EPA
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                    Wasnington DC 20460
                       540G87003
          Superfund
Data Quality
Objectives for
Remedial Response
Activities RECEIVED
              ,£8211989
D eve lop nrent
                              Agency
                         U.S. Environmental Protection

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                                                          EPA/540/G-87/003 \/
                                                  (OSVVER Directive 9355.0-7B)
                                                                March 1987
*                              DATA QUALITY OBJECTIVES

3                         FOR REMEDIAL RESPONSE ACTIVITIES
• t
i4                                   Development Process
                                       Prepared for:

                         Office of Emergency and Remedial Response
                                           and
                            Office of Waste Programs Enforcement
                        Office of Solid Waste and Emergency Response

                            U.S. Environmental  Protection Agency
                                  Washington, DC  20460
                                       Prepared by:

                             CDM Federal Programs Corporation
                            7611 Little River Turnpike, Suite 104
                                   Annandale,  VA  22003
                                EPA  Contract No. 68-01-6939
                                        March 1987

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                      NOTICE

This document has been reviewed in accordance with
U.S. Environmental Protection Agency policy and
approved for publication.   Mention of trade names
or commercial products does not constitute endorse-
ment or recommendation for use.

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                                          PREFACE

This document. Data Quality Objectives For Remedial Activities (Development Process), guides the
user through the process of developing data quality objectives (DQOs) for site-specific remedial
activities.  Remedial response activities include remedial investigations (RI). feasibility studies
(FS). remedial design (RD). and remedial action (RA). This guidance manual  should be used in
conjunction  with the Data Quality Objectives For Remedial Response Activities  (Example Scenario -
RI/FS Activities at a Site With Contaminated Soils And Ground Water) which provides an outline of
how the DQO process is applied to a hypothetical site situation.

These guidance documents will be updated in the future to focus on quantification of DQO's and other
statistical issues.

This is one of a series of guidance documents prepared in accordance with the National Oil and
Hazardous Substance Pollution Contingency Plan (NCP) final rule, published in the Federal Register
November 20, 1985 and effective February 18.  1986.  These guidance documents will be updated in the
near future to be consistent with provisions of the Superfund Amendments and Reauthorization Act
(SARA) and the new  NCP.  The guidance document series includes the following titles:

     •   Guidance on Remedial Investigations Under CERCLA (EPA 540/G-85/002)

     t   Guidance on Feasibility Studies Under CERCLA (EPA 540/G-85/003)

     •   Superfund Remedial Design and Remedial Action Guidance (OSWER Directive 9355.0-4A)

     •   Compendium of Field Operations Methods (planned June 1987)

     •   Superfund Public Health Evaluation Manual  (OSWER Directive 9285.4-1)

     0   Superfund Exposure Assessment Manual (OSWER Directive 9285.5-1)

Collectively, these documents provide guidance for the development and performance of technically
sound and cost-effective remedial response activities which will support the program goals of both
the Office of Emergency and Remedial Response (OERR)  and the Office of Waste Programs Enforcement
(OWPE).  These documents are also available for use  by state agencies and private parties conducting
remedial response activities to ensure consistency with the intent of CERCLA and SARA.
                                              in

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                             TABLE OF CONTENTS

Section

1.0  INTRODUCTION                                                     1-1
     1.1   PURPOSE                                                      1-1
     1.2   DATA QUALITY OBJECTIVE POLICY BACKGROUND                    1 -2
     1.3   FORMAT                                                       1-2

2.0  DATA QUALITY OBJECTIVE DEVELOPMENT PROCESS                       2-1

     2.1   DQO STAGES                                                   2-1
          2.1.1  STAGE 1 - IDENTIFY DECISION TYPES                         2-1
          2.1.2  STAGE 2 - IDENTIFY DATA USES/NEEDS                       2-1
          2.1.3  STAGE 3 - DESIGN DATA COLLECTION PROGRAM               2-1
     2.2   RI/FS PROCESS                                                  2-1
          2.2.1  GENERAL APPROACH                                      2-3
          2.2.2  PHASED RI/FS APPROACH                                   2-3
     2.3   REMEDIAL DESIGN                                              2-3
     2.4   REMEDIAL ACTION                                              2-5
     2.5   DATA QUALITY OBJECTIVES DOCUMENTATION                      2-5
     2.6   REFERENCES                                                   2-5

3.0  RI/FS DQO STAGE 1 - IDENTIFY DECISION TYPES                          3-1

     3.1   IDENTIFY AND INVOLVE DATA USERS                              3-1
          3.1.1  DECISION MAKER'S ROLE                                   3-1
          3.1.2  DATA USERS' ROLE                                       3-3
     3.2   EVALUATE AVAILABLE INFORMATION                              3-3
          3.2.1  DESCRIBE CURRENT SITUATION                             3-3
          3.2.2  REVIEW AVAILABLE DATA                                  3-5
          3.2.3  ASSESS ADEQUACY OF DATA                                3-6
     3.3   DEVELOP CONCEPTUAL MODEL                                   3-6
          3.3.1  EVALUATION OF THE CONCEPTUAL MODEL                   3-6
          3.3.2  COMPUTER MODELS                                       3-9
     3.4   SPECIFY OBJECTIVES/DECISIONS                                   3-10
          3.4.1  OBJECTIVES                                              3-10
          3.4.2  DECISION TYPES                                          3-10
     3.5   REFERENCES                                                   3-12

4.0  RI/FS DQO STAGE 2 IDENTIFY DATA USES/NEEDS                          4-1

     4.1   IDENTIFY DATA USES                                           4-3
          4.1.1  DATA USE CATEGORIES                                    4-3
          4.1.2  RI/FS USES                                               4-7
     4.2   IDENTIFY DATA TYPES                                           4-7
     4.3   IDENTIFY DATA QUALITY  NEEDS                                  4-9
          4.3.1  DATA QUALITY FACTORS                                   4-9
          4.3.2  COST ANALYSIS OF ALTERNATIVES                           4-13
     4.4   IDENTIFY DATA QUANTITY NEEDS                                 4-13
     4.5   EVALUATE SAMPLING AND ANALYSIS OPTIONS                      4-14

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                             TABLE OF CONTENTS
                                  (Continued)
          4.5.1  SAMPLING AND ANALYSIS APPROACH (PHASING)               4-14
          4.5.2  RESOURCE CONSIDERATIONS                               4-17
     4.6   REVIEW PARCC PARAMETER INFORMATION                         4-17
          4.6.1  PRECISION                                               4-17
          4.6.2  ACCURACY                                               4-18
          4.6.3  REPRESENTATIVENESS                                     4-18
          4.6.4  COMPLETENESS                                           4-19
          4.6.5  COMPARABILITY                                          4-19
     4.7   UTILIZING PARCC PARAMETER INFORMATION                       4-19
     4.8   REFERENCES                                                   4-20

5.0   RI/FS DQO STAGE 3 - DESIGN DATA COLLECTION PROGRAM                 5-1

     5.1   ASSEMBLE DATA COLLECTION  COMPONENTS                        5-1
     5.2   DEVELOP DATA COLLECTION DOCUMENTATION                     5-1
          5.2.1  SAMPLING AND ANALYSIS PLANS                            5-1
          5.2.2  WORK PLANS                                             5-4
          5.2.3  ENFORCEMENT CONCERNS                                 5-4
     5.3   REFERENCES                                                   5-5

6.0   REMEDIAL DESIGN (reserved)

7.0   REMEDIAL ACTION (reserved)

APPENDIX A  STATISTICAL CONSIDERATIONS
APPENDIX B  ANALYTICAL CONSIDERATIONS
APPENDIX C  SAMPLING CONSIDERATIONS
APPENDIX D  REVIEW OF QAMS DQO CHECKLIST
APPENDIX E  POTENTIALLY APPLICABLE OR RELEVANT AND APPROPRIATE REQUIREMENTS
APPENDIX F  HISTORICAL PRECISION AND  ACCURACY DATA CLASSIFIED BY MEDIA BY
            ANALYTICAL LEVEL
APPENDIX G  RCRA APPENDIX VIII CLP HSL COMPARISON
APPENDIX H  CONTRACT REQUIRED DETECTION LIMITS FOR HSL ANALYSES USING CLP IFB
            PROCEDURES

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                                      LIST  OF  FIGURES
Figure
2-1   DQO Three-Stage Process                                                            2-2
2-2   Phased RI/FS Approach and the DQO Process                                          2-4
3-1   DQO Stage 1  Elements                                                               3-2
3-2   Decision Makers Data Users Interaction                                                3-4
3-3   Elements of a Conceptual Evaluation Model                                            3-7
3-4   Example Conceptual Model Illustration                                                3-8
3-5   Relationship of Risk and Data Quality/Quantity                                        3-13
4-1   DQO Stage 2 Elements                                                               4-2
4-2   Sample Type Specification Logic Diagram                                              4-8
4-3   Integration of Analytical Support Levels                                               4-16
5-1   Stage 3 Elements Design Data Collection Program                                      5-2
                                      LIST OF TABLES
Table

3-1    Generic RI/FS Objectives                                                             3-11
4-1    Data Uses                                                                            4-4
4-2    DQO Summary Form                                                                 4-5
4-3    Summary of Analytical Levels                                                         4-11
4-4    Appropriate Analytical Levels                                                          4-12
5-1    Quality Assurance Project Plan Elements                                                5-3

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                                   LIST OF ACRONYMS

ARAR        Applicable or Relevant and Appropriate Requirements
ATSDR       Agency for Toxic Substances and Disease Registry
CERCLA      Comprehensive Environmental Response. Compensation, and Liability Act of 1980 (Superfund)
CDC          Centers for Disease Control
CLP          Contract Laboratory Program
COE          U.S. Army Corps of Engineers
DQO         Data Quality Objective
EMSL-LV     Environmental Monitoring and Support Laboratory - Las Vegas
ESD          Environmental Services Division (of EPA)
FIT          Field Investigation Team
FS           Feasibility Study
GC/MS       Gas Chromatograph/Mass Spectrograph
HSL          Hazardous Substance List
MDL         Method Detection Limit
NBS          National Bureau of Standards
NCP          National Contingency Plan
NEIC         National Enforcement Investigation Center
NPL          National Priorities List
ORC          Office of Regional Counsel
PARCC       Precision. Accuracy. Representativeness, Completeness, Comparability
PRP          Potentially Responsible Party
QAMS        Quality Assurance Management Staff
QAPP         Quality Assurance Program Plan
QAPjP        Quality Assurance Project Plan
QA/QC       Quality Assurance/Quality Control
RA           Remedial Action
RAS          Routine Analytical Service
RD           Remedial Design
RI            Remedial Investigation
ROD          Record of Decision
RPM          Remedial Project Manager
RSCC         Regional Sample Control Center
S&A          Sampling and Analysis
SARA         Superfund Amendments and Reauthorization Act of 1986
SAS          Special Analytical Service
SMO          Sample Management Office
SRM          Standard Reference Material
TAC          Technical Advisory Committee
TAT          Technical Assistance Team
TIC           Tentatively Identified Compounds
TSCA         Toxic Substances Control Act
VOC          Volatile Organic Compounds

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                                  ACKNOWLEDGMENTS
This document was developed for the Office of Solid Waste and Emergency Response (OSWER) by a task
force composed of the following individuals:

      Randall  Kaltreider (Hazardous Site Control Division.  OERR)
      Linda Boornazian (CERCLA Enforcement Division, OWPE)
      Andrew Szilagyi (CDM Federal Programs Corporation)
      Jeffery Sullivan (Camp Dresser & McKee Inc.)
      RoseMary Ellersick (CDM Federal Programs Corporation)
      Tom Pedersen (Camp Dresser & McKee Inc.)
      James Occhialini (Camp Dresser & McKee Inc.)
      Dennis Gagne (Region 1, Waste Management Division)
      Bill CoakJey (Region 2, Environmental Services Division)
      Edward  Shoener (Region 3, Hazardous Waste Management Division)
      Diane Moshman (Region 5, Waste Management Division)
      Steve Lemons (Region 6, Environmental Services  Division)
      Bill Bunn  (Region 7, Environmental  Services Division)
      Mike Carter (Hazardous Response Support Division.  OERR)
      Duane Geuder (Hazardous Response  Support Division, OERR)
      Michael Kosakowski (CERCLA Enforcement Division, OWPE)
      Dennisse Beauchamp (CERCLA Enforcement Division, OWPE)
      Gary Liberson (Lloyd Associates)
      Craig Zamuda (Policy Analysis Staff, OERR)
      John Warren (Statistical Policy Branch. OPPE)
      Wendy Sydow (CDM Federal Programs Corporation)
      Paul Clay  (NUS Corporation)

Helpful suggestions and comments on the draft document were provided by the following as well as
other EPA and contractor staff.

