larger hydrocarbons and a series of
PAHs would be found during  the analysis.  The aliphatic
hydrocarbons are not especially toxic, but the PAH compound
class contains carcinogens and would be of greater concern.


3.3.3   IDENTIFICATION AND
         QUANTITATION

           The type of detection or quantitation
           limit must be defined for reporting
           purposes; the sample quantitation limit
           i SQL) should be requested for risk
           assessment.
A risk assessor generally wants to confirm chemical identifica-
tion first, and then determine the level of quantitation. This
section summarizes the effects of detection limit and sample
contamination considerations on the confidence in  analyte
 ..... , ., '  JJIM ana qudiiULiiion  Requirements are ipccilied in
                    EXHIBIT 3-24

          REQUIREMENTS FOR CONFIDENT
         IDENTIFICATION AND OUANTITATION
                 Analyle present a:ove •-'a "Mrument
                 detection lirp>!

                 Organic - Retentio- '. —a a^c.or mass spectra
                 matches autnemc sta^ca'ds

                 Inorganic - Spect'a. aMo-3"ons compared to
                 auihenic standarcs

                 Knowledge o( bia-< co~'a~ -anon (if any)
                 Instrument response «.-ow~ 'rom analysis of an
                 aj:hent,c stanaa-c

                 Detected concenfa .o- aoove me limit ot
                 quanntat.on a~c * — "~e  "'t of linearity
                 (instrument response " s-a'^'atod)
Exhibit 3-24. When analytcs have concentrations of concern
approaching method detection limits, the confidence in both
identification and quantitation is low. This case is presented in
Exhibit 3-25.  In addition, confidence  in identification and
quantitation as representative of site conditions is potentiallv
diminished if theanalytesarc present as chemicals of potential
concern from laboratory or field  procedures.  This section
identifies analytes and cites situations in which this is most
likely to occur.

The first area of analytical consideration is confidence in the
identification of chemicals of potential concern. Identification
means that the  chemical was detected in the environmental
sample. Chemicals can be correctly identified at lower concen-
trations than are suitable for accurate quantitation.  If lower
quantitation limits are required for risk assessment purposes, a
larger  initial sample size may be processed, or the sample

                     EXHIBIT 3-25

        COMPARISON OF DETECTION LIMIT WITH
            CONCENTRATION OF CONCERN
          TO SELECT AN ANALYTICAL METHOD
Relative Position of Detection Limit
(DL) and Concentration of Concern
(COC)
l ConfKjgnre
Confidence rt COC '"""J'5
Limns » J^x— ~-Jv. ' /
Concentration i ™
t
DL COC
Concentration i
COC DL
Concentralion j ^
\
COC Cl
Concentration ' w~
i
Consequence
Non-Detects and
Detects
Quantitatively
Useable
Possibility ot
False Positives and
False Negatives
Possibility ot
False Positives and
High Possibility of
False Negatives
Non-Detects
Not Useable
Delects Quantitatively
Useable
Possibility ot False
Negatives
                                                        40
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                          Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
extract may be concentrated to a smaller final volume. The risk
assessor should discuss these issues with an analytical chemist
to determine the best approach. A further discussion of limits
to quantitation is presented in Section 3.3.4.

To assure maximum confidence in  the identification of an
organic chemical contaminant, an instrumental technique such
as mass spectrometry that provides definitive results is neces-
sary.  Although alternative techniques are  available, GC-MS
determination is the routinely available procedure for confident
qualitative identification or confirmation of volatile and ex-
tractable organic chemicals of potential concern. The applica-
tion of these techniques minimizes the risk of error in qualita-
tive identification and measures chemicals of potential concern
at environmental levels above the detection or quantitation
limits listed in Appendix III. In cases where  the necessary
detection  limit is too low to allow identification by mass
spectrometry, more sensitive methods can be used.

The identification of inorganic chemicals is more certain. A
reported concentration determined by atomic absorption (AA)
or inductively coupled plasma (ICP) atomic emission spectro-
photometry is considered evidence of presence at the desig-
nated level reported, provided there is no interference.


3.3.4    DETECTION AND QUANTITATION
          LIMITS AND RANGE OF LINEARITY

           The closer the concentration of
           concern is to the detection limit, the
           greater the possibility of false  nega-
           tives and false positives.


           The wide range ofctemical concentra-
           tions in the environment may require
           multiple analyses or dilutions  to obtain
           useable data. Resales from all analy-
           ses should be requested.


The following discussion is intended to provide the RPM and
risk assessor with an understanding of the various ways that
detection or quantitation limits  can  be reported.  The term
detection limit is frequently used * ithout qualification.  How-
ever, there are several methods for calculating detection limits.
It is the responsibility of the RPM. in consultation with the risk
assessor and the project chemist, to specify the nature  of the
detection limits that must be reported; it is the laboratory's
responsibility to adhere to this requirement. If no requirement
has been specified, then it is the responsibility of the laboratory
to explicitly describe the types of ihe detection limits it reports.
Detection limits can be calculated for the instrument used for
measurement, for the analytical  method, or as a sample-spe-
cific quantitation limit. The risk assessor should request that
the sample quantitation limit be reported whenever possible.
The term detection limit should be considered generic unless
the specific type is defined. Exhibit 3-26 illustrates the rela-
tionship between instrument  response  and the quantity oi
analyte presented to the analytical system (i.e., a calibration
curve).


                      EXHIBIT 3-26

  THE RELATIONSHIP OF INSTRUMENT CALIBRATION
           CURVE AND ANALYTE DETECTION
        Region of
        ' High Uncertainty in
        Identification and Quantitation
8

I
o>
a:
m

i=
t/i
c
                  J
              1
          Region
          of Less
          Certain
        Quantitation
                       Region of Known
                        Quantitation
                                             Region of
                                            Less Certain
                                            Quantitation
        Region
        of Less
        Certain
        Identifi-
        cation
           i!    !
                            IDL
                           MDL
                           LOQ
                           LOL
                               » Instrument Detec;ion Lin
                               - Method Detectio" Limit
                               = Limit of Quantita*. on
                               = Limit of Linearity
                                         1
              I    I
   OIDL    MDL  LOQ
                        Concentration
                                      LOL
The following definitions are intended to provide the RPM and
risk assessor with an understanding of the various methods for
calculating detection limits, the terms used to describe specific
detection limits, and the limitations associated with identifica-
tion  and quantitation  of chemicals  of potential concern at
concentrations near specified detection limits. Understanding
the different terms used to describe detection limits helps avoid
reporting problems from confusion in their use. Exhibit 3-27
provides examples of calculations of the three most commonly
reported types of detection limits.

Instrument detection limit

The instrument detection limit (IDL)  includes only the instru-
ment portion of detection, not sample preparation, concentra-
tion/dilution factors, or method-specific parameters. The IDL
                                                       41
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Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
                        EXHIBIT 3-27

        EXAMPLE OF DETECTION LIMIT CALCULATION



  IOL = 3 143 X S D.1of replicate injections

        Example   100 ppb pentachtorophenol standard

            If   S D - 5 ppb

          Then   IDL - 3 X 5 ppO- 15 ppO


  MDL = 3 143 X S.D. of replicate analyses (extraction and injection)

        Example   100 ppb pentachbropnenol spked in sample producing average measured
                concentration at 50 ppb (not all anatyte «recovered or measured)

            II   SD .18 ppb

          Then   MOU-3X18ppb-57ppb
  SQL = MDL corrected for sample parameters

        Example   100 ppb pentachtorophenol producing MDL of 57 ppb

             II   Dilution factor. 10 (sample Is cWji«c due to matrix interference or high
                concentrations of other analyses)

          Then   SQL-10X57 ppb. 570 ppb
   SO. Standard Deviation
is operationally defined as three times the standard deviation of
seven replicate analyses at the lowest concentration level that
is statistically different from a blank.  This represents 99%
confidence that the signal identified is the result of the presence
of the analyte, not random noise. The IDL is not the same as the
method detection limit, although the IDL is often the quantity
reported for inorganic analyses.

Method detection limit

The method detection limit (MDL) is the minimum amount of
an analyte that can be identified by using a specific method.
The MDL can be calculated from the IDL by use of sample size
and concentration factors and assuming 100% analyte recov-
ery. This estimate of detection limit ma\ be biased low because
recovery is frequently less than lOO^r  NIDLsareoperationally
determined as three times the standird deviation of seven
replicate spiked samples run according to thecomplete method.
Since this estimate includes sample preparation effects, the
procedure is more accurate than reported IDLs.  However, the
evaluation is routinely completed on reagent water. As a result,
potentially significant matrix interferences that decrease ana-
lyte recoveries are not addressed.

Sample Quantitation Limit

The sample quanti tation limit (SQL h< the most useful lim it for
the risk assessor and should be requested whenever possible.
An individual sample may require adjustments in preparation
or analyses, such as dilution or use of a smaller sample aliquot
for analysis due to matrix effecLs or iho high concentration of
   some analytes.  The reported sample quantitation limit is
   adjusted to reflect the sample-specific action. Therefore,
   sample quantitation limits will be ihe quantitation limits of
   interest for most samples.

   In fact, for the same chemical, the SQL in one sample may
   be higher than, lower than, or equal to SQL values for other
   samples. In addition, preparation cranalyticaladjustments,
   such as dilution of the sample  for quantitation of an ex-
   tremely high level of one chemical, could result in non-
   detects for other chemicals included in the analysis even
   though these chemicals may have been present at trace
   quantities in the undiluted sample. The risk assessor should
   request results of both original and dilution analyses  in this
   case.  The reported SQLs will  take into account sample
   characteristics,  sample preparation, and analytical adjust-
   ments, so the SQLs are the most relevant quantitation lim its
   for evaluating non-detected chemicals.

   Contract Required Quantitation
   (Detection) Limit
   The CLP utilizes a Contract Required Quantitation Limit
   (CRQL) for organics and a Contract Required Detection
Limit (CRDL) for inorganics. This quantity is related to the
SQL that has been shown through laboratory validation to be
routinely within the  defined linear ranges of the required
calibration procedures. The use of CRQLs and CRDLs main-
tains the analytical requirements within performance limits that
consider the laboratory variability using a variety of instru-
ments. CRQLs are typically two to five times the reported
MDLs and they generally correspond to the limit of quantita-
tion (LOQ) described below.

Other quantitation measurements

In addition to the measurements described, two other terms
relate to confidence in quantitation: the limit of quantitation
and the limit of linearity.

The  limit of quantitation (LOQ) is the  level above which
quantitative results may be obtained with a specified degree of
confidence.  At low analyte concentrations but  above the
method detection limit, the uncertainty in quantitation is rela-
tively high. Although the presence of the analyte is accepted at
99% confidence, the quantity reported may be in the range of
+/- 30%.   To demonstrate a level at  which confidence in
quantitation is maximized, ten times the standard deviation
measured for instrument detection is recommended as the LOQ
(Borgman  1988). LOQ is more frequently used for organic
compounds.

The limit of linearity (LOL) is the point at which the relation-
ship between the quantity present and the instrument response
ceases to be linear (Taylor, 1987).  This point is at or above the
upper end of the calibration curve. Instrument response usual K
                                                       42
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                           Chapters CriteriaforEvaluatingDataUseabilityin Baseline RiskAssessments
decreases at this point, and the concentration reported is less
than the amount actually present in the sample because of
instrument saturation. For samples in which analyte concentra-
tions exceed this amount, dilution is necessary to analyze the
chemical within the linear range.  However, dilutions corre-
spondingly increase SQLs.


3.3.5    MEDIA VARIABILITY

This section relates method detection and general confidence in
the analytical determination to specific media  types.  The
medium (matrix) is the sample type to be analyzed for the
presence of chemicals of potential concern. The impact of
matrix interference on detection limits, identification, and
quantitation is discussed. Examples include:
•  Oil and hydrocarbons affecting GC-MS analyses.

•  Phthalates and non-pesticide chlorinated compounds that
   can interfere with pesticide analyses.

•  Iron spectral interference affecting ICP sample results.

The type of medium in which a chemical is present affects the
potential sensitivity, precision, and accuracy of the measure-
ment. Sharp distinctions occur in applying a single method to
media such as water, oil, sludge, soil, or tissue.  Medium or
matrix problems are indicated by the presence of analytical
interferences, poor recovery of analytes from the matrix, physi-
cal parameters such as viscosity (flow parameters), and panicu-
late content that affect sample processing. Exhibit 3-28 shows
the sources of uncertainty across media. Spiked environmental
samples monitor the effect of these parameters on the recovery'
of target compounds from the matrix. Duplicates quantify the
effect of these parameters on precision.  The method must be
chosen carefully if a difficult medium such as oily waste or soil
is to be analyzed. Routine methods usually specify the medium
or media for which they are applicable. The following discus-
sion provides three examples of interferences and is not meant
to be comprehensive.

Oil  and hydrocarbons

The  presence of appreciable concentrations of oil and other
hydrocarbons may interfere with the extraction or concentra-
tion process.  Also, even at low concentrations, oil in a sample
usually produces a large series of chromatographic peaks that
interfere with the detection of other chemicals  of potential
concern during gas chromatograph>. Any chemicals of poten-
tial concern that may elute concurrently are obscured by the
hydrocarbon response and may not present a distinct spectrum.
Also, hydrocarbons that are present  in significant quantity are
identified  often as LSCs or TICs. potentially adding a large
number of compounds for consideration by the risk assessor.

During RI planning, the nsk assessor 
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Chapters CriteriaforEvaluating DataUseability in Baseline RiskAssessments
Iron

Large quantities of iron in a sample affect the detection and
quant itation of other metallic elements analyzed by TCP/atomic
emission spectroscopy at wavelengths near the iron signals.
The  strong iron response overlaps  nearby signals, thereby
obscuring the results of potentially toxic elements present at
much lower concentrations. An interference check sample for
ICP  analyses monitors the effect of such elements.  High
concentrations of iron are analyzed with low concentrations of
other metals in these samples to indicate if interference may
have been present which affected the certainty of detection of
metals at lower concentrations. Data are usually qualified as
underestimated when spectral interferences are observed. The
risk assessor or RPM can request an interference check.


3.3.6   SAMPLE PREPARATION

Several factors contribute to the effect of sample preparation on
the analytical data. These factors  include sample matrix,
desired detection limit, extraction solvent, and whether the
analysis is performed in the field or in a fixed laboratory. In
addition, parameters such as turnaround time may preclude the
use of some sample preparation alternatives.

An extraction method must be able to release the compounds of
concern from the sample matrix. For example, organic solvents
will extract non-polar organic compounds from water. Polar
and ionic compounds may require additional techniques for
extraction from water.  The choice of solvent is also critical to
the extraction efficiency.   Methanol would be expected to
extract a larger quantity of volatile organic material from soils
or sediments than  from water. For inorganic analyses, the
matrix may have to receive additional acidification to dissolve
metal salts that have precipitated from the solution.

Sample preparation procedures for volatile organic compounds
include head-space and purge-and-trap.  Extraction alterna-
tives for the analysis of less volatile (extractable  organics)
organic chemicals include separatory funnels, soxhlet extrac-
tion apparatus, continuous liquid-liquid extractors, and solid
phase cartridges. Strengths and weaknesses of each of these
preparation procedures are described in Exhibit 3-29.

Prior to elemental analysis by flame atomic absorption or ICP
atomic emission spectrophotometry, the sample matrix is usually
digested in concentrated acid to release the metals for introduc-
tion into the instrument. The selection of the acid for digestion
influences the detection limit.
•  If digestion is  not used, the sample measurement corre-
   sponds to a determination of soluble metals rather than total
   metals.  If soluble metals have a greater toxicological sig-
   nificance, this difference may be important to the  risk
   assessment process.
•  If the sample is filtered in the field or the laboratory before
   digestion, any metals associated with particulates are re-
   moved before analysis. If particulates are an exposure path-
   way in the risk assessment, the risk would be uncleaMi-
   mated.

The analytical rcquestmust specify ifthesampleistobe filtered
and whether or  not it is  to be digested (to measure soluble
metals). Unless otherwise specified, samples are usually di-
gested but not filtered.


3.3.7   FIXED LABORATORY VERSUS
         FIELD ANALYSES

Field instruments are usually divided into three classes:

•  Field portable instruments that can be carried by a single
   person.

•  Field transportable instruments that can be moved and used
   in the field or in a mobile laboratory.

•  Mobile  laboratory instrumentation  that is installed  in a
   trailer for transport to a site.

           Use of the CLP or other fixed labora-
           tory sources is most appropriate for
           broad spectrum analysis, or confirma-
           tory analysis.


Analyte-specific field screening and monitoring tests are cur-
rently available and fall  in the category of field portable or
transportable instruments. Chem ical field test kits, lon-speci fie
probes, and immunoassays have provided semi-quanuiativc
results free of significant matrix interference.  The data can be
used quantitatively if confirmed by  additional types of quanti-
tative analysis.   Different tests can be chosen to examine
organic compounds, inorganic analytes, nutrients, or water
quality indicators. Field instruments are also frequently used to
indicate the presence of contamination. Forexample, they may
provide a total vapor concentration.  These analyses can be used
to identify critical locations and to modify sampling plans in ihc
field to determine the extent of contamination. Consequently,
for some sites,  these procedures can provide data that are
directly useful to the risk assessment. The number of samples
needed and turnaround requirements must be considered when
evaluating field screening data as a source for risk assessment.

           Data should be available from a broad
           spectrum analysis from each medium
           and exposure pathway to minimize the
           potential for false negatives.
                                                      44
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                            Chapters CriteriaforEvaluating Data Useability in Baseline RiskAssessments
                                              EXHIBIT 3-29

                 COMPARISON OF SAMPLE PREPARATION OPTIONS
Fraction
& Matrix
Preparation
             Strengths
          Weaknesses
Volatile
Soil/Water
 Head-space
               Purge/Trap
 Extractable
 Organics
 in Water
 Separatory
 Funnel
               Continuous
               Extraction
               Solid Phase
               Extraction
 Extractable
 Organics in
 Soil
 Sonication
 Inorganics
              Soxhlet
              Extraction
 Acid
 Digestion
              045 um
              Membrane
              Filtration
  Rapid, simple, potentially automated and
  minimal interferences if standards are prepared
  using sample media to minimize the effects of
  ionic strength variability between samples and
  standards.
                  Generally recommended for this analysis
                  (comparabilities); can be automated; broadly
                  applicable and allows concentration factor,
                  good recoveries across analyte list.

                  High precision and recoveries for waters.
   Relatively rapid processing and low set-up
   costs, relatively high PAH recovery
                  Minimal matrix problems; generally higher
                  analytical precision and high phenol
                  recoveries; overall high extraction efficiency
                  (accuracy).

                  Very rapid, simple technique; samples can be
                  extracted in the field for laboratory analysis,
                  potentially low MDL in a clean matrix
1   Rapid sample preparation, relatively low
   solvent requirement; good efficiency of
   ana'yte recovery/matnx exposure to solvent
                  Re a'jvely routine requirement for direct
                  ana'ytical support, relatively good exposure of
                  sa/npie to solvent if sample texture
                  appropriate. Relatively low initial cost.
                               Promotes ionic speciation, dissolves
                               partxxilates, provides results for total metals
                               Isolates dissolved metals species
                  No oreparation required, provides results for
                  dissolved metals
Qualitative identification, comparison of
concentration possible but quantitative
standardization is difficult, especially true for
complex matrix (e.g., particulates and clay in soil).
No mechanism for concentration. Application and
sensitivity are very analyte-specific.

Sacrifice of either highly volatile analytes or
inadequate purge of low volatility analytes;
dependent on purge and  trap parameters
Soils have variable response dependent on soil
characteristics.  Efficiency of soil purge is not
monitored.

Generally low recovery of target analytes, high
potential for matrix problems, poor method
precision

Lower recovery of PAH and phthalates (especially
higher MW), time-consuming procedure and high
initial  set-up costs; more potential for
contamination

Procedure has limited available performance data
Presence of interference and matrix problems can
affect extraction efficiency and data quality'  Each
batch of extraction medium must be tested for
efficiency by recovery of standards, preferably  in
the same matrix   Breakthrough (loss) at high
sample concentrations

Labor intensive, constant attention  to procedure
Relatively high initial cost  Methylene
chloride/acetone solvent mixture results in many
condensation products and often in method blank
contamination
                                                  Relatively high operating cost-replacement
                                                  apparatus, solvent, for some matrices may not
                                                  provide efficient sample/solvent contact (e g ,
                                                  channeling, very slow sample output)
                                                                 Some compounds are acid insoluble; digestion may
                                                                 promote interference effects
                                                                 Filtration problems in field  Does not provide a total
                                                                 metals assay  Is an extra step in sample collection
                                                  Particulates affect sample introduction
                                                              45
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Chapters CriteriaforEvaluatingDataUseabilityin Baseline RiskAssessments
Rugged versions of fixed laboratory instrumentation, such as
X-Ray fluorescence spectrometers (XRF) and gas chromato-
graphs, can often be installed in trailers if adequate ventilation
and power supplies arc available. Therefore, analytical meth-
oJs that hoAC UaJitiuiiaily been iCiUiUeJ to off-site laborato-
ries can now be employed in the field.  In addition, the quality
ol field instrumentation ha.s improved steadily allowing for
better measurements at the site. Through the useof field instru-
mentation, samples can be analyzed immediately or with short
holding times with no shipping and storage requirements. It is
recommended that 10% of field analyses be confirmed by fixed
laboratory analyses.

Fixed laboratory analyses are particularly useful for conduct-
ing some broad spectrum analyses for target compounds to
avoid the possibility of false negatives. They generally provide
more information for a widerrange of analytes than field data.
Fixed laboratory data sources are generally more reliable than
field screening or field analytical techniques. Mass spectrome-
try combined with  gas chromatography provides greatly en-
hanced abilities for compound identification and is the recom-
mended means of  identification  for organic analyses.  For
inorganics, AA spectroscopy or ICP emission spectroscopy
should be used for reliable identification of target compounds.
Once the broad spectrum analysis and contaminant identifica-
tion has occurred, other methods may be employed that better
quantitate specific  analytes of concern and
that are less expensive. Other methods may
also offer lower detection limits.

Characteristics that should be considered in
the selection of field or fixed laboratory in-
strumentation include: turnaround ume, de-
'.cciio.'i and identification abilit) of the instru-
ments, precision and accuracy requirements
of the measurements, and operator qualifica-
tions. Exhibit 3-30 compares the characteris-
tics for field and fixed laboratory analyses.
The risk assessor and RPM should consider
the available options  and make a choice  of
analysis ba^ed on method parameiers, turn-
around time, and cost, as well as other data re-
quirements perunenuo risk assessment needs
(e.g., legal defensibility). Exhibit 3-31 com-
pares the strengths and weaknesses of field
and fixed laboratory analyses.

Field instruments usually produce data in a
shorter time because sample shipment is not
required and  analytical methods ire often
streamlined.  The  fixed  laboratory  instru-
ments usually  have lower dcux'_on  limits
because of the stability of the installation and
less  likelihood of  contamination /-om site
              chemicals of potential concern. However, if a fixed laboratory
              is using GC-MS methods and the field analyses are using GC
              methods, then the field analyses will  have  lower detection
              limits.  Because mass spectrometers can now be installed in
              trailers for transport to a .site, both fixed laboratory and fu!,!
              installations can provide good organic  analyte identification
              Identification by  high  resolution mass spectromctry is still
              restricted to the fixed laboratory, however.

                          Field methods can produce legally
                          defensible data provided that appropri-
                          ate quality control and documentation
                          are available.
              The precision and accuracy of individual measurements may
              be lower in the field than at a fixed laboratories .butthequicker
              turnaround and the possibility of analyzing a larger number of
              samples at the same cost may compensate for this factor. A
              final consideration is the qualifications of operators in the field.
              The RPM, in consultation with chem ists and quality assurance
              personnel, should set proficiency levels required  for each
              instrument class and review the qualifications of personnel
              proposed as instrument operators for compliance with these
              specifications.


                         EXHIBIT 3-3Q

CHARACTERISTICS OF FIELD AND FIXED LABORATORY ANALYSES
CHARACTERISTIC
Prevention of
false negatives
Prevention of
false positives
Analytical
Turnaround Time
Sample
Preparation
Cost of
Acquisition
FIELD ANALYSIS
Immediate analysis means
volatiles not lost due to
shipment and storage
No sample to sample
contamination during
shipment and storage
Data available immediately
or in up to 24-48 hours
(additional time necessary
for data review)
Limited ability to prepare
samples prior to analysis
Cost is relatively low for
individual organics and
inorganics analyses (More
samples may be collected
for better precision and
accuracy )
FIXED LABORATORY
ANALYSIS
More extensive sampio
preparation available to
increase recovery of
analytes
Contamination by laboratory
solvents minimized by
storage away from analytical
system
Data available in 7 35 da1, s
unles.s quick turnaround time
requested (at increased
cost)
Samples can be extracted or
digested, thereby increasing
the range of analyses
available
Cost is relatively high
(Individual analyses provide
better precision and
accuracy )
                                                       46
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                        Chapters Criteriafor Evaluating Data Useability in Baseline Risk Assessments


                                          EXHIBIT 3-31

                   STRENGTHS AND WEAKNESSES OF FIELD
                        AND FIXED LABORATORY ANALYSES
      ANALYSIS
                STRENGTHS
         WEAKNESSES
   Field XRF (Metals)
Extremely high volume sampling and analysis,
compatible with sophisticated sampling and
data handling software; detection limit may be
above laboratory instrument values but
applicable to specific site levels of interest
                                                                        Confirmation technique recommended
                                                                        Comparability may require external
                                                                        standardization of calibration because
                                                                        quantitation is based on soil surface area
                                                                        vs. a soil volume  Results often lower than
                                                                        from AA analyses.
   Field GC
                        Rapid analysis supporting high volume sampling
                        for variety of volatile and extractable organic
                        target compounds (includes pesticide/PCB).
                        Minimization of sample handling variability and
                        data quality indicators comparable to fixed
                        laboratory methods
                                                Requires prior site knowledge to assure
                                                applicability to specific conditions (e g ,
                                                soil gas may not be appropriate for
                                                investigation in sandy area)  Confidence
                                                in identification is matrix- and site-specific
                                                and highly variable depending on sample
                                                complexity. Confirmation technique
                                                recommended.
   Mobile Laboratory
   XRF, AA (Metals)
Combines the high volume sample capacity of
field analyses with the detection limits, data
quality and confidence associated with
laboratory analyses
Requires significant  resources, time,
and personnel to transport, maintain
and operate  Generally most
appropriate at high volume sites,
especially remote
   Mobile Laboratory
   Luminescence
j  Mobile Laboratory
Rapid survey of analytes that routinely
require sample preparation (e.g., polynuclear
aromatcs and PCBs) Detection limits can
be adjusted within limits to site-specific
concentrations of concern
Technique has had minimal use in EPA
site investigation.  Comparability may
be an issue and require extensive
confirmation analyses
Combines high volume capacity of field
analyses with increased confidence in
identification (GC-MS) or improved data
quality (GC).  GC methods may be identical
to laboratory procedures but quality is
intermediate due to site conditions (e g ,
temperature, humidity and power requirements)
                                                                       Same weaknesses as for mobile
                                                                       laboratory inorganics An additional
                                                                       weakness is the increased training
                                                                       requirements and decreased availability
                                                                       of experienced GC-MS operators for
                                                                       totally independent system operation
                                                                       Possibiity of site contamination and
                                                                       cross-contamination

  Fixecf Laboratory
  XRF, AA, ICP
  (Metals - Routine
  Available Methods)
"ighest comparability and representativeness
Data quality, including detection limits,
generally predictable  Efficient match of analyses
required to instrument (e g , multiple analyses
run simultaneously by ICP)
Slow delivery of data, increased
documentation requirement due to
the number of participants • relatively
high sample cost
  Fixed Laboratory
  GC & GC-MS
  (Organics - Routine
  Available Methods)
Highest comparability and representativeness
Necessary confirmation of qualitative
identification  Data quality and detection
limits generally predictable  In depth
araiysis and sample archives for follow-up
Same weaknesses as for fixed
laboratory metals  Analyte-specific
performance
   ICP = Inductively Coupled = asma Spectroscopy.  Graphite AA = Graphite Furnace (electrothermal) Atomic
   Absorption Spectroscocy  "ameAA = Flame Atomic Absorption Spectroscopy. ICP-MS = Inductively Coupled
   Plasma-Mass Spectror-ef,  XRF = X-Ray Fluorescence. GC = Gas Chromatography.  GC-MS = Gas
   Chromatography-Mass Soec"ometry  AA = Atomic Absorption Spectroscopy.
                                                       47
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Chapters Criteriafor Evaluating Data Useability in Baseline Risk Assessments

                                              EXHIBIT 3-31
                         STRENGTHS AND WEAKNESSES OF FIELD
                             AND  FIXED LABORATORY ANALYSES
                                               (continued)
              ANALYSIS
            STRENGTHS
       WEAKNESSES
              ICP
Simple, automated, extremely rapid. Can
assay metals simultaneously. Can detect ppb
levels.
Subject to salt or iron interferences.
Lacks detection capability at low
levels. Not suitable for less than 20
ppb Arsenic, Lead, Selenium,
Thallium, Cadmium, Antimony.
Requires background and
interelement correction.
             Graphite AA
Simple, automated.  Can assay most metals.
Can assay low level metals. Can detect ppb
levels.
Lower precision and accuracy unless
methods of standard seditions used.
Method is time-consunmg  Requires
background correction.  Requires
matrix modifiers. Graphite tube
requires replacement frequently.
Subject to spectral interferences.
             Flame AA
Simple, rapid, very suitable for high
concentration sodium and potassium assays.
Commonly used and rugged.
Not as sensitive as graphite AA.  Salts
can interfere.  Limited by lamp
capabilities. Detects pprn levels.
             ICP-MS
Rapid. Can detect low levels. Accurate.
Subject to isobaric molecular and ion
interferences. Nebulization, transport
process, and memory physical
interferences occur. Method is
relatively new and is expensive.
Specialized training is required.
              ICP-Hydnde        Rapid  Can detect low levels of Antimony,
                               Arsenic, Selenium. Hydride formation
                              I eliminates spectral interferences.
                                         Dependent on analyte oxida'jon state.
                                         Especially sensitive to copper
                                         interference.  Method is relatively new
                                         Specialized training is required
              ICP = Inductively Couo'ed Plasma Spectroscopy. Graphite AA = Graphite Furnace (electrothermal) Atomic
              Absorption Spectrosccov Rame AA  = Flame Atomic Absorption Spectroscopy. ICP-MS = Inductively Coupled
              Plasma-Mass Spectronetry.  XRF = X-Ray Fluorescence. GC = Gas Chromatography.  GC-MS = Gas
              Chromatography-Mass Spectrometry. AA = Atomic Absorption Spectroscopy.
                                                      48
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                          Chapters CriteriaforEvaluatingDataUseabilityin Baseline RiskAssessments
3.3.8   LABORATORY PERFORMANCE
         PROBLEMS

The  RPM should be aware of problems that occur during
laboratory analyses, even though the resolution of such prob-
lems will usually be handled by the project chemist.  This
section discusses common performance problems and explains
how  to differentiate  laboratory performance problems from
method performance problems.