      David F. Doyle (Camp Dresser & McKee Inc.)
      Dean Neptune (QAMS)
      Gene Brantly (RTI)
      Daniel Michael (RTI)

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                                       1.0  INTRODUCTION
 Data quality objectives (DQOs) are qualitative and quantitative statements which specify the quality
 of the  data required to support Agency decisions during remedial response activities.  DQOs are
 determined based on the end uses of the data to be collected.  For example, depending on the project
 phase, sufficient data may have to be collected  to characterize the site, evaluate remedial
 alternatives,  determine design criteria, or monitor site conditions and/or remedial action
 effectiveness. DQOs are applicable to all data  collection activities,  including those performed for
 preliminary assessments/site investigations (PA/SI), remedial investigations (RI), feasibility
 studies (FS), remedial design (RD), and remedial actions (RA).  The level of detail and data quality
 needed will vary based on the intended  use of the data. The variability of site characteristics
 makes it impossible to apply a generic set of DQOs to all CERCLA activities, however investigators
 are expected to take advantage of previous experience and data collected for similar sites.

 DQOs are established prior to data collection and are not considered a separate deliverable.
 Rather, the DQO development process is integrated with the project planning process, and the results
 are incorporated into the  sampling and analysis (S&A) plan, quality assurance project plan (QAPjP)
 and, in general  terms, into the work plan for the site.  The DQO process results in a well thought
 out sampling and analysis plan which details the chosen sampling and analysis option and statements
 of the  confidence in decisions made during the remedial process.  Confidence statements  are possible
 through the application of statistical  techniques to the data.

 Data quality objectives should be specified  for each data collection activity associated with a
 remedial response.  The majority of these data collection activities take place during a remedial
 investigation (RI) but additional data needs may be identified during the feasibility study (FS).
 remedial design (RD), and remedial action (RA).

 All investigation activities should  be conducted  and documented in a manner  that ensures that
 sufficient  data of known quality are collected to support sound decisions concerning remedial action
 selection.   This  applies to  fund-lead, federal  or state enforcement-lead, and potentially
 responsible party-lead projects.

 1.1    PURPOSE

 The purpose of this guidance document is to identify the framework and  process by which DQOs  are
 developed and the individuals  responsible for development of DQOs.  This document  is intended to
 guide the  user through the process of DQO development.  Each site will have a unique history, data
 availability, and other factors. Therefore, a  unique set of DQOs  must be developed for each site.

 This DQO guidance acts as a supplement to existing remedial program guidance by providing procedures
 for determining  a quantifiable degree of certainty which can be used in making site-specific
 decisions.  In actual practice to date, projects conducted under CERCLA have complied with the
 intent of the DQO process. DQOs have been incorporated as parts of sampling and analytical  plans,
 quality assurance project plans or work  plans.  The purpose of this guidance is to provide a more
 formal approach to integration of DQO development with S&A plan development and  to improve the
 overall quality and cost effectiveness of data collection and analysis activities.

 This guidance focuses specifically on the DQO process.  RI/FS activities (planning and
 implementation) are presented only as a framework for DQOs and  as such are not fully developed in
accordance with RI/FS guidance.  Similarly,  this document  is not meant to be guidance on overall
development of sampling and analysis plans,  quality assurance project plans,  or work  plans.  Future
documents will emphasize statistical considerations in the DQO process.

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 1.2    DATA QUALITY OBJECTIVE POLICY BACKGROUND

 Mr. Alvin Aim, then Deputy Administrator of the EPA, in his memorandum of May 24,  1984 to the
 Assistant Administrators (AAs), stated that one of the most important steps in assuring the quality
 of environmental data is development of DQOs.  He requested active participation of the AAs in the
 development of DQOs during the stages in which policy and guidance is crucial, and asked  for
 identification of significant ongoing environmental data collection activities.  The Quality
 Assurance Management Staff (QAMS) issued guidance on development of DQOs in October 1984.  A
 checklist for DQO review was then issued in a memorandum from Stan Blacker on April  3, 1985.
 Appendix D includes a comparison of this checklist with this DQO guidance document. Additional
 guidance on the development of DQOs, specifically related to Stages  1 and 2 of the process, was
 provided in a draft document issued by QAMS March  17, 1986.

 The approach to developing and implementing DQOs for remedial response activities has  been
 established by a DQO Task Force comprising technical personnel from EPA Headquarters (OERR and
 OWPE), Regions 1, 2, 3, 5, 6 and 7; and EPA remedial contractors. The methodology used by the DQO
 Task Force was to apply the guidance provided by QAMS to the remedial response process. The efforts
 of the Task Force included identifying the elements of the DQO process within existing planning
 documents and organizing them into a formal implementation approach. The DQO development process
 presented in this document is based on the best available  information but may be revised as
 additional information becomes available.

 1.3    FORMAT

 This document includes the following sections:

         1.0    Introduction

         2.0    DQO Development Process - the process for developing DQOs and how DQO development
                relates to the remedial response program.

         3.0    RI/FS-DQO Stage 1  - identification and involvement of data users, development of a
                conceptual site model and definition of decision types that will be made during the
                RI/FS process.

         4.0    RI/FS-DQO Stage 2  - determining data needs and uses, establishing criteria for
                decisions, and identifying and selecting analytical and sampling options.

         5.0    RI/FS-DQO Stage 3  - assembling sampling and analytical components into an overall
                sampling design  and  documentation required  for  a sampling and  analytical program.

         6.0    Remedial Design - Reserved

         7.0    Remedial Action - Reserved

Appendix A    Statistical Considerations  - provides a description of some statistical approaches which
               may be applied during a remedial action program.

Appendix B    Analytical Considerations - describes the  various  options that are available for
               analyzing samples from uncontrolled hazardous waste sites.

Appendix C    Sampling Considerations  - provides discussion of sampling rationale related  to the DQO
               development process.

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Additional appendices to the DQO document provide information on the QAMs DQO checklist, established
criteria for RI/FS activities,  and CLP performance criteria.

Sections of this manual are applicable to specific components of the remedial response process.
Sections  1 and 2 are applicable to all remedial response activities; Sections 3. 4 and 5 apply
specifically to the RI/FS process.  Sections 6 and 7 are forthcoming and will provide guidance for
the application of DQOs to Remedial Design Activities (Section 6) and to Remedial Actions (Section
7).

A  companion to this guidance is the Data Quality Objectives For Remedial Response Activities Example
Scenario) (EPA  1987) which provides an example case study of implementation of the DQO process.

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                2.0   DATA QUALITY OBJECTIVE  DEVELOPMENT  PROCESS

 Data quality objectives are identified during  project scoping and development of sampling and analysis
 plans.  DQOs are established to ensure that the data collected are sufficient and of adequate quality for
 their intended uses. Data collected and analyzed in conformance with the DQO process described  in this
 document can be used in assessing the uncertainty  associated with decisions related to remedial
 response.

 2.1    DQO STAGES

 Data quality objectives are developed through a three-stage process, as illustrated in Figure 2-1.
 Although the three stages are discussed sequentially in this guidance document, they should be undertaken
 in an interactive and iterative manner,  whereby all the DQO elements are continually reviewed and
 reevaluated.  As such, the DQO process is integrated with development of the S&A plan and is  revised as
 needed based upon the results of each data collection activity.  This process is  illustrated in the
 example document.

 2.1.1    STAGE  1 - IDENTIFY DECISION TYPES

 Stage 1 of the DQO process defines the types of decisions which will be made  regarding site remediation
 through identifying data users, evaluating available data, developing a conceptual model, and specifying
 objectives for the project.  Available information is compiled and analyzed to develop a conceptual model
 of the site.  This model describes suspected  sources, contaminant pathways, and potential receptors.   The
 model facilitates identification of decisions which must be  made and deficiencies in the existing
 information. Stage 1 results  in the specification of the decision making process and identification of
 why new data are needed.

 2.1.2    STAGE 2 - IDENTIFY DATA USES/NEEDS

 Stage 2 stipulates criteria for  determining data adequacy.   This stage involves specifying the data
 necessary to meet the objectives set in Stage  1.  Stage 2 includes selection of the sampling approaches
 and the analytical options for the site, including evaluation of multiple-option approaches to effect
 more timely or cost-effective data collection and evaluation.

 2.1.3    STAGE 3 - DESIGN DATA COLLECTION PROGRAM

 Stage 3 results in the specification  of the methods by which data of acceptable quality and quantity will
 be obtained  to make decisions.  This information is provided in documents such as the S&A plan, and is
 summarized in the work plan.

 2.2    RI/FS PROCESS

 2.2.1    GENERAL APPROACH

 The overall objective of an RI/FS is to determine the nature and extent of the threat posed by the
 release of hazardous substances and to evaluate  proposed remedies.  The ultimate goal is to select a
cost-effective remedial alternative which  mitigates threats to and provides protection of public  health.
welfare, and the environment, consistent with the NCP.

  The term, uncertainty,  is used as a catchall term  to describe the likelihood of all types of errors
  associated with a particular decision.  There is not a precise statistical definition of the term since
  (lie precise definition varies from decision  to decision: however,  it can be stated that uncertainty is
  always a function of the distribution of the statistics used in making the decision.

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              STAGE 1
      IDENTIFY DECISION TYPES
     • IDENTIFY & INVOLVE DATA USERS
     • EVALUATE AVAILABLE DATA
     • DEVELOP CONCEPTUAL MODEL
     • SPECIFY OBJECTIVES/DECISIONS
               I
           STAGE 2
  IDENTIFY DATA USES/NEEDS
  • IDENTIFY DATA USES
  • IDENTIFY DATA TYPES
  • IDENTIFY DATA QUALITY NEEDS
  . IDENTIFY DATA QUANTITY NEEDS
  • EVALUATE SAMPLING/ANALYSIS OPTIONS
  • REVIEW PARCC PARAMETERS
             STAGE 3
 DESIGN DATA COLLECTION PROGRAM
 • ASSEMBLE DATA COLLECTION COMPONENTS
 • DEVELOP DATA COLLECTION DOCUMENTATION
          FIGURE  2-1
DQO THREE-STAGE PROCESS

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RIs consist of data gathering activities undertaken  to determine the degree and extent of contamination
at a site.  The data are  used  in the identification, screening, and evaluation  of remedial alternatives.
The objective of the RJ is to  collect the  necessary  data to determine the distribution and migration of
contaminants; identify cleanup criteria;  and identify and support the  remedial alternative evaluation.

Feasibility studies entail development, screening, and evaluation of remedial alternatives.  The
objectives of the FS  are to develop and evaluate the remedial action alternatives with respect to
protection of public  health and the environment, compliance with ARARs, and reduction of mobility and/or
toxicity.  In order to ensure that adequate and sufficient data are collected for performance of the FS,
site managers must continually coordinate the evaluation and re-evaluation of data collected during the
RI.

The RI/FS typically  addresses data collection and site characterization from  the perspective of
contaminant source and contaminant migration pathways.  Once pathways are established and human and
environmental receptors are identified, further data collection efforts can be directed toward evaluating
the potential impact  upon  receptors, and for use in evaluating potential remedial technologies and
alternatives.

Through the process of developing DQOs, a series of statements and definitions of the types, quantity and
quality of data required for specific uses will be developed.

2.2.2    PHASED RI/FS APPROACH

The amount and quality of data required to support selection of a remedial alternative will vary by site.
In many situations it may not be possible to identify all data needs during the  initial scoping
activities. Rather, data needs will  become more clearly defined as additional data are obtained and
evaluated.  By separating  the remedial investigation into phases, data can be collected and evaluated
sequentially,  with a refinement or  redefinition of data collection needs at the completion of each phase.
Figure 2-2 illustrates the  phased RI/FS  approach.

It is seldom possible to identify fully all the data needed to complete an RI/FS at the outset of the
scoping process.  For complex sites, the phased approach provides more control of investigative
activities than a singular sampling/analysis event.  Applying the DQO process to a phased investigation
improves the usability of the  data and the cost effectiveness of the investigation.

2.3    REMEDIAL  DESIGN

Following selection of a remedy (based  on the RI/FS) and approval of the Record of Decision (ROD) or
Enforcement Decision Document (EDD), design activities are initiated. Additional field data collection
activities may be required during the remedial design phase to  supplement the technical data available
from the RI/FS.

Cost estimates should be refined to the  + 15/-IO percent range based on data collected during the RD (EPA
1986).  The type of data required  during the RD varies depending on the type of remedies.  For soil
excavation, a good estimate of contaminated soil volume is needed: for treatment options, a refined
estimate  of the physical/chemical waste  character may be  required.  If the RI  is carefully planned with
accurate  foresight of FS and  RD data needs, sampling activities during the RD phase should be minimized.
The practical application of DQOs to RD activities will be described in  future updates to this document.

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INITIATION OF
  RI/FS
                          FIGURE  2-2
         PHASED  RI/FS APPROACH AND THE DQO  PROCESS
                               2-4

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2.4   REMEDIAL ACTION

RA activities entail the actual implementation of the alternative selected in the ROD/HDD.  As with the
RD, additional data collection activities may have to be conducted during the RA. and the DQO process
utilized.  Data collected during  the RA are used to evaluate the progress of the RA and to verify that
the set performance criteria were achieved.

2.5   DATA QUALITY OBJECTIVES DOCUMENTATION

The DQO development process is initiated during project scoping and is completed  in conjunction  with the
development of an S&A plan for each project phase. The three  stages of the DQO  development process are
interactive in  nature.  As additional details regarding the site are discovered, the decisions which will
be made during the project are  refined.  This allows for further specification of data needs and for
design of the  data collection program.

As the DQO process continues, the scoping of  the project will become refined.  Additional decision types
may be needed (Stage  1), or data collection  activities may be modified (Stage 2 and Stage 3) based on
evaluation of data (Stage 1).

Development  of DQOs in a formal manner ensures that the appropriate data are obtained to meet the
objectives  of the RI/FS, RD or  RA.  Documentation of DQOs can be provided primarily in the S&A plan
(which includes QAPjP elements), and summarized in the work plan.

2.6   REFERENCES

U.S. Environmental Protection  Agency.  1985a.  Guidance on Remedial Investigations Under CERCLA.
   Office of Emergency and Remedial Response, Office of Waste Programs Enforcement, Office of Solid
   Waste and  Emergency Response, Washington, DC.  Office of Research and Development. Cincinnati,
   Ohio.   EPA/540/G-85/002.  June.

	.  1985b.  Guidance on Feasibility Studies Under CERCLA.  Office of Emergency and Remedial
   Response,  Office of Waste Programs Enforcement, Office of  Solid Waste and Emergency Response,
   Washington, DC.  Office of  Research and Development, Cincinnati, Ohio.  EPA/540/G-85/003.  June.

	.  1986.  Superfund Remedial Design and Remedial Action Guidance, Office of Emergency and
   Remedial Response.  OSWER Directive 9355.0-4A. June.
                                             2-5

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                  3.0  RI/FS DQO  STAGE  1 - IDENTIFY DECISION TYPES

 Stage 1 of the DQO process  is undertaken to identify the individuals responsible for decisions, to
 identify and involve data users, and to define the types of decisions  which will be made as part of each
 RI/FS.  Decisions are made  following evaluation of data at various  points during the RI/FS.  The general
 decision types are identified early in Stage 1 to ensure that an investigative approach which will yield
 data sufficient to support the decisions.

 The major elements of Stage 1 include:

      •    Identifying and involving data users

      •    Evaluating available information

      •    Developing a conceptual model

      •    Specifing RI/FS objectives and decisions

 Stage 1  of the DQO process  is an inherent part of the project scoping process.  The thought process by
 which a work plan is developed naturally encompasses the Stage 1 DQO elements. Figure 3-1 illustrates
 the Stage 1 elements.  Although the  elements of Stage I can  be thought of as distinct steps, they are a
 continuous thought process.

 3.1    IDENTIFY AND INVOLVE DATA USERS

 DQO development requires involving the data  users during planning of remedial activities.  Because of the
 interdisciplinary nature of remedial activities, it is important that the appropriate technical expertise
 is identified and obtained for the DQO development process.

 3.1.1     DECISION MAKER'S ROLE

 The key RI/FS decision is  remedy selection (i.e., ROD/EDD signature).  For the majority of RI/FS
 projects, remedy selection is  the responsibility of the Regional Administrator (RA).  Program management
 responsibilities are delegated  to the Waste Management Division Director and managers, with project
 specific management and oversight assigned to Remedial  Project Managers (RPMs).  Senior management  staff
 are likely to be involved primarily in scoping of the RI/FS and review and approval of the  decision
 document.

 The EPA RPM is the designated decision  maker for the DQO development process.  In this role, the RPM is
 responsible for coordinating the DQO development process; and overseeing remedial  contractors, state
 officials, or private parties conducting the RI/FS.

 For federal lead projects, day-to-day decision making  becomes the responsibility of the remedial
contractor's site manager under the direction of the RPM.  Remedial contractors incorporate technical
 review and oversight by senior level  management and technical experts into their internal scoping and
 project planning process.  For state lead or private party lead projects,  the state project manager  or
 private party project manager will be a key decision maker assisted by their contractor's site manager.
The RPM should be in close contact with  the federal remedial contractor, state  project manager,  or
private party project manager to ensure that project activities are proceeding on  track and are
consistent with  EPA policy  and guidance.
                                               3-1

-------

-------
                           IDENTIFY & INVOLVE DATA USERS
   EVALUATE
AVAILABLE DATA
DEVELOP CONCEPTUAL MODEL

- CONTAMINANT SOURCES
- MIGRATION PATHWAYS
- POTENTIAL RECEPTORS
- CONTAMINANTS OF CONCERN
                           SPECIFY OBJECTIVES/DECISIONS
                                 FIGURE 3-1
                          DQO STAGE 1 ELEMENTS

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3.1.2    DATA USERS'ROLE

The interactions of decision makers and various data users during the DQO development process is
illustrated in Figure  3-2  and discussed below.

Primary Data Users

Primary data users are those individuals involved in ongoing RI/FS  activities.  These activities include
RI/FS planning and  implementation, project management and oversight, site specific decision making, and
DQO development.  For federal lead projects, this includes the RPM and the remedial contractor's site
manager and staff.  For state lead or private party lead projects, this includes the state or private
party manager and their contractor's site manager/staff, along with the RPM.

The contractor site manager must identify the appropriate contractor technical staff based upon the
overall problems at the site. For example, if ground water contamination is a concern,
geologists/hydrogeologists and water supply/treatment engineers may be involved, at a minimum.  If
surface water contamination is a concern, aquatic biologists, limnologists and water resource engineers
may be involved.   Analytical chemists can assist in specifying the types of analyses to be used and  the
limitations of the particular techniques or  methods.  Individuals familiar with  the interactions  of
chemicals in the environment, such as geochemists, soil scientists, and chemists, must also be involved
to assess environmental impacts.  Geostatisticians can provide assistance in  evaluating  spatially
distributed data.  Toxicologists and  individuals familiar with risk assessments  should also be involved
early in the  scoping  process to ensure that appropriate consideration is given to potential migration
pathways, receptors and contaminants of concern.

Secondary Data Users

Secondary data users rely on RI/FS outputs to support their activities.   Secondary data users provide
input to the  decision maker and primary data users by communicating generic or site specific data  needs.
Depending on project lead, secondary data users may include the state, enforcement personnel, ATSDR, U.S.
Army Corps of Engineers, and others. The level of involvement of secondary data users will vary
according to site specific requirements, program lead, or Agency policy.

Technical Support and Project Review/Audit

At the request of the RPM, technical specialists  may provide support related to project specific sampling
and analytical activities, regulatory requirements, etc.  Project review  and audit personnel such as  ESD,
Office of Regional Counsel, and EPA HQ help ensure QA program integrity  and compliance  with  program
policy.

3.2    EVALUATE  AVAILABLE INFORMATION

Available information is  reviewed and evaluated  as the initial step in the RI/FS process.  This review
provides the foundation for additional on-site activities and serves as the database for RI/FS scoping.
The review  and an initial site visit are used for a preliminary interpretation of site conditions.

3.2.1    DESCRIBE CURRENT SITUATION

The initial data review should  be as thorough and accurate as possible.  Information should be obtained
from EPA technical  and  enforcement files, state/local regulatory agency files. USGS files, and other
relevant sources.   Files from potentially responsible parties (PRPs)  should also be referred  to when
available.  A detailed list of potential data sources is  contained in Section 2.0 of the Guidance for
Remedial Investigations Under CERCLA (EPA 1985a).