           Communication with the chemist
           ensures proper laboratory selection
           and minimizes laboratory and/or
           methods performance problems that
           occur during the analysis of environ-
           mental samples.


Laboratory performance problems may occur for routine or
non-routine analytical services and can happen with the most
technically experienced and responsive laboratories. Labora-
tory problems include instrument problems, personnel inexpe-
rience or insufficient training, and overload of samples. Issues
that may appear to be laboratory problems, that are actually
planning problems, include access to standards, unclear re-
quirements in the analytical specifications, difficulty in imple-
menting non-routine methods, and some sample-related prob-
lems. Another problem for the RPM may be a lack of labora-
tories with appropriate- experience  T available capacity :< > PI _\-t
RI analytical needs.

   Instrument problems can be revealed by requiring a unique
   identifier for each  instrument in  the laboratory that is
   reported with the analyses. Instrument blanks and calibra-
   tion and performance standards should be specified in the
   analytical method to monitor the performance  of each
   instrument.

•   Some degradation in data quality may appear  when new
   personnel are utilized or when the sample load for a labo-
   ratory is too high.  The contributing personnel for each
   analysis should be identified clearly in laboratory records
   and reports, and required qualifications of personnel should
   be documented in contracts.

•   Sample and method problems can  be distinguished from
   laboratory problems because they are usually not associ-
   ated with a specific instrument or analyst.
                                                     49
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
Chapter 1
Introduction and Background
    Chapter 2
    The Risk Assessment Process
        Chapter 3
        Criteria for Evaluating Data Useability in Baseline
        Risk Assessments
                     Chapter 4
                     Steps for Planning for the
                     Acquisition of Useable
                     Environmental Data in
                     Baseline Risk Assessments

                     •   Provides guidelines for designing
                         sampling plans and selecting
                         analytical methods.

                     •   Provides worksheets to support
                         sampling plan design and
                         analytical method selection.
                                 51
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
                          ACRONYMS FOR CHAPTER FOUR
                         BNA     Base/Neutral/Acid
                         CLP      Contract Laboratory Program
                         CV       Coefficient of Variation
                         DQO     Data Quality Objective
                         GC       GasChromatography
                         GPC      Gel Permeation Chromatography
                         MS       Mass Spectrometry
                         PCB      PolychlorinatedBiphenyl
                         QA       Quality Assurance
                         QC       Quality Control
                         RCRA    Resource Conservation and Recovery Act
                         RI        Remedial Investigation
                         RME     Reasonable Maximum Exposure
                         RPM     Remedial Project Manager
                         S AS      Special Analytical Services
                         VOA     Volatile Organics Analysis
                                             52
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   Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
4.0    STEPS FOR PLANNING FOR
        THE ACQUISITION OF
        USEABLE ENVIRONMENTAL
        DATA IN BASELINE RISK
        ASSESSMENTS

This chapter provides planning guidance to the RPM and risk
assessor on designing an effective sampling plan and selecting
suitable analytical methods to collect environmental analytical
data that will be optimally useable in baseline risk assessments.
The first section of the chapter describes the process of select-
ing an appropriate sample design  strategy and developing a
sampling plan that is devised to resolve the four fundamental
risk assessment decisions presented in Chapter 3:
    What contamination is present and at what levels?
•  Are site concentrations sufficiendy different from back-
    ground?
•  Are all exposure pathways identified and examined?
•  Are all exposure pathways fully characterized?

A Sample Design Selection  Worksheet is  used as a data
collection and decision-making tool in this process. Guidance
for evaluating alternative sampling strategies and designing
statistical sampling plans is included.

The second section of the chapter provides detailed guidance
on  selecting the methods for analyzing samples collected
during the RI.   A Methods Selection Worksheet is used to
compile the list  of chemicals of potential concern and deter-
mine analytical priorities so that the most suitable combination
of methods is selected.

The risk assessor or RPM, in consultation with other technical
experts, will usually complete several worksheets, represent-
ing different media,  exposure pathways, potential sampling
strategies, chemicals of potential concern, and analytical pri-
orities, in order to compile sufficient information to communi-
cate basic risk assessment requirements to the RPM so that
these requirements will be addressed in the SAP. Section 4.1
explains how to complete the Sample Design Selection Work-
sheet and how to obtain summary information from the work-
sheets to support the final decision on the RI sample design.
Section 4.2  gives instruction? for completing  the Method
Selection Worksheet and tells hov.  to use worksheet  informa-
tion to select the appropriate ar.al> tical methods for the RI.

The final selection of sampling plans and analytical methods is
bused  on  the specification of the performance measures dis-
cussed in tins chapter. These measures arc data quality indica-
tors (DQIs) that are component of t'v data quality objectives
(DQOs) developed by the RPM for Lie total data collection and
evaluation effort.
4.1    Strategies for designing
        sampling plans

This section provides guidance for evaluating the neecssii\ ui
alternative  sampling  strategies  and designing  a  statistical
sampling plan. The objective is to determine a ^ti ueov il>.!i
collects data representative of conditions at the sue and  is
withm resource limitations.  The data must have acceptable
levels of precision and accuracy, obtain minimum requiied
levels of detection for chemicals of potential concern, and
minimize the probability of Type I and Type II errors. Meeting
these objectives involves optimizing the precision of concen-
tration estimates and the power to detect difference- between
site and background levels.  To accomplish these objectives
the RPM can:

•   Increase the number of samples.

•   Improve the sample design.

•   Improve the efficiency of statistical estimators.

Increasing the number of samples may increase initial COSTS to
a varying degree, depending on whether fixed or field anah ti-
cal methods are used for analysis, but is necessary  in certain
situations (see Section 4.1.2). The sample design c in often be
improved by stratifying within a medium to reduce \aruh>!ity
or by selecting a different sampling approach, such as .-.geosta-
tistical estimation procedure termed "krigmg." Improving tiic
efficiency of the statistical estimators involves specif) ing the
type of data distribution if parametric procedures  are bei u"
used, or switching from nonparametric  to parai^cinc if  oaring the ii_ ."i;n.i  • v i,;   •   -
often reduced in clean-up efforts

Exhibit 4-1  is a Sample Design  Selection Works;vc.. ^ir •,
tured to assist design selection for the most complex < HI; •,,-
mental situation, which is usually soil sanipiin.'   Ii,e u.
sheet contains the elements needed to support ihr .1.x • s,oii i;
RI sample design  The RPM and risk assessor -'•-. .." - ,,<•;>.
worksheet, or use Has a model to create on*. --70 'U  .' . • T ^
to their needs, to design a site samparr plan tl; r v> .1 tr • ; ;h.
data uscability requirements of the risk assessment T\ . i:s^ uv
worksheet,  initially assume a stratified random design am!
complete the worksheet on this  basis for ead/ meJun  -. :i'H
exposure pathway at the site. Once completed, u\\- ',,•',.;  e'
of worksheets can be modified to assume al^maiix - Nam;>!,.i.'
strategies. Completion of a sctof worksheet^ (i.e., a uork>lu-ei
for each medium  and exposure pathway at a -ate. Kue,! <>!' .
single samp!ing strategy) specifics the total numbcroi s.riipk
to be taken  for an exposure palhwa\  and sai.'ple break,!.'  ;'
according to type (i.e., site environmental samples, qualit;.
control samples, background sample-).
                                                     53
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
                                       EXHIBIT 4-1
                        SAMPLE DESIGN SELECTION WORKSHEET
1.
Pathway
Medium and
Design
Alternative
II.
Contaminant
Characteristics
for Medium
III.
Performance
Measures
IV.
Number of
Samples
A. Exposure pathway (specify):
B. Medium (specify):
A.
Chemicals of Potential Concern

E. Chemical distribution: I 1
a
B. Frequency
of Occurrence

C. Est
C
Design alternative:
Stratified Random
C
(
>ther
specify)



Concentration
Avg (ppb)

Max (ppb)

D. Coefficient
of Variation
(C.V.)

Homogeneous
Heterogenerous (define potential strata)
Stratum C\
'specify): * — r/
A Confidence level .'percent)
B Power (percent




C Minimum de'ec'?N-3 real's e difference |
D Mi'asjierner.' e^rc- precision
E! CoTipieleness for c'ltica1 samples


















? Other p^rforT^a'H'i? measures (sp&cify)



B Ojaii'y Co'iiro1 Samples N^ of Duplicates
No of Blanks
Total Samples for Stratum:








i






Contamination concerns n any (specify)
C. No of backg'ound samples:






D Total no o' sar~ Dies for pathway/medium (sum of strata plus background)'


                                            54
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  Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
     v.
     Modifying
     Sample
     Design
                                           EXHIBIT 4-1
                         SAMPLE DESIGN SELECTION WORKSHEET
                                           (continued)
A. Hot spots known''
  Probability of
  missing hot spot:
                  B. Historical data?
                     Are current and
                     historical data sets
                     comparable?
                  C. Resource issues?
                  D. Other issues (specify):
No
Yes (describe, include estimate of radius)
                           No
                           Yes (specify design):
                           Yes
                           No  (CONSIDER MODIFYING SAMPLE DESIGN)
                           No
                           Yes (specify issues and potential design modification):
The remainder of this section ;s a step-by-step guide to completing the Sample Design Selection NV'orksh ::t  Che-mica'-- x
potential concern listed on the Sample Design Selection Worksheet should be the same as those used for the Method Selection
Worksheet (Exhibit 4-6).
4.1.1    COMPLETING THE SAMPLE
         DESIGN SELECTION WORKSHEET

           Use of the Sample Design Selection
           Worksheet will help the RPM or
           statistician to determine an appropri-
           ate sampling des^n.

Step I - Pathway, medium and design
alternatives

Step IA - Specify exposure pathway.
In this worksheet space, enter the identifier for the exposure
pathway to be sampled.
                                    Step IB • Specify medium.

                                    The  worksheet is specific to a particular medium within a
                                    pathway, so that sample design alternatives will be examined
                                    independently for each medium in the exposure pathway. Emer
                                    the name of the medium on the worksheet (e.g., soil, sediment,
                                    ground water, surface water, air, biota).

                                    Step 1C - Identify sample design alternatives.

                                    Forplanning purposes, first assume a stratified random design.
                                    This assumption  facilitates  the discussion  of performance
                                    measures and sample sue. For systematic grid and gcosuuv
                                    tical designs, the basic planning concepts are  the same but the
                                    calculation and setup procedures arc significant!)  more com-
                                    plex Deuilcd discussions of the advantages and di.sads-anuccs
                                    of classical and geostalistical designs arc a\ailablc (lioigraan
                                                    55
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
           U'/n/c ether dc'siuns rna\ be as apprn-
           priate in rminy c.uxcx, stratified random
           or systematic, samphny designs are
           (//uriv.s arccptahli'
and Quimby 1988 and EPA 1989e). An overview of these   Step II -  Specify contaminant                    ^
procedures is provided here.                                characteristics for medium.

                                                         Sample design selection and the determination of the required
                                                         number ol samples to meet pert ormance objectives are depend-
                                                         ent on the spatial and temporal variation of contaminants in the
                                                         medium at the site  This variation is a function of fate and
                                                         transport properties, in addition to use and disposal practices,
                                                         and any unusual physical site characleristics. Key measures of
                                                         contaminant characteristics to be recorded include: frequency
                                                         of occurrence, average and maximum concentrations, coeffi-
                                                         cient of variation, and chemical distribution. Estimates of key
                                                         characteristics can be derived from historical site data, pilot
                                                         studies and/or reported  data from Superfund site databases
                                                         such as  CLP  Analytical Results Database (CARD) and the
                                                         Statistical Database (STAT). These databases have analytical
                                                         results for over 8,900 sites across 10 Regions.  A weighted
                                                         approach to using existing data should be adopted if any of the
                                                         sources are considered more (or less;) reliable.

                                                         Step HA - Identify chemicals of potential concern.

                                                         Based on historical data, list the known or suspected contami-
                                                         nants as chemicals of potential concern.

                                                         Step IIB • Estimate frequency of occurrence.

                                                         Estimate the  frequency of occurrence of each  chemical of   ™
                                                         potential concern on a sample basis and enter this number on the
                                                         worksheet.  The frequency of occurrence is the percent of
                                                         samples in which the contaminant was identified, and can be
                                                         calculated only  if sample data are available for the site or
                                                         exposure area  An estimate of frequency of occurrence can also
                                                         be made as an  exposure area estimate based  on  fate  and
                                                         transport or disposal.  Estimates of exposure area occurrence
                                                         frequency:
                                                         •  Over 50%  - should cause few chemical identification prob-
                                                            lems ard the probability of false negatives will be low.
                                                         •  Below 10%  -  may  be  missed by less  rigorous sample
                                                            designs
                                                            Between 1  0% and 50% - the nature of the distnbuuon v. ill
                                                            determine  the probability of a false negative.

                                                         Step IIC - Record estimated average and maximum
                                                         concentrations.

                                                         For each chcm ical of potential concern, record the average and
                                                         maximum estimated concentration obtained from historical
                                                         data or data from similar sites. This information v, ill indicate
                                                         that the concentration of concern, method detection limit, and/
                                                         or background levels may  be close and that the detection of
                                                         statistically significant differences willbe difficult. This would    -
                                                         cause an increase in the number of samples required to meet   m
                                                         performance objectives  Conversely, high average conccntra-
Sampling procedures used in environmental sampling may be
unbiased or biased. Two alternative statistical models are
available for use in unbiased sample evaluation and hypothesis
testing:  the classical model and the geostatistical model. The
classical model is based on random or stratified random proce-
dures and the geostatistical on optimizing co-variance.  Sys-
tematic grid sampling can be utilized by either model.  Biased,
or purposeful designs require the use of different approaches to
planning and evaluation.

•  Classical model: The classical  model using a stratified
   random sampling design is appropriate for use in sampling
   any medium. This design produces unbiased estimates and
   is always acceptable, although for some situations other
   designs are more effective or efficient. A stratified random
   design provides the RPM and nsk assessor with great
   flexibility. If the nature and extent of the exposure areas are
   not yet well defined, a pilot study  can be conducted and the
   results included in the final design.  The  data  can  be
   averaged for any area of concern using the samples of
   interest  without losing the advantages of the stratified
   random  sampling probability model.  This model is the
   basis for calculating confidence  levels, power, and mini-
   mum detectable differences. The classical approach is not
   subject i.o judgmental  biases,  .ir.cl produces know.n esti-
   mates and recognized statistical measures and guidelines
   because of the simplicity  of assumptions.

•  Geostatisucal model' Geosuustica' techniques are good
   for identifying hot spots and reasonable maximum expo-
   sure (RME) site characten/auon.  These techniques require
   complex judgmental and calculation  procedures. Even
   with the use of available conpuier programs, a statistician
   should be consulted since different approaches to estimat-
   ing key parameters can produce different estimates.

•  Systematic grid sampling: Systematic grid sampling proce-
   dures are good for identify ing unknown hot spots and also
   provide unbiased estimates of chemical occurrence and
   concentration (Gilbert, 14S~i  S \stcmauc sampling can be
   used in geostatistical or classical  estimation models.  Spe-
   cial  variance calculations are required to estimate confi-
   dence limits  on the average concentration.  Systematic
   sampling is especially powerful for complete site or expo-
   sure area charactcn/ation v. hen the sampled area is known
   to be heterogeneous Houe\er .1 large number of samples
   are often required
-------
   Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
lions and maximum concentrations potentially indicate a large
coefficient of variation, which would also require an increased
number of samples.  Exhibit 3-12 shows frequency of occur-
rence of analytes and median coefficients of variation for
selected contaminants at Supcrfund sites.

Step IID - Estimate coefficient of variation.

Estimate the coefficient of variation for each analyte from prior
information or from the guidelines given in Chapter 3.
•  Coefficients below 20% indicate  little variability  in the
   concentrations of the chemical of potential concern.
•  Coefficients over 40% indicate greater variability, and
   potentially require a large number of samples  to meet
   performance objectives.

Step HE • Classify chemical distribution.

Once the contaminant characteristic table is completed, make
a judgment  as to whether the distribution of contaminant
characteristics can be classified as homogeneous or heteroge-
neous and mark this on the worksheet Chemical distribution
can be classified by answering the question: Is there similarity
or dissimilarity in properties between characteristics that have
been predicted across the compounds of potential concern at
the site?
•  If the distribution is heterogeneous (dissimilar properties)
   determine the basis for the differences and consider strati-
   fication.  Define potential strata on the worksheet.  An
   example of a heterogeneous distribution is one where many
   contaminants occur at the site at similar frequencies and one
   contaminant occurs at a very different frequency.
•  If the distribution is homogenous' similar properties), strati-
   fication is not needed.

Step II!  - Select performance measures

During planning, propose and discuss quantitative perform-
ance measures based on performance objectives for complete-
ness, comparability, representativeness, precision, and accu-
racy. Performance measures are specified as minimum limits
for each stratum. Based on the coefficients of variation of the
analyte concentrations, these limits will determine thenumbers
of samples required.  The actual  values or  objectives  are
determined by the level of acceptable uncertainty including
that associated with hot spot identification.  Recommended
minimum criteria for statistical performance measures associ-
ated with risk assessment variability, confidence level, power,
and minimum detectable difference are specified in Steps III A,
IIIB.andlllC.

Five basic  performance measures are employed in the evalu-
ation of statistical sampling designs  Three of these measures,
confidence level, power, and minimum detectable difference,
are related to sample variability.  Measurement error is the
fourth performance measure, and completeness for critical
samples is the fifth. These performance measures are discussed
in the following sections.

Step HI A-C Set minimum acceptable limits for
confidence level (IIIA), power (IIIB), and minimum
detectable relative difference (IIIC).

Confidence level, power, and  minimum detectable relative
difference are  three measures of sample design precision.
These measures are ultimately determined by the coefficient of
variation of chemical concentration and the number of samples.
Each measure is briefly defined as follows:
•  Confidence level: One hundred minus the confidence level
    is the percent probability of taking action when no action is
    required (Type I error or false positive).
•  Power:  One hundred  minus the power is the percent
    probability of not taking action when action is required
    (Type II error or false negative).
•  Minimum Detectable Relative Difference: Percent differ-
    ence required between site and background concentration
    levels before the difference can be detected statistically.

For risk assessment, power and the ability to detect differences
between site concentration levels compared to background
levels are critical. Given acoefficient of variation, the required
levels of certainty, power, and minimum detectable differences
sign ificantly affect thenumher of samples. The following table
illustrates the effect when the C.V. is  equal to 25%  (EPA
1989c):
         Confi-
         dence

         90%
         90%
         80%*
         80%
         80%
         80%*
Power

  90%
  90%
  80%
  80%
  90%
Detectable%
 Difference

     10
     20
     20
     10
     20
     40
No. of
Samples

42
12
8
19
5
3
"The minimum recommended performance measures for risk
assessment purposes are: confidence (80%) and power (90%)

It is important to note that the number of samples required to
meet confidenceand power requirements will be very low if the
acceptable minimum detectable difference is larger; that is, if
site contamination is easily discriminated from background
levels.

Step HID • Determine required measurement error
precision.

The major field quality control samples of importance to the
precision of measurement error are field duplicatesand blanks
                                                      57
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 Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
Duplicates provide an estimate of total measurement error
variance, including variance due to sample collection, prepara-
tion, analysis, and data processing. They do not discriminate
bctwecn-batch error variance. If the duplicate is collocated,
contaminant sample variation caused by a  heterogeneous
medium is also included in the measure. The precision of the
measurement error estimate is subject to the number of dupli-
cates on which the estimate is based.  Exhibit 4-2 gives the
estimated precision of the measurement error based on the
number of duplicate pairs. With three duplicates, the true
measurement error variance could be as much as  13.89 times
the observed variance, if a 95% level of confidence is required.

                      EXHIBIT 4-2
       CONFIDENCE LEVELS FOR THE ASSESSMENT
             OF MEASUREMENT VARIABILITY
   Numb«r of OC Samples
    (duplicate pairs)
Interval lor 99% Confidence that
 Measurement Error Is Within Unit*
          2
          3
          4
          5
          6
          7

          8
          9
          10
                                    * 39.21
                                        s2
    .36s2 sO2* 8.26s2
    .39s2 sO2 4 6.02s2
    42»2i<^i 4.84s2
    .44s2 £0*4 4.1 4s2
    Mi £
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   Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
             EXHIBIT 4-3

 FACTORS IN DETERMINING TOTAL
 NUMBER OF SAMPLES COLLECTED
   NUMBER OF EXPOSURE PATHWAYS THAT
   WILL BE SAMPLED

      Media within exposure pathway
      Strata within exposure pathway medium

   NUMBER OF SAMPLES FOR EACH
   EXPOSURE PATHWAY GROUPING GIVEN
   REQUIRED STATISTICAL PERFORMANCE

    •  Confidence (1 - o ) Type I Error
    •  Power (1 - P ) Type II Error
      Minimum detectable relative difference

   NUMBER OF QUALITY CONTROL SAMPLES

    •  Field duplicate (collocated)
    •  Field duplicate (split)
      Blank (trip, field, and equipment (rmsate))
      Field evaluation

   NUMBER OF BACKGROUND SAMPLES

      Number of site samples collected
      Minimum detectable relative difference
                              EXHIBIT 4-4

RELATIONSHIPS BETWEEN MEASURES OF STATISTICAL PERFORMANCE
                 AND NUMBER OF SAMPLES REQUIRED
Coefficient
of Variation (%) Power (%)
10
15
20
25
30
35
Note.
95
95
95
95
95
95
Number of samples required
Confidence
Level (%)
90
90
90
90
90
90
Samples Required to Meet
Minimum Detectable
Relative Difference
5%
36
78
138
216
310
421
10%
10
21
36
55
78
106
n a one-sided one-sample t-test to
detectable relative difference at confidence level and power

Source:
mean for transformed data
EPA 1989c




20%
3
6
10
15
21
28
achieve a minimum
C V based on geometric




•  Quality assurance  objectives  (based  on quality control
   samples).

•  Background samples (based on minimum detectable rela-
   tive difference).
Enter the numbers of samples for each stratum.

Step IVA - Determine number of environmental
samples.

The number of environmental sue samples is ultimately con-
trolled by statistical performance requirements, given the sta-
tistical sample design.  The relationship between number of
samples and measures of statistical performancedepends upon
the variability of the chemicals of potential concern, expressed
as the coefficient of variation.  The relationship between  the
coefficient of variation for a chemical of potential concern and
measures of statistical performance is the basis for determining
the number of samples.

The number of samples can be calc ulated given a coefficient of
variation, a required confidence level or certainly, a required
statistical power,  and a minimum detectable relative differ-
ence.  Exhibit 4-4 illustrates the relationships between  the
number of samples required given typical values for the coef-
ficient of variation  and statistical performance objectives.
Calculation formulas in Appendix IV facilitate the examination
of effects beyond the example^ Cited
                  Step IVB - Determine number of quality control
                  samples.

                  NUMBER OF DUPLICATES

                  Guidance for determining the number of required duplicate
                  samples is usually one duplicate for every 20 environmental
                  samples. However, this procedure should be modified ba.->cd on
                  site conditions.  For example, the number of duplicates and
                  other quality control samples may be set high for the beginning
                  of site sampling, evaluated after several duplicates to determine
                  routine measurement error, and subsequently adjusted accord-
                  ing to observed performance. The information in Exhibit 4-2
                  associated with Step HID shows that confidence in measure-
                  ment error  increases  sharply when four or more pairs of
                  duplicate samples are taken per medium. Critical samples .ire
                  recommended for designation as duplicates in  the quality
                  assurance sample design.

                  NUMBER OF BLANKS

                  Blanks provide  an  estimate of bias due to contamination
                  introduced by sampling transportation, preservation, or stor-
                  age.

                  At least one field blank per medium should be collected each
                  day,andat least one blank must be collected for each sampling
                  process.
                                                       59
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 Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
Examine results from duplicate and blank samples as early as
possible in the sampling operation to ascertain if presumed
sampling characteristics are accurate and discover areas where
the sampling strategy requires modification.

TOTAL NUMBER OF SAMPLES FOR STRATUM

Add the environmental and quality control samples for each
stratum and record these totals on the worksheet. Do not double
count duplicate samples in the total.

CONTAMINATION CONCERNS

Indicate on the worksheet if there are any concerns regarding
blank contamination and describe the concerns. For example,
the number of blank samples may need to be increased if
volatile compounds are suspected in a medium.

Step IVC - Determine number of background
samples

A sufficient number of background samples must be taken to
determ ine the validity of the assertion that there is no difference
between the site and the  background at the desired level of
confidence. Early sampling and analysis of background samples
will indicate the ease with which background levels can be
discriminated, and allow modifications to be made to the SAP
if necessary.

A minimum of one background sample per medium and expo-
sure area should be taken. As with quality control samples,
results from the background sample should be assessed early to
see if background levels  will severely impact the sampling
design. In those instances where background levels are close
to on-site contamination levels, it may be necessary to collect
as many background samples as site samples. Small numbers
of background samples increase the probability of a Type II,
false negative error (i.e., that no difference exists between site
and background, when a difference does in fact exist). How-
ever, rigorous statistical analyses im ol\ mg background samples
may be unnecessary if site and non-site related contamination
clearly differ.

           Background samples should be
           collected and analyzed prior to the
           final determination of the sampling
           design  since  the number of samples is
           significantly reduced if little back-
           ground contamination is present.


Background levels of contaminants vary by medium and the
type of contamination. If a detectable background level of a
contaminant occurs infrequently, the number of background
samples analyzed might be kept small  Metals often have high
rates of detection in background samples. Some pesticides,
such as DDT, are anthropogenic and also have high rates of
detection in particular matrices. Anthropogenic background
levels are also found in sites near industries and urban areas.

Background  sampling  must HC increased in the following
situations:
•  Contamination exists in more than one medium.
•  Expected coefficients of variation in chemicals of concern
   are high and confirmed by actual data.
•  Relativedifferences between siteand background levelsare
   small.
•  Site concentrations and concentrations of concern are low.

Step IVD -  Calculate total number of samples for
medium/pathway.

Obtain the total number of samples required for the exposure
pathway in the medium of concern b> adding the numbers of
environmental, quality  control, and background samples re-
corded on the worksheet. (Duplicate samples should not be
double-counted in the  total).  Record the total number of
samples for each stratum (if stratified) on the Sample Design
Selection Worksheet.

Step V - Modify sample design as
appropriate.

A  stratified random sample design is  proposed  for initial
assessment of planning strategy. Now, given the total number
of samples required, reconsider the utility of this design on the
basis of three factors: hot spot identification, comparability
with previous sampling events, and resource issues.

           Random or systematic sampling is the
           best strategy for identifying hot spots.


Step VA - Describe known hot spots and indicate
probability of missing a hot spot.

A critical issue for the RPM and nsk assessor is whether hot
spots exist in the exposure area and the probable size of the hot
spot. This information can frequently be deduced from histori-
cal data.  Hot spots are primarily an issue in soil sampling.
Procedures for determining the probability of missing a hot
spot are not as effective in random designs as in systematic and
geostatistical designs.   However, a search strategy which
stratifies the area based on grids and then randomly samples
within each grid can be used within the classical framework.
Systematic and geostatistical design approaches provide the
best approach to hot spot identification as described in Step I.

Appendix IV describes numerical procedures and assumptions
to determine the probability that a given systematic design \ull
detect a hot spot and provides a calculation formula based on a
                                                     60
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   Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
 geometrical argument. To employ this formula, the distance
 between grid points and the estimated size of the hot spot as a
 radius must be specified.

 Indicate on the Sample Design Selection Worksheet if a hot
 spot is known to exist in the exposure pathway. If yes, describe
 each hot spot, including an estimated radius.  Based on the
 calculations in this step, record the probability of missing a hot
 spot.

 Step VB • Establish historical data comparability

 The ability  to combine data from different sampling episodes
 or different sampling procedures is a very important considera-
 tion in selecting a sampling design.  The RPM may wish to
 assess historical data along with current results or may antici-
 pate that the current data will need to be compared with results
 from future sampling activities. Consult a statistician in either
 of these cases to determine if the current sampling design will
 allow the production of data of known comparability.

 Indicate if  historical data exist and whether or not they are
 comparable to current planned data  on  the Sample Design
 Selection Worksheet.

 Step VC - Identify and address resource issues.

 Resource limitations are a major reason for sample design
 modification.  The number of samples required to achieve
 desired performance measures may exceed resource availabil-
 ity. Modifying the sample design and the efficiency of statis-
 tical estimators can reduce sample size, thereby reducing costs
 and improving overall timeliness for the risk assessment
 Alternatively, analytical methods such as field analyses can
 also reduce cost.  Systematic and eeostatistical designs can
 often  achieve the required performance measures with fewer
 samples (Gilbert 1987). Interim samplingcanbe used to verify
 initial assumptions of the SAP, increase knowledge of contami-
 nant distribution, and support S.AP modifications to reduce the
 number of samples.  On the Sample Design Selection Work-
 sheet, indicate whether resource issues are a concern.  If so,
 explain the  issues and record potential design modifications.


 4.1.2   BALANCING ISSUES  FOR
         DECISION-MAKING

 Completing a number of Sample Design Selection Worksheets
 (Exhibit 4-1) for different exposure areas, media, and sample
 design alternatives will enable the RPM and risk assessor  to
 compare and eval uate sample design options and consequences
and select the appropriate sample design for each medium and
exposure pathway. Practical tradeoffs between response time,
analytical costs, number of samples, sampling costs,  and level
of uncertainty can then be weighed For example,perhaps more
samples can be collected if less expensive analyses  arc used.
Or, if the risk assessment is based on a point source, collection
of additional samples to estimate chemical concentrations and
distribution can be avoided.

Computer programs arc useful tools in developing and eva1':-
ating sampling strategies, especially in trading off costs against
uncertainty, and identifying situations when additional samples
will not significantly affect the useability of the data (i.e., the
point of diminishing returns).  Each automated  system has
specific data requirements and is based on specific site assump-
tions. The major systems which support environmental sam-
pling decisions are listed, contacts for information given, and
brief descriptions provided in Exhibit 4-5.

                      EXHIBIT 4-5
           AUTOMATED SYSTEMS1 TO SUPPORT
              ENVIRONMENTAL SAMPLING
SYSTEM
Data Quality Objecttve
(Tranng) • Expert
System
ESES
Environmental SampHng
(Plan Oeilgn) - Expert
Syttem
QEO- EAS
Geoitaflftlc*
Environment*
Assessment Software
SCOUT
Murlivanate Statistical
Analyses Package
ASSESS
AcMMrrwnt of Error* in
Sampling of Salt
EPA CONTACT
Dean Neptune
USEPA
Quality Assurance
Management Staff
(202) 475-9464
Jeft Van £e
Exposure As**s*ment Ov
USEPA. EMSL-LV
(702) 798-2367
Evan Engiund
Exposure Assessment L>v
USEPA. EMSL-LV
(702) 798-2248
Jeff Van £e
Exposure Assessment Ov
USEPA EMSL LV
(702) 798-2367
Jeft Van Ee
Exposure Assessment L>v
USEPA. EMSL-IV
(702) 798-2367
DESCRIPTION
Training system designed to assist in
planning of environmental
investigations based on data quality
objective process
Expert system designed to assist in
planning sample collection Induces
mode** that address statistical
design. OC, sampling procedures
sample handling, budget, and
documentation Current system
addresses metal contaminants *n a
soil matnx (Expanded application
under development, contact
EMSL-LV )
Collection of software tools for
two-dimensional geoslaDstical
analysis of spatially distributed data
points Programs include file
management, contour mapping
knging, and variogramanalysis
A colleoon of statistical ;y iQ-ar"-
that accept GEO-EAS files for
rrxiftivanme analysis
System designed to assist in
assessment of error in sampling of
so»l» Estimates measurement e'-or
variance components Presents
scatter plots of quality control daia
and error plots to assist in
determining th« appropriate amount
of quality control samples
^ All system! wtfl run on any IBM compatible PC AT wilh 640 K RAM (minimum)
A fixed disk is recommended
4.1.3   DOCUMENTING SAMPLE DESIGN
         DECISIONS

It is important to document the primary issues considered in
balancing tradeoffs to accommodate resource concerns, and
their impact on data useability. Fully documental! final sample
design decisions, including the rationale for each decision.
During the course  of the RI, continue to document pertinent
issues that arise and any sampling plan modifications which arc
implemented.
                                                     61
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Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
                                                EXHIBIT 4-6
                                    METHOD SELECTION WORKSHEET
1 AnalylM
A
Chemical or Class of
Chemicals of
Potential Concern

B
Reporting
Requirement1
(YorN)

II Medium

III Critical Parameter*
A
Turnaround
Time

B
ID Only or
ID Plus
Quant
(ID or ID+Q)

C.
Concen-
tration of
Concern

D
Required
Method
Quantitation
Limit2

IV Routine Available Methods 3

Y= Total reported for compound class
N = Each analytfl reported separately
Method detection limit should be no greater than 20% of concentration of concern.
Refer to Appendix III for specific methods Recommend consultation with chemist and/or automated methods search to determine all methods ava.lable
(Exhibit 4-7 lists computer systems that support method selection )
4.2    Strategy for selecting
        analytical methods

This section describes how to use the Method Selection Work-
sheet shown in Exhibit 4-6 as a data collection and decision-
making tool to identify analuical methods that meet the needs
of the risk assessment and to select the most appropriate method
for each analyte. The RPM and risk assessor should use this
worksheet, or use it as a model to cre-ate a worksheet specifi-
cally suited to their needs, in order to aid in method selection.
Methods selected in this process may be routine or non-routine.

          The risk assessor should ensure that
          critical requirements and priorities are
          specified on the "Method Selection
          Worksheet" so that the best available
          methods can be selected.

          Routine methods should be used
          wherever possible since method
          development is time consuming and
          may result in problerr.s with laboratory
          implementation
•  Routine methods are issued by an organi/ation with appro-
   priate responsibility (e.g.. Suite or Federal agcnc> v>nh
   regulatory responsibility, professional organization), are
   validated, documented, and published, and contain infor-
   mation on minimum performance characteristics such as
   detection limit, precision and accuracy, and useful range

•  Non-routine methods address situations with unusual or
   problematic matrices, low detection limits or new parame-
   ters, procedures or techniques; :.hey often contain adjust-
   ments to routine methods.


4.2.1    COMPLETING THE METHOD
         SELECTION WORKSHEET

Step I - Identify analytes.

List the chemicals of potential concern to the risk assessment
for the site on the Method Selection Worksheet. Use the same
list of chemicals that appears on the Sample Design Selection
Worksheets for the site. Indicate whether each analyte should
be reported separately or the total for the compound clu^s
reported.
4
                                                    62
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   Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
Step II - Identify medium for analysis.

Specify theanalysis medium, e.g., soil, sediment, groundwatcr,
surface water, air, biota.

Step III - Decide on critical parameters.

Specify the required data turnaround time (III A) as a number of
hours or days from the time  of sample collection.  Indicate
whether chemical identification alone is desired or identifica-
tion plus quantitation (IIIB).  Specify the concentration of
concern (IIIC) and required method quantitation limit (HID).

Step IV -  Identify available routine
methods

Use the final worksheet column to list the methods available
that satisfy the requirements in the preceding steps. Reference
sources and software are available to assist in identifying
routine analytical methods  applicable  for  environmental
samples.  Routine methods for organics and inorganics analy-
ses are listed in Appendix III.  The methods are  from the
following sources:
•  EPA CLP-RAS (EPA 1988g, 1988h).
•  EPA SW-846 (EPA 1986a).
                      EXHIBIT 4-7

         AUTOMATED SYSTEMS1 TO SUPPORT
                 METHOD SELECTION
SYSTEM
List of Lists
Smart Method
Index
Geophysical
Techniques
Expert System
EPA Sampling
and Analyse
DataBase
CONTACT
W A Telliard
USEPA
OH 108 0' Water
(202)382-7120
John Nocenno
Quality Assurance Dur
USEPA, EMSL-LV
(202)798-2110
Aide Maggela
Advanced Monnorng
Dtv
USEPA. EMSL-LV
(202) 798-2254
Lewis Publisher
1 -800-272-7737
DESCRIPTION
An automated sorting and
se*~ on software pacKage that
currently contains 1 50 methods
and 1 .700 anaiytes. These are
cross-referenced to facilitate
selection based on required
neecs (e g.. analyte detection
limn, instrument).
Nafj'al language expert system
prototype thai provides
interactive querns ol databases
cross-referenced by method,
ana.'yte, and performance
features.
An expert system that suggests
and ranks geophysical
technique*. Including soil gas, for
«pp«aD«lty ol use based on
st*-pcs
                                                       described in the method. Data quality issues (precision, accu-
                                                       racy, interferences) are usually described in the method. Exam-
                                                       ine available methods with respect to the criteria defined on the
                                                       Method Selection Worksheet.  It may be helpful to divide the
                                                       analyte list into suitable categories  based on  the types  of
                                                       analysis. For example, a requirement for chromium, cadmium,
                                                       and arsenic data could not be generated by the same analysis as
                                                       data for chlorinated hydrocarbons. It may be possible to use
                                                       several methods independently and combine the data sets for
                                                       risk assessment purposes. This is done routinely by the CLP,
                                                       where inorganics (elemental analysis), volaules, extractable
                                                       organics, and pesticides are analyzed by different methods.  In
                                                       somecases, no routine method or scncs of methods will be able
                                                       to satisfy all critcriaand compromises must be considered The
                                                       risk assessor,  with the advice of an analytical chemist, must
                                                     63
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 Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
then determine which criteria are of highest priority and which
can be modified. For example, if a low detection limit isof high
priority, turnaround time and cost of analysis will likely in-
crease. If an initial broad spectrum analysis to quickly deter-
mine the largest number of chemicals present at the site is of
high priority, low detection limit and precision requirements
may need to be modified.
TURNAROUNDTIME

Turnaround time is determined by the available instrumenta-
tion , sample capacity, and methods requirements. Turnaround
times for field analyses can be as short as a few
hours, while those for fixed laboratory analy-
ses include transport time, and  range from
several days to several weeks. Field instru-
ments, can provide the quickest results, espe-
cially if the data do not go through a formal
review process. However, the confidence in
chemical identification, and particularly quan-
titation, may not be as high.   In  general,
methods with quick turnaround times are of-
ten less precise and have higher detection
limits.   If data are needed quickly, a field
method can be used for initial results and a
fixed laboratory method used to produce more
detailed results (or confirm the earlier results),
thereby  increasing the  confidence  in field
analyses.
     USEFUL RANGE

     The useful range of a method specifics the concentrations of
     chemicals for which precise and accurate results can be gener-
     ated. Tins range is diialyle-specific. The lo^crcndof the useful
     range is the method detection limit, often generically referred
     to as the "detection limit." If a lower detection limit is required,
     use of  a larger sample or smaller final extract  volume can
     sometimes compensate. However, any interfering chemicals
     are also concentrated, thereby producing greater interference
     effects. Above the useful range, the response may not be linear
     and may affect quantitation. This causes inaccurate and/or

                 EXHIBIT 4-8a
COMMON LABORATORY CONTAMINANTS AND
   INTERFERENCES BY ORGANIC ANALYTE
SAMPLE QUANTITATION LIMITS

Risk assessment often requires a sample quan-
uta tion limit at or below the detection limit for
routine methods for many chemicals of toxi-
cological concern.  The sample quantitation
limits vary according to sample medium. The
quantitation limits for chemicals in  water
samples are often far lower than for the same
chemicals in soils, because of co-extractable
components in the soil, as well as limitations
in sample weight or volume. Pay particular
attention to interferences reported by  the
method, because these will hinder acquisition
of acceptable quality data.  Interference ef-
fects are more pronounced near the method
detection limit. Compare documented method interferences
with site conditions to identify potential method problems.
Somecommon sourcesof interference in organic and inorganic
analyses are summarized in Exhibits 4-8a and4-8b. If needed
sample quantitation cannot be met by available methods, con-
sult with an analytical chemist for the feasibility of detection at
the desired level in the required sample type. The chemist can
help determine if method adaptation can resolve the problem or
if a non-routine method of anal\ sis can be used.
Contamin-
ation or
Interference
Fat/Oil




Sulfur



Phthalate
Esters







Laboratory
Solvents




Fraction
Extractable
organ ics.
pesticides, and
PCBs

Extractable organics,
chlorinated and
phosphorus-
containing pesticides

Chlorinated
pesticides, PCBs,
ana extractable
organ ics





Volatile or a an ics
(mettiylene chloride.
acetone, and
2-butanone)


Matrix
Tissue,
waste,
soils


Sediment,
waste,
soils


All








All




Effects on
Analysis
Increased
detection limit,
decreased
precision/
accuracy
Presence/
absence,
detection limits.
precision/
accuracy
False positive
identification
1
Removal /
Action
GPC (all groups), flonsil
(pesticides), acid
digestion (PCBs only)


GPC. copper,
mercury, tetrabutyl
ammonium sulfate


F.ofs • GC-MS
conf "*ial'on of identity
(pesticides anc i (pes'c.ces, PCBs)
extractable
organics) or
positive bias
(pesticides and
extractable
organics)
False positive
identification 01
positive bias

evaluation of reagen's
and method blanks for
contamination



Conficence in data use
based on interpretation
of olan< data

1
Source. EPA19S6a.
     imprecise measurements. Reducing the sample size for analy-
     sis or diluting the extracted material will bring the concentra-
     tion within the useful range.  With environmental samples,
     some chemicals are frequently present at the low end of the
     useful range of the method, while others are above the useful
     range. In this situation, two analyses are necessary to produce
     accurate and precise  data on all chemicals.  The laboratory
     should be instructed to notify the RPM or responsible contract
     manager if this occurs in sufficient time for re-analysis within
     the specified holding time. Both analyses should be reported to
                                                      64
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   Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
maximize the useability of both detected and non-detected
analytes.

           All results should h: Deported for
           samples anlayzed at more than one
           dilution.
PRECISION AND ACCURACY

 Routine methods often specify precision and accuracy with
respect to specific analytes (chemicals) and matrices (sample
media).  However, be aware that environmental samples are

                             EXHIBrT4-8b

               COMMON LABORATORY CONTAMINANTS AND
                 INTERFERENCES BY INORGANIC ANALYTE
ANALYTE
ARSENIC
BERYLLIUM
CADMIUM
CHROMIUM
LEAD
MERCURY
SELENIUM
CYANIDE
TECHNIQUE
GFAA
ICP
ICP
GFAA
ICP
GFAA
ICP
INTERFERENCE
Iron, Aluminum
Aluminum
Titanium, Vanadium
^n« except possible
iarrcle matrix effects
Iron
CaJctum
"txi. Manganese
GFAA S jnat«
I
ICP ' A'^Tinum
CVAA SJ"«B. High Chlofkte
GFAA
ICP
-oo. Aluminum
A urn num
Cotonmetric/ *c«3s. Sulfid*.
spectropholofnetrc C^onne oxidizing
ajwts
REMOVAL/
ACTION
Background correction
(not deuterium) (Zeeman)
It above 100 ppm,
correction factor utilized.
tf above 100 ppm,
correct en factor utilized
Background correction
for matrix effects.
rt above 100 ppm,
correction factor utilized.
Add calcium, standardize
suppression, background
correct on.
rf above 1 00 ppm,
correction factor utilized
Lanthanum nitrate
addition as matrix
modifier, background
correction
tf above 1 00 ppm,
correction fe«^or utilized
Remove interferences with
cadmium carbonate
(removes sulfide),
potassium permanganate
(removes chloride), excess
hydroxylamine sulfaie
(remove* tree chlorine)
Alternate wavelength for
analysis, background
correction (not deuterium)
(Zeeman)
Above 1 00 ppm.
correction factor utilized.
Increase pH to > 12 m field to
remove acids, cadmium
carbonate (removes sulfide),
ascorbtc add (removes free
chlorine)
Key: ICP - tnduclrvely couplec z»as~a.
GFAA - Graphrte 1 jrnac« £?-—< ar-wption
CVAA - Cokj vapor alomc ac-s >-;
                                                                     will not hinder the availability of laboraton and
                                                      65
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments

                                      EXHIBIT 4-9a
                    COMPARISON OF ANALYTICAL OPTIONS FOR
                            ORGANIC ANALYTE3 !M V/ATER
Qualitative Precision &
Method MDL Confidence Timeliness Accuracy
CLP RAS
VOA
BNA
Pesticides
Dioxin
600 SERIES
GC
VOA
BNA
500 SERIES
GC
VOA
BNA
SW846
GC
VOA
BNA

n
n
n
n

s
s
s

s
s
s

s
n
n

s
s
n
s

(w)n
s
s

(w)n
s
s

(w)n
s
s
FIELD SCREEN/FIELD ANALYSIS (Assumes
GC(PCB)
GC(Pesticides)
GC(VOA)
GC(Soil Gas)
GC(BNA)
PHOTO VAC
Detector
CLP LOW CONC
GC
VOA
BNA
1600 SERIES
GC
VOA
BNA
Dioxin
Key: s
w
rt
s
s
s
s
s

n

s
s
s

s
n
n
n
Method
Method
Neither
s
w
w
w
w

w

(w)n
s
s

(w)n
s
s
s
strength.
weakness.

w
w
w
w

n
n
n

n
n
n

n
n
n
preparation step)
s
s
s
s
s

s

n
n
n

n
n
n
n



n
n
n
s

n
n
n

n
n
n

n
n
n

n
n
n
; t
n

n

s
s
s

s
s
s
s


Comparability Cost

s
s
s
s

s
s
s

s
s
s

s
s
s

s
s
s
s

w

s
s
s

s
s
s
s



n
n
n
n

n
n
n

n
n
n

n
n
n

n
n
n
n
s

n

n
n
n

w
w
w
w


1
1


































strength nor weakness 1
           Cost per sample does not consider funding source
                                             66
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
                                        EXHIBIT 4-9b

                     COMPARISON OF ANALYTICAL OPTIONS FOR
                              ORGANIC ANALYTES IN SOIL
           Method
       Qualitative
MDL   Confidence
Timeliness
Precision &
 Accuracy
Comparability   Cost
          Key:      s   - Method strength.
                   w  = Method weakness.
                   n   = \either strength nor weakness
           Cost per sample ooes not consider funding source
          CLP RAS
           VGA
           BNA
           Pesticides
           Dioxin

          SW846
           GC
           VOA
           BNA

          FIELD  SCREEN
           GC(PCB)
           GC(Pesticides)
           GC(VOA)
           GC(Soil Gas)
           GC(BNA)
           PHOTO VAC
           Detector

          1600 SERIES
           GC
           VOA
           BNA
           Dioxin
                                             67
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Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments

EXHIBIT 4-9C
COMPARISON OF ANALYTICAL OPTIONS FOR
INORGANIC ANALYTES IN WATER AND SOIL
Qualitative Precision &
Method MDL Confidence Timeliness Accuracy 1 Comparability Cost 3
CLP RAS
ICP w(n) s w s s n
GFAA s s w s s n
Flame AA w(n) n w n n n
200 Series
GFAA s s w s s n
AA w(n) n w n n n
ICP-MJif s s w n s w
4
ICP-Hydride s w w n n w
Reid Screen
XRF w(n) n s w w s
AA w(n) n s n n s
Key: s = Method strength.
w = Method weakness.
n = Neither strength nor weakness.
1
CLP inorganic water assays are more accurate and precise than soil assays.
2
ICP and GFAA are comparable at medium to high ppb levels. For As, Pb, Se, Tl and Sb at less than 20 ppb,
GFAA is the method of choice.
3
Cost per sample does not consider funding source.
4
ICP-MS and ICP-Hydnde methods are relatively new; therefore, precision, accuracy, and comparability
estimates based on large statistical sampling are not available.



                                               68
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Chapter 4  Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
                                    EXHIBIT 4-9d

                 COMPARISON OF ANALYTICAL OPTIONS FOR
                  ORGANIC AND INORGANIC ANALYTES IN AIR
        Method
 MDL
Qualitative
Confidence
Timeliness
Precision &
 Accuracy
Comparability   Cost
        CLP VGA
         Cannister

        CLP VOA
         Tenax
        CLP BNA


        CLP Metals
2-5 ppb
2-30 ppb     s
(for most)

0.00001-     s
0.001 ug/m3
                    3-10 ng/m3
      Key:      s    = Method strength.
               w   = Method weakness
               n    = Neither strength nor weakness.
                                                                                            I
       The methods described are new Statements of Work.
       Cost per sample does not include funding source.
                                              69
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Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
field data for preliminary use if a tiered data review sequence
is incorporated. Use of a tiered approach to both data genera-
tion and review will often provide a good compromise between
use of a swift analysis of abroad spectrum of compounds and
longer-term accurate quantuume meas-urement nf indivi^'ial
chemicals of potential concern.

When using the  tiered approach consider the use of .split
samples (i.e., sending sample splits for analysis by field and
fixed laboratories). Quantitative comparison can then be made
between the precision and accuracy of the field analyses and
that of the fixed laboratory. Confirmation of identification by
both field and fixed laboratories also increases data confidence
and useability. Field methods should be used with a 10% rate
of confirmation or comparison by CLP or other fixed labora-
tory analyses.


4.2.3   DEVELOPING ALTERNATIVES
         WHEN ROUTINE METHODS ARE
         NOT AVAILABLE

If routine methods are not available to suit the  parameters of
interest, it is often due to one or more of the following factors:
•  The detection limit of commonly available instrumentation
   has been reached, and a lower detection limit is required for
   the risk assessment.

•  An unusual combination  of  chemicals are of potential
   concern.

•  The sample matrix is complex.

•  The chemicals of potential concern or other analjtical para-
   meters are unique to a particular site

Consult an analytical chemist for specific guidance on the po-
tential limitations of alternative  approaches. These may in-
clude adaptation of a routine method or use of a non-routine
method. Be aware that certain conditions, such as extremely
low detection limits for some chemicals, may be  beyond the
capability of current analytical technology.

ADAPTATION OF ROUTINE METHODS

Adapting routine methods ma>  be a solution  when routine
methods will not suffice to provide the desired data even after
compromises have been made with respect to parameters such
as turnaround lime and cost.  Using  the completed method
selection worksheet as the starting point, work closely with an
analytical chemist to formulate suitable  modifications to the
routine method. Evaluate and document any effects on data
quality that will result from the modifications.

Within the  CLP, such  anahses can bo  obtained by Special
Analytical Services (SAS) requests   Before analysis of site
samples, it may be advisable to confirm the laboratory's ability
to perform the adapted method with preliminary data.

USE OF NON-ROUTINE METHODS

II a routine method cannot be adapted lo provide the nece.ssai >
data, then existing non-routine methods that meet the criteria
can be used.  Such analyses can be  found  in the research
literature, usually catalogued by analytc or instrument. On-line
computerized search services can be of considerable help in
identifying such methods. Work interactively with an analyti-
cal chemist in reviewing selected methods.  Recognize that
non-routine analyses require  a greater level of capability and
experience from the analytical laboratory, and that turnaround
time can be longer because the method may need alteration
during analysis if problems develop.

DEVELOPMENT OF NEW METHODS

Developing new methods should be the option of last resort.
The RPM and risk assessor should consider recommending the
development of new  methods only for chemicals of potential
concern that are difficult to analyze and for which analyses will
be required for a number of sites over a period of several years.

Although designing a method based  on data available for a
given instrument and analytes may seem straightforward, the
process is time consuming and expensive. Unforeseen prob-
lems can arise when the method is implemented in the labora-
tory, often with environmental matrices such as effluents and
soils. Problems can occur even when laboratory personnel
have superior training and experience.  Consider the following
points when requesting the development of a new method"

   Select a lahora'ory  with a recogm/ed rcpiitniion for ;vr
   tormance and flexibility in a related area. Treat laboratory
   personnel as partners in the development process. This is
   true whether a commercial or a government laboratory is
   used.

•  Identify sources for authentic standards of the chemicals in
   question to support  method development Computer pro-
   grams such as the EPA List of Lists may be useful for such
   a determination.

•  Be aware that turnaround time for useable data may be long
   (potentially several months) because of the likelihood of
   trying different approaches before discovering an accept-
   able procedure.


4.2.4   SELECTING ANALYTICAL
         LABORATORIES

in selecting a laboratory to  produce  analytical data for risk
assessment purposes, identify and evaluate the following labo-
ratory qualifications:
                                                     70
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   Chapter 4 Steps for Planning for the Acquisition of Useable Environmental Data in Baseline Risk Assessments
    Possession of appropriate instrumentation and trained per-
    sonnel to perform the required analyses, as defined in the
    analytical specifications.
    Experience in performing the same or similar analyses.
    Adequate laboratory capacit} to perform all analyses in the
    desired time frame.
    Intra-laboratory quality control review of all generated
    data, independent of the data generators.
•   Adequate  laboratory  protocols for documentation  and
    sample security.

For non-routine analyses, the laboratory should have highly
trained personnel and instrumentation not dedicated to produc-
tion work, espec ially if new methods or untested modifications
are requested.

Accreditation programs, where established, monitor the level
of quality  of laboratory performance within the scope of their
charters.  Many of these programs periodically provide per-
formance evaluation samples that the laboratories must analyze
within certain limits in order to maintain their status. Prior to
laboratory selection, request that laboratories provide informa-
tion about their performance in accreditation programs. This
information can be used for evaluation of laboratory quality, in
the case of similar matrices and analytes.  Laboratory adher-
ence to standards of performance such as the Good Laboratory
Practices regulations also provides a measure of laboratory
quality.


4.2.5   WRITING THE ANALYSIS
         REQUEST

Include the following items in the analysis request:
•    A clear, complete description of the sample preparation, ex-
    traction, and  analysis procedures  including detailed per-
    formance specifications. For adaptation of routine meth-
   ods, specify the routine method and explicitly state altera-
    tions with applicable references
•   Documented reporting requirements.

•   Laboratory  access to required authentic chemical stan-
    dards.
    Mechanism fur the lahoMtop. to obtain F-PA technical
    assistance in implementing method modifications or per-
    forming non-routine methods

In the analysis request for a non-routine method, reference the
published material with a detailed specification of procedures
and requirements prepared by the analytical chemist who has
been working with the RPM and risk assessor. The specifica-
tion must include the frequency, acceptance criteria, and cor-
rective action requirements for each of the following:

•   Standardization, including initial and continuing calibra-
    tion.

•   Method blank performance (permissible level of contami-
    nation).

•   Spike sample recovery requirements.

•   Duplicate analysis requirements.

•   Performance evaluation or quality control sample results.