-------
 DIVISION  MGMT
 • PROGRAM & PROJECT
   OVERSIGHT
 • ROD/SETTLEMENT
   RECOMMENDATIONS TO RA
                          RA/AA
                          • ROD/SETTLEMENT
                            DECISIONS
 ENFORCEMENT
 • NEGOTIATIONS
 STATE     -4-
 • REMEDY
   CONCURRENCE
         DQO
    DECISION MAKER
         RPM
  PRIMARY DATA USERS

RPM
CONTRACTOR'S SITE MANAGER & STAFF
STATE PROJECT MANAGER
 (STATE LEAD PROJECT)
PRIVATE PARTY PROJECT MANAGER
 (PRIVATE PARTY LEAD)
        ATSDR
        • HEALTH
          ASSESSMENTS
                                                          CORPS

                                                        RD/RA ACTIVITIES
                                                        (FEDERAL LEAD PROJECT)
SECONDARY USERS
(ENF/STATE/ATSDR/CORPS)
• GENERIC DATA NEEDS
  ATSDR PAPER
  STATE STANDARDS
  CHAIN OF CUSTODY
• SITE SPECIFIC DATA NEEDS
  SPECIAL PATHWAY INFO
  PRP IDENTIFICATION
  RD/RA NEEDS
  TECHNICAL SUPPORT
  (ESD/OTHERS/TAC TEAM)
  • UPON REQUEST BY RPM
    SAMPLING/ANALYTICAL
    SUPPORT
    REGULATORY REQUIREMENTS
PROJ  REVIEW/AUDIT
(ESD/ORC/HQ)
• QA INTEGRITY
• COMPLIANCE WITH POLICY
                                          INPUT
                                    	  OUTPUT
                                  FIGURE  3-2
               DECISION MAKER DATA USERS INTERACTION
                                   3-4

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The preliminary data are confirmed by on-site observations.  The goals of the initial site inspection are
as follows:

     •   Utilizing field analytical procedures, obtain data on volatile chemical contaminants,
         radioactivity, and explosivity hazards to determine appropriate health and safety levels.

     •   Estimate if any conditions could pose an  imminent danger to public health

     •   Confirm the information contained in previous  documents.

     •   Record observable data missing in previous documents.

     •   Update site conditions if undocumented changes have occurred.

     •   Perform an inventory of possible off-site  sources of contamination.

     •   Obtain data such as location of access routes, sampling points and the site organization
         requirements for the field investigation.

Geophysical surveys, limited field screening, or limited field analysis  may be performed during the
initial site inspection.  This type of initial sampling may  help determine the variability of the media,
provide background  information, or determine if site conditions have changed.

3.2.2    REVIEW AVAILABLE DATA

For many sites, previous studies have provided useful information upon which further investigations can
be based. The quality of the data should be analyzed to determine its usability.  These evaluations
determine the uncertainty associated with the conclusions drawn from the data.

A number of factors relate to the quality of data and its adequacy for use in the RI/FS process,
including the following considerations:

     •   Age of the data

     •   Analytic methods used

     •   Detection limits of methods

     •   QA/QC procedures and documentation

Methods used for sample collection are as  important to consider as the methods used for sample analysis.
These considerations fall into two broad categories:  statistical and standard operating procedures
(SOPs). The statistical considerations relate to the representativeness of the data and the level of
confidence that may be placed in conclusions drawn from the data (confidence levels are discussed  in
Appendix A).   Following SOPs ensures sample integrity  and data comparability and reduces sampling  and
analytical error.  Typical issues to consider include the following:

     •    Sampling objective and approach

     •    Sample collection methods

     •    Chain of custody documentation

     •    Sample preservation techniques

-------
      •   Sample shipment methods

      •   Holding times                                                                                        |

 If limited or no information exists on  sample collection, preservation techniques or holding times, the
 data should be interpreted with caution.                                                                         I

 3.2.3     ASSESS ADEQUACY OF DATA

 The uncertainty associated with each data measurement activity should be considered when data are
 evaluated. Although data may be validated  analytically, the level of precision of a particular data
 point may not provide sufficient certainty for use in a decision.  (Precision and its use in decision
 making is discussed in Appendix A.)

 It is important to recognize the distinction between uncertainty associated with a measurement activity
 and uncertainty associated with a decision during development of DQOs. The uncertainty associated with a
 measurement activity is a function of the statistical  distribution of errors for each reported
 concentration value.  At a typical site, many measurement activities are performed and many data are
 obtained.  Decisions are made after analyzing and summarizing the data. The uncertainty associated with
 a decision is a function of the statistical distributions of the factors  (statistics)  which  were used in
 reaching the decision.  Assessment of data adequacy, then, has two steps.  The first step is data
 validation. The second step is determining  if the data is sufficient to reduce  the uncertainty
 surrounding  a decision to an acceptable level.

 Data validation identifies invalid data and qualifies the usability of the remaining data. The output of
 data validation  is qualitative or quantitative statements of data quality. Once the quality of
 individual  measurements are known, a compilation of all data points into a cohesive  statement regarding,
 for example, the areal extent of contamination can be made. Areas requiring remediation can then be
 delineated based on  specific action levels. The confidence associated with such a remediation decision
 incorporates  both the confidence in individual measurements as well as in the estimated area requiring
 remediation.  These types of confidence statements can only be made if a detailed statistical evaluation
 of the data is undertaken.  Details regarding establishment of criteria and action levels are discussed
 in Section 4.0 of this document.
                                                                                                               F

 3.3    DEVELOP CONCEPTUAL MODEL                                                                    ,

 Conceptual models describe a site and its environs and present hypotheses regarding the  contaminants
 present, their routes of migration, and their  potential impact on sensitive receptors.  The hypotheses                 ,
 are tested, refined and modified throughout  the RI/FS.  Figure 3-3 depicts the basic elements of a
 conceptual model for an uncontrolled hazardous waste site. The development of a conceptual model  for a           *•
 hypothetical site is presented in Section 3.4  of the Example Scenario document.

 3.3.1    EVALUATION OF THE CONCEPTUAL MODEL                                                     \
                                                                                                               t
The conceptual model should be detailed enough to address potential or suspected sources, types and                |
concentrations of contaminants, affected media,  rates and routes of  migration, and receptors.  Figure 3-4            •
presents an illustration which supports a conceptual  model.                                                       I
                                                                                                               I
                                                                                                               *

                                                                                                               "I
                                                                                                               i
                                               3-6

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                  SOURCE
                CONTAMINANTS
                CONCENTRATION
                TIME
                LOCATION
                               MEDIA
                               RATE OF MIGRATION
                               TIME
                               LOSS FUNCTIONS
                                 TYPE
                                 SENSITIVITY
                                 TIME
                                 CONCENTRATION
                                 NUMBER
HYPOTHESIS
TO BE
TESTED
• SOURCE EXISTS

• SOURCE CAN BE
 CONTAINED

> SOURCE CAN
 BE REMOVED
 AND DISPOSED

•SOURCE CAN
 BE TREATED
 PATHWAY EXISTS

 PATHWAY CAN
 BE INTERRUPTED

• PATHWAY CAN
 BE ELIMINATED
• RECEPTORS ARE NOT
 IMPACTED BY MIGRATION
 OF CONTAMINANTS

• RECEPTOR CAN
 BE RELOCATED

• INSTITUTIONAL  CONTROLS
 CAN BE APPLIED

• RECEPTORS CAN BE
 PROTECTED
                                               FIGURE 3-3
                        ELEMENTS OF  A CONCEPTUAL EVALUATION  MODEL

-------
     VOLATILIZATION
                     POTENTIAL SOURCES
SURFACE RUNOFF
LAGOON
                        PERCHED
                      WATER TABLE
          GLACIAL TILL
                                         DRUMS
                         CONTAMINATED
                            SOILS
                           UNCONFINED AQUIFER

                                              /   '/ x   X  ^
                     BEDROCK
                     FIGURE  3-4
            EXAMPLE CONCEPTUAL MODEL
                   ILLUSTRATION

-------
               The following are assessed during development of the conceptual model to determine appropriate remedial
               and/or removal  actions at a site:

                    •   Population, environmental, and welfare concerns at risk

                    •   Routes of exposure

                    •   Spatial distribution of contaminants

                    •   Atmospheric dispersion potential and proximity of targets

                    •   Amount, concentration, hazardous properties,  environmental  fate and form of the substance(s)
                        present

                    •   Hydrogeological  factors

                    •   Climate

                    •   Extent to which the source can be adequately identified and characterized

                    •   Potential for reuse,  recycling or treatment of substances at the site

                    •   Likelihood of future releases if the substances  remain on-site

                    •   Extent to which natural or man-made barriers  currently contain the substances and the
                        adequacy of the barriers

                    •   Assessment of the potential pathways of migration and a model of such

                    •   Extent to which the substances have migrated or are expected  to migrate from their source and
                        whether migration poses a threat to public health, welfare, or the  environment

                    •   Extent to which contamination levels exceed applicable or relevant and appropriate federal or
                        state requirements (ARARs) relating to public  health or environmental standards and criteria

,               Data evaluation  should  be  undertaken at the initiation of any remedial action program and at each point
?               within the program that additional data are obtained.  Additional data collected during the RI are used
               to expand the conceptual model and determine if sufficient data  of adequate  quality have been obtained to
=               address the issues of concern.
I
-               3.3.2    COMPUTER MODELS
!

               Common, but difficult, questions to be addressed during a remedial  action program deal with defining the
i               extent of contamination, setting action limits and establishing the acceptable likelihood of an incorrect
•               decision.  These types of questions generally require that data be evaluated utilizing tools such as
               ground water models, air quality models, and/or geostatistical methods.  Ground water models include
               several levels of analysis:  simple graphical techniques, analytical  solution techniques, and numerical
               solution techniques.  Using this broad definition of modeling, one of these techniques is almost always
               applied to examine a ground water  contamination problem.  Thus, the primary question becomes not when to
I               use modeling, but what level  of analysis is required to meet the objectives of the  study.

,               The role  of modeling must be evaluated with respect to the entire site investigation.  The  evaluation of
?               small sites with  relatively uniform geology may be accomplished by the use of simple analytical models.
                                                              3-9

-------
 Larger sites with complex stratigraphy, involving contamination in multiple layers and with variable
 aquifer parameters, can only be represented by a sophisticated numerical model.

 A common misconception about ground water modeling and geostatistical techniques is that they are applied
 only during the final stages of an  RI, after all tlie data are collected.  Modeling techniques can be
 applied throughout the RI.  For example, during the early stages of an RI,  modeling can be used to guide
 the  data collection program. Sensitivity analyses can help identify the types of data needed, as well as
 critical sampling locations.  As data collection  proceeds during a phased RI. or when a large amount of
 data exist from previous investigations,  models can be used to provide a consistent framework for
 organizing  the data.  During the latter stages of an FS, models can be applied to predict the  future
 behavior of a ground water system under natural or artificial stresses, such as varied pumping schemes.

 3.4    SPECIFY OBJECTIVES/DECISIONS

 In a broad  sense, the objective of a remedial action program is to determine the nature and extent of the
 release or threat of release of hazardous substances and to select a cost effective remedial action to
 minimize or eliminate that threat.   Achieving this broad objective requires that several complicated and
 interrelated activities be performed, each having objectives, acceptable levels of uncertainty, and
 attendant data quality requirements. The expression of these objectives in clear precise decision
 statements is the first step toward  the development of a cost-effective data collection program.