Allow time for the laboratory to review the analysis request and
question  any part  of the description that seems unclear or
unworkable according to its experience with the analues or
sample matrix.  Preliminary data, such as precision and accu-
racy data on a  subset of the  analytes, can  be  requested to
determine if the laboratory can implement the proposed method.
Should the criteria not be met in the preliminary analyses, the
analytical chemist should advise the laboratory on additional
method modifications to produce the required dau.  !;: some
Cubes, e\er, quahv.L: .^ J..:a.. .  '"  .   '.'.:, r \e :N -; • :-e  .••*•
chemicals of potential concern

In all cases, require the laboratory performing the anah scs to
contact the RPM, responsible chemist, or risk assessor at the
first sign  of a problem that ma> affect data quality The EPA
technical team can then judge the magnitude of the problem and
determine appropriate corre^u\ e action
                                                       71
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 Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
Chapter 1
Introduction and Background
    Chapter 2
    The Risk Assessment Process
       Chapter 3
       Criteria for Evaluating Data Useability in Baseline
       Risk Assessments
            Chapter 4
            Steps for Planning for the Acquisition of Useable
            Environmental Data in Baseline Risk Assessments
                           Chapter 5
                           Assessment of Environmental
                           Data for Useability in Baseline
                           Risk Assessments
                               Describes minimum requirements for
                               useable data.

                               Explains how to determine actual
                               performance compared to objectives.

                               Recommends corrective actions for
                               critical data not meeting objectives.

                               Describes options for combining data
                               from different sources and of varying
                               quality into the risk assessment.
                                        73
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Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
                           ACRONYMS FOR CHAPTER FIVE
                         CLP      Contract Laboratory Program
                         CRDL    Contract Required Detection Limit
                         CRQL    Contract Required Quantitation Limit
                         DQO     Data Quality Objective
                         GC       GasChromatography
                         ICP       Inductively Coupled Plasma Atomic Emission
                                  Spectroscopy
                         MS       Mass Spectrometry
                         Q APjP    Quality Assurance Project Plan
                         QC       Quality Control
                         RAGS    Risk Assessment Guidance for Superfund
                         RI        Remedial Investigation
                         RME     Reasonable Maximum Exposure
                         RPD      Relative Percent Difference
                         RPM     Remedial Project Manager
                         S AP      Sampling and Analysis Plan
                         SOP      Standard Operating Procedure
                         SQL      Sample Quantitation Limit
                         UCL      Upper Confidence Limit
                                              74
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   Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
5.0    ASSESSMENT OF
        ENVIRONMENTAL DATA FOR
        USEABILITY IN BASELINE
        RISK ASSESSMENTS
                                    EXHIBIT 5-1

                                 DATA USEABILITY
                               ASSESSMENT PHASES
                                      PHASE I
                                   Assessment of
                                   Reports to Risk
                                     Assessor
                                       (5.1)
                                     PHASE II
                                   Assessment of
                                   Documentation
                                       (5.2)
                                     PHASE III
                                   Assessment of
                                   Data Sources
                                       (5.3)
Th is chapter provides specific guid-
ance for the assessment and inter-
pretation of environmental data for
use in baseline risk assessments.
The discussion of data assessment
is organized according to six phases
that define the assessment process
for each data useability criterion.
Exhibit 5-1 lists each of the data
useability assessment phases in the
order in which a risk assessor would
evaluate them, and gives references
to the sections where they are dis-
cussed in the manual.

As presented in Chapter 3, the four
basic decisions to be made with the
data collected in the RI are:
•  What contamination is present
   and at what levels?
•  Are site concentrations  suffi-
   ciently different from  back-
   ground?
•  Are all exposure pathways iden-
   tified and examined?
•  Are all exposure path ways full>
   characterized?

The uncertainty associated with
each data useability criterion af-
fects the level of confidence asso-
ciated with each of these major
decisions.

How to Conduct the
Data Assessment

The risk assessor or RPM examines the data, documentation,
and reports for each assessment phase (I - VI) to determine if
performance is within the  hmit< required  by  the planning
objectives. The data assessment process for each phase should
be conducted according to  the step-by-stcp procedures dis-
cussed later in this chapter. For ej;h phase, minimum require-
ments are listed, potential effects of not meeting the minimum
requirements are discussed, and corrective action options arc
presented. Exhibit5-2 summarize* ie major impact on assess-
ment associated v, ith each daia Lv.M?ilily criterion.
                                     PHASE IV
                                   Assessment of
                                  Analytical Method
                                 and Detection Limit
                                       (5.4)
                                     PHASE V
                                   Assessment of
                                    Data Review
                                       (5.5)
                                     PHASE VI
                                   Assessment of
                                    Data Quality
                                     Indicators
                                       (5.6)
The following activities are performed for each assessment
phase being evaluated:
«  Identify or determine performance objectives and mini-
   mum data requirements.
   Performance objectives should have been specified in plan-
   ning for all components of the acquisition of environmental
   data, as discussed in Chapter 4.  The first step of each
   assessment phase is to assemble these performance objec-
   tives and note any changes made to them since their initial
   specification.  The performance objectives should also be
   compared with the minimum acceptable requirements for
   data useability that are presented in the following sections
   of this chapter.  These minimum requirements can  be
   adopted as the performance objectives for any component
   if current objectives are inappropriate or were not specified.
•  Determine actual performance compared  to performance
   objectives.
   The next step in the assessment of each phase is to examine
   the analytical results to determine the performance that was
   actually achieved for each data useability criterion.  This
   performance should then be compared with the objectives
   established during planning.  Take  particular note  of
   performance for samples or analyses that are critical to the
   baseline risk assessment.  All deviations from the objec-
   tives should be noted. In those cases where actual perform-
   ance was better than that required in  the objective, it is
   useful to determine if this is due to unanticipated character-
   istics of the site, or to superior performance in some stage
   of thedataacquisition. Where actual performance docs not
   meet performance objectives for data critical to the risk
   assessment, the next step is corrective action

•  Determine and execute any corrective action required

           Corrective  action should be focused on
           maximizing the useability of data from
           critical samples.

   Correctiveacuon should be taken to improve dau useabili;\
   when actual performance fails to meet performance obiec-
   tives for data critical to the risk assessment  Corrective
   action options are described in Exhibit 5-3.  These may
   involve communication with the RPM and the technical
   team to resolve the problem or actions on the part of the risk
   assessor. Sensitivity analysis may be performed by the risk
   assessor to estimate the effects of not meeting performance
   requirements on the certainty of the risk assessment  Cor-
   rective actions can improve data quality and reduce uncer-
   tainty, and may eliminate the need  to qualify or reject the
   data.
                                                    75
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Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
                                      EXHIBIT 5-2

                     MINIMUM REQUIREMENTS, IMPACT, AND CORRECTIVE
                          ACTIONS FOR DATA USEABILITY CRITERIA
DATA USEABIUTY
CRITERION
5 1 Reports To Risk
Assessor












5.2 Documentation






5.3 Data Sources







5 4 Analytical
Method and
Detectio" L" '.

MINIMUM
REQUIREMENT
• Site description
• Sample design with sample
locations
• Analytical method and
detection limit
• Results on per-sample basis.
qualified for analytical
limitations
• Sample-specific quantitation
limits (SQLs) and detection
limit for non-detects
• Reid conditions for media
and environment
• Preliminary reports
• Sample results related to
geographic location
(chain-of-custody records,
SOPs, field and analytical
records)


• Analytical data results for
one sample per medium
per exposure pathway
• Broad spectrum analysis for
one sample per medium
per exposure pathway
• Field measurements data
for media and environment
• Routine methods used for
critical samples and
chemicals of potential
concern
• Detection limit iess than



5 5 Daa Revew










5.6 DataQ-a.ry
Indicators










20% of concentration of
concern

• Correctness of analytical
results reviewed









• Sampling variability
quantified for each analyte
• QC samples required to
identify and quantify
precision and accuracy
• Sampling and
analytical precision and
accuracy quantified




IMPACT ON RISK
ASSESSMENT
• Unable to
perform
quantitative risk
assessment










• Unable to assess
exposure
pathways
• Unable to identify
appropriate
concentration for
exposure areas
• Potential for false
negatives and
positives
• Increased
variability in
exposure
modeling

• Unqualified
precision and
accuracy
• False negatives




• Potential for false
negatives
or false positives
• Increased
variability and
bias due to
analytical
process,
calculation or
transcription
errors
• Unable to
quantify
confidence
levels for
uncertainty
• Potential for false
negatives
or false positives




CORRECTIVE
ACTION
• Request missing
information
• Perform qualitative
risk assessment










• Request locations
identified
• Resampling




• Resampling or
reanalysis for
critical sample:!





• Reanalysis
• Resampling and
analysis for cr tical
samples
• Doc-rr.er.ted
statements of
limitation for non-
critical samples
• Perform data
review









• Resampling for
critical samples
• Perform qual tative
risk assessment
• Perform
quantitative
risk assessment
for non-critical
samples with
documented
discussion of
potential limitations
                                          76
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    Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
                     EXHIBIT 5-3

           CORRECTIVE ACTION OPTIONS
             WHEN DATA DO NOT MEET
            PERFORMANCE OBJECTIVES
             Retrieve missing information

             Resolve technical or procedural problems
             by requesting additional explanation or
             clarification from the technical team.

             Request reanalysis of sample(s) from
             extract

             Request construction and re-interpretation
             of analytical results from tfie laboratory or
             project chemist.

             Request additional sample collection and
             analysis for site or background
             characterization.

             Model potential impact on risk assessment
             uncertainty using sensitivity analysis to
             determine  range of effect.

             Adjust or impute data based on approved
             default options and imputation routines.

             Qualify or reject data for use in risk
             assessment.
 Using a Worksheet to organize the data
 assessment

 The level of certainty associated with the datacomponentof the
 risk assessment varies according to the amount of data meeting
 performance objectives. For each performance measure, the
 risk assessor determines whether the data involved are satisfac-
 tory (data accepted), questionable .  Phase VI includes the
 asscssmentof sampling (Section 5  6 1> and analytical perform-
ance (Section 5.6.2) according to each of five data quality
indicators:  completeness, cornparjbility, representativeness,
precision, and accuracy.
5.1    Phase I:  Assessment of
        Reports to  Risk Assessor

The data and documentation supplied to the risk assessor must
be evaluated for completeness and appropriateness, and to
determine if any changes were made to the work plan and the
SAP during the course of the work.  The SAP discusses the
sampling and analytical design and contains the Quality Assur-
ance Project Plan (QAPjP) and a list of data quality objectives,
where such indicators were  developed.  The risk assessor
should receive preliminary and final  data and reports, as de-
scribed in the following sections.


5.1.1   PRELIMINARY REPORTS

           Preliminary data should be used as a
           basis for identifying sampling or
           analysis deficiencies and taking
           corrective action.
Preliminary analytical data and reports allow the risk assessor
to begin assessment once the sampling and analysis effort has
begun. These initial reports have three functions:

•  The risk assessor can begin to characterize the baseline risk
   assessment on  the basis  of actual data.   Chemicals of
   interest will be identified and the concentration variability
   confirmed.

•  Potential problems in either sampling or analysis can be
   identified and the need for corrective action can be as-
   sessed.  For example, based on the preliminary data, addi-
   tional samples may be required or the method nia\ need to
   be modified because of matrix interferences.

•  RI  time schedules are more likely to be met if the risk
   assessment process can begin before the final data reports
   are produced.

The major advantage to preliminary review of data by the risk
assessor is the potential for feedback and corrective action
while the RI issulhn process to improve the quality of the data
for the risk assessment.


5.1.2   FINAL REPORT

           Problems in data useability due to
           sampling can affect all chemicals
           involved in the risk assessment
           whereas problems due to analysis may
           only affect specific chemicals
                                                       77
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 Chapters Assessment of Environmental Data For Useability in Baseline Risk Assessments
                                  EXHIBIT 5-4
                       DATA USEABILITY WORKSHEET
Data Useability Criteria
1
2.
3.
Reports to Risk Assessor
Documentation
A. WP/SAP/QAPJP
B. SOPs
C. Field and
Analytical Records
Data Sources
A. Analytical
Decision










Comments





     B Non-analytical
    Analytical Methods
I 5
Data Review
  Decision: Accept, Qualified Acceo' Reject
                                           78
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   Chapter 5  Assessment of Environmental Data For Useability in Baseline Risk Assessments
                                        EXHIBIT 5-4
                          DATA USEABILITY WORKSHEET
                                        (continued)
Data Useability Criteria
6
|
I
'-r
Data Quality Indicators
A. Completeness
B. Comparability
C. Representativeness
Decision
Sampling
Analytical
Combined
Sampling
Analytical
Combined
Sampling
Analytical
Combined
D. Precision
Sampling
Analytical
Combined
E Accuracy Samp|ing
Analytical
Combined















Comments






j   Decision: Accept, Qualified Accept, Reject
The m inimum data, documentation, and report materials needed
to prepare the risk assessment are

•   A description of the site, including a detailed map showing
   the location of each sample, the site relative to surrounding
   structures, terrain  features, receptor populations,  indica-
   tions of air and water flo*. and a description of the opera-
   tive industrial process (if an\
A map with sampling locations.
A description and rationale of the sample design and
sampling procedures.
A description of the analytical methods used.
Results for each analyte and each sample, qualified for
analytical limitations.
                                                   79
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Chapter 5 Assessment of Environmental Data For Useability in Baseline  Risk Assessments
•  Sample-specific quantitation limits (SQLs) and detection
   limits for undetected analytes, with an explanation of the
   detection limits reported and detection limit qualification
   for analytical limitations.
•  A narrative explanation of the level ol data rev iew used and
   the resulting data qualifiers indicating direction of bias,
   based on the assessment of the results from quality control
   samples (i.e., blanks, duplicates, and spikes for field and
   laboratory).
•  A description of field conditions and physical parameter
   data as appropriate for the media involved in the exposure
   assessment

If any of these materials are not  available and cannot  be
obtained, it may not be possible to  perform  a quantitative
baseline risk assessment.  The RPM or risk assessor should
attempt to retrieve missing deliverables from the source.

Additional reports and data that are useful to the risk assessor,
such as data results on CLP diskettes, are listed in Exhibit 3-10.
Access to this information will  improve  the efficiency and
quality of the risk  assessment,  but  not  having it  will not
necessarily require the data to be qualified or rejected. Mini-
mum requirements for reports to the risk assessor are listed in
Exhibit 5-2.


5.2    Phase II: Assessment of
        Documentation

Three types of documentation must  be assessed:  chain-of-
custody records, SOPs, and field  and analytical records.

Chain-of-custody records for ri.sk assessment must document
the sample locations and the date of sampling  so that sample
results can be related to geographic location and to specific
sample containers. If a sample result cannot be  related to a
sampling date and the point of sample collection, the results are
unuseable for the quantitative risk assessment.   Full scale
chain-of-custody procedures (extending from sample collec-
tion through analysis) are not  required for risk assessments,
although they are required for other purposes, such as enforce-
ment or cost recovery.

SOPs are products of the planning process and serve todcscribe
and specify the procedures to be followed during samp] ing and
analysis.  They are quality assurance procedures that increase
the probability that a data collection  design  will  be  properly
implemented. SOPs increase consistency in performing tasks
and, as a result, determine the level of systematic error and
reduce the random error associated v. ith sampling and analysis.
Knowledge that SOPs were developed and followed increases
confidence in the data.  However, once the data have  been
collected and anal y/.ed, the existence of SOPs docs not increase
the data quality.  The existence of SOPs for each process or
activity involved in data collection is not a minimum require-
ment, but the value of SOPs if data problems occur is signifi-
cant.

Field and analytical records are the products ol sampling and
analysis, and document the procedures followed and the condi-
tions of the procedures.  Field and analytical records such as
field logs and raw instrument output may be useful to the risk
assessor as back-up documentation but they arc not minimum
requirements. Like SOPs, such records are critical to resolving
problems in interpretation, but they may not directly affect the
level of certainty of the risk assessment.

Minimum requirements for documentation are listed in Exhibit
5-2.
5.3    Phase III:  Assessment of Data
        Sources

Data source assessment involves the evaluation and use of
historical and current analytical data. Historical analytical data
should be evaluated according to dataquality indicators and not
source.

The minimum analytical data requirement for risk assessment
is that one sample per medium exposure pathway be analyzed
using a broad spectrum analytical technique, such as GC-MS
methods for organic analytes, or ICP for  inorganic analytes.
The impact of not having a broad spectrum analysis from a
fixed laboratory source is  an increased probability of false
negatives because all chemicals of potential concern at the site
may not be identified. In the absence of a broad  spectrum
analysis, the best corrective action is to uikeaduuionai samples
If additional samples cannot be obtained, the probability of
false negatives and positives should be considered high, and the
level of certainty of the risk assessment is  decreased.

The broad spectrum analysis and any other analytical data are
subject to the basic documentation and data review require-
ments discussed in this chapter.  The location of the sample data
point  must be known, as  well as Ihe method  and  sample
quantitation limn achieved for analyiical results.

Guidance  for the assessment of analytical data to  determine
false positives and false negatives and the precision and accu-
racy of concentration results is provided in Section 5.6.2.

Field  measurements of physical  characteristics of the site,
medium, or contamination source arc a critical data source.
whose omission can significantly  affect the ability  of the risk
assessor to perform a quantitative assessment.  Physical site
information is required to perform exposure fate and transport
modeling.  Examples of such data are particle si/c, pll, clav
content, and porosity of soils,  wind direction  and  speed
-------
    Chapter 5  Assessment of Environmental  Data For Useability in Baseline Risk Assessments
 topography, and percent vegetation. RAGS Exhibit 4-2, Ex-
 amples of Modeling Parameters for Which Information may
 nee
-------
 Chapter 5 Assessment of Environmental  Data For Useability in Baseline Risk Assessments
           Qualified data can usually be used for
           quantitative risk assessments.

The assessmcntof data quality indicators foreither sampling or
analysis involves the evaluation of five indicators: complete-
ness, comparability, representativeness, precision, and accu-
racy. The effects of uncertainty in completeness, comparabil-
ity and representativeness influence the certainty of chemical
identification by increasing the probability of false negatives
and positives. Variation in completeness, comparability, repre-
sentativeness, precision and  accuracy and affects the uncer-
tainty of estimates of average concentration and reasonable
maximum exposure.  Once  the indicator is examined or a
numerical value is determined, the results can be compared to
the performance objectives established during RI planning.
This comparison determines the useability of the data and any
required corrective actions.
A summary of the  minimum requirements for data quality
indicators is presented  in Exhibit 5-2, and the evaluation
process is illustrated in Exhibit 5-5. Specific requirements for
each indicator are presented in the sampling and analytical data
quality indicator assessment sections below.

5.6.1.  ASSESSMENT  OF SAMPLING
         DATA QUALITY INDICATORS

The major activity in determining the useability of data based
on sampling is assessing the effectiveness of the sampling
operations performed.  Samples provided for analysis must
answers to the four basic decisions to be made with RI data in
the risk assessment cited at the beginning of this chapter.

Completeness

Minimum requirements:
   100% for critical samples. Background samples and broad
   spectrum analyses are usually critical.
   Sufficient number of samples to meet specified perform-
   ance measures.

Impact:
•  A reduction in the number of samples reduces site coverage
   and may affect representativeness.
•  Ability to differentiate site levels from background.
•  False negatives.
   Reduction in confidence levels and power.
   Upper confidence limit estimates may be inflated.
Corrective action:
•  Resampling.

•  Additional analysis of samples already at laboratory.
Cuinpleienc.s.-> i.s uilcuialcd In ilie lolloping lunnulu.
                            (Number of accepted
                             data points) X 100
    Percent Completeness =   --  	
                              Total number of
                             samples collected

This measure of completeness is useful for data collection and
analysis management but misses the k;y risk assessment issue,
which is the total number of data points available and accepted
for each chemical of potential concern.  All occurrences of
incompleteness should be assessed to determine if an accept-
able level of data useability can still be obtained or whether the
level of completeness  must  be increased, either by  further
sampling or by other corrective action.  Any decrease in the
numbcrof samples from that specified in the sample design will
affect the final results. In this case, the option of obtaining more
samples should be reviewed.

Typical causes for sample attrition include: site conditions
preventing sample collection  (e.g., a well runs dry), sample
breakage, and invalid or unusable analytical results. Complete-
ness can affect the uncertainty involved in risk assessments by
reducing the available number of samples on which idcntifica-
tion of chemicals at the site  and estimates of concentration
levels are based. The reduction in the number of samples from
the original design further affects representativeness by reduc-
ing site coverage and increases the variability in concentration
estimates.   Only the collection of additional  samples will
resolve the problem, unless the samples involved were dupli-
cates or splits.  In this case,  or if the cause was laboratory
performance, the extracts may be considered tor reanal) sis.

Comparability

Minimum requirement:
   Unbiased sample design or documented reasons for select-
   ing another sample design

Impact:
•  Non-additivity of sample results.
•  Reduced confidence, power, and ability to detect differ-
   ences, given the number of samples available.

Corrective action:

•  Statistical analysis of effects of bias.

Comparability issues have little impact on performance meas-
ures associated with sampling provided that the sample design
is unbiased, and the sample design or analytical  methods !u\ e
                                                     82
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Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
                                      EXHIBIT 5-5
                         USING DATA QUALITY INDICATORS
                         TO DETERMINE DATA USEABILITY
               Environmental
                  Data
                Statistical
               Assumptions
 Consult a
Statistician
                                   Group Data by
                                  Medium/Stratum
               Estimate Statistical
                  Performance
                    Require
                   Performance
                   Achieved'
                                      Accep
                                     Probability
                                     Missing Hot
                                       Spot'

Judgmental
Model
yte
No

Yes
t

Non-Statistical
Treatment

No
•^
s
r
Modify Performance
Objective
                                                   Estimate Sampling
                                                   Measurement Error


Accept and Qualify
Data or Reject
A
Total Error Estimates



4


4


No



Accept Quantitativ
Data
No},
Estimate Analytical
Measurement Error
i

S XYes
9 4 No / SignificantV_l
1 V Effect' }^_





Determine
Corrective
Action
                                            83
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 Chapter 5  Assessment of Environmental Data For Useability in Baseline Risk Assessments
not changed over time. If any of these factors change, the risk
assessor may experience difficulties in combining data sets to
estimate the reasonable maximum exposure. The determina-
tion of RME is based on the principal of estimating risk over
!'PT" for the exposure area.  The ideal situation occurs when
samples can be added within the basic design and the only effect
is to decrease  the level of uncertainty.

Representativeness

Minimum requirements:
•  Sample data representative of exposure area.
•  Sample preparation procedures (i.e., filtering, compositing,
   and sample preservation) do not affect representativeness.

Impact:
•  Bias high or low in estimate of RME.
•  False negatives.

Corrective action:
•  Additional sampling.
•  Examination of effects of sample preparation procedures.

Representativeness of data is critical to risk assessments. The
results of the risk assessment will be biased to the degree that
the data do not reflect the chemicals and concentrations present
in the exposure area of interest.  Non-representative chemical
identification will result in false negatives. Non-representative
estimates of concentration  levels may be high or low. Only
additional sampling will resolve the problems associated with
unrepresentative sampling, unless  the risk assessment is ac-
cepted with explicit discussion of its potential limitations.

It is important to determine whether any changes have occurred
in the actual sample  collection that convert an originally
unbiased sampling plan into a biased sampling episode. Bias
in unbiased designs is difficult to assess because no measure of
the true value is known.   Bias  in non-statistical designs is
assumed.

R eprcscntauvcness is primarily a planning concern. The solu-
tion is in the design of a sampling plan that is representative.
Once the design is implemented, only the sampling variability
is evaluated during the assessment process, unless contamina-
tion occurs in  thequality control samples or blanks. Complete-
ness problems decrease representativeness  and increase the
potential for  false negatives and the bias in estimations of
concentration, but only the increase in variability can be esti-
mated.
Precision

Minimum requirements:

•  Confidence level of 80%.
•  Power of 90%.
   Minimum detectable relative differences specified in SAP
   and modified after analysis of background samples if nec-
   essary.
•  One set of duplicates or more, as specified in the SAP.
«  Measurement error specified.

Impact:

•  Errors in decisions to act or not act based on analytical data
•  Unacceptable level of uncertainty.

Corrective action:
•  Add samples.
•  Adjust performance objectives.

The two basic activities performed in the assessment of preci-
sion  are estimating sampling variability from  the observed
spatial variation and estimating the measurement error attribut-
able  to the data collection process.  Assumptions concerning
the sample design and data distributions must be examined
prior to interpreting the results. This ex animation will provide
the basis for selecting calculation form ulas and knowing when
statistical consultation is required.

The type of sample design selected is critical to the estimation
of sampling variability as discussed in Sections 3,2 and 4.1  If
the sample design is purposive, the nature of the sampling error
cannot be determined and estimates ot the average concentra-
tions of analytes may not be representative of the site.

           The distribution of the data must
           always be determined before applying
           statistical measures.
The nature of the observed chemical data distribution
estimation procedures. The estimation of variability and con-
fidence intervals will become complex if the distribution can-
not be assumed normal or to approximate normal when trans-
formed using standard procedures such as the transformation to
log normal.  Estimates of the 95%  upper confidence limit
(UCL) of the average concentration for the RME should be
                                                      84
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    Chapter 5  Assessment of Environmental Data For  Useability in Baseline Risk  Assessments
based on an analysis of the frequency distribution of the data
whenever the database is sufficient to support such analysis.
The use of statistical tests comparing the distribution of the
observed data with the  normal or some other distribution is
p;cf:rrcd.  O.iphs '.if data '.'• 'thout statistic.!1, test results may
also be acceptable for some data sets.  Statistical computer
software can assist in the analyses of data distribution.

If the analysis shows thai the data are not normally distributed,
the risk assessor should transform the data to a normal distribu-
tion, if possible, lo facilitate statistical analyses.  After data are
normalized, the UCL of the arithmetic mean can be calculated
for data that are logarithmically distributed (RAGS Section
6.4.1).   If log normal  is not appropriate, other  parametric
models such as pareto, gamma, or beta, might be used or non-
parametric approaches utilized.  A statistician should be con-
sulted, since the wrong choice of the distributional model can
result in low power and even invalid tests.

Sampling Variability

Exhibit 5-6 summarizes  the assessment procedures for the
evaluation of  variability from different sampling procedures.
The estimation of confidence levels, power, and minimum
detectable relative differences requires assumptions about the
coefficients of variation from  sampling variability for each
chemical of potential concern.  The RPM or risk assessor
should discuss the implications of these assumptions with a
statistician to determine their potential impacts on data useabil-
ity.

            The statistical measures of perform-
            ance most app!i;abl<: :o site conditions
            should he determineJ Before assessing
                                                                                       EXHIBIT 5-6

                                                                       STEPS TO ASSESS SAMPLING PERFORMANCE
                                                                   Confirm slaoslcal assumptions
                                                                   Summarize anaryre detection data by strai
                                                                   vwrffxn nwdia
                                                                3  Transform analyte concentration data so distribution ts approximately noma/

                                                                4  Calculate the coefficient of variation lor eacn analyte detected

                                                                5  Using Exhibit 4-4 "Relationships Between Measures o( Statistical Perlormarv-s and Numnyr
                                                                   of Samples Heojuired." loo* up tfw range of power confidence Mvyl and minimal delect ir<. =
                                                                   differences for the calculated coefficient of variation

                                                                6  Compare the statistical performance measures required to those achievable given :he
                                                                   coefficient of variation and sample size

                                                                7  If the performance objectives are achieved, go to step 9

                                                                   if ttie required statieflcal performance levels an not met, men additional samples must be
                                                                   taken or one or more of tw performance parameters must be changed

                                                                   If lampta are to be added. Exhibit 4-4 and *w calculation formula! In Appendix IV can t*.
                                                                   used 10 determine t^e number needed

                                                                0  If the performance parameters are to be changed. **e parameter to be changed shoukj he
                                                                   the one which wffl ncrease tie probabiity of taking unnecessary acnon as opposed to
                                                                   unnecessary risk.

                                                                9  Examine the reeurts of tf^e quafty convol samples If none exist, the sample rssutts ~xs- -
                                                                   considered to be qualitative

                                                                to  If ffie quakty control sample resurs indicate possible bias through contarmndrjon  !a>.»
                                                                   appropriate correcwe action
                                                                for data useability in risk assessment are provided in Exnibr.
                                                                5-7.
To determine whether the performance objectives have
met, first summarize the sample results at the ana! vte !•:•
stratum, including media within a site or site subgroup
stratum within media. If a particular combination <.M s.
andanalyte yields only asingle data point, the issue of: sa;v,
error is notconsidered relevant, and theassessment proce
the assessment of analytical error for that stratum and -I'
combination. SituationsinvolvingasingledatajH.ii,: .!•,.  ,
trcvi'C'J as  instances of risk  T-'scssmcn; Ku ^ '  ••'   >•<
concentration.
                                                                                                                       been
                                                                                                                       e! h\
Unce !!v sf;.i!i ;tic;»,i aspirins ion-; a;,.: olxei vcd an jlj te vanabi!
ity are known, selected statistical performance measures can be
assessed to determine the aaia qujMu achieved.  Additional
samples may be needed or m-xlificd data qualu> objccuves
required as a result of evalu iiT.t: su:'ip!,ng variability. Three
issues are m\"Ived  in 'iv .:s-.^-s- .'-: of -;.|;iiro(i suitistica!
performance.

    Level of certainty or confidence
•   Power

    Minimum detectable difference

The required level for ea.h  of  r:  Ciree critical  statistical
performance measures should be Deluded in the SAP as data
quality objectives (DQOs)  The L-^or's data quality require-
ments defined  by these  stalls^...,  •:-, \isures determine the
number  of samples  that  are  taker  during data collection.
Recommended  minimum  sti: si.u.:  ;--;rformance parameters
                                                                                       EXHIBITS-?

                                                                       RECOMMENDED MINIMUM STAT!ST;CA'
                                                                             PERFORMANCE PARAMETERS
                                                                                FOR RISK ASSESSMENT
                                                                        NULL HYPOTHES'S. ON-SITE CONTA'/I'J/''
                                                                         CONCENTRATIONS ARE NOT HIGHER "i t-/
                                                                                    THE BACKGROUND
                                                                        Confidence level (Type I error) 80% mi'iirr^r,. rt-^i t
                                                                        when true (take unnecessary action)

                                                                        Power (Type It error) 9C% minimum, acceo' "j.i A'^pr
                                                                        (fail to take action when nskj

                                                                        Minimum de'ectable difference 10% - 20% ^'.^n''^
                                                                        depends on concentration of concern
                                                                       Source  EPA 1989C
                                                            85
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 Chapter 5  Assessment of Environmental  Data For Useability in Baseline Risk Assessments
Inihcmajontyofcascs, for.siratum/analytc combinations with
multiple data points, the distribution should be examined for
normality and transformed to log normal  The coefficient of
variation is calculated for each stratum/analyte combination. If
tlv distribution resulting from !N:!r 'rKfnpmnon is not normal,
a new distributional model will need to be  identified and
validated in consultation with a statistician. Non-parametric
procedures which lequire no distributional assumptions may
also be used.

Once the coefficient of variation is calculated, the number of
samples required to achieve any specific statistical perform-
ance measure  can be  determined  from tables or statistical
formulas.  Conversely, the statistical performance achieved,
given the coefficient of variation, can also be determined. The
statistical performance achie\ed should be compared to the
requirements stated in planning. If the performance objectives
are achieved, the risk assessor can proceed to the assessment of
measurement error.

For example, if the required statistical performance objectives
are not met, additional samples must be taken or one (or more)
of the performance parameters must be changed. Ifsamplesare
added, the tables  or formulas can be used to calculate  the
number of samples required. If a performance parameter is
changed, the one to be changed should be the one which will
increase the  probability of taking unnecessary action as op-
posed to unnecessary risk. As a result, the confidence level
would be reduced first,  the n. immurn detectable relative differ-
ences would be increased second, and the level of power would
be reduced last. Minimum recommended levels of reduction
for use in risk assessment are 80~c confidence levels, 90%
power, arid 10-20% minimum deteeiub'e relative differences.
Exhibit 5-7 summan/vs the KV v~'~,^d-:d data qualitvobjec-
tives for statistical performance parameters.

Measurement Error

Measurement error is estimated u^ing the results of duplicate
samples.  The estimate represents, the difference, between the
reported values. This type of variation has four basic sources:
sample collodion procedures  x^--'.e Handling  an:1 storage
procedures, analytical proced^r-r^  _:,:! data processing proce-
dures. The use of duplicate -.am r:e^'o determine measurement
error is discussed in section 5.5 under data review procedures.

Variability due to  sampling can be estimated from field dupli-
cates, orcollocated samples il the sample isalsoanaly/.ed as the
laboratory duplicate. Otherwise, tr: field duplicates determine
total withm-batch measurement C~.T including analytical er-
ror.
The  formula for computing the relative percent difference   fl
(RPD) between duplicates is:                                ^
                                R1  - R2|
                RPD=         	   -  x 100
                               (R1 + R2)/2

where Rl  and R2 are the results fiom the first and second
duplicate samples, respectively.

Accuracy

Minimum requirements:

•  Spikes to assess accuracy of non-detects and positive sample
   results if specified in the SAP.

•  Blanks to determine contamination.

Impact:

•  False negatives.

•  False positives.

Corrective action:

•  Consider re-sampling at affected locations.

Accuracy is controlled primarily by the analytical process and
is reported as bias.  The bias of the sample design cannot be
determined since the true value of the chemicals of concern in    —
the exposure area can never be known. However, certain   fl
sample designs describe in Chapter 4 produced unbiased re-
sults if followed.

The  bias associated with the  measurement process can be
estimated using field spikeson field evaluation or audit samples
to assess the accuracy and comparability of results.  The-.:
estimates will rc-flec'the effectsofs;>-r.pl-?c '".• '!'•'•' .!> T-i'ni '
holding times, and the analytical process on the value of the
sample collected.

Bias is estimated for the measurement process b\ computing
the percent recovery for the spiked or reference coin pound .•-,
follows:
                        (Measure amount - Amount
                        i" 1i - e n i. .-> H 53 — -i o\ v -r^
                        Ml UllOfJ v'x^J Cl d < «J ' C j ,^ I ^ .-/
     Percent Recovery =
                              Amount SpiKed

Because of the inherent problems associated with the spiking
procedure and the interpretation of recovery, spikes are consid-
ered minimum requirements only if specified in the SAP.

Field matrix spikes arc currently not recommended for use in
soils (EPA 1989c).
                                                      86
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   Chapter 5 Assessment of Environmental Data For Useability in Baseline Risk Assessments
Field blanks are evaluated to estimate the potential bias caused
by contamination from sample collection, preparation, ship-
ping and/or storage.  Results for the analysis of field blanks
indicate whether contamination resulted in bias but are not
,>-;iirmtf >. of nfviirnr\  R'I;K is romputed as follows:
    Percent Bias =
(Measured amount) X 100

 Required Detection Limit
Blanks areof primary concern for the analysis of bias involved
in sampling because of the difficulty in performing field spikes
and the  availability of appropriate reference standards and
matrix for evaluation samples.


5.6.2   ASSESSMENT OF ANALYTICAL
         DATA QUALITY INDICATORS

Determining the useability of analytical results for the risk
assessor begins with the review of quality control samples and
qualifiers. The review is used to determine an overall assess-
ment of analytical performance as determined by laboratory
and method performance.  Note that it is more important to
evaluate the effect on the data than to determine the source of
the error. The data package is re% lewed as a whole for some
criteria.   Data are reviewed at the sample level for certain
criteria, such as holding time. Factors affecting the accuracy of
identification  and  precision and accuracy of quantitation of

                    EXHIBFT 5-8
USE OF QUALITY CONTROL DATA FOR RISK ASSESSMENT
QUAIJTY
c-»;,^
(High Recovery)
(Low Recovery)
-X ~r\»*

CAWMon

Internal Standards^
iReorcdoobiifty)
Irtwmal Standards
iHign Recovery)
interned Standards
(Low Recovery)
1 False negative o
2 Effect on bias de
EFFECT OH
ISMCT MET S-A£ USE
~T -"°"U"-IJ"-"1^
""'•"^ - -*— to~"™
None unl»«» anafyte -t-y -y -•* iJ«ta <• *«tj,-r«te poor
and not in« om«r
nownve or neoanv*
EctM Dosicve -< y S^t TonflderMre level Sx Ijtank
> ^-M data above confidenc* level
^** data below mn^Oeoce leve^
| M estimate
| - T :r ^*4 data as estimate
j _;W jriess problem is extreme
False Neo/rtrvw ^-^ect dita or examne raw data ariJ
-s«» professional judgment
,se data as estimate • poor
_ r . _ ^e data M lower limit
False NeQatrve1 -.-3- ~se data as upper limit
Tly likely il recovery is near :e-:
ermcied t^ e»arnnaton o' ~4*j T> *-»i^~ rxttvid^jal annlyte
individual chemicals, such as calibration and recoveries, must
be examined analyte-by-analyte.  The qualifiers used in the
review of CLP data arc presented and the effect on data quality
is discussed in this section. Exhibit 5-8 presents a summary of
the qiialiiv control samples and the data use implication of
qualified data. Corrective action options are shown in Exhibit
5-1.

In environmental analysis, sample mediacan be more complex
than expected as in the case of sludge or oily wastes or can
contain interfering chemicals whose presence cannot be pre-
dicted in both precision and accuracy measurements. The risk
assessor must examine the reported precision and accuracy
data to determine useability. Ranges  used for rejection  and
qualification of CLP data have been determined based on the
analysisof target compounds in environmental media (soil, and
water). These ranges, documented in "Laboratory Data Vali-
dation: Functional Guidelines for Evaluating Organics/Inor-
ganics Analyses" can be used in the absence of specifications
in the planning documents (EPA 1988d, 1988e).

Completeness

Minimum requirements:

•  Percentage of sample completeness  determined during
   planning.
•  100% for critical samples (one sample per medium per
   exposure pathway).
•  All data from critical samples considered crucial.

Impact:
   Consequences generally decrease as the number of samples
                                                           Data (or critical samples have significantly more impact
                                                           than incomplete data for non-critical samples.
                                                        •   For critical samples, decrease useability of data.
                                                        •   For non-critical samples, potential decrease in useability of
                                                           data.

                                                        Corrective action:
                                                        •   Determine whether the missing data are crucial to the risk
                                                           assessment (i.e., data from critical samples).
                                                        •   Resampling or sample re-analysis to fill data gaps.

                                                        The completeness for analytical data required for risk assess-
                                                        ment is defined as the number of chemical-specific data results
                                                        for an exposure area that are determined acceptable after data
                                                        review, expressed as a percent of the total:
                                                                                 (acceptable samples) x 100
                                                            Percent Completeness = --
                                                                                       total samples
                                                     87
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 Chapter 5 Assessment of Environmental Data For Useability in Baseline  Risk Assessments
An analysis is considered complete if all data generated are
determined to be acceptable measurements as defined in the
SAP.  Data for each analyte should be present for each sample.
In addition, data from quality control samples necessary to
determine precision and accuracy should be present. Quality
control samples and the effects of problems associated  with
these samples are discussed in section 5.6.2.

Comparability

Minimum requirements:
•  The analytical methods used must have common analytical
   parameters.
•  Same units of measure used in reporting.
•  Similar detection limits.
•  Equivalent sample preparation techniques.

Impact:
•  Increase in overall error.

Corrective action:
•  Preferentially use those data that provide the most defini-
   tive identification and quantitation  of the chemicals of
   potential  concern.  For organic chemical identification,
   GC-MS data are preferred over GC data generated  with
   other detectors. For quantitation, examine the  precision
   and accuracy data along with the reported detection limits.
•  Sample re-analysis using comparable methods.

           The need to combine data from
           different sampling events and/or
           different analytical me;hods should be
           anticipated.


Comparability is a very important qualitative data indicator for
analytical assessment, and is a cnueal parameter when consid-
ering the combination of data sets from different analyses for
the same  chemicals of potential concern.  The assessment
determines if analytical results being reported arc equivalent to
data obtained from similar analyses.  Only comparable data sets
can readily be combined for the purpose of generating a single
risk assessment calculation.

The use of routine methods simplifies  the determination of
comparability because all laboratories use the same standard-
ized procedures and reporting parameters. In other cases, the
risk assessor may have to consult v. nh an analytical chemist to
evaluate whether different methods are sufficiently  compa-
rable  to combine data sets. The nsk assessor should request
complete descriptions of non-routine methods. A preliminary
assessment can be made by comparing the analytcs, useful
range, and detection limit of the methods. If different units of
measure have been reported, all measurements must be con-
verted to a common set of units before comparison.

Representativeness
          requirements:
   As  specified in the SAP.

Impact:
•  Inaccurate identification or estimate of concentration that
   leads to inaccurate calculation of risk.
•  If a large portion of the data are rejected or if all data from
   analyses of samples at a specific location are rejected, the
   remaining data may no longer sufficiently represent the
   site.

Corrective action:
•  For critical samples, re-analyses of samples or resampling
   of the affected site areas. For non-critical  samples, re-
   analyses or re-sampling should be decided by the RPM in
   consultation with the technical team.

•  If the re-sampling  or re -analyses cannot be performed,
   document in the site assessment report what areas of the site
   are not represented  due to poor quality of analytical data.

Representativeness is determined by examining the sampling
plan, as discussed in Section 3.2. In determining the represcn-
tativeness of the data,  the evaluator examines the degree to
which the data meet the performancs standards of the method
and to which the analysis represents the sample submitted to the
laboratory.  Analytical  data quality affects representativeness
since data of low quality  may be rejected for use in risk
rK
-------
    Chapter 5  Assessment of Environmental Data For Useability in Baseline Risk Assessments
•  If there is too much variability in the analyses, the risk
   assessor can use the larger sample result to set an upper
   bound on the risk.

Precision is a measure of the repeatability of a single measure-
ment and is evaluated from the results of duplicate samples and
splits. The relative percent difference (RPD) between dupli-
cates is calculated using the following formula:
                                -R2|
            RPD =
x 100
                                R2)/2
Low precision can be caused by poor instrument performance,
inconsistent application of method protocols, or by a difficult,
heterogeneous sample matrix. The last effect can be distin-
guished from the others because it is unique to a single sample
or set of samples from the same location.
If split samples have been analyzed by different methods or
different laboratories, then data users have a measure of the
quality of individual techniques.  Splits are particularly effec-
tive in cases when one laboratory is a reference laboratory. If
both sets of data exhibit the same problems, then laboratory
performance can usually be ruled out as a source of error. Splits
are also useful when using non-routine methods or comparing
results  from different analytical methods.

Accuracy

Minimum requirements:
•  Use of methods (routine methods whenever possible) that
   specify expected or required recovery ranges using spike or
   other quality control measures.
•  As  specified in the SAP.
•  No chemicals of potential concern detected in the blanks.
Impact:
•  Potential for false negatives. If spike recovery is low, it is
   probable that the method or analysis is biased low for that
   analyte and values in all related samples may underestimate
   the actual concentration.
•  Potential for false positives.   If spike recovery exceeds
   100%, interferences may be present, and it is probable that
   the method or analysis is biased high. Analytical results
   overestimate the actual concentration of the spiked analyte.
Corrective action:
•  In many validated methods the percent recovery is used as
   a correction factor in calculating the analyte concentration.
   However, no correction factor is applied for CLP data.
•  If recoveries are extremely low or extremely high, the risk
   assessor should consult with an analytical chemist to iden-
   tify a more appropriate method for re-analysis of the samples.
                 Accuracy is a measure of ovcrestimation or underestimation of
                 reported concentrations and is evaluated from the results of
                 spiked samples. Recoveries from spiked or performance evalu-
                 ation samples can be calculated using the following formula:
                     Percent Recovery =
                         (measured amount - amount
                          in unspiked sample) x 100

                               amount spiked
The procedures will vary according to differences in the num-
ber of measurements and the precision of the estimates. Data
that are not reported by confidence limits cannot be assigned
weights based on precision and should not be combined for use
(Taylor 1987).

Spiked samples are particularly useful in the analysis of com-
plex sample types because they help the reviewer determine the
extent of bias on the sample measurement. A set of standards
at known concentrations is mixed into a portion of the sample
or into distilled water prior to sample preparation and analysis.
The analytical  results are compared  to the amount spiked to
determine the level of recovery.  It is important to note that
unless every  sample is spiked, spike recoveries present only a
trend rather than a specific quantitative measure.

Results from blanks can be used to estimate the extent of high
bias in the event of contamination. The following procedures
should be implemented to prevent  the assignment of false
positive values due to blank contamination:
•  If the field blanks are contaminated and the  laboratory
   blanks are not, the risk assessor can conclude that contami-
   nation occurred prior to receipt of the samples by the
   laboratory.  If the contamination  is significant (i.e., it will
   interfere  with the determination of risk), consider resam-
   pling at affected locations.

•  Ifitisnotpossibletoresample.theriskassessormustassess
   the effect of the contamination on the potential for false
   positives. Often, this determination can be made by exam-
   ining data from samples located nearby. If all samples and
   blanks show the same level of a particular chemical, the
   presence of the chemical in the samples is most likely due
   to contamination.

•  If the laboratory blanks are contaminated, the laboratory
   should be required to rerun the associated analyses. This is
   especially important  in  the case of critical analytes or
   samples.  Before reanalyses, the  laboratory must demon-
   strate freedom from contamination by providing results of
   a clean laboratory blank. Note:  if laboratory  blanks are
   contaminated, field blanks will generally also be contami-
   nated.

•  If reanalysis is  not possible, then  the sample data must be
   qualified. "Laboratory Data Validation: Functional Guide-
   lines for  Evaluation of  Organics/Inorganics  Analyses"
                                                      89
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 Chapter 5  Assessment of Environmental Data  For Useability in Baseline Risk Assessments
   provides examples of blank qualification (EPA 1988d,
   1988c).  Chemicals detected  in the associated samples
   below the action level defined in the Functional Guidelines
   arc considered undetected.

Data qualifiers

All of the data generated by the Routine Analytical Services
(RAS) of the CLP are  reviewed  and qualified by regional
representatives according to the guidelines found in "Labora-
tory Data Validation: Functional Guidelines for Evaluation of
Organics/Inorganics Analyses" (EPA 1988d, 1988e) as modi-
fied to fit the requirements of the individual regions.

           Data qualified "U" or "f are useable
          | for risk assessment purposes.


Analytes qualified with a U are considered not detected. If data
precision and accuracy are good (as determined by the quality
control samples), data are entered in the data summary tables in
the data validation report as the SQL or corrected quantitation
limit (method detection limit corrected for sample factors such
as dilution and percent moisture), and qualified with a U.  Note
that the same chemical can be reported undetected in a series of
samples at different concentrations because of sample differ-
ences. The use of zero, half the detection limit, or the corrected
method detection limit as reported in the data summary tables
will determine minimum, average, or maximum risk, respec-
tively.

Data qualified with an R are rejected because performance
requirements in the sample or in associated quality control
analyses were not met.  For example, if a mass spectrometer
tune  is not within specifications neither the identification nor
qjanutation of chemicals can  be  accepted with confidence.
Extremely low recoveries of a chemical in a spiked sample
might also  warrant an  R designation for that chemical in
associated samples because of the risk of false negatives (see
Appendix VI).

Data qualified with a J present a more complex issue.  J
qualified data are considered estimated because quantitation in
the sample or in associated qua! 11\ control samples did not meet
specifications.  The justification  for qualifying the data is
explained in the validation report and is proposed to be included
on a  qualifier summary table submitted with the validation
report by draft revisions of the functional guidelines. Data can
be biased high or low for qualification as estimated. The bias
can often be determined by examining the results of the quality
control samples used to quahf>  the data.  For example, if
interfering levels of aluminum are found in inorganic analysis
of the interference check sample • ICS), the sample results arc
probably biased high because the iron signal overlap is added
to the signal being reported. When volatilcorgamc compounds
arc qualified J for holding time violation, the results arc usually
biased low because some of the volatile compounds may have fl
evaporated during storage.                                ^

Data associated with contaminated blanks are not considered
estimated and arc no! flagged T  The presence of the hl.ink
contaminant chemical in the analytical samples is questionable
at levels up to  five  to ten times those found in the blank,
depending on the nature of the analytc.   An action level is
determined for each chemical based on the quantity found in the
blank, and data above the action level is accepted without
qualification.  Data between the Contract Required Quantita-
tion (Detection) Limit (CRQL, CRDL) and the action level are
qualified U (undetected) because die confidence in the detec-
tion is low due to blank contamination.

Estimated organics and inorganics data  that are below the
CRQL or CRDL are qualified as UJ.  This qualifier signifies
that the chemical is  not detected but the precision of the
measurement is not good enough for confidence in the quanti-
tation limit.  UJ is used for  the same reasons as J but the
appropriate quantitation limit is reported rather than the amount
found.

Other qualifiers may be added to the analytical data by the
laboratory. A set of qualifiers (or flags) has been defined by the
CLP for use by the laboratories to denote problems with the
analytical data.  These qualifiers and their potential use in risk  _
assessment are discussed in RAGs (EPA  1989a).            fl


5.6.3   COMBINING THE ASSESSMENT OF
         SAMPLING AND ANALYSIS

Once the quality of the sampling and analysis effort has been
assessed using  the five  data quail)   indicators, the  p;ol le.;,
becomes one of combining the results to determine the overall
assessment of a particular indicator across sampling and analy-
sis. Combining the assessment for completeness, comparabil-
ity, and representativeness is discussed  in this section as a
qualitative procedure.  Statistical models arc  available for
combining data sets with different variability and bias. Theri.sk
assessor should consult a chemist or statistician if the magni-
tude of the sampling and analysis effort warrants the use of a
formal statistical treatment of comparability.

The basic model for estimating total variability across sam-
pling and analysis components is presented in Exhibit 5-9. A
non-statistical approach to combining the assessment results is
suggested in Exhibit 5-10.  Using this approach, each data
quality indicator is assessed to determine  whether a problem
exists in cither sampling or analysis.  This assessment leads to
different combinations of problem determination.  For ex-
ample, completeness  may have been a problem in sampling
[yes] but not aproblcm in analysis [no]; the combinauon is
no].
                                                  yes/
                                                  '
                                                     90
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  Chapter 5 Assessment of Environmental Data For Useability in Baseline  Risk Assessments
                    EXHIBIT 5-9

  BASIC MODEL FOR ESTIMATING TOTAL VARIABILITY
  ACROSS SAMPLING AND ANALYSIS COMPONENTS
   where   o,  = total variability
          n  = measurement variability
          a  = population variability
   where  c^ = sampling variability (standard deviation)
          q, = handling, transportation and storage variability
          qs = preparation variability (subsamplmg variability)
          oj, = laboratory analytical variability
          <$, = between batch variability

   NOTE: It is assumed that the data are normally distributed or that a
   normalizing data transformation has been performed.

   Source  EPA 1990b
                    EXHIBIT 5-10

  COMBINING DATA QUALITY INDICATORS FROM
     SAMPLING AND ANALYSIS INTO A SINGLE
          ASSESSMENT OF UNCERTAINTY
     Data
    Quality
    Indicators
Assessment of P'oo'ers  Combined Sampling
                      and Analytical
 Sampling   Analytical
                                       Determination
  Completeness
   YES

   NO
YES

NO
            YES/YES

             YES/NO

             MO'YES
  Comparability
  YES

   NO
YES

MO
             YES/YES

             YES/NO

             NO/YES
! Representativeness
  YES

   NO
                             NO   I
             YES/YES

             YES/NO

             NO/YES
    Precision
  YES

  NO
            YES/YES

             YES/NO

             NC/YES
    Accuracy
  YES

  NO
                            VES
            YES/YES

             YES/NO

             NO/YES
  The combination NO NO inc ca'es ~a" "~e data quah'y indicator wil
  not affect the level o' i-0cer'a -% - ca'a -seability
Once assessment patterns based on the determination of a prob-
lem have been established, some basic guidance is given on the
combinations. This guidance is qualitative in nature and is pre-
sented only to assist in organizing the data assessment problem
for the application of professional judgment. If the assessment
pattern  is [no/no], the issue of  combining results  i-> not a
problem. Conversely, if the pattern is [yes/yes), the issue of
combining  results is an  issue of the effects of the combined
magnitudes.  Instances of combined sampling and  analysis
problems for a single indicator will have significant effects on
the risk assessment uncertainty. The most complicated assess-
ment pattern to interpret is encountered when a problem occurs
in one area but not in another (e.g., in sampling but  not in
analysis). This situation is briefly discussed for each indicator
in the following sections.

Completeness

An incomplete sample can be considered incomplete  for all
analytes, while analytical incompleteness is usually related to
particular analytes. In the instance of a completeness problem
[yes/yes], the effect on the risk assessment will vary according
to chemical. For some chemicals, the data points will be lost in
both sampling and analysis.

The  effects of a loss in the number of  sample points for a
particular chemical could be as much as 50%. For example, if
collection of 10 samples was planned and one sample could not
be collected because of site access problems, one was broken
in transport, and the laboratory experienced anaK sis problems
with three  samples  for the chemical of potential  concern
causing  the data  to be rejected, then only five data  points
remain.

If the assessment pattern is [yes/no], the effects arc distributed
across all chemicals involved in the risk asse.ssinei.'i  1; me
pattern  is [no/yes] the effects are localized to the particular
chemical affected.

Comparability

Comparability problems in sampling are primarily due to
different sample designs and time periods. Seasonal \,aruiiior,s
are treated  like spatial variations since the risk assessment is
calculated as risk over time. Data can be as eraged and consid-
ered as  a single data set.  For analytical data, comparability
problems arc related primarily to the use of different methods
and laboratories.  A pattern of [yes/yes] w ill indicate that the
risk assessor will have considerable difficulty mcombmmg the
various  data SCLS into a single assessment of risk. In situations
of [yes/no]  and (no/yes], the problem of sampling comparabil-
ity is more  difficult to resolve. Models exist lor determining
comparability between methods and integrating results  across
laboratories.   These models involve  the general statistic a!
approach to confirming data sets with dilferent but kno'.ui
variability and bias (Ta\ lor 19S7)
                                                      91
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Chapter 5 Assessment of Environmental Data For Useability in Baseline  Risk Assessments
Representativeness

Representativeness is critical to the risk assessment in the
sampling area.   Non-representativeness  affects both false
negatives (chemicals not identified) and estimates of concen-
tration magnitudes and, therefore, affects estimates of reason-
able maximum exposure (RME).  Analytical representative-
ness involves the question of whether the analysis  results
represent the sample collected. For example, holding times and
sample preservation can cause the analytical results not to be
representative of the sample collected. These questions should
be treated separately in the discussion of effects.

Precision

Sampling variability typically overshadows variability intro-
duced by the measurement process, which includes analytical
variability.  If precision is a problem  in both sampling and
analysis, the risk assessor should focus on the impact of
sampling variability on the estimate of RME and the resulting
confidence limits. Unless the sampling variability is low and
the analytical variability very high, the effects of analytical
variability will be minimal in comparison to  the effects of
sampling variability.

Accuracy

The assessment of accuracy with regard to sampling is focused
primarily on recoveries from spiked or performance evaluation
samples. Blank contamination can indicate the likelihood of
false positives.  For analysis, both  blank contamination and
analytical performance arc reflected by spike recoveries.  If ihe
pattern is [yes/yes] for accuracy, this may require identifying
blank contaminants and integrating the identification of con-
tamination across field and laboratory blanks.

If the accuracy pattern is [no/yes], then the issue is the analyti-
cal performance. Low variability in sampling as measured by
low coefficients of variation for chemicals of potential concern
should increase the risk assessor's concern over an analytical
accuracy problem.  High sampling variability (CV>25) will
greatly reduce the real effects of analytical bias on the level of
certainty of the risk assessment.
                                                      92
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                                    Chapter 6  Application of Data to Risk Assessment
Chapter 1
Introduction and Background
    Chapter 2
    The Risk Assessment Process
       Chapter 3
       Criteria for Evaluating Data Useability in Baseline
       Risk Assessments
1
            Chapter 4
            Steps for Planning for the Acquisition of Useable
            Environmental Data in Baseline Risk Assessments
                Chapter 5
                Assessment of Environmental Data for Useability in
                Baseline Risk Assessments
                              Chapter 6
                              Application of Data to
                              Risk Assessments
                                  Provides procedures to determine
                                  the uncertainty of the analytical data.

                              •   Explains how to distinguish site from
                                  background levels of contamination
                                  and determine presence (absence)
                                  of contamination.

                                  Discusses how to characterize
                                  exposure pathways.
                                      93
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Chapter 6 Application of Data to Risk Assessment
                       ACRONYMS FOR CHAPTER SIX
                    RAGS
                    SAP
                    SOP
Risk Assessment Guidance for Superfund
Sampling and Analysis Plan
Standard Operating Procedure
                                      94
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                                                    Chapter 6  Application of Data to Risk Assessment
6.0    APPLICATION OF DATA TO
        RISK ASSESSMENT

This chapter provides guidance for integrating the assessment
of data useability to determine the overall level of confidence
of the completed risk assessment. This guidance builds on each
of the previous chapters in this manual.
•  Chapter 2 explained the risk assessment process and the
   roles and responsibilities of key participants. Exhibit 2-3
   defined a continuum of level of certainty in the baseline risk
   assessment result based on the ability of the risk assessor to
   quantify or qualify the level of confidence associated with
   the analytical data.

•  Chapter 3 defined six data useability criteria and examined
   preliminary issues that must be considered during sampling
   and analysis planning to increase the certainty of the ana-
   lytical data collected for the risk assessment.
•  Chapter 4 presented strategies for planning sampling and
   analysis activities based on the six data useability criteria.
•  Chapter 5 described how to use each data useability crite-
   rion in a separate assessment phase to determine the effect
   of sampling and analysis problems on data quality and on
   the useability of the data in the baseline risk assessment.