 3.4.1    OBJECTIVES

 Project objectives should address major areas of the remedial process.  These include characterizing the
 site  with respect to the environmental setting, proximity and size of human population, and nature of the
 problem; identifying potential  remedies; and determining specific performance levels of the potential
 remedies.

 Specifying the objectives can be thought of as identifying problems to be solved.  Objectives tend to be
 geared toward separate media or sources. However, these objectives  should be consistent with the
 ultimate objective of selecting a remedial alternative(s) to address the  entire site.  Table 3-1 lists
 general RI/FS objectives.

 Defining the types of decisions which will be made regarding remedial actions requires a clear
 understanding of the problems  posed by the site and awareness of the consequences of making a wrong
 decision.

 3.4.2    DECISION TYPES

 The  consequences of making a wrong decision  regarding site remediation  will vary depending on the
 situation. For example, a decision may be made not to implement a remedial alternative designed to
 mitigate  the migration  of contaminants in ground water because the data indicate that dispersion and
 degradation  of the contaminants will reduce  concentrations to health-based levels.  If the contaminants
 actually migrated beyond the site and were encountered in the ground water system,  it may be suggested
 that  a wrong decision was made.   The consequences of this wrong decision at a site where residents derive
 their drinking water from the contaminated aquifer would  be different from the consequences of
contamination of an aquifer which was not used as a water supply.

The  consequences of a wrong decision must be weighed for each  major decision to be  made during the
remedial action process. Where the consequences of a wrong decision carry significant public health.
safety or environmental impacts, greater attention must be paid to obtaining the data required to ensure
that the decision is sound.
                                               3-10

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                                                             TABLE 3-1

                                                     General RI/FS Objectives
            Objective
              RI
           Activity
                    FS
                 Activity
  Determine presence or absence
   of contaminants
- Determine types  of  contaminants
  Determine  quantities  (concentrations)
   of contaminants

  Determine  mechanism of  contaminant
   release to  pathways

  Determine  direction of  pathway(s)
   transport
  Determine boundaries  of  source(s)
   and pathways
  Determine environmental/public.
   health factors
  Determine source/pathway  contaminant
   characteristics with respect  to
   mitigation (bench studies)
- Establish presence/absence of
   contaminants  at  source  and in
   all  pathways.

- Establish "nature"  of  contaminants
   at source and in pathways; relate
   contaminants  to  PRP-cost  recovery

- Establish concentration  gradients
  Establish mechanics  of  source/
   pathway(s)  interface

  Establish pathway(s)/transport
   route(s),  Identify  potential
   receptor(s)

  Establish horizontal/vertical
   boundaries  of  source(s)  and
   pathway(s)  of  contamination

  Establish routes  of  exposure,
   and  environmental and  public
   health  threat

  Establish range of contaminants/
   concentrations
  Evaluate applicability of no action alternative
   for source areas/pathways.
  Evaluate environmental/public health threat;
   identify applicable remedial technologies.
  Evaluate costs to achieve applicable or
   relevant and appropriate standards

  Evaluate effectiveness of containment
   technologies

  Identify most effective points in
   pathway to control transport of
   contaminants

  Evaluate costs to achieve relevant/applicable
   standards; identify applicable remedial
   technologies

  Evaluate applicable standards or risk; identify
   applicable remedial technologies
- Evaluate treatment schemes

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The risk of making a wrong decision is related to the quantity and quality of information available.  As
shown in Figure 3-5. as the quantity and quality of data increase, the risk of making a wrong decision
generally decreases.  This is not a linearly inverse relationship since at some point the collection of
additional data or improvement of data quality will not significantly decrease the risk of making wrong
decisions.

Data quantity and data quality are independent variables which  must be considered jointly during
assessment of the consequences of making a wrong decision.  Collecting  increasing  quantities of data
points which  are of low quality may  not add significantly to the reduction of risk of  making a wrong
decision.  Increasing the data quality of a limited  number of samples may not add significantly to the
body of knowledge to be used in making a decision.

The value of  obtaining additional data or increasing data quality has traditionally been  based on
professional judgment for RI/FS projects.  The intent of the DQO process is to provide a systematic
approach for  the evaluation of the risk associated with  making a wrong decision and for determining
levels of uncertainty associated with decisions to provide a framework for the RPM.

3.5   REFERENCES

Federal  Register.   1985.  National Oil and Hazardous Substances Pollution Contingency Plan.  Final
   Rule.  Vol. 50. No. 224. November 20.

U.S. Environmental Protection Agency (EPA).  1985a.  Guidance on Remedial Investigations Under
   CERCLA.  Office of Emergency and Remedial  Response.  Office of Waste Programs Enforcement, Office
   of Solid  Waste and Emergency Response. Washington, D.C. Office of Research and Development,
   Cincinnati, Ohio.  EPA/540/G-85/002.  June.

	. 1985b,  Guidance on Feasibility  Studies Under CERCLA. Office of Emergency and Remedial
   Response,  Office of Waste Programs Enforcement, Office of Solid Waste and Emergency Response,
   Washington, D.C. Office of Research and Development. Cincinnati. Ohio.  EPA/540/G-85/003.  June.
                                             3-12

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 INCREASING
   RISK OF
MAKING WRONG
  DECISIONS
                        INCREASING DATA QUALITY/QUANTITY
                           FIGURE  3-5
               RELATIONSHIP OF RISK  AND DATA
                      QUALITY/QUANTITY
                             3-13

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                4.0   RI/FS  DQO  STAGE 2  -  IDENTIFY DATA  USES/NEEDS

Stage 2 of the DQO process defines data uses and specifies the types of data needed to meet the project
objectives. Although  data needs are identified generally during Stage 1,  it is during Stage 2 that
specific data uses are  defined.

The major elements of Stage 2 of the DQO process, as identified in Figure 4-1, are:

     •   Identify data uses

     •   Identify data types

     •   Identify data quality needs

     t   Identify data quantity needs

     •   Evaluate sampling/analysis options

     •   Review PARCC parameters

Stage 2 begins after the conceptual model is developed and overall project objectives are established.
The conceptual model and the  general decisions become the basis for determining data uses and data needs.
Stage 1  determines if  existing data meet the project objectives.  If the  existing data are sufficient,
there is no need to collect additional data.  If the data are insufficient, the types, quality, and
quantity of data which must be collected will be determined in Stage 2.

4.1   IDENTIFY DATA USES

Data uses must be stated very specifically to serve their purpose in development of DQOs.  This task
should not be taken lightly.

As a demonstration of the  importance of accurately specifying data uses consider the following example.
Ground water samples are to be obtained at a site with known shallow ground water contamination. The
homes in the area  derive water from private wells which tap a deeper  bedrock aquifer. Based  upon the DQO
approach, professional experience, and program guidelines provided by the RPM, the contractor decides
that ground water from the bedrock aquifer should  be sampled.  However, additional questions to address
during Stage 2 of the DQO process include:

     •   How many samples are required?

     •   Where should samples be obtained?

     •   How many QA/QC samples are needed (field  trip blanks,  collocated samples, field and
         laboratory duplicates, spikes)

     •   Will data be used to determine if an alternative water supply should be provided to affected
         homes?

     •   At what contaminant  level are water supplies believed to be affected?

     •   Will decisions be based upon  analysis of samples from private water supply wells  or  from
         monitoring wells?
                                              4-1

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-------
                      IDENTIFY
                     DATA USES
                    IDENTIFY DATA
                       TYPES
  IDENTIFY
DATA QUALITY
   NEEDS
   IDENTIFY
DATA QUANTITY
    NEEDS
                     EVALUATE
                SAMPLING/ANALYTICAL
                      OPTIONS
                    REVIEW PARCC
                    PARAMETERS
                 FIGURE  4-1
          DQO STAGE  2 ELEMENTS
                       4-2

-------
     •  If contaminants are not detected in private water supply wells but are detected in monitoring
         wells, how will data be used to assess risks to receptors?

As demonstrated, the list of questions which can be generated to evaluate a simplistic problem in one
medium can be quite extensive.

4.1.1    DATA USE CATEGORIES

RI/FS  data uses can be described in general purpose categories.  These categories represent generic uses
but vary on a site-by-site basis.  Further, specific sites may require data for purposes other than those
described here. The categories listed in Table 4-1 represent the most common RI/FS data uses.  Tables
4-1 and 4-2 are forms that can be used by project managers to document the thought processes involved in
DQOs and the S&A plan. The categories do not represent different data qualities, only different uses
which  may require data of a given quality.  In other words, data collected for a site at a given level of
quality may be used for different purposes.   The data  use categories are briefly described below:

     •  Site Characterization -  Data are used to determine the nature and extent of contamination at
         a site.  This category is usually the one that requires the most data collection.  Site
         characterization data are generated through the sampling and analysis of waste sources and
         environmental  media.

     •  Health and Safety - Data are typically used to establish the level  of protection needed for
         investigators or workers at a site,  and if there  should be an immediate concern for the
         population living within the site vicinity.

     •  Risk Assessment -  Data are used to evaluate the threat posed  by  a site to public health and
         the environment.  Risk assessment data are generated through the sampling and analysis of
         environmental  and biological media, particularly where the potential for human exposure is
         great.

     •  Evaluation of Alternatives - Data are used to evaluate various remedial technologies.
         Engineering data are collected in support of remedial alternative evaluation and to develop
         cost estimates.   This may involve  performing bench-scale or  pilot scale studies to determine
         if a particular process or material may be effective in mitigating site contamination.

     •  Engineering Design of Alternatives - Data collected during the RI/FS can be used for
         engineering design purposes to develop a preliminary data base in reference to the
         performance of various remedial technologies.  Data types collected during  the RI/FS which
         are applicable to the RD process include waste characterization and preliminary volume
         estimates (these estimates usually  need to be refined further by additional data collection
         activities during the RD/RA).

     •  Monitoring During Remedial Action - During the the remedial action, samples can be taken to
         assess the effectiveness of the action.  Based on the analysis of these samples, corrective
         measures may  be taken.

     •  PRP Determination - Data  may be used to help establish liability at multiple-party sites.
         For known RPs, data are used to  link their wastes to those found on  the site and to
         pollutants  released to the environment, and for unknown RPs, by comparing the site wastes to
         pollutant profiles of known waste  streams.  Data are also  used for injunctive actions and
         cost  recovery.

Once the data  use categories are listed, the intended uses must be prioritized.  Establishing an order  of
priority for the intended data uses will help identify the  most  demanding use of each type of data,  i.e.,

                                                4-3

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 SITE
 NAME	
 LOCATION.
 NUMBER _
 PHASE	
                                                  TABLE  4-1
                                                   DATA USES
EPA REGION
       RI1  RI2  RI3  ERA  FS  RD RA
DATE	
CONTRACTOR _
SITE MANAGER
^^^^ DATA USE
MEDIA ^^^x.
SOURCE SAMPUNG
TYPE

SOIL SAMPLING
GROUND WATER SAMPUNG
SURFACE WATER/SEDIMENT
SAMPLING
AIR SAMPLING
BIOLOGICAL SAMPLNG
OTHER

SITE
CHARACTERIZATION
(NCLUDNG
HEALTHS
SAFETY)







RISK
ASSESSMENT







EVALUATION OF
ALTERNATIVES







ENGNEERNG
DESIGN OF
ALTERNATIVES







MONrTORNG
DURNG
REMEDIAL ACTION







PRP
DETERMINATION







OT>IER








NOTE: CHECK APPROPRIATE BOX (ES)
                                                      COM SFDQO 1.001

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                                            TABLE  4-2
                                      DQO SUMMARY FORM
   1.  SITE
       NAME.
       LOCATION.
       NUMBER —
                                                    EPA
                                                    REGION 	

                                                    PHASE	
                                                     Rl 1 Rl 2  Rl 3 ERA FS  RD  RA
                                                            (CIRCLE ONE)
2. MEDIA
(CIRCLE ONE)
3. USE
(CIRCLE ALL THAT

SOL

SITE
CHARAC.

GW

RISK
ASSESS.

SW/SED

EVAL.
ALTS.

AIR

ENGG
DESIGN

BIO

PRP
DETER

OTH3!

MONfTORNG
REMEDIAL
ACTION
OTT-BR

   4. OBJECTIVE
   5.  SITE  INFORMATION
        AREA.
                                            DEPTH TO GROUND WATER
        GROUND WATER USE.
        SOIL TYPES	
        SENSITIVE RECEPTORS .
     DATA TYPES (CIRCLE APPROPRIATE DATA TYPES)
              A. ANALYTICAL DATA
                                                                       B. PHYSICAL DATA
        PH
        CONDUCTIVITY
        VOA
        ABN
        TCLP
        PESTICIDES   TOX
        PCS         TOG
        METALS      BTX
        CYANIDE      COD
PERMEABILITY
POROSITY
GRAN SIZE
BULK DENSITY
HYDRAULIC HEAD
PENETRATION TEST
HARDNESS
   7. SAMPLING METHOD (CIRCLE METHOD(S) TO BE USED)

        ENVIRONMENTAL         BIASED         GRAB

        SOURCE
                              GRD
                                           COMPOSfTE
                                             NON- INTRUSIVE

                                             NTRUSIVE
                                                                  PHASED
   8. ANALYTICAL LEVELS (INDICATELEVELfS) AND EQUIPMENTi, METHODS)

       LEVEL 1  FIELD SCREENING - EQUIPMENT	

       LEVEL 2  FIELD ANALYSIS - EQUIPMENT	

       LEVELS  NON-CLP LABORATORY - METHODS	
       LEVEL 4

       LEVELS
CLP/RAS - METHODS.

NON STANDARD	
   9.  SAMPLING  PROCEDURES

       BACKGROUND - 2 PER EVENT OR

       CRITICAL (LIST)  	

       PROCEDURES  	
  10. QUALITY  CONTROL SAMPLES (CONFIRM OR SET STANDARD)
        A- FIELD                                   B. LABORATORY
        COLLOCATED - 5% OR 	            REAGENT BLANK -1 PER ANALYSIS BATCH OR
        REPLICATE -  5% OR 	            REPLICATE    -1 PER ANALYSIS BATCH OR
        FIELD BLANK - 5% OR 	            MATRIX SPIKE  -1 PER ANALYSIS BATCH OR
        TRIP BLANK - 1 PER DAY OR	            OTHER	
  11.  BUDGET  REQUIREMENTS

       BUDGET 	

       STAFF	
                         SCHEDULE
   CONTRACTOR _
   SITE MANAGER
                                              PRIME CONTRACTOR
                                                                   DATE
FOR DETAILS SEE SAMPLING S ANALYSIS PLAN
                                                                                    COM SF DQO 1 002
                                                 4-5

-------
                                                            TABLE  4-2  (CONTINUED)

                                                  DQO SUMMARY FORM INSTRUCTIONS
1. SITE  - Identify the site and phase ot the work to be conducted
     •  NAME - Site name or assignment as stated in the WA
     •  LOCATION  - City or Town Counly and State where site is located
     •  NUMBER - Site number as staled In the WA
    •  EPA REGION - EPA Region where (he site is located
     •  PHASE • CIrde work phase for which DOG'S are being developed: (number
           sequentially for each phase as appropriate):
          Rl - Remedial
         ERA • Expedited Response Action
         FS • Feasibility Study
         RD -  Remedial Design
         RA - Remedial Action

2. MEDIA   - CIrde the media being Investigated; only one form will be completed
             for each media.
     •  SOIL • Surface and subsurface soils
     • GW • Ground water
     •  SW/SED - Surface water and sediment (a sediment sample will be taken if
           possible at each surface water sampling location)
     • AIR • Air quality and resplrable dust monitoring
     •  BIO - Biological monitoring, flora and fauna
     •  OTHER - Indicate other 'media' being Investigated I.e. buildings,
           underground conduits, etc.

1_US£ • Circle the Intended use(s) of the data to be developed.
     •  SITE CHARAC. (H&S) - Site characterization which Includes a determination
          of the lavel(s) of health and safety protection required at the  site
     •  RISK ASSESS - Risk assessment, data to be used to perform the
          endangerment assessment or public health evaluation
     •  EVAL ALTS. - Evaluate alternatives, data  will be used to evaluate or screen
            remedial/technological alternatives
     • ENG'G DESIGN - Data will be used to perform detailed engineering design
           of remedy
     •  MONITORING - Data will be used to monitor during remedy Implementation
           or establish baseline conditions for long term monitoring after site
           remediation
     •  PRP DETERMINATION - Data will be used to confirm/fingerprint
           contaminants to specific potentially responsible parties for possible
           furture  or pending enforcement actions
     • OTHER • Indicate other specific data uses
 4.
    OBJECTIVE    - Provide a concise, specific statement that answers the question
          •Why am I taking these samples?'
 5.  SITE  INFORMATION    - Provide the site Information necessary to
            gain an overview of the site and the relative complexity and extent
           of data requirements
     • AREA - Indicate the area of the site in acres and an indication of the
           configuration (length and width)
    • DEPTH TO GROUND WATER - Indicate the depth to ground water from the
           ground surface, to the extent known Identify seasonal fluctuation and the
           depth and thickness of multiple aquifers
    • GROUND WATER USE - Identify both potable and non-potable ground water
           use(s) by aquifer, if appropriate, and the point(s) of extraction relative to
           the site
     • SOIL TYPES - Identify, to the extent known, the site soil strata and relative
           depths below ground surface
    • SENSITIVE  RECEPTORS - Identify population and environmental concerns,
            relative to the site, which could be impacted by contaminant migration

«.  DATA  TYPES   . circle the appropriate analytical and physical data required to
           to determine the type, degree, extent and migration characteristics of
           the contaminants and the required site characlensllcs.  The selection of
          data types required  must be developed by the sue manager with the data
           users as described In section 3.2

7.  SAMPLING METHODS   - Circle the appropriate sampling method(s) to be used
           In obtaining  the required  data In accordance with the objectives above
    • ENVIRONMENTAL • Refers to media sampling of air, water,  soils and the
           biological environment to determine the extent  of contamination
    • SOURCE - Refers to the sampling of the  actual contamination source(s)
    • BIASED - Refers to sampling which focuses on a specific site area,
           characteristic  or problem factor based upon site knowledge and/or modeling
    • GRID • Refers to unbiased sampling which provides a representative estimate
           of contamination problem over the entire site
     • GRAB - Refers to discrete samples which are representative of a specific
           location at a specific point In time.
    • COMPOSITE • The  mixture ol a number ot grab samples to represent the average
           properties of the parameters of concern over athe extent of the area
           sampled
    • NON-INTRUSIVE - Refers to obtaining data using methods and equipment
         that do not require the physical extraction of sample from the media
         being sampled
    •  INSTRUSIVE -  Refers to physically extracting  samples from the media
         being sampled
    • PHASED- Refers to performing discrete time-phased sampling events and
         using the information obtained in the previous event to refine the
         subsequent sampling event

 8. ANALYTICAL  LEVELS   . The analytical levels are descrbed in Section 9
         of the Guidance
    • LEVEL  f FIELD SCREENING - EQUIPMENT • Identify the field monitoring
         equipment to be used and the manufacturer's specified detection limits
         when known
    • LEVEL 2 FIELD ANALYSIS - EQUIPMENT - Identify the field analysis to be
           used and the historically achievable Instrument detection limits
    • LEVEL 3 NON-CLP LABORATORY - METHODS  -Identify the laboratory
          method(s) to be used and the historically achleveable precision
          and accuracy when available
    • LEVEL 4 CLP'RAS - METHODS - Identify the CLP  laboratory method(s)
          to be used  and the historically achievable precision and accuracy
    • LEVEL 5 NON-STANOARO • Specify requirement for non-standard
         analysis, analytical procedures to be used and  required precision
         and accuracy

 a.  SAMPLING PROCEDURES   . The procedures to be used In obtaining the
          required samples are to be defined, a description of the critical
          samples Is to be provided and the requirement ot obtaining a
          minimum of two background samples per sampling event Is to be
          confirmed or the  deviation from this minimum  standard defined
10. QUALITY CONTROL  SAMPLES    . The identified minimum standards
          for the field and laboratory quality control samples must be
          confirmed or revised on a site specific basis

11. BUDGET REQUIREMENTS   . Based upon the analysis summarized above
          the critical resource requirements shall be defined
   • BUDGET - The estimated cost of the sampling and analysis shall be
         presented in dollars
   • SCHEDULE - The total time required to perform the sampling and the
          estimated time, as appropriate to perform the analysis shall be
         presented by calendar days, by phase
    • STAFF  - The key staff disciplines required to perform the sampling shall
         be Identified

The form shall Identify the contractor  directly responsible for the work the
   prime contractor and must be signed and dated by the site manager.
                                                                     4-b

-------
the use requiring the highest level of confidence, and therefore the lowest level of uncertainty.  The
data quality required will  be a function of the acceptable limits of uncertainty established by the
decision maker.  The limits on uncertainty will drive the selection of both the analytical and sampling
approaches.

4.1.2    RI/FS USES

During the evaluation of data uses, the potential remedial options which will be considered during the
RI/FS must be reviewed.

As  mandated by the Superfund Amendments and Reauthorization Act of 1986 (SARA), treatment alternative;
should be developed ranging from an alternative which minimizes long term management of residuals to an
alternative  involving treatment that significantly reduces toxicity,  mobility, or volume as a principal
element.  In addition, a containment option involving little or no treatment and a no action alternative
should also be developed.

For each of the appropriate action categories, the following information or analyses should be considered
during the DQO process:

      •   List of candidate remedial  actions

      •   Method by which the initial alternatives will be screened, including effectiveness criteria.
         implementability criteria, and cost criteria

      •   Detailed effectiveness screening will examine whether the alternatives protect public health
         and the environment: meet ARARs; cause a reduction in toxicity,  mobility, or volume; and
         provide acceptable reliability.

      •   Detailed implementability screening will examine the technical feasibility, availability, and
         administrative feasibility of each alternative.

      •   Detailed cost screening will examine the capital,  O&M, and replacement cost as well  as  the
         present worth of the alternatives.

      •   Both the  short and long-term effects of the screening factors must  be assessed and the
         alternatives must be compared to identify their relative  strengths and weaknesses.