The Data Useability Worksheet (Exhibit 5-4) was designed to
assist the risk assessor in summarizing the determination of
data quality across the various assessment phases. The work-
sheet forms the basis for this chapter's discussion of the impact
of the quality of the analytical data on the level of confidence
of the risk assessment.
6.1    Assessment of Level of
        Certainty Associated with the
        Analytical  Data

This section explains how to assess the level of confidence in
sampling and analytical procedures in the context of the four
major decisions to be made by the nsk assessor with environ-
mental analytical data. Exhibits in this section apply the data
useability criteria, defined in Chapter 3 and appearing on the
Data Useability Worksheet, to the four decisions. The data
useability criteria affect the level of confidence involved in
each decision. Accordingly, the level of certainty in the data
collection and evaluation component of the risk assessment
will affect the overall certaint} of the risk estimate.
6.1.1   WHAT CONTAMINATION IS
         PRESENT AND AT WHAT LEVELS?

The risk assessor's first task is to use the analytical data to
determine what contamination is presentat the site and at what
levels (i.e., what potential exists for increased risk from this
contamination). Exhibit 6-1 lists the criteria from the Daia
Useability Worksheet that affect this decision.  The most
critical question to be answered about the analytical data before
calculating the risk is the probability of false positives or false
negatives in the data.  Risk assessors are concerned primarily
with false negatives because their occurrence causes the assess-
ment of risk to be biased low.   False positives cause the
calculated risk to be biased high.

           The major concern with false negatives
           is that the decision based on the risk
           assessment may not be protective of
           human health.
Probability of false negatives

False negatives occur when chemicals of potential concern are
present but are not detected by the analytical method or the

                        EXHIBIT 6-1
DATA USEABILITY CRITERIA AFFECTING CONTAMINATION PRESENCE
 Work»h«*t
 Rafaranca
     Dm* Uaaablllty
        Criterion
Data ColWction and
Evaluation Decision
   1
   28
   2C
   3A
   4
   5
   6A
   6C
   60
   6E
Reports to risk assessor
Documentation (SOPs)
Documentation (analytical raco'dsj
Data sources (anaiy'icai)
Analytical methods
Data review
Completeness (analytical)
Representativeness (sampling)
Precision (analytical)
Accuracy (sampling and analytical)
sample design.  The following parameters  from the  Dam
Useability Workshectcan be used to determine the probability
of false negatives: analytical methods, data review, samplum
completeness,  sampling representativeness, analytical com-
pleteness, analytical precision and accuracy, and combined
error.

           False negatives can occur if sampling
           is not representative, if detection  limns
           are above concentrations of concern,
           or if spike recoveries are very low.
                                                      95
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 Chapter 6  Application of Data to  Risk Assessment
Sampling contributes to the probability of false negatives if too
few samples were taken or if sections of the site were not
sampled.  If sampling of any exposure pathway was not
representative, the probability of false negatives increases.

Knowing the analyte-specific detection limits is critical to the
determination of the probability of false negatives. Recovery
values from spikes, internal standards, surrogates, and system
monitoring compounds are used to assess the level of accuracy
and precision in the laboratory data and determine whether the
detection limits stated in the analytical methods have been met
by the laboratory.
•  If the concentration of concern is at or below the detection
   limit for any analyte, the probability of false negatives for
   that  analyte  is high.  This probability should have been
   documented during planning if no analytical methods were
   found with detection  limits below  the concentration of
   concern.

•  If the spike  recoveries are acceptable or biased high as
   documented during the data review process and the detec-
   tion  limits are below the concentration of concern for each
   analyte, the probability of false negatives is low.

•  If the spike  recoveries are biased low and the detection
   limits are below the concentration of concern for each
   analyte, the probability of false negatives is directly related
   to the amount of bias. The effect is  more pronounced the
   closer the concentration of concern is to the detection limits.

•  The possibility of false negatives should be carefully evalu-
   ated whenever  samples have been highly diluted (i.e.,
   diluted beyond normal method specifications).

Probability  of false positives

False positives occur when a chemical of concern is not present
at the site but is detected by the anal\ tical method. Assessment
of the following parameters from the Data Useability Work-
sheet can be used to determine the probability of false positives:
analytical methods, data review, sampling accuracy, analytical
completeness, analytical  precision and accuracy, and com-
bined error.

           False positives can occur when blanks
           are contaminated or spike recoveries
           are very high.
Sampling and analysis uncertainties connected with false posi-
tives can be assessed by examining the results of quality control
samples. Blank contamination is the most important indicator
for determining the probability of false positives, particularly
when accompanied by high spike recoveries. As described in
Chapter 5, samples can be contaminated  during sampling,
storage, or analysis, resulting in false positives.  Field  and
laboratory blanks identify this problem by determining the
level and point of contamination. Sample matrix interferences
also cause false positives. High spike recoveries indicate that
matrix interference has occurred.
•  If a chemical of potential concern has been detected in any
   of the blanks, the probability of false positives associated
   with that analyte is high.  False positives should be sus-
   pected for any sample value less than five times the blank
   concentration (ten times for common laboratory contami-
   nants). High spike recoveries compound problems with
   blank contamination and increase the likelihood of false
   positives.

•  If chemicals of potential concern are detected in the blanks
   and spike recoveries for any analyte are biased high, the
   probability of false positives for that analyte is directly
   related to the amount of bias.  The probability of false
   positives is highest when the reported concentration is near
   the detection limit for an analyte.

•  If chemicals of potential concern have not been detected m
   any of the blanks and spike recoveries are not biased high,
   the probability of false positives is  low.


6.1.2   ARE SITE CONCENTRATIONS
         SUFFICIENTLY  DIFFERENT  FROM
         BACKGROUND?

Background samples serve as a baseline measurement to deter-
mine the degree of contamination. Background samples arc
collected and analyzed for each medium of concern in the same
manner as other site samples. They require the same degree of
quality control and data review.  Background samples differ
from other samples because the sampling location, as defined
in the samplingand analysis plan (SAP), is intended to be in an
area that has not been exposed to the source of contamination
Exhibit 6-2 lists the criteria from the Data Useability Work-
sheet that affect this decision.

As part of the risk assessment process, the risk assessor must
determine if the samples collected as background samples arc
actually uncontaminated. If chemicals of potential concern are

                        EXHIBIT 6-2
             DATA USEABILITY CRITERIA AFFECTING
              BACKGROUND LEVEL COMPARISON
                                                         WorfcshMt
                                                                         O«l« UmMbllity
                                                                           Criterion
                                        Data Collection and
                                        Evaluation Decision
1
2A
3A
6A
6B
60
6E
Reports to risk assessor
Documentation (SAP)
Data sources (analytical)
Completeness (sampling)
Comparability (analytical)
Precision (analytical)
Accuracy (sampling and
analytical)
                                                      96
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                                                    Chapter 6 Application of Data to Risk Assessment
not found in the background samples, the entire data collection
process will be simplified. If chemicalsof potential concern are
found in the background samples, the risk assessor must deter-
mine whether they are at naturally occurring levels, are of
anthropogenic origin,  are due to contamination during the
sampling process, or if they are site contaminants.

Both naturally occurring chemicals and anthropogenic chemi-
cals have significance for risk assessment. Naturally occurring
chemicals are those expected at a site in the absence of human
influence. Metals are naturally occurring chemicals that are
often included in risk analysis. They are generally present in
varying concentrations depending on the medium.  For ex-
ample, soils of high organic content, such as humus, would
have a low concentration of metals by weight, while soils with
a high clay content would contain higher metal levels. Anthro-
pogenic chemicals are defined  in RAGS (EPA 1989a) as
concentrations of chemicals that are present in the environment
due to man-made,  non-site sources (e.g., industry,  automo-
biles). Chemicals of anthropogenic origin include organic
compounds such as phthalates (plasticizers), DDT, or polycy-
clic aromatic hydrocarbons and inorganic chemicals such as
lead (from automobile exhaust).
•  If chemicals of potential concern are found in background
   samples, they should not be cons idered naturally occurring.
   They can be present because they are either site contami-
   nants or are of anthropogenic origin. They also could be a
   result of contamination during sampling.
•  If chemical concentrations in the background samples fall
   within naturally-occurring levels and there is no risk asso-
   ciated with those levels, the risk assessor may eliminate the
   chemicals from the risk assessment calculations.
•  If chemical concentrations in the background samples are
   higher than naturally occurring levels, it is an indication of
   contamination,  either  site-specific or  anthropogenic in
   nature. The risk assessor may include the analytical data
   with other site data or perform a separate risk assessment
   based on best professional wdgment.
   Anthropogenic chemicals
   the risk assessment
should not be eliminated from
   Statistical analysis may be necessary to determine if site
   levels are distinctly different from those found in the
   background samples.
   Statistical analysis may also be necessary in those cases
   where chemicals of potential concern are detected in site
   samples at very low concentrations.  It is difficult to
   distinguish adifference between background andsite sample
   concentrations at levels close to the detection limit.
                               6.1.3
                                         Statistical analysis may be able to
                                         determine if site concentrations are
                                         significantly above background
                                         concentrations when the differences
                                         are not obvious.
         ARE ALL EXPOSURE PATHWAYS
         IDENTIFIED AND EXAMINED?
                               The identification and examination of exposure pathways is
                               discussed in detail in RAGS (EPA 1989a). Exhibit 6-3 summa-
                               rizes the criteria that the risk assessor must assess to determine
                               the probable level of certainty that all pathways have been
                               identified and examined.

                                                     EXHIBIT 6-3
                                          DATA USEABILITY CRITERIA AFFECTING
                                           EXPOSURE PATHWAY EXAMINATION
                                                                       Data Collection and
                                                                       Evaluation Decision
Worksheet Dili U«»bility
Reference Criterion
1
2A
3B
6A
6B
Reports to risk assessor
Documentation (SAP)
Data sources (non-analytical)
Completeness (sampling)
Comparability (sampling)
The nature of the pathways to be examined is critical to the
selection of a sample design and analytical methods.  If the
exposure pathways are not identified properly, the resulting
characterization will be inappropriate. The risk assessor should
determine which pathways are not adequate and determine the
effect on the risk assessment if those pathways are excluded
from study.

•  If additional samples can be collected to include the inade-
   quately represented exposure pathway in the risk assess-
   ment, the risk assessor should recommend their acquisition.
   (Sampling considerations presented in Chapter 3 of this
   manual should be re-examined).
•  If additional samples cannot be collected from an inade-
   quately represented pathway, the risk assessor should in-
   vestigate whether computer simulation modeling is fea-
   sible. For example, if the contamination of the soil and
   water at the site is fully characterized but no air samples
   were obtained, air flow models could be used to estimate
   transport of volatile contaminants.

•  If additional samples cannot be collected from an inade-
   quately represented pathway and no simulation models arc
   appropriate, the risk assessor should note in the report that
                                                      97
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 Chapter 6  Application of Data to Risk Assessment
   the risk could not be determined for that pathway or use
   simple  chemical/physical relationships  to estimate the
   exposure. For example, equilibrium partition coefficients
   can he used toestimate movement in the vadose /.one of soil
   il insufficient data exist to calibrate a groundwater transport
   model.


6.1.4   ARE ALL  EXPOSURE  PATHWAYS
         FULLY CHARACTERIZED?

Assessing how well exposure pathways have been character-
ized involves evaluation of completeness, comparability, and
representativeness across analytical and sampling data quality
indicators. Exhibit 6-4 lists the criteria from the worksheet that
affect this decision.  To be fully characterized, the exposure
pathway must have been appropriately sampled. Broad-spec-
trum analyses also must have been conducted for the media of
concern and analyte-specific methods used where appropriate.
The uncertainty in the data collection and analysis depends on
the evaluation of completeness, comparability and representa-

                      EXHIBIT 6-4
           DATA USEABILITY CRITERIA AFFECTING
          EXPOSURE PATHWAY CHARACTERIZATION
Worksheet
Reference
      D«U UswbNKy
         Criterion
Data Collection and
Evaluation Decision
  1
  2A
  28
  2C
  3A
  3B
  6A
  6B
  6C

  60
Reports to risk assessor
Documentation (SAP)
Documentation (SOPs)
Documentation (field records)
Data sources (analytical)
Data sources (non-analytical)
Completeness (sampling and analynca1)
Comparability (sampling and ana'y"ca v
Representativeness (sampling
 and analytical)
Precision (sampling)
tiveness as discussed in Section 5.6. Based on these indicators,
the risk assessor should determine the magnitude of the effect
of analytical data uncertainty on the risk assessment.

•  If the uncertainty associated with the data for an exposure
   pathway is not significant, the risk assessor should use the
   data and note in the report the high level of certainty
   associated with assessment of the affected exposure path-
   way.
•  If the uncertainty associated with the data for any exposure
   pathway is significant but does not warrant resampling and
   rcanalysis, statistical procedures may be necessary to inter-
   pret the data.
•  If the uncertainty associated with the data is high, the risk
   assessor may  determine that an exposure pathway is not
   fully characterized.


6.2    Assessment of  Uncertainty
        Associated With  the Baseline
        Risk Assessment for Human
        Health

The level of certainty in making each of the four decisions
discussed in Section 6.1 contributes to the overall uncertainty
in the data collection  and analysis component of  the risk
assessment.  The critical factor in  assessing the effect  of
uncertainty on the environmental analytical data component of
the risk assessment is not that uncertainty exists, but rather that
the risk assessor be able to qualify ami/or quantify the uncer-
tainty. The certainty levels for risk assessment represented in
Exhibit 6-5 are based on the ability to quantify the uncertainty
in analytical data collection and evaluation.  Data collection
and evaluation, however, comprise on ly one  source of uncer-
tainty in the risk  assessment.  Other components of the risk
assessment process, such as toxicity of chemicals and exposure
assumptions, influence the four decisions to  be made and
contribute significantly to the  uncertainty of  the baseline risk
assessment.
                                    icni occurs when ihc
                 The most quantitative lev el of risk
                 uncertainty in the data can be determined quantitatively. The
                 next level occurs when the uncertainty can be determined
                 qualitatively, or the impact of the uncertainty is assessed using
                 sensitivity analysis. The least desirable situation occurs when
                 the uncertainty in the data is unknown. This situation can occ ur
                 if the minimum requirements given in Chapter 5 for the data
                 useabilily criteria have not been achieved.

                            The primary planning objective is that
                            uncertainty levels are acceptable,
                            known and quantifiable, not that
                            uncertainty be eliminated.
                                                     98
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                                      Chapter 6 Application of Data to Risk Assessment
                                  EXHIBIT 6-5

UNCERTAINTY IN DATA COLLECTION AND EVALUATION DECISIONS
        AFFECTS THE CERTAINTY OF THE RiSK ASSESSMENT
  Decisions To
      What
  Contamination
  is Present and
  at What Levels
     Are Site
  Concentrations
   Sufficiently
  Different From
   Background
     Aie A
   Exposure
   Pathways
  Identified and
   Examined
    Are All
   Exposure
 Pathways Fully
 Characterized
Risk Assessment
    Process
 Data Collection
 and Evaluation
   Exposure
  Assessment
 ;  Toxicity
i Assessment
     Risk
Characterization
Nature of Risk
 Assessment
                                  Quantitative
                                  (Uncertainty
                                 explicitly stated)
                                  Quantitative
                                 (Uncertainty not
                                    known)
                                                                   Qualitative (No
                                                                     uncertainty
                                                                     estimate)
                                        99
-------
                                                                                                    Glossary
                                               GLOSSARY
Accuracy. A measure of ihe closeness of an observed concen-
tration to the true value.

Aliquot.  A measured portion of a sample or extract taken for
analysis.

Analvte.  One of the chemicals or chemical species for which
a sample is analyzed.

Anthropogenic Background Levels. Concentrations of chemi-
cals that are present in the environment due to human-made,
non-site sources (i.e., industry, automobiles).

Background Sample.  A sample taken from a location where
chemicals present in the ambient medium are assumed due to
natural sources.

Bias.  A measure of overestimauon or underestimation of
reported values.

Biased Sampling. A sampling plan in which the data obtained
may be systematically different from the true mean.  Bias in
sampling is caused by systematic error in data location, such as
clustering data points.

Blank. A  clean sample that has not been exposed  to the
analyzed sample stream in order to monitor contamination
during sampling, transport, storage, or analysis.

Broad Spectrum Analysis. An analytical procedure capable of
providing identification and quantitation of a wide variety of
potential  chemicals.

Calibration. The comparison of a measurement iUuiuard or
instrument with another standard or instrument to report or
eliminate, by adjustment, any variation (deviation) in accuracy
of the item being compared. The le\ els of calibration standards
should bracket the range of levels for which actual measure-
ments are to be made.

Chem ical of Potential Concern Chemical initially identified or
suspected to be present at a site that ma\ be hazardous to human
health.

Chromatogram. The plot of a detector response as a function
of time that represents the separation of compounds during
analysis.

Classical Statistics. The theor. of statistics that assumes that
data  points are mutually independent.  Classical methods
consider  that one point is not  related to another.

Co-clution. When the time of relea^ of two or more analytcs
from  the column of a gas chromaiograph  cannot be distin-
guished.
Co-extractable. When two or more analytes are released from
the matrix under Cample preparation conditions

Coefficient of Variation.  A measure of relative dispersion
(precision) used  in parametric  statistics. It is equal to the
standard deviation divided by the mean, multiplied by 100 and
is expressed as a percentage.

Collocated Sample.  Independent samples  that are equally
representative of the parameters of interest at a given point in
space and time.

Comparability. A measure of the equivalence of data.

Completeness.  A measure of the  amount of  useable data
resulting  from a data collection activity, given the sample
design and analysis.

Composite Sample.  A sample that is combined from several
sampling locations in order to reduce cost and provide an
estimate of the mean of the population from which the samples
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 sample.

Compound Class.  A group of organic compounds that are
structurally related.

Confidence.  A measure of the probability  of taking action
when action is required.

Concentration of Concern. A site-specific level of concentra-
tion that the risk assessor determines to be of concern; may be
health-based,  required by statute or of environmental signifi-
cance.

Contract  Laboratory Program (CLP).   Analytical  program
developed for analysis of Superfund site samples to provide
analytical results of know quality, supported by a high level of
quality assurance and documentation.

Contract Required Ouantitation Limit (CROP The chemical
specific quantitation levels that the CLPrcquircs to be routine!)
and reliably quantitated in specified sample matrices.

Data Assessment.  The  determination of the  quantity and
quality of data.

Data Quality  Indicator (DOT).  A performance measure for
sampling and  analytical procedures.

Data Quality Objectives (POPs'). Qualitative and quantitative
statements that  specify the quality of the  data required to
support decisions. DQOs arc determined based on the end u>e
of the data to be collected.
                                                      101
-------
 Glossary
Data  Review.  The evaluation process that determines the
quality of reported analytical results. It involves examination
of raw data (i.e., instrument output) and quality control and
method parameters by a professional with knowledge of the
tests performed

Data Validation. CLP specific evaluation process that exam-
ines adherence to performance based acceptance  criteria as
outlined in the Functional Guidelines for Evaluating Organics
or Inorganics Analyses.

Data  Useabilitv.  The process of  assuring or determining
whether the quality of the data generated meet their intended
use.

Detection Limit.  The minimum concentration or weight of
analyte  that can be detected by a single measurement with a
known confidence level.

Digestion.  The application of acid  and heat to a solution or
suspension to bring metals into solution for elemental (inorgan-
ics) analysis.

Dilution. Adding solvent to a sample, with analyte concentra-
tion higher than the standard calibration curve, to bring the
analyte concentration into a quantifiably measurable range.

Dissolved Metals.  Metals present in solution rather than sorbed
on suspended particles.

Dose-Response Evaluation.  The process of quantitatively
evaluating toxicity information and characterizing the relation-
ship between die dose of a contaminant administered or re-
ceived and the incidence of adverse health effects in the
exposed populations.
Duplicate.  Tv»u sample^ Uikeu iruui Uic .saiu^ sumce ai the
same time and analyzed under identical conditions.

Exposure Pathway. The course of a chemical or physical agent
from a source to an exposed organism . Each exposure pathway
includes a release from  a source, an  exposure unit, and an
exposure route.

ExtractablcOrganics. Compounds that can be partitioned into
an organic solvent from  the sample matrix and that are ame-
nable to gas chromatography (CLP designation).

Extraction. The process of releasing compounds from a sample
matrix.

False Negative (Type II or beta error).  A statement that a
substance is not present \>.hen the substance is present.

False Positive (Type I or alpha error).  A statement that a
substance is present when it is not.
Geostatislics  A theory of statistics that rccogm/.es obsci\ od
concentrations as dependent on one another and governed by
physical processes. Geostalistical methods consider Uic loca-
tion of data and the si/c of the site for calculations.

1 leterogencous Distribution. Sample property that is unevenK
distributed in the population.

Historical daia.  UaUi collected before the remedial investiga-
tion.

Holding time. The length of time from the date of sampling to
the date of analys is. CLP designates the holding time as the date
from laboratory receipt of sample until date of analysis.

Homogeneous Distribution. A sample property that is evenly
distributed over the population.

Hot Spot.  Location of substantially higher concentration of a
chemical of concern than in surrounding areas of a site.

Hydrocarbon. An organic compound composed of carbon and
hydrogen.

Identification.   Confirmation  of the presence of a specific
compound or analyte in a sample.

Instrument Detection Limit (IDL).  The lowest amount of a
substance that can be detected by an instrument without correc-
tion for the effects of sample matrix, handling and preparation.

Intake Estimate. A measure of exposure expressed as the mass
of a substance in contact  with the exchange boundary per unit
body weight per unit time.

Integrated Risk Information System (IRTS). An EPA database
containing verified RfDs, slope factors, up-to-date health risks
and EPA regulatory information for numerous chemicals. IRl.s
is EPA's preferred source for toxicity information for Super-
fund.

Internal Standard. A compound added to organic samples and
blanks at a known concentration prior to analysis. It is used as
the basis for quantitation of target compounds.

Judgmental Sampling. The process of locaungsumplmg pmn^
based on the investigator's judgment of where the  sample
should be taken.

Kriging.  A procedure utilizing the covariancc  function that
determines an acceptable spacing for sampling locations on a
square grid.

Limit of Detection (LQD). The concentration of a chemical
that has a 99 percent probability of producing an analytical
result above zero using a specific method.
-------
                                                                                                      Glossary
 Limit of Ouantitation CLOQ'). The concentration of a chemical
 that has 99 percent probability of producing an analytical result
 above the LOD. Results below LOQ are not quantitative.

 Linearity. The agreement between an actual instrument read-
 ing and the reading predicted by a straight line drawn between
 calibration points that bracket the reading.

 Lowest-Observable-Adverse-Effect-Level (LOAEL). In dose
 experiments, the lowest exposure  level at which there are
 statistically or biologically significant increases in frequency
 or severity of adverse effects between the exposed population
 and its apparent control group.

 Mass Spectrum. A characteristic pattern of ion fragments of
 different masses resulting from analysis that can be compared
 with a mass spectral library for analyte identification.

 Matrix/Medium.  The predominant material comprising the
 sample to be analyzed (e.g., drinking water, sludge, air).

 Measurement Error. The difference between the true sample
 value and the observed value.

 Method Detection Limit (MDLX The detection limit that takes
 into account the reagents, sample matrix, and preparation steps
 applied to a sample in specific analytical methods.

 Minimum Detectable Difference. Percent difference between
 two concentration levels that can be detected in analyses.

 Naturally Occurring Background Levels.  Ambient concentra-
 tions  of chemicals that are present in the environment in the
 absence of human intervention (e.g., aluminum, manganese).

 No-Observed-Adverse-Effect-Level (NOAEL).  In dose re-
 sponse experiments, an exposure ION el at which there are no
 statistically or biologically significant increases  in the  fre-
 quency or severity of adverse effects between the exposed
 population and its appropriate control.

 Noise. The random errors of observation and other uncontrol-
 lable effects that are not related to the presence of the analyte
 being measured.

 Nonparamclric. Statistical equations that assume the data set is
 not normally distributed.

 Normal Distribution. A probabilit> density function that ap-
proximates the distribution of man>  random variables and has
the form generally called the "bell-shaped curve."

 Null Hypothesis.  For risk assessment, statistical hypothesis
that states on-sitc contaminant concentrations are not higher
than background.

Parameter.  A specified component of a procedure or method.
 Parametric. Statistical equations that assume the data set is
 normally distributed.

 Particulatc. Solid material suspended in a fluid (air or water)
 medium.

 Performance Evaluation Sample. A sample of known compo-
 sition provided for laboratory analysis to monitor laboratory
 and method performance.

 Power.  A measure of the probability of taking no action when
 no action is required.

 Precision. A measure of the reproducibility or variability of a
 measurement under a given set of conditions.

 Preservation. The sample treatment for maintaining represen-
 tative sample properties.

 Qualifier. Acode appended to an analytical result that indicates
 possible qualitative or quantitative uncertainty in the result.

 Qualitative. An analysis that identifies an analyte in a sample
 without numerical certainty.

 Quantitative.  An analysis that gives a numerical  level of
 certainty to the concentration of an analyte in a sample.

 Random Sampling.  The process of locating sample points
 randomly within a sampling area.

 Reasonable Maximum Exposure (RME).  The highest expo-
 sure that is reasonably expected to occur at a site.

 Recovery. A determination of the accuracy of the analytical
 procedure made by  comparing measured values for  a spiked
 sample  against the known spike  values.
Reference Dose
EPA's preferred toxicity value for
evaluating noncarcinogenic effects resulting from exposures at
Superfund sites.

Representativeness.  The extent to which data measure the
objectives of the data collection.

Resolution.  The degree of difference between t^o measure-
ments.

Retention Index. Retention time data specific to an anak ucal
gas chromatography column compared with retention times of
standards.

Retention Time. The length of time that acompound is re Limed
on an analytical column (GC, HPLC, 1C).

Routine Method.  A method issued by an organi/ation  with
appropriate responsibility.  A routine method has been vali-
dated and published and contains information on minimum
performance characteristics.
                                                      103
-------
 Glossary

Sample Integrity. The maintenance of the sample in the same
condition as when sampled.

Sample Quantitation Limit (SOU). The detection limit that
accounts  for sample characteristics, sample preparation and
analytical adjustments such as dilution.

Sensitivity. The capability of methodology or instrumentation
to discriminate between measurement responses for quantita-
tive differences in a parameter of interest.

Slope factor. A plausible upper-bound estimate of the proba-
bi li ty of a response per unit intake of a chemical over a lifetime.
The slope factor is used to estimate an upper-bound probability
of an individual developing cancer as a result of a lifetime
exposure to a particular level of a potential carcinogen.

Solvent  A liquid used to dissolve and separate analytes from
the matrix of origin.

Spatial Variation.  Term describing the manner in  which
contaminants vary as a function of space. The magnitude of
difference in contaminant concentrations in samples separated
by a known distance is a measure of spatial variability.

Spike. A known amount of a chemical added to a sample for the
purpose of determining efficiency of recovery; a type of quality
control sample.

Split. A single sample divided for the same measurement by
two processes  for the purpose of monitoring precision, accu-
racy or comparability of two analyses.

Standard Deviation. The most common measure of the disper-
sion of observed values or results expressed as the magnitude
of the square root of the variance.

Stratified Random Sampling. The  process of locating samples
randomly within distinct populations, unit areas or strata.

Stratify.  To divide into strata.

Surrogate.  A substance added to  environmental samples for
quality control purposes that is not likely to be found in an
environmental sample but that mimics the analyte of interest.

Systematic Random (Grid) Sampling. A non-biased sampling
plan using a grid comprised of equidistant parallel lines at right
angles to each other.
Target Compound. The compound of interest in a specific
method. The term also has been used in the Federal Register to
denote compounds of regulatory significance.

Temporal Variation. Variation ohscrved in chcmic il concen-
trations that is dependent on time.

Tentatively Identified Compound (TIC). Organic compound^
detected in a sample that are not target compounds, internal
standards or surrogates.

Toxicological Threshold. The concentration at which a com-
pound exhibits toxic effects.

Turnaround Time. Thetimefrom laboratory receiptof samples
to receipt of a data package by the client.

95% Upper Confidence Limit (UCL).  The upper limit on a
normal distribution curve below which the observed mean of a
data set will occur 95% of the time.

Useful Range. That portion of the calibration curve that v, ill
produce the most accurate and precise results.

Variance. A measure of dispersion. It is the sum of the squares
of the difference between the individual values and the arithme-
tic mean of the set, divided by one less  that the number of
values.

Viscosity.  The  physical property  of a  fluid  that offers a
continued resistance to flow.