The remedial process involves a number of data collection activities, each having specific objectives.
Since the objectives require varying degrees of data quality, it is  critical to identify the specific  use
to which each  set of data  will be applied.

4.2   IDENTIFY DATA TYPES

Data use categories define the general purposes for which  data will be collected during the RI.   Based on
the intended uses,  a concise statement regarding the data types needed can be developed.   After
identifying the data types  and uses, data quality  needs can  be defined, and a systematic evaluation of
sampling and analysis options can be performed.

Data types can be specified in broad groups initially, such as background samples or  media  samples,  and
then  these broad groups are divided into more specific components.  Figure 4-2 illustrates the process of
continual refinement of data types for a hypothetical ground water contamination problem.  The process
should  be  followed for each media of interest or each source material.  The result of  completing the
entire decision matrix is the specification of the data type needed for each intended data use.


                                                4-7

-------
                                                                   _	c
VOLATIE
ORGANIC
RECT
NTACT
1


GRO
WA

UNO
TER



SUH
WA

FACE
TER
	
           I  ORGANIC       INORGANIC
           CONTAMINANTS    CONTAMINANTS
            HAZARDOUS
            SUBSTANCE
               LIST
            APPENDIX VIII
                                                                                 FOOD
                                                                                 CHAIN
                                                                                 J
                                    FIGURE 4-2
                 SAMPLE TYPE SPECIFICATION LOGIC DIAGRAM

-------
               Since environmental media and source materials are interrelated at uncontrolled hazardous waste sites,
               data  types used to evaluate ground water contamination may also be used to evaluate soil contamination.
               By identifying data  types by media, overlapping data needs are identified.  The types of analyses
               performed on each  sample must be determined while identifying data types. The analytical requirements
               are dictated  by  the use of the data.

               The  data types  specified in Stage 2 should not be limited to chemical analytical parameters, but should
               also  include physical parameters such as permeability and porosity, which are needed to evaluate
               contaminant migration.  The level of detail in data type definition must be sufficient to allow  for
  I             evaluation of sampling/analysis options during subsequent stages of the DQO process.

               4.3    IDENTIFY DATA QUALITY NEEDS

               4.3.1    DATA QUALITY FACTORS

  j             Consideration of data quality needs should begin with the identification of data uses and data  types.
               Important factors in defining data quality include:

  I                  •   Prioritized data uses

  i                  •   Appropriate analytical levels

                    •   Contaminants  of concern

 :                  •   Level of concern

                    •   Required detection limit

                    •   Critical samples

               These factors should be considered to define data quality needs in a general way at the start of an
               RI/FS.  As work proceeds and more data become available, more precise statements can be made.

               Appropriate Analytical Levels

               There is little or no information on many factors which critically affect data quality such as:  sample
 I              variability,  sample container cleanliness, effect of different sample collection and  analytical
               preparation techniques,  etc.  Most available measurement data quality information addresses  only the
 j              analytical technique. To provide some guidance, this section defines analytical levels and then
 ;              indicates the levels appropriate to different generic RI/FS data uses.  Appendix B of this document
 j              provides a more detailed discussion of analytical considerations.

 j              The  analytical levels are defined as follows:

                    •   Level I - field screening or analysis using portable instruments.  Results are often  not
                        compound specific and not quantitative but results are available in real-time.   It is the
                        least costly of the analytical options.

I                   •   Level II - field analyses using more sophisticated portable analytical  instruments: in some
j                       cases, the instruments may be set  up in a mobile laboratory on site.   There is a wide range
'                       in the quality of data that can be generated. It depends on the  use of suitable calibration
j                       standards, reference materials, and sample preparation equipment: and  the training of the
                        operator.  Results  are available in  real-time or  several  hours.
i

                                                              4-9

-------
      •   Level  III - all analyses performed in an off-site analytical laboratory. Level III analyses
          may or may  not use CLP procedures,  but do not usually utilize the validation or documentation
          procedures required of CLP Level IV  analysis.  The laboratory may or  may not be a CLP
          laboratory.

      t   Level  IV - CLP routine analytical services (RAS).  All analyses are performed in an off-site
          CLP analytical laboratory following CLP protocols.  Level IV is characterized by rigorous
          QA/QC protocols and documentation.

      •   Level  V - analysis by  non-standard methods.  All analyses are  performed in an off-site
          analytical laboratory which  may or may not be a CLP laboratory.  Method development or method
          modification  may  be required for specific constituents or detection limits.  CLP special
          analytical services (SAS) are Level V.

Levels III, IV and V all incorporate some time  lag between submission of samples to the laboratory and
receipt of results.  Table 4-3 provides more information  on these analytical levels; Table 4-4 identifies
appropriate analytical levels for  generic RI/FS data uses.

It can be seen from Table 4-4 that, for each generic data use, several analytical levels may be
appropriate.  The decision  maker needs further  criteria to select the most appropriate.  Important
criteria are the contaminants of concern and the level of concern  for each contaminant.

Engineering design (see Table  4-4) usually  requires considerations beyond analytical levels for chemical
analyses. Physical property data (viscosity,  soil  organic carbon, etc.) are often necessary for
engineering design. While most of the chemical analysis requirements for engineering design data needs
can be accomplished by Level II, III  and  IV analyses, the physical  property type  analyses  will usually
fall within the Level V and  "other" categories.

Contaminants  of Concern

At some sites it  may be clear which contaminants are of  concern because they have known adverse impacts
on human health.  In such  cases, the appropriate health  standards can be used to set levels of concern.
Often a large number of contaminants are found at a site.  In such cases it is not feasible or desirable
to specify levels  of concern  for each observed contaminant.  Rather,  a small number of indicator
chemicals are  selected  and  levels of concern are determined for these chemicals.  Indicator chemicals are
the most toxic, mobile, persistent, or  frequently  occurring contaminants found on site.  The process of
selecting indicator contaminants is described in the Superfund Public  Health Evaluation Manual (EPA
1985).

Levels of Concern and ARARs

The level of concern specifies a concentration range above which some action  may need to be  taken. The
level of concern is  intimately linked with the action level, which defines the "level of cleanup" for
remedial activities under SARA.  In general, levels of concern are site specific issues and relate to
site characterization and assessment.   The applicable or  relevant and appropriate requirements  (ARARs),
as mandated by  SARA, are related to  defining remedial design criteria and legal requirements.

An exact action  level is not required before initiating an RI field  investigation:  however,  a  rough
estimate  is necessary to ensure  that the chosen analytical  methods are accurate at the level  of concern.
Also,  knowledge of the level of concern can influence the number of samples required and the selection of
analytical methods. For these reasons,  an acceptable range of values should be specified.  As  work on a
site progress and more data  become available, the level of concern will be further refined and
incorporated into the ROD  as an action level.
                                              4-10

-------
                TABLE  4-3
SUMMARY OF ANALYTICAL LEVELS APPROPRIATE TO
                DATA USES
DATA USES ANALYTICAL LEVEL
SITE CHARACTERIZATION
MONITORNG DURING LEVEL 1
IMPLEMENTATION
SITE CHARATERIZATION
EVALUATION OF ALTERNATIVES LEVEL II
ENGrCERNG DESIGN
MONITORWG DURING
IMPLEMENTATION
RISK ASSESSMENT
PRP DETERMINATION
SITE CHARACTERIZATION
EVALUATION OF ALTERNATIVES
ENGNEERNG DESIGN LEVEL III
MONJTORNG DURING
IMPLEMENTATION
RISK ASSESSMENT
PRP DETERMINATION LEVEL IV
EVALUATION OF ALTERNATIVES LEVEL w
ENG KEFUNG DESIGN
RISK ASSESSMENT LEVEL V
PRP DETERMINATION
TYPE OF ANALYSIS
- TOTAL ORGANIC/INORGANIC
VAPOR DETECTION USNG
PORTABLE INSTRUMENTS
- FIELD TEST KITS
- VARIETY OF OHGANICS BY
GC; INORGANICS BY AA;
XRF
- TENTATIVE ID; ANALYTE-
SPECIFIC
- DETECTION LIMITS VARY
FROM LOW ppm TO LOW ppb
- ORGANICS/INORGANICS
USNG EPA PROCEDURES
OTHER THAN CLP CAN BE
ANALYTE-SPECIFIC
- RCRA CHARACTERISTIC TESTS
- HSL ORGANICS/INORGANICS
BY GC/MS; AA; ICP
- LOW ppb DETECTION LIMIT
- NON-CONVENTIAL
PARAMETERS
- METHOD-SPECIFIC
DETECTION LIMITS
-MODIFICATION OF
EXISTING METHODS
- APPENDIX 8 PARAMETERS
LIMITATIONS
- INSTRUMENTS RESPOND TO
NATUFIALLY-OCCURING
COMPOUNDS
- TENTATIVE ID
- TECHNIQUES/INSTRUMENTS
LIMITED MOSTLY TO
VOLATILES, METALS
- TENTATIVE ID IN SOME
CASES
- CAN PROVIDE DATA OF
SAME QUALITY AS
LEVELS IV, NS
- TENTATIVE IOENTFICATON
OF NON^HSL PARAMETERS
- SOME TIME MAY BE REQUIRED
FOR VALIDATION OF PACKAGES
- MAY REQUIRE METHOD
DEVELOPMENT/MODIFICATION
- MECHANISM TO OBTAIN
SERVICES REQUIRES
SPECIAL LEAD TIME
DATA QUALITY
- IF INSTRUMENTS CALIBRATED
AND DATA INTERPRETED
CORRECTLY, CAN PROVIDE
INDICATION OF CONTAMINATION
- DEPENDENT ON QA/QC
STEPS EMPLOYED
- DATA TYPICALLY REPORTED
W CONCENTRATION RANGES
- SIMILAR DETECTION
LIMITS TO CLP
- LESS RIGOROUS QA/QC
- GOAL IS DATA OF KNOWN
QUALITY
- RIGOROUS QA/QC
- METHOD-SPECIFIC

-------
                                              TABLE 4-4
                           APPROPRIATE ANALYTICAL LEVELS - BY DATA USE
^"^v^ DATA USE
ANALYTICAL ^\-
LEVEL ^\
LEVEL 1
LEVEL II
LEVEL III
LEVEL IV
LEVEL V
OTHER
SITE
CHARACTERIZATION
(NCLUDNG
HEALTH 4
SAFETY)
x/
^/
N/



RISK
ASSESSMENT


N/
N/
N/

EVALUATION OF
ALTERNATIVES

N/
N/
N/


ENGNEEFtNG
DESIGN OF
REMEDIAL ACTION


N/
N/
S/
N/
MONITORMG
DURJNG
IMPLEMENTATION
OF
REMEDIAL ACTION
N/
N/
-y



FflP
DETERMINATION


N/
N/
N/

OTHER







NOTE. CHECK APPROPRIATE BOX (ES)
                                                                                               CDM SF DQO 1 001
                                                                                                    HEM 1006

-------
Determination of levels of concern is a site specific activity.  The decision maker and data users
(toxicologists. geologists,  and engineers) must meet to determine the appropriate action level range for
the site.  Tables in Appendix E summarize potentially applicable or relevant and appropriate requirements
and toxicity values.  The standards do not consider simultaneous exposure from  multiple routes.
Standards may also be based on levels, durations,  or  frequencies of exposure that differ from those at a
specific site. The standards and criteria  that are used, especially when conducting public health
assessments, must correspond to the media for which they  are developed.