Volatile Organics. The solid or liquid compounds that may
undergo spontaneous phase change to a gaseous state at stan-
dard temperature and  pressure.

Wavelength.  The linear distance between successive <^:^ •-
mum or minima of a wave form.

Weight-of-Evidence  Classification.  An  EPA  classification
system for characterizing the extent to which available dau
indicate that an agent is a human carcinogen. Recently, EPA
has developed weight-of-evidence systems for other kinds of
toxic effects, such as developmental effects.
                                                     104
-------
                                                                                             References
                                           REFERENCES
American Chemical Society (ACS) Committee on Environ-
    mental Improvement. 1983. Principles of Environmental
    Analysis. Analytical Chemistry.  55:2210-2218.

Annual Book of ASTM Standards.  American Society for
    Testing and Materials. Philadelphia, PA.

American Society for Testing and Materials (ASTM). 1979.
    Sampling and Analysis of Toxic Organics in the Atmos-
    phere. ASTM Symposium. American Society for Testing
    and Materials. Philadelphia, PA.

Baudo,R.,Glesy,J.,andMuntan,H.(Eds). 1990. Sediments:
    Chemistry and Toxicitv of In-Place Pollutants.  Lewis
    Publishers, Inc. Ann Arbor, MI.

Borgman, L.E., and Quimby, W.F. 1988. Sampling for Tests
    for Tests of Hypothesis When Data are Correlated in Time
    and Space. In: Principles of Environmental Sampling. L.
    H. Keith, Ed. American Chemical Society. Washington,
    D.C.

CARD.  CLP Results Database.  1988.  U.S. Environmental
    Protection Agency.  Office of Emergency and Remedial
    Response.

Clcsccri et al. (Eds). 1989. Standard Methods for the Exami-
    nation of Water and Wastewater. 17th Edition. American
    Public Health Association. Washington, D.C.

Dragun,J.  1988. The Soil Chemistry of Hazardous Materials.
    Hazardous Materials Control Research Institute.  Silver
    Spring, MD.

Environmental Protection Agency (EPA). 1983.  Methods for
    Chemical Analysis of Water and Wastes (EPA 200 and
    300 Methods). Environmental Monitoring Services Labo-
    ratory.  EPA/600/4-79/020.

Environmental Protection Agency (EPA). 1984. Methods for
    Organic Chemical Analysis of Municipal and Industrial
    Wastewater (EPA 600 Methods) as presented in 40 CFT
    Part 136, Guidelines Establishing Test Procedures for the
    Analysis of Pollutants under the Clean Water ACL

Environmental Protection Agency (EPA). 1985. Methodology
    for Characterization of Uncertainty in Exposure Assess-
    ment Office of Research and Development. EPA/600/8-
    85/009.
Environmental Protection Agency (EPA). 1986a. Test Meth-
    ods for Evaluating Solid Waste .(SW-846V  Ptnsical/
    Chemical Methods. Third Edition. Office of Solid Waste.

Environmental Protection Agency (EPA). 1986b. Guidelines
    for Carcinogenic Risk Assessment.  51 Federal Register
    33992 (September 24,1986).

Environmental Protection Agency (EPA). 1986c. Guidelines
    for the Health Risk Assessment of Chemical Mixtures. 51
    Federal Register 34014 (September 24,1986).

Environmental Protection Agency (EPA). 1986U CLP Statis-
    tical System Database. Office of Emergency and Reme-
    dial Response.

Environmental Protection Agency (EPA). 1987a. Data Qual-
    ity Objectives for Remedial Response Activities: Devel-
    opment Process. EPA/540/G-87/003 (NTIS9B88-131370).

Environmental Protection Agency (EPA). 1987b. Data Qual-
    ity Objectives for Remedial Response Activities. Example
    Scenario: RI/FS Activities at a Site with Contaminated
    Soil and Ground Water. Office of Emergency and Reme-
    dial Response.  EPA/540/G-87/004.

Environmental Protection Agency (EPA). 1987c. Field Screen-
    ing Methods Catalog.  Office of Emergency and Remedial
    Response.

Environmental Protection Agency (EPA). 1987J. A Compen-
    dium of Superfund Field Operations Methods.  Office 01
    Emergency and Remedial Response. EPA /540/P-87/001
    (OSWER Directive 9355.0-14).

Environmental Protection Agency (EPA). 1988. Field Screen-
    ing Methods for Hazardous  Waste Site Investigation
    Presented at the U.S.  Environmental Protection Agency
    First International Symposium. Las Vegas, NV.

Environmental Protection  Agency  (EPA).  1988a.  Guidance
    for Conducting Remedial Investigations and Feasibility
    Studies under CERCLA.  Office of Solid Waste  and
    Emergency and Remedial Response. EPA/540/G-89/004.

Environmental Protection Agency (EPA). 1988b. National Oil
    and Hazardous Substances Pollution Contigencv  Plan
    fNCP). 53 Federal Register 51394.(December 23. 1988).
                                                    105
-------
 References
Environmental Protection Agency (EPA). 1988c. Suoerfund
    Exposure Assessment Manual. Office of Emergency and
    Remedial Response.  EPA/540/1-88/001.  (OSWER Di-
    rective 9285.5-1).

Environmental Protection Agency (EPA). 1988d. Laboratory
    Data Validation Functional  Guidelines for Evaluating
    Inorganics Analysis.  Office of Emergency and Remedial
    Response.

Environmental Protection Agency (EPA). 1988e. Laboratory
    Data Validation Functional  Guidelines for Evaluating
    Organics Analysis.  Office of Emergency and Remedial
    Response.

Environmental Protection Agency (EPA). 1988f. Review of
    Ecological Risk Assessment  Methods.  Office of Policy
    Analysis. EPA/230/10-88/041.

Environmental Protection Agency (EPA).  1988g.  Contract
    Laboratory Program Statement of Work for Inorganic
    Analysis: Multi-media. Multi-concentration. Office  of
    Emergency and Remedial Response. SOW No. 788.

Environmental Protection Agency (EPA).  1988h. Contract
    Laboratory Program Statement  of Work for Organic
    Analysis: Multi-media. Multi-concentration. Office  of
    Emergency and Remedial Response. SOW No. 288.

Environmental Protection Agency (EPA). 1988L Guidance
    Document for the Assessment of RCRA Environmental
    Data.  In Draft. Office of Solid V> aste

Environmental Protection Agency (EPA). 1988J. Methods for
    the Determination of Organic Compounds in Drinking
    Water (EPA 500 Methods).  Environmental Monitoring
    Services Laboratory. Las Vegas. NV. EPA/600/4-88/039.

Environmental Protection Agency ("EPA).   1988k.  Toxic
    Release Inventory System (data base).  Office of Solid
    Waste and Emergency and Remedial Response.

Environmental Protection Agency (EPA). 1989. Integrated
    Risk Information System (data baseX Office of Research
    and Development.

Environmental Protection Agenc> (EPA). 1989a. Risk As-
    sessment Guidance for Superfund  Human Health Evalu-
    ation Manual Part A.  Office of Solid Waste and Emer-
    gency and  Remedial  Response    EPA/540/1-89/002.
    (OSWER Directive 9285.701 A >
Environmental Protection Agency (EPA).  1989b.  Risk As-
    sessment Guidance for Suncrfund:  Volume II Environ-
    mental Evaluation Manual.  Office of Solid Waste and
    Emergency and Remedial Response.  EPA/540/1 -89/001

Environmental Protection Agency (EPA). 1989c. Soil Sam-
    pling Quality Assurance User's Guide.  Environmental
    Monitoring Systems Laboratory.  Las Vegas, NV.  EPA.
    600/8-89/046.

Environmental Protection Agency (EPA).  1989d.  Health
    Effects Assessment Summary Tables. Fourth Quarter FY
    1989.   Office  of  Research  and  Development.
    (OERR 9200.6-303).

Environmental Protection Agency (EPA).  1989e.  Proposed
    Amendments to the Guidelines for the Health Assessment
    of Suspect Developmental Toxicants. 54 Federal Register
    9386 (March 14, 1989).

Environmental Protection Agency (EPA). 1989f. Ecological
    Assessment of Hazardous Waste Sites:  A Field and
    Laboratory Reference. Environmental Research Labora-
    tory. EPA/600/3-89/013.

Environmental Protection Agency (EPA).  1989g.  Data U^e
    Categories for the Field Analytical Support Project.  In
    Draft. Hazardous Site Evaluation Division Office of Solid
    Waste and Emergency and Remedial Response.

Environmental Protection Agency (EPA).  1989h. Office of
    Water Regulations and Standards/Industrial Technology
    Division (ITDi Mctluxis (EPA 1600 Methods). OlYijeui"
    Water.

Environmental Protection Agency (EPA). 1989i. Methods for
    Evaluating the Attainment of Cleanup Standards. Volume
    I: Soils and Solid Media. Office of Policy, Planning and
    Evaluation. EPA/230/2-89/042.

Environmental Protection Agency (EPA). 1990b. A Rationale
    for the Assessment  of Errors in the Sampling of Soils.
    Office of Research  and Development. EPA/600/4-90/
    013.

Environmental Protection Agency (EPA).  1990c.   Health
    Effects Assessment  Summary Tables.  First and Second
    Quarters FY 1990. Office of Research and Development
    (OERR 9200.6-303).

Finkcl, A.M.  1990. Confronting Uncertainty in Risk Manage-
    ment: A Guide for  Decision-Makers.  Center for  Risk
    Management.  Washington, D.C.
                                                    106
-------
                                                                                               References
Gilbert, R.O.  1987. Statistical Methods for Environmental
    Pollution Monitoring. Van Nostrand.  New York, NY.

Hclrich, Kcnnith,(Ed).  1990. Official Methods of Analysis of
    the Association of Official Analytical Chemists.  15th
    Edition.  Association of Official  Analytical Chemists.
    Washington, D.C.

IRIS. Integrated Risk Information System (data base). 1989.
    U.S. Environmental  Protection  Agency,  Office of Re-
    search and Development.

Keith, L.H.  1987.  Principles of Environmental Sampling.
    American Chemical Society. Washington, D.C.

Keith, L.H.  1990a. Environmental Sampling and Analysis. In
    Print. American Chemical Society. Washington, D.C.

Keith, L.H.  1990b.  Environmental Sampling:  A Summary.
    Environmental Science and Technology. 24:610-615.

Manahan, S.E.  1975.  Environmental Chemistry.  Willard
    Grant Press.  Boston, MA.

Neptune, D.E., Brantly, E.P., Messner, M., and Michael, D.I.
    1990.   Quantitative Decision  Making in Superfund.
    Hazardous Materials Control. 18-27.
National Research Council (NRC). 1983. Risk Assessment in
    the Federal Government: Managing the Process. National
    Academy Press. Washington, D.C.

Oak Ridge National Laboratory (.ORNL). 1982. Mcihuduluuv
    for Environmental Risk Assessment. Environmental Sci-
    ences Division. ORNL/TM-8167.

Oak Ridge National Laboratory (ORNL). 1986. User's Manual
    for Ecological Risk Assessment. Environmental Sciences
    Division. ORNL/TM-8167.

Pohlmann,K.F.,Hess,J.W. 1988. Generalized Ground-Water
    Sampling Device Matrix. Desert Research Institute. Las
    Vegas, NV.

Seller, F.A. 1987. Error Propagation for Large Errors.  Risk
    Analysis. 7:509-518.

Taylor, J.H. 1987. Quality Assurance of Chemical Measure-
    ments. Lewis Publishers, Inc. Ann Arbor, MI.

Whitmore, R.W.  1985. Methodology for Characterization of
    Uncertainty in  Exposure Assessments.   EPA/600/8-85/
    009.
                                                    107
-------
Chapter 1
Introduction and Background
    Chapter 2
    The Risk Assessment Process
       Chapter 3
       Criteria for Evaluating Data Useability in Baseline
       Risk Assessments
            Chapter 4
            Steps for Planning for the Acquisition of Useable
            Environmental Data in Baseline Risk Assessments
                Chapter 5
                Assessment of Environmental Data for Useability in
                Baseline Risk Assessments
                     Chapters
                     Application of Data to Baseline Risk Assessments
                                   APPENDICES

                                   •  Provide technical
                                      reference tables for
                                      sampling  and analysis.

                                   •  Describe data review
                                      packages and meanings
                                      of selected data
                                      qualifiers.
-------
                                      APPENDIX I
    DESCRIPTION OF ORGANICS AND INORGANICS DATA REVIEW
                                       PACKAGES
      The purpose of Appendix I is to familiarize the reader with a model for data review deliverahles. This
appendix consists of the following items:

      1.     A description of the five major components of a typical data review package.

      2.     An example of a data review summary.

      3.     Example data review forms.

      Please note that the example forms are designed for the validation of Contract Laboratory Program
(CLP) type data packages. An example form is included for each analytical fraction (volatiles, semivolatilcs,
pesticide/aroclors and metals)  and  for samples from soil/sediment and aqueous matrices.  These forms
nevertheless  include the necessary  information for the review of most types  of data (analytical  results,
sample quantitation/detection limits, data qualifiers, etc.) not associated with the CLP.
                                             111
-------
                                       AlMM.\m\ I
                     i.  nr.sc KIITION 01  DYIA iu:\ir.\\ PACKAGES
A  typical data  review  package consists of  the folluv. ing components

o    Narrative
o    Gkv-sa'-'.  >f' 'I '"' '? 'i;'"' "
o    Data Summaiv Form-

o    AnalvMs Data Sheet-- foi  Fach Yin,;','

o    Support Documentation
Narrative

      The  Narrative  provides  sample  identification information, and  describes  the  problems
found that affect  data qualitv

Glossary of Data Qualifiers

      This glossary  is  a list of Data  Review  Qualifiers  and  their  meanings for use in data
e\ aluation.

Data Summary  Form;

      A  Data  Summaiv Form  consist  of a grid of sample  numbers  and analv tes where  all
positive  results are  lepoited for  each anahte  in  each  sample along with  the  applicable data
validation qualifier if  piesent.  The Data  Summarv Forms provide a  reference  which indicate?
both  position sample les'Jts and sample "-pecific quantitation  limits

Analysis Data Sheei^ •':: F.ac!'.  SampL

      The results  of all anahtes analvzed  fiom each sample are  reported bv  the  laboratory  on
Anahsis  Data  Sheers    This  include"  the  testily of  the L.aboratorv  search  compounds  or
Tentativ elv   Hent'f;-? °
undetected  analv te^   : •
applicable

      Some  data  re  r-
appropriate  data  le  e
Analv sis Data Sheet-  '.  .

Siu^port  Document?.:  ^
      Su|?port Docu:".-"  M-n ;i, :;'!.!,  'i.-j i.-o in  : ita  •.";.',>  packaj-e^ ate Mimmane- ••: the
qualitv control re-ul:>   •  ;aw  data that  hav .• caused the data to be ciualified  Documents  winch
discuss data analvsjs  •  -ep^iting 1^11-^  mav also he included
                                            1 11'
-------
                      APPENDIX I (Continued)

                    2. DATA REVIEW SUMMARY

             ORGANIC DATA SUMMARY FORMS UTILIZED
                    BY REGION III IN THE CLP

   DATE:

SUBJECT:


   FROM:


     TO:


   THRU:


OVERVIEW

Case       consisted  of  four (4)  low level water and  two  (2)  low
level soil samples, submitted  for -full organic analyses.  Included
in this data set  was  one  (1)  equipment blank and one  (1)  trip
blank.  The trip  blank was  analyzed for volatiles only.   The
samples were analyzed as  a  Contract Laboratory Program  (CLP)
Routine Analytical  Service  (RAS).

SUMMARY

All samples were  successfully analyzed for all target compounds
with the exception  of 2-Butanone  and 2-Hexanone in the volatile
fraction.  All remaining  instrument? and method sensitivities  were
according to the  Contract Laboratory Program (CLP) Routine
Analytical Service  (RAS)  protocol.

MAJOR PROBLEM.

The response factors  (RF) for  2-Butanone and 2-Hexanone were  less
than 0.05 ir. one  of the continuing volatile calibration.  The
quantitation li-its for this compound in the affected samples
were qualified unreliable,  "R".   (See Table I in Appendix F for
the affected, sarcles.)

MINOR PROBLEMS

Several compour.is failed  precision criteria for initial and/or
cont inu ing,, cal ir rations .  Quantitation limits and the reported
results  for ~r.-z~~- compounds may  be biased and, therefore,  have
been quail: Lei -intimated, "UJ"  and "J",  respectively.   (See Table
I in Apporv.: ^x  F :JT the affected  samples) .
                             113
-------
                     APPENDIX I (Continued)

                   2. DATA REVIEW SUMMARY
NOTE?
                                                Page 2 of 3
     The soil semivolatile MG/MGD analyses were  originally
     extracted within the technical and  contractual holding
     times.  Re-extractions were required because  of surrogate
     recoveries, and these re-extractions were performed outside
     of  holding times.  Surrogate recoveries were again outside
     of the QC limits, therefore, original sample  results are
     being reported.

     The maximum concentration of compounds  found  in the trip
     blanks, field blanks, or method blanks  are  listed below.
     All samples with concentrations of  common laboratory
     contaminants less than ten times  (<10X) the blank
     concentration, and uncommon laboratory  contaminants less
     than five times (<5X) the blank concentration have been
     qualified "B" in the data summary table.  (See Appendix F) .
                Compound

          Methylene chloride *
          Acetone *
                                      Concentration  (ug/L)

                                             7 J
                                             9 J
          3is(2-ethylhexyl)phthalate *       10 J

          *    Common Laboratory Contaninant
  o  The semivolatile MS/MSD analyses had compounds  other than
     the spiking compounds present.  The following  is a table of
     results and precision estimates for the   non-sniked
     comDouncs:
                 MS/MSD Non-Spiked Co-pounds
                                     Concentration fua/L]
      Compound

Phenanthrene
Fluoranthene
Benzo(a)anthracene
Chrysene
Bis (2-ethyihexyl) phthalatc
P.enTTi ( h 1 rv> •••" r*-: ••>
Benzo (b)pyrer.e
Benzo (k) pyren-
Benzo (a) pyre."-

      RSD=-- Rele
1 r)0
340
290
290
1 6 0
190
730
740
j"
J
J
J
J
J
J
J
190
4 7 0
310
330
200
240
200
190
u
J
J
J
J
-~
J
, J
1 .', ()
4 4 0
320
300
240
240
220
2 4 0
T
J
J
J
J
J
J
J
                 _ve Standard  Deviation
                             114
-------
                     APPENDIX I (Continued)

                   2. DATA REVIEW SUMMARY
                                                       Page  3 of  3
 o The pesticide/PCB  analyses of all  soil sampler, and associated
   QC samples  had  surrogate recoveries in excess of the QC limit.
   Since  no  positive  results were reported for any pesticide or
   PCB compounds  for  any of the samples in this case no data was
   affected.  (See  Appendix  F) .

 o The reported Tentatively Identified Compounds (TIC's) in
   Appendix  D  have been  reviewed and  accepted or corrected.

 o All data  for Case        were reviewed in accordance with the
   Functional  Guidelines for Evaluating Organic Analyses with
   modifications  for  use within Region III.  The text of this
   report addresses only those  problems affecting usability.
ATTACHMENTS

APPENDIX A - Glossary of  Data  Qualifiers
APPENDIX B - Data Summary.   These "include:
      (a)  All  positive  results for  target compounds with
          qualifier codes where applicable.
      (b)  All  unusable  detection limits  (qualified "R").
APPENDIX C - Results as Reported by the  Laboratory for All
                 Target Compounds
APPENDIX D - Reviewed and Corrected Tentatively Identified
             Compounds
APPENDIX E - Organic Regional  Data  Assessment Summary
APPENDIX F - Support Documentation
                            115
-------
                                  APPENDIX I (Continued)



                                  3.  DATA REVIEW FORM
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                                                              APPENDIX  I (Continued)


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                                    APPENDIX I (Continued)



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                                                           APPENDIX  I (Continued)


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                                                        APPENDIX I (Continued)




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APPENDIX I (Continued)




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                                                    APPENDIX I (Continued)


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                                                APPENDIX I (Continued)


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                                       APPENDIX II
      LISTING OF COMMON POLLUTANTS GENERATED BY SEVEN
                                       INDUSTRIES
                li idciHiho .seven mdusti ; > lli .i 5- ujia'e ..    .'•'••   •<•• -\-    -"  -      ~  •
to pose human and enMronmcntal hazards This appendix 1.1 intended to aid ihe leader in ilire^ ->v\

      o      I o ass is i in the identification oi i.ugct eorip' )uii,_U j,' ! poieniu! .j .;  < • ,,.•; „ -.

      o     To predict associated contaminants that potentially yield interferences.

      o     To assist in early identification of sites  that contain high levels of compounds that may not be
            included as target analytes for routinely available methods.

      The data for these tables were obtained by  searching the  US EPA Toxic  Release Inventory  System
using the Standard Industrial Classification (SIC) Codes listed below:

               Indusm                                      SIC Code

        1       Battery Recycling                              3691,3692
        2       Munitions / Explosives                         2892
        3       Pesticides Manufacturing                      2842, 2879
        4       Electroplating                                59,3471
        5       Wood Presenatives                            2491
        6       Leather Tanning                              3111
        7       Petroleum Refining                            2911

      The appendix consists of seven tables and depicis the pollutants associated v. ;tr each u'~ " „• -J..T:
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      The list is not mclushe of all pollutants or industrial sources.  The seven industries were selected
based on the recommendation of the Risk .Assessment Subgroup of the Data Ijscabilm Workgroup ^ jcau^e
of the frequence ot'oc'. _rreroe 'f the pollutants produced bs 'l-^t.- Hdu\rr'.-s i" Si'^erionJ 'Mtes
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Appendix II
LISTING OF COMMON POLLUTANTS
GENERATED BY SEVEN INDUSTRIES
INDUSTRY 6 : LEATHER TANNING
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                                     APPENDIX III
        LISTING OF ANALYTES, METHODS, AND DETECTION OR
  QUANTITATION LIMITS FOR POLLUTANTS OF CONCERN TO RISK
                                     ASSESSMENT
      The purpose of this appendix is to familiarize the reader with the variety of EPA methods that are
available for analysis of pollutants of concern in risk assessment.  The  appendix facilitates  appropriate
method selection for pollutants in the matrix of interest.

      Appendix III consists first of a glossary of method abbreviations and definitions. Tables I, II, and III
depict detection limit estimates achievable for 32  organic and inorganic pollutants of potential concern to
risk assessment in air soil, air and water matrices respectively.

       Table IV provides a methods summary of each method of analysis for these pollutants.  The 32
pollutants listed were chosen because they have reported cancer risk, and occur at a frequency of greater
than 2% in 141 National Priorities List (NPL) sites .

      Tables V-A and V-B  provide an additional comparison of analytical  methodologies  for  selected
organic compound classes and organics analytes  including method detection ranges and the applicable
analytical system and preparation procedures.
1  Source CLP Statist -.  Datehase (STAT)
                                            149
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                                       APPENDIX II!

                                        TabieV-  A
            COMPARISON OF ROUTINE METHODS  BY MAJOR CHARACTERISTIC?

                                    Organic Compounds
Drinkino Water (U3EPA, Office of Water)
                                                           Samole
                              EPA                          Introduction/
                              Method No.   Analytical System  Preparation
Compound Class
Acrolein and Acrylonitrile         503
Base/Neutrals, Acids and         625*
    Pesticides
Benzidines                     605
    Carbamates and Urea         632
    Pesticides
Chlorinated Acias
Chlorinated Hydrocarbons
Chlorinated Pesticides
1,2-Dibromoethane and 1,2 -      504
    Dibromo-3-Chioropropane
Dithiocarbamate Pesticides       630
Extractable Organics            525*

Haloethers                     611
Nitroaromatics and Isophorone    509

Nitrogen and Pnospuorojs       l~7
"
Mitrosamines
N-Methylcarbamates and N
    Methylcarbamoyioximes
Organonalide Pesticides ana
    PCBs
Organophospnate Pesticides
Drganoonospnate Pesticides
3er.;hionr,ation Screening c'
    PCBs
Pesticide and PCBs
Pesticiaes and PCBs
    Organcnlorme
Phenols
Phthalate Esters
Purgeable Aromatics
                                                                          Detection Ranae
517

g-i 4

50SA

505*
             GC/FID          P&T
             GC/MS          XTN

             HPLC/Electrochem XTN
             HPLC/UV        XTN

             ECD             XTN
             Capillary Column
             GC/ECD         XTN
             ECD             XTN
             Capillary Column
             GC/ECD
                                                                          0.5-0.6
                                                                          .09-44.0

                                                                          0.08-0.13
                                                                          0.003-11.1

                                                                          EDL, 0.1-1.0

                                                                          C.03-1.34
                                                                          EDL. 0.01-0.5 (most
                                                            XTN
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                             CS? Liberation  1.9-153
                                           GC/MS          XTM
                                           Capillary Column
                                           GC/HECD        XTN
                                           GC/RD +         XTN
                                           GC/TMPD
                                           r-PLC            3
                                           Fuorescence Detector
                                           GC/ECD          XTN
                                           GC/FPD or NPD
                              506
                              502*
                                           ECD/HECD
                                           or Capillary Column
                                           GC/ECD
                                           Caoillarv Column
             GC/ECD

             GC/FID
             GC/ECD
             GC/PID
XTN
XTN
°&T
               0.3-3.9
               0.01-15.7

               EOL 'Estimated D L.\

               C "i5-0 81
               0.5-4 0

               0.002-0.176
               '\;ariable
               Pesticide 0.005-1.0
               Herbicide 0.2-7.0
               PCBsO 1-0.5
               0 002-J 24
0 M-150
0.29-3 0
0.2-0.4
                            e:noa.
-------
                        APPENDIX III
                        Table V- A
COMPARISON OF ROUTINE METHODS BY MAJOR CHARACTERISTICS

                Organic Compounds (continued)
Industrial and Municipal Waste Water (USEPA, Office
Development)
Compound Class
Purgeable Halocarbons
Purgeable Organics
Purgeable Organics
Purgeables
Volatile Aromatics and
Unsaturated Compounds
Volatile Halocarbons
Volatile Halocarbons
2,3,7,8-Tetrachlorodibenzo-p
dioxin
Triazine Pesticides
EPA
Method No. Analytical System
601* GC/HECD
524.1 GC/MS
Capillary Column
524.2* GC/MS
Capillary Column
624* GC/MS
503.1 GC/PID
502.1 GC/ECD
Packed Column
502.2* GC/HECD/PID
Capillary Column
613 GC/MS
619 GC/NPD
of Research
Samplp
Introduction/
Preparation
P&T
P&T
P&T
P&T
P&T
P&T
P&T
XTN
XTN
and
Detection Range
fpob)
0.02-1.81
0.1-1.0
0.02-0.2
1.6-7.2
0.002-0.03
0.001-0.01
0.01-0 10
0.002
0.03-0.07
Aqueous and Solid Matrices (USEPA, Office of Water)
EPA
Compound Class Method No Analytical System
Semivolatile Organics 1625 Isotope Dilution by
GC/MS (Capillary
Column)
Tetra- through octa- 1613
chlorinated dioxins
and furans
Volatile Organics 1624
Isotope Dilution by high
resolution GC/high
resolution MS
Isotope Dilution by
GC/MS (Capillary
Column)
Introduction/
Preparation
XTN
XTN
P&T
Detection Range
fpob)
most 20-100 ppb
(dependent on
% solids)
10-100 parts per
quadrillion in water
1-10 parts per trillion
in soil
5-100 ppb
(dependent
on % solids)
" Frequently requested rethod.
                               240
-------
        APPENDIX III
                         TablaV- A
COfViPAFUSGr* Or ROUTINE METHODS BY r^AJ
                         I-i CHARACTERISTICS
Organic Compounds (continued)
Solid Matrices (USEPA, Test Methods for Evaluating
Solid Waste, SW846, November, 1986.)
EPA
Compound Class Method No. Analytical Svstem
Acrolein, Acrylonitrite,
Acetonitrile
Aromatic Volatile Organics
Chlorinated Herbicides
Chlorinated Hydrocarbons
Nitroaromatics and Cyclic
Ketones
Organophosphorus Pesticides
Organochlonne Pesticides and
PCBs
Phenols
Phthalate Esters
Polynuclear Aromatic
Hydrocarbons
Polynuclear Aromatic
Hydrocarbons
Purgeable Halogenated Volatile
Organics
Purgeable Non-Halogenated
Volatile Organics
Semivolatile Organics
Volatile Organics
8030
8020*
8150
8120
8090
8140
8080*
8040
-. ^ « ->
OwO U
S". CO
8310
8010
5015
8270*
3240"
GC/FID
GC/FID
GC/ECD or HECD
GC/ECD
GC/FID or ECD
GC/FPD or NPD
GC/ECD
GC/FID
GC/ECD
GC/FID
HPLC/UV and Fluor
GC/HECD
GC/FID
GC/MS
Capillary Column
GC/MS
Sample
Introduction/
Preparation
5030
5030
3550
3550
3550
3550
3550
3550
3550
3550
3550
5030
5030
3550
5030
Detection Range
0.5-0.6
0.2-0.4
0.1-200
0.03-1.3
0.06-5.0
0.1-5.0
70-1000
0.14-16
0.29-31
Not Reoorted
0.013-2.3
0.03-0.52
Not Reported
Not Reported
1.6-7.2
* Frequently requested Tier.cd.
            241
-------
                                     APPENDIX III

                                      Table V-  A
           COMPARISON OF ROUTINE METHODS BY MAJOR CHARACTERISTICS

                            Organic Compounds (continued)
Sample Preparation Method Used:

XTN   Extraction Methods that could be used
       include 3510, 3520, 3540 and 3550
P&T   Purge and Trap
Dl     Direct Injection of liquid samples; solid
       samples mixed then injected.
3510   Separatory Funnel Extraction of Liquid
       Samples

3520   Liquid-Liquid Extraction
3540   Soxhlet Extraction of Solid Samples
3550   Sonication Extraction of Solid Samples
5030   Purge and Trap.
Nomenclature:

Instruments:

GC     = Gas Chromatograph

GC/MS  = Gas Chromatcgraph/Mass Spectrometer

HPLC   = High Performance Liquid Chromatograph


Detectors:

ECD   =   Electron Caot'jre

FID    =   Flame loniza:,cn

Fluor   =   Fluoresence

FPD   =   Flame Phc;c~eiric

HECD  =   Hall Electrocyte Conduction

NPD   =   Nitrogen/Phcsohorus

PID    =   Photoionizat.cn

UV     =   Ultraviolet
                                          242
-------
                       APPENDIX III
                        TABLE V-B
COMPARISON OF  ROUTINE  METHODS BY MAJOR CHARACTERISTICS
                   ttJORGANIC ANALYTES
Analvte
Total/Dissolved Metals
Total/Dissolved Metals
Total/Dissolved Metals
Aluminum
Aluminum
Aluminum
Aluminum
Antimony
Antimony
Antimony
Antimony
Antimony
Arsenic
Arsenic
Arsenic
Arsenic
Arsenic
Arsenic
Barium
Barium
Barium
Barium
Banum
Beryllium
Beryllium
Beryllium
Beryllium
Beryllium
Boron
Cadmium
Cadmium
Cadmium
Cadmium
Cadmium
Calcium
Calcium
Calcium
Calcium
Chromium
Chromium
Chromium
Chromium
Chromium Hexavalent
EPA
Method No.
1620
6010
7000
200.7 CLP
202.1 CLP
202.2 CLP
7020
200.7 CLP
204.1 CLP
204.2 CLP
7040
7041
200.7 CLP
206.2
206.3
206.4
7060
7061
200.7 CLP
208.1 CLP
208.2 CLP
7080
7081
200.7 CLP
210.1 CLP
210.2 CLP
7090
7091
212.3
200.7 CLP
213.1 CLP
213.2 CLP
7130
7131
200.7 CLP
215.1 CLP
215.2
7140
200.7 CLP
218.1 CLP
218.2 CLP
218.3
218.4
Analytical
System

ICP
ICP
AA
ICP
AA
GFAA
AA
ICP
AA
GFAA
AA
GFAA
ICP
GFAA
AA-Hydride
Spectrophotometric
GFAA
AA-Hydride
ICP
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
GFAA
Spectrophotometric
ICP
AA
GFAA
AA
GFAA
ICP
AA
Titrimetric
AA
ICP
AA
GFAA
AA-Chelate
AA
Sample
Preparation
3005,3010
3005,3010
3005,3010
1
1
1
3005,3010
1
1
1
3005,3010
3005,3010,3020
1
1
2
6
3020
3005,3010
1
1
1
3005.3010
Nitnc acid, ret'tix
1
1
1
3005,3010
3020
Hydrochloric acid
1
1
1
3005,3010
3020
1
1
1
3005,3010
1
1
1
2
2
Detection
Range (ppb)


1,000
200
100
3
4300-5700
60
20

70
20
25

10
10
1
10
200
100
2
30
2
5
5

460-540
5
200
5
5

2
5
5,000

100,000
4800-5200
10
50
1


I










1
1
I
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
1
1
1
1
1
1
1
1
1
                                 243
-------
                      APPENDIX III
                       TABLE  V-B
COMPARISON  OF ROUTINE  METHODS  BY MAJOR CHARACTERISTICS
                  INORGANIC ANALYTES
                       (continued)


Analyte
Chromium
Dissolved,
Hexavalent
Chromium
Chromium
Chromium

Chromium,
Hexavalent
Chromium,
Hexavalent
Chromium,
Hexavalent
Cobalt
Cobalt
Cobalt *
Cobalt
Cobalt
Copper
Copper
Copper
Copper
Copper
Cyanide

Cyanide


Cyanide



Cyanide,
amendable to
chlorination,
after
distillation

Cyanide,
Amendable to
Chlorination,
without
distillation

EPA
Method No.
218.5


7190
7191
7195

7196

7197

7198

200.7 CLP
219.1 CLP
219.2 CLP
7200
7201
200.7 CLP
220.1 CLP
220.2 CL*P
7210
7211
335.2

335 2


355.1



4500-CN-G
Standard Method
for the Examin-
ation of Water
and Wastewater
1989
4500-CN-H
Standard Method
for the Examin-
ation of Water

Analytical
System
GFAA


AA
GFAA
Coprecipitation
AA, GFAA
colorimetric

Chehtion/
extraction, AA
Differential
Pulse Polarography
ICP
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
GFAA
Total, Titrimetnc,
Spectrophotometric
Midi, Distil'at'cn
Total, Colorimetric,
Automated UV
Amenable to
Chlorination/
Titrimetric,
Spectrophotometric
T'rtnmetic
colonmetnc.
ion selective
electrode


Spectrophotmetric




Sample
Preparation
2


3005,3010
3020
Nitric acid, hydrogen
peroxide
Aqueous, acetic
acid extract (EP)
Aqueous, acetic
acid extract (EP)
Aqueous, acetic
acid extract (EP)
1
1
1
3005-3010
3020
1
1
1
3005,3010
Nitric acid, reflux
4

&


5



Alkaline distillation


distillation


pH greater
than 12


I
Detection I
Range (ODD)



3300-6700
5
See 7190,7191

500

500 !
3000

10
50
50
1
3400-4600
50
25
20
1
3700-4300
1
10

K


10



10





20



and Wastewater 1989
-------
                                          APPENDIX III
                                           TABLE  V-B
                COMPARISON  OF  ROUTINE  METHODS BY MAJOR CHARACTERISTICS
                                      INORGANIC  ANALYTES
                                           (continued)
  Analyte

  Cyanide,
  Weak and
  dissociable
  Cyanide
  iron
  Iron
  Iron
  Iron
  Iron
  Indium

  Indium


  Lead
i  Lead
  Lead
  Magnasium
  Magnesium
  Manganese
  Manganese
  Manganese
  Manganese
  Manganese
  Mercury
  Mercury
  Mercury
   EPA
Method No.

4500-CN-l
Standard
Method for the
Examination
of Water and
Wastewater 1989
335.3
231.1


231.2


200.7 CLP
236.1 CLP
236.2 CLP
7380
7381
235.1


235.2


200.7 CLP
239.1 CLP
?3D ? CLP
7420
7421
242.1 CLP
7450
200 7 CLP
243 1 CLP
243 2 CLP
7460
7461

245.1 CLP
245.2 CLP
245.5 CLP
                                                    Analytical
                                                    System
Titrimetic,
colorimetric,
ion selective
electrode
Total, Spec-
trophoto-
metric
AA

GFAA
AA
GFAA
Cold Vapor
AA/Manual
Aqueous
CVAA7
Automated/
Aqueous
CVAA/Manual/
Sediment
                Sample
               Preparation
pH greater
than 12
                  Detection
                 Range fppb
10
                                                                                     10
Nitric acid, Aqua
Regia
Nitric acid, Aqua
Regia
1
1
1
3005,3010
Nitric acid, reflux
Nitric acid, Aqua
Regia
Nitric acid, Aqua
ReQia
1

1
3005,3010
3020
1
3005,3010
1
1
1
3005,3010
Nitric acid, reflux

100

1
100
30
1
4400-5600
1

3000

30
30
100
1
50
1
100
970-1030
15
10
0.2
10
0.2
                                                                                     0.2
                                                245
-------
                      APPENDIX III
                       TABLE V-B
COMPARISON  OF ROUTINE METHODS BY MAJOR CHARACTERISTICS
                  INORGANIC ANALYTES
                       (continued)

Analvte
Mercury

Mercury
Molybdenum
Molybdenum
Molybdenum
Molybdenum
Nickel
Nickel
Nickel
Nickel
Osmium

Osmium
Osmium
Palladium
Palladium
Platinum
Platinum
Potassium
Potassium
Potassium
Rhenium
Rhenium
Rhodium

Rhodium
Ruthenium
Ruthenium
Selenium
Selenium
Selenium
Selenium
Selenium
Silver
Silver
Silver
Silver
Silver
Sodium
Sodium
Sodium
Sodium
Thallium
EPA
Method No.
7470

7471
246.1
246.2
7480
7481
200.7 CLP
249.1 CLP
249.2 CLP
7520
252.1

252.2
7550
253.1
253.2
255.1
255.2
200.7 CLP
258.1 CLP
7610
264.1
264.2
255 1

265.2
267.1
267.2
200.7 CLP
270.2 CLP
270.3
7740
"741
200.7 CLP
272.1 CLP
272.2 CLP
7760
7761
200.7 CLP
273.1 CLP
273.2 CLP
7770
200.7 CLP
Analytical
System
CVAA/
Liquids
CVAA/Solids
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
AA

GFAA
AA
AA
GFAA
AA
GFAA
ICP
A A
AA
AA
GFAA
A '*

GFAA
AA
GFAA
ICP
GFAA
AA- Hydride
Gi7AA
AA Hydride
ICP
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
ICP
Sample
Preparation

3
3
2
2
3005,3010
3020
1
1
1
3005,3010
Nitric.sulfuric
acids
Nitric acid
3005,3010
Nitric acid
Nitric acid
2
2
1
1
3005,3010
Nitric acid
Nitric acid
Nitric acid
Reqia
Nitric acid
Hydrochloric acid
Hydrochloric acid
1
1
2
3020
3005,3013
1
1
1
3005,3010
Nitric acid, reflux
1
1
2
3005,3010
1
_^_^^^_^«__._H_.H._Mi
Detection
Range (ppb)
1 -

0.2
0.2
100
1
10,000
-
40
40
1
4900-5100
300

20
-
100
5
1000
20
5000
10
1000-2200
5000
200

5C
5
200
20
5
2
-
3-5
4
10
10
0.2
1200-2800
0.2
5000
2
1-30
4800-5200
10
•••••^^^l^^BMMi
                           24 G
-------
                      APPENDIX III
                       TABLE  V-B
COMPARISON OF ROUTINE METHODS  BY MAJOR CHARACTERISTICS
                  INORGANIC ANALYTES
                       (continued)

lAnalyte
Thallium
Thallium
Thallium
Thallium
Tin
Tin
Titanium
Titanium
Vanadium
Vanadium
Vanadium
Vanadium
Vanadium
Zinc
Zinc
Zinc
Zinc
Zinc
EPA
Method No.
279.1 CLP
279.2 CLP
7840
7841
282.1
282.2
283.1
283.2
200.7 CLP
286.1 CLP
286.2 CLP
7910
7911
200.7 CLP
289.1 CLP
289.2 CLP
7950
7951
Analytical
System
AA
GFAA
AA
GFAA
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
GFAA
ICP
AA
GFAA
AA
GFAA
Sample
Preparation
1
1
3005,3010
3020
2
2
2
2
1
1
1
3005,3010
3020
1
1
1
3005,3010
Nitric acid, reflux
Detection
Range fppb^
100
1

1-10
800
5
400
10
50
200
4
49400-50600
50
20
5
0.05
5
0.05
                           247
-------
                                             APPENDIX  III
                                              TABLE V-B
                 COMPARISON OF  ROUTINE METHODS BY MAJOR CHARACTERISTIC^
                                        INORGANIC  ANALYTES
                                              (continued)


Sample Preparation Methods

3005  Acid Digestion of Waters for Total Recoverable Dissolved  Metals tor Analysis oy name A;OI,;IC /v.;,;/,,./;,,,,
       Spectroscopy or Inductively Coupled Plasma Spectroscopy

3010  Acid Digestion of Aqueous Samples and Extracts for Total Metals for Analysis by Flame Atomic Absorption
       Spectroscopy or Inductively Coupled Plasma Spectroscopy.

3020  Acid Digestion of Aqueous Samples and Extracts for Total Metals for Analysis by Furnace Atomic Absorption
       Spectroscopy.

1.      CLP preparation methods are categorized by water/soil, ICP, AA, and GFAA instrumentation

       CLP methods are based on the 200 series Methods tor Chemical Analysis of Water and Wastes  U S
       Environmental Monitoring and Support Laboratory.  Cincinnati, Ohio March, 1983.

1.1     Water sample preparation for GFAA uses nitric acid, hydrogen  peroxide and mild heat  SOW 788,  0-5

1.2    Water sample preparation for ICP and AA uses nitric acid,  hydrochloric acid and mild heat  SOW 738, D-5.

1.3    Soil sample preparation for ICP, AA, GFAA uses nitric acid, hydrogen peroxide, and mild heat  Hydrorhlor;,~
       acid  is used as the final reflux acid for several analytes. SOW 788, D-5,6.

2.      Nitric and hydrochloric acids are used for digestion.

3.      Mercury is prepared using a permanganate/persulfate oxidation procedure

4      Total cyanide is  determine- cy a refiux-disMlat.or, pro.:cJ...:- ;,:-•..., - :-•.; 
-------
                                         APPENDIX IV
                 CALCULATION FORMULAS FOR STATISTICAL EVALUATION

       Appendix IV provides calculation formulass to enable responsible risk assessment
  personnel samples necessary to meet statistical performance objectives. This appendix also
    provides statistical guidelines  on the  probability that a given sampling plan will identify a
  hot spot, and the probability that no hot hot spot exists given none was found after sampling.


                 CALCULATION  FORMULAS  TO DETERMINE  THE NUMBER  OF
                SAMPLES REQUIRED GIVEN  COEFFICIENT OF  VARIATION AND
                         STATISTICAL  PERFORMANCE  OBJECTIVES
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 one of the following equations below.
 Equation # 1: for one-sided, one-sample t-test
n>[(Za + Zb)/D]2 + 0.5Z2a
                                                            2      2
 Equation # 2: for a one-sided, two-sample t-test     n > 2[(Zct + Zb)/D]  + 0.25Z a
 where: Za is a percentile of the standard normal distribution such that P(Z > Za) =a,ZR 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'  NOTE: Data must be transformed (Z a).

As an example of application of the first equation above, assume CV = 30%, Confidence Level = 80%, Power =
95%, and Minimum Detectable Relative Difference = 20%  For infinite degrees of freedom (t distribution becomes
a normal one) Za= 0.842 and Zp = 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 = 14269

             n > 15 sarnie required (round up)
 Source: Adapted from EPA 1990c.
                                          M9
-------
                                         APPENDIX IV
                                         (continued)


                     CALCULATION FORMULAS FOR THE
                     STATISTICAL  EVALUATION  OF THE
                          DETECTION  OF  HOT  SPOTS
Hot Spot  Will Be Identified:   Example # 1

These formulas are useful to evaluate the probability that a particular sampling plan will identify a hot spot.
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 C?  centered at the center of the hot spot. From this
concept, it follows that the probability of sampling a hot spot P(H) is given by:
                                                                     if D/2 < R < DV2/2
               P(H) =   (TiR'O/D2    ifR<;D/2

                    =   (R  [7t-2arccos(D/(2R))] + (D/4)V(4R2-D2)}/D2

                    =   1     if R > DV2/2

where the angle with cosine as D/(2R) is expressed in radian measure.

An example is if the grid spacing is taken to be D = 2R and the probability of a hit is rc/4 = 0.785, which
implies that the probability that this grid spacing would not hit a hot spot if it exists is 0.215.

No Hot  Spot  Exists:  .Example #  2

This set of formulas addresses the second question concerning the probability that no hot spot exists
(given that none were found). This argument 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 H is the event that no hot spot is sampled in the
survey, Bayes formula gives:


               P(E | H) = P(H | E) P(E) / [P(H | E) P(E) + P(iT| E) PJEJ]

                             = P(H|E)P(E)/P(H|E)P(E)+P(E)].

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.
Source:  Adapted from EPA 1990C.
                                              250
-------
                                          APPENDIX V

                      "J" D-VTA QUALIFIER SOURCE AND MEANING

      Appendix V lists, the paiameteis and criteria that produce a "J" Hag in accoidancc \\ith ihe Laboiatoix
Data Validation Functional Guidelines lor Oruanics and Inoruamcs Analyses (EPA l')88) as applied to data
from the Contract Laboratory  Program.  The appendix also indicates the likcK implication ol ihis Hag on the
associated rcsult(s)

The ciitena listed in  this document should be used to Hag CLP daia as "J". 01  estimated eoncemiation (the
associated numeiical  '.alue is an  estimate ol the amount actually present  in  the sample i   With  piopet
in;erpretation, the results o! anal\ tes which are llagged "J" can olten be used in making decisions
ANALYSIS: Organic (2/88) VGA & BNA
PARAMETER
CRITERIA
ACTION
    LIKELY
IMPLICATION
Holding times
Mass
Calibration

Ion Abundance
Calibrations
- initial
 - continuing
     <30clavs
Blanks
Several data elements in
expanded window

Average RRF < .05
       > 30%
Average RRF < .05
^rD between initial and
c?-:inuing calibration >
I:" associated result is
retween detection limit and
CRQL
.Associated   samples
(4- results)
All associated data
Compound specific
(+ results)
Compound specific
(+ results)

Compound specific
(+ results)

Compound specific
( + results;

Compound specific
           L

           N
           L
           P
                                                                                            H
      Implication Ke\

      L =      Low  Tht

      H =      High  T

      P=

      X=
    ,ated result may underestimate the true value

    .'.Heel result may overestimate the true value

    :-sociaicd result may be of poor precision (high vanabitv)

    ••1  Nil geneiali/ation can he made as to the likely implication
                                               251
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                                     APPENDIX V (CONTINUED)
PA RA METER
CRITERIA
ACTION
     LIKELY
IMPLICATION
Nili ro
II suno'jale ieco\ci it's
lo\\ bin >  \U'-i
l-~i.it i inn specific ( 4-
icsults) (negative
results are flawed
\\/samplc
quaiHitation limil as
estimated (UJ)
                          Any surrogate in a fraction
                          shows < 10% recover)'

                          If surrogate recoveries arc
                          hieh
                                   Fraction specific-
                                   posit ive results

                                   Fraction specific ( +
                                   results)
                                 H
Internal
standards
If an IS area count is outside
-50^ or +100% of the
associated standard
TICs                      None

ANALYSIS: Pesticides (2 88)

Holding Times             >~ davs <30 davs
Instrument
Performance
Calibration -
     initial
DDT breakdown >2
                                 breakdown
I:" criteria for linearity not
                          '.:   D between calibration
                          :. .'.jrs >15c;c(2U% for
                          jc-ipounds being
Associated
compounds ( +
results) (non-detects
flagged w/sample
quantitation limit -
UJ)

All TIC results - (NJ)
Associated positive
results f negative
results - UJ)

Associated positive
DDT results (J)
Results for DDD
and/or DDE (NJ)

Associated positive
Endrm results (J)
Results for endnn
ketone (NJ)

Associated   positive
results

Associated   positive
results
           N
                                                                    N
           N
                                                                    N
-------
                                    APPENDIX V (CONTINUED)
PARAMETER
CRITERIA
ACTION
    LIKELY
IMPLICATION
                         l\i.xo'.ei\ outside hl'A
                         control limns
                                  Associated  samples
                                  >!DL
Duplicate

Matrix Spike
Sample
AA Post
Digestion Spike
Recovery lower ihan EPA
control limns

Outside appropriate control
windows

Recovery > 125% or <75%
Recovery within range 30-
74%
Duplicate injection outside
+ 20%
RSD (or CV) and sample
not rerun once
Associated   sample^
[IDL
Associated   samples
[ 115, or <85%

                         li sample absorbance is
                         < 50% of post digestion
                         spike absorbance and if
                         furnace post digestion spike
                         recovery not within 85 -
                         115%
                                  Associated data
Associated
>IDL
                                                 data
                                  Associated      data
                                  [IDL
                                  [<1DL(UJ)
                                H L
                         .VSA not done

                         Am samples run by MSA
                         not spiked at appropriate
                         levels
                                  Associated data

                                  Associated data
                         VSA correlation coefficient
                                  Associated data
ICP Serial
Dilution
Criteria not met
Associated data
                                               253
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                                    APPENDIX V (C:O.\TIMi
!»\RAVIKTER
                         CRITKRIA
ACTION
    LIKKLY          M
IMPLICATION1      ^
                         'Untamed
 <•.!',• pi >i;nd
 ^udiitiuuion.
 md Detection
ANALYSIS:  Inorganic (7 88)
Ho Id i nil
. ,mes Preservation
e alirrations
                         Not met
                         Correlation coclficient
                         Midrange CM- standard not
                         jL-ullecT
Associated   samples
>IDL
[IDL           (J),
IDL
           : H
                           CS recover
                                                          Associated   surnr>io
                         / ^CS recoverv' fails between
                         •- o"-parable or higher levels
                         .•:  rierferents and wuh
                         .".r.al'.te concentrations - 1CS

                         Ai. Ca, Fe, and Mg
                         ir.iert'enng elements
                         >Z\CRDLand 10%
                         '." irted concentration of
                         : -: .rfectcd element
                                                          Associated   samples
                                                          >IDL
                                                          [120%
                                                          /Vssociated   samples
                                                          >IDL
                                                          [
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                                    APPENDIX V (CONTINbliI»







                        Selected Acronym Key




C'RDL   --   Contiact Required Detection Limn (CLP inoipanics)




('R( )1    --   C i in trai. I Reunite"1 Ouaniitation 1 .mnl i C[ .I-1 ui ".lino /




CV      --   Coefficient of Y'a; laiion




IC'S      --   Interleience Check Sample




ICV      --   Initial Calibration Verification




IDL      --   Instrument Detection Limit




IS       --   Internal Standaid




LCS      --   Laboratory Control Sample




RRF     --   Relame Response Factor




RSD     --   Relative Standard Deviation




TIC      -   Temauveh Identified Compound
                                                !55
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                                       APPRNDIX VI
                 "R" DATA QUALIFIER SOURCE AND MEANING


       \ppcmlix VI lists iho parnmotets and critoi la thai pmducc an "R" Hag  in aecoidancc \\ith the l,aboi;i(oi\
^
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PAR \MKTKK
                               APPENDIX \ [ (CONTINUED)
                                 •\CTION
                          1.1KK1A
                       IMPLICATION
    i uincni PCI fomiaiKC
    DDT Retention
 ladeijuate Reparation
•Xlfccted Compounds
     DDT/Endrin
     Deuradation
     Retention Time
     Check
Sunonates

Compound
Quanutation, and
Detection Limits
Peaks o! Concern OuiMdc
Windows

Not Detected and
Breakdown Concentrations
Positive

DBC > 2.0% (Packed)
     >0.3% (Narrow-Bore)
    > \.57c (Wide-Bore)

Not Present

Larse Off-Scale Peaks
Prolessional Judgmen't (PoMU\c
Results and Quantitation Limits)

Samples Following Last In-
Control Standard (Quantitation
Limit - DDT and Endrin)
Professional Judgment
U
Suggested (Negative Results)       L

Quantitation Limits               U
ANALYSIS:  Inorganic (7/88)

Holdins Times            Exceeded
Calibrations
    ICVorCCV


1CS (for !CP)


LCS (Aqueous)

Matrix Spike Sample
AA Post Digestion
Spike
Minimum Number of
Standards Not Used; Not
Calibrated Daily or Each
7."^ instrument So; L p

-~R Outside Windows
Al. Ca, Fe or Mg in Samples
          !CS <50^
         < 50' '(

Recovery < 30%


Recoxerv < }()',!
ProfcssionalJudement (Results     L
-------
                                APPENDIX VI (CONTINUED)






                       Selected Acromm Key




         --   Continuing Cahhiation VeiilKaiion




I )|H      --   Dilniiyl OhlorciKljic




1CP      --   Induclivcly Cou|'k\l Plasma Aioinic EmiSMon Spcciit>>LOp\




ICS      --   Intci lercucc Chock Sample




ICV      --   Initial Calibration Verification




1DL      --   Instrument Detection Limit




LCS      -   Laboratory Control Sample
                                               258
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                                       APPENDIX VII
       SUMMARY OF COMMON LABORATORY CONTAMINANTS, CONCENTRATION
                 REQUIREMENTS. AND  RISK  ASSESSMENT IMPLICATIONS


      Appendix VII  lists common oiganic  laboratory contaminants  that may  appear  in  blanks
 1 he  purpose ot this appendix is to inform  ihe  reader of chemicals  that may appeal  in anahse-^
but may not  be present at the site.  The implications for risk assessment aie included
Common Laborator>
  Contaminants
Concentration Requirements
Risk Assessment Implications
Target Compound

Methylene Chloride
Acetone
Toluene
Sample concentrations less than
10X that detected  in method
blanks will be reported as
undetected.
Sample concentrations less than
10.X that detected in  method
blanks will be reported as
undetected.
Sample concentrations less than
10X that detected in  method
blanks will be reported as
undetected.
Include analyte if
concentration  is greater than
10X blank.
Include analyte if
concentration  is less than 10X
greater than blank
concentration  and multiple
chlorinated analytes are
detected.
Exclude analyte in all other
situations.

Include analyte  if
concentration  is greater  than
10X blank.
Include analyte  if
concentration  is less than 10X
greater than blank
concentration  and multiple
ketones aie  detected
Exclude analyte in al! ether
situations.

Include analyte  if
concentration  is greater  than
10X blank.
Include analyte  if
concentration  is less than 10X
greater than blank
concentration  and multiple
aromatic or fuel  hydrocarbons
are detected.
Exclude analyte in all other
situations
                                              259
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                                AIM'F.NWX \ II (CONTINUCD)
C'omnioii Laboiatoi\
  Co ma mi nan ts
Concenti at ion Requuements
Risk Assessment Implications     '
1- Bum no no (me tin i
etin Iketoiie)
Sample concentrations less than
10X  that detected m method
blanks will be reported as
undetected.
Include anal\ te if
concentiatio'n  is greater than
10X blank.
Include anah te if
concentration  is less than  10X
greater than blank
concentration  and mutiple
ketones are detected.
Tentatively  Identified
     Compounds

Carbon  dioxide
Not -eported if present  in the
method blank.
Exclude ana!\ te in  all
situations.
Dieth\ 1 ether
Not reported if present in the
method.
Exclude analyte in all
situations.
Hexanes
Freons (i.e .  1,1.2-
tnchloro- 1.2.2-
t r i f 1 u r oe t h a n e,  f 1 uo r o t r i -
chlorome thane)
Not reported if present in the
method blank.
Not reported if present in  the
method blank.
Exclude if analyte
concentration not 10X method
blank.
Exclude if analyte
concentration not 10X field
blank (EPA definition).
Exclude if sample not anahzed
v, itliin  7 da> >

Exclude if analyte
concentration not 10X method
blank.
Exclude if analyte
concentration not 10X field
blank (EPA  definition)
Exclude if sample not analysed
within  7 da\s.
                                              260
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                                M'l'f M)l\ N II ((ONI IM  I l>!
th> i!u \;, 1) phthalate. di
:-'>ct\ I phthalate)
\"ot reported it
method blank
                                                    in the
                                                                  Include anal', ;.• if
                                                                   • i'K -'Hi! ail' 'i1  ' ^  gl C.'tei t1' :!'
                                                                  I OX blank
                                                                  H \ciudo anai te  in all  .ulij!
                                                                  situations
Exclude  if hexane
concentration not 10X  method
blank.
Exclude  if hexane
c o nee n t ra t i o n n o t 10 X  f i e I d
blank (EPA definition^
Exclude  if sample not analyzed
within 7 da\s
    ieportecl if p'e^-jpt n
    tV,j blank.
                                                                  Include anah te if
                                                                  concentration is greater than
                                                                  10X  Dlank
                                                                  Include anal\ te if
                                                                  Create: than blank
                                                                  concentiation and mutiple
                                                                  ketones are detected
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