In the listing of applicable standards which can be used for selecting action levels, few standards  are
available for soil contamination. Generally, some type of modeling may be required to specify the level
of concern for soil.  The  type of model selected will be based on the potential route of exposure.  If
contaminated soil is carried  in the air and inhaled  by receptors, air modeling may be required. If
contaminants leach from soils into ground water and are transported to receptor  wells, a  ground water
model may be required. These models are useful  in assessing the potential impact resulting from
migration of contaminants at a specified level of concern to a receptor at a specified cancer risk level.
for instance. The available  models are specified in the Superfund  Exposure Assessment  Manual  (EPA 1985).

Detection Limit Requirements

The level of concern selected directly affects data quality requirements.  The  sampling and analysis
methods  used must be accurate at the level of concern.  Since sampling accuracy is hard to evaluate or
control, it is extremely important that the analytical technique chosen has a detection limit well below
the level  of concern.  This factor must be considered in evaluating analytical  options. Appendix B
provides  more detailed information on detection limits.  Appendix H lists CLP contractually required
detection limits.

Critical Samples

Critical samples are those for which valid data  must be obtained to satisfy the objectives of the
sampling and analysis  task.  An example of a critical  data point may be an upgradient well in a ground
water contamination study or any other data point considered vital to the decision making process. In
some  cases, taking  critical samples in duplicate is appropriate.

4.3.2    COST ANALYSIS OF ALTERNATIVES

The program goal for  developing cost estimates in  feasibility studies is to estimate to  within +50
percent and -30  percent of the actual  cost of the selected remedial alternative. This puts  requirements
on the type and amount of data which  must be collected during the field investigation and requires the
decision  maker to consider the range of potential remedial  alternatives before planning the field
investigation.

Where a possible alternative is source removal or treatment, the cost criteria  may be used to determine
the number of data required. If the cost of the remedial alternative is strictly proportional to the
volume of material  removed or  treated, sufficient data must be obtained to determine the  volume of
material  to within  +50 percent and -30 percent.  Normally, however, there is some  uncertainty in the
capital costs and the efficiency of the treatment or  removal  procedure.  Therefore, it  is necessary to
determine the volume of contaminated soil as accurately as  possible.

4.4    IDENTIFY  DATA QUANTITY NEEDS

The number of samples which should be collected can be determined using a variety of approaches.  The
validity of the approach utilized is dependent on the characteristics of the media  under investigation
and the assumptions used to select sample locations   In  situations uliere data are  unavailable or


                                              4-13

-------
limited, a phased sampling approach may be appropriate.  Phase I data can be evaluated to determine the
appropriate number of samples to be obtained in subsequent phases  of the RI.

In the absence of available data,  the data users and decision makers will be required to develop a
rationale for selecting sampling locations.  Questions to guide the data users in selecting appropriate
locations could include:

      •   Do source materials still exist  on the soil surface?

      •   Is there evidence of soil disturbance or vegetative stress based upon review of aerial
          photographs?

      •   Do geologic features in the area control ground water and surface water flow patterns?

      •   Do site conditions favor surficial soil erosion or wind  erosion?                                          :
                                                                                                              it
      •  Are sensitive receptors located in the vicinity of the site?

These types of questions can be addressed  in the absence of any analytical data and  will assist in
identifying additional data needs.  Subsequent discussions may lead to the recommendation that
geophysical surveys or soil gas and other field  screening studies be conducted  in areas of soil                      \
disturbance.  Collection of a limited number of samples from identified source materials or pathways,              !
such  as streams, may also be considered.  Limited air sampling may also be warranted during the early
stages of the RI to determine if organic vapors  or particulates could  pose a problem.                              |

In situations where data are available, or as new data are added to the site's data base, statistical                   '
techniques may be  utilized in determining the number of data required.   Appendix  A provides examples of        ;
the applicability and methodology of various statistical  techniques.                                                j

Following evaluation of the data, the adequacy  of the data to support a decision can  be determined.  If a
higher degree of certainty in the decision is required (e.g.,  a more definitive statement regarding the
extent of contamination), then additional data should be obtained in subsequent sampling phases.  In all
cases, the actual  level of confidence in a decision can only be established following collection and                   j
evaluation of data.  Therefore, at the completion of each data collection activity, data evaluation is
critical.
                                                                                                               t

4.5    EVALUATE SAMPLING/ANALYSIS OPTIONS

Following the identification of data uses, data types, and data quality needs, an evaluation of sampling
and analysis options can  be undertaken.  Numerous sampling and analysis options could be developed for
any data collection activity.  The possible options are a function of the data types needed.

4.5.1    SAMPLING AND  ANALYSIS APPROACH (PHASING)                                                ;

Data collection activities must be designed to ensure maximum use of the data.  Developing a sampling and
analysis approach which ensures that appropriate levels of data quantity and quality are obtained  may be             \
accomplished by  use of a phased RI approach and by the use of field screening techniques to direct the
data collection activities.  By subdividing the data collection program  into a number  of phases, the data
can be obtained in a sequence which allows it to be  used to direct subsequent data collection activities.
                                                                                                               i
The time required for receipt of analytical data  from laboratories often results in delays in  an RI
program.  By utilizing field techniques for assessing contaminant concentrations or media
characteristics, the RI can proceed with  fewer delays.


                                               4-14

-------
Direct reading instruments which should be considered for use during the evaluation of a
sampling/analysis approach  include:

     •   Photoionization detectors  (PIDs)

     •   Flame ionization detectors (FIDs)

     •   Hydrogen sulfide analyzers

     •   Hg vapor analyzers

     •   Respirable particulate meters

     •   Radiation meters

     •   Oxygen/explosi meters

     •   pH and conductivity meters

Other devices and field tests which  allow for assessment of site conditions without the need for
laboratory support include:

     •   Oil/water interface units

     •   Slug tests

     •   Infiltrometers

These direct reading instruments can be taken into the field to obtain data without extensive calibration
procedures.  Additional levels of quantification can be obtained with transportable instruments such as
gas chromatographs (GC), x-ray fluorescence, or atomic adsorption devices.  For these instruments.
however, calibration using known standards must be completed prior to field use.

Conceptually, an analytical approach can be thought of as a large "inverted funnel" whereby large numbers
of samples are analyzed quickly  and cost effectively in the field, with  succeedingly smaller numbers of
samples analyzed further using successively more sophisticated procedures.  The type and design of this
analytical approach is determined by how the data  will be used.  By strategically  selecting the samples
analyzed at  each  level, a much higher degree of certainty can be obtained for the overall data set
without sacrificing either the quantity of samples analyzed or the quality of data collected.

For example, consider a hazardous waste  site where the soil  is contaminated with volatile organic
compounds  (VOCs).  For this example, the objectives of the sampling are determination of VOC
concentrations at site boundaries and assessment of the  direct contact threat.  It is assumed that  a
photoionization detector will detect  contaminants at the levels of concern for this example.

The analytical approach for  this  hypothetical situation is illustrated in Figure 4-3 and summarized
below:

     •   Samples from all locations are analyzed in  real time using photoionization  field headspace
         techniques (Level  I).

     •   A limited number of samples  for which nothing was detected and nil samples for which VOCs
         were detected are analyzed oil-site using  a portable gas chromatograph (Level II).


                                               4-15

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              DATA
             QUALITY
   Q
   in
   co
   a
   CO
   &
   3*
   O


   g
   LU
   Q
                                                    COST
                                                    AND
                                                 TURNAROUND
                                                    TIME
1
 Although not applicable to the example situation, level III support
 is shown to indicate that this is a viable option for confirmatory
 analyses.
                     FIGURE  4-3
         INTEGRATION  OF  ANALYTICAL
                 SUPPORT  LEVELS
                           4-ir.

-------
     •   A number of samples are selected for analysis by CLP RAS (Level IV) for the Hazardous
         Substance List (HSL) compounds.  All samples identified as critical data points are included.
         This step provides confirmation for  all preceding work, including verification that indicator
         parameters are representative of contaminants of concern and are identified appropriately.
         The results of all split samples analyzed by different levels are interpreted for quality
         control purposes.

This approach can also be utilized in a time-phased manner, i.e., by using the results of an initial
sampling round  with a lower level of analysis to fine-tune the sampling approach for a subsequent
sampling round  using higher level(s) of analytical support.  Another approach involves complete GC/MS
analysis of the initial sampling round to identify the organic compounds present, followed by GC analysis
of specific compounds of interest in later rounds. Gas chromatography with the appropriate detector can
provide lower cost analyses, often with lower  detection limits and higher precision and accuracy, than
GC/MS. It is necessary, however, to verify by GC/MS that interfering compounds are not present.

4.5.2    RESOURCE CONSIDERATIONS

The resources available for performing a remedial  action must be evaluated during the scoping process.
Within Stage 2 of DQO development, the time required for obtaining data,  the personnel resources and
equipment required, and the costs for data collection must be evaluated. This evaluation is most
effectively performed as sampling/analysis options are identified.

The cost for analytical  support varies considerably depending on the type of analysis required.  Schedule
requirements dictating the need for rapid turnaround escalate analytical costs. The cost associated with
obtaining samples must also be considered during the evaluation of sampling/analysis options.  Cost
savings can  be achieved by performing multiple media sampling activities simultaneously (e.g., sample
ground  water and surface water during the  same sampling event).

Critical path activities and technical staff resource needs should be identified early to facilitate
efficient planning for the RI/FS.

4.6    REVIEW PARCC PARAMETER INFORMATION
The PARCC (precision, accuracy, representativeness, completeness, and comparability) parameters are
indicators of data quality.  Ideally, the end use of the measurement data should define the necessary
PARCC parameters.  In the ideal situation, numerical precision, accuracy, and completeness goals would be
established and these goals would aid in selecting the measurement methods.

As  noted earlier. RI/FS work does not fit  this ideal situation.  RI/FS sites are so different and
information on overall measurements (sampling plus analysis) is so limited that it is not practical to
set  universal PARCC goals at this time. Rather, the historical precision and accuracy achieved by
different analytical techniques should be reviewed to  aid in selecting the most appropriate technique.

To  indicate achievable precision and accuracy, tables in Appendix F present  historical precision and
accuracy information for analytical techniques classified by level.  EPA will continue to make
information of this type available so that a data base of numerical precision and accuracy requirements
appropriate to different data uses will develop.

4.6.1     PRECISION

Precision measures the reproducibility of  measurements under a given set of conditions.  Specifically, it
is a quantitative measure of the variability of a group of measurements compared to their average value.
                                              4-17

-------
 Precision is  usually stated in terms of standard deviation but other estimates such as the coefficient of
 variation (relative standard deviation), range (maximum value  minus minimum value), and relative range
 are common.

 The overall precision of measurement data is a mixture of sampling and analytical factors.  Analytical                4
 precision is much easier to control and quantify than sampling precision.  There are more historical data             J
 related to individual method performance and the "universe" is limited to  the samples received  in the
 laboratory.  In contrast, sampling precision is unique to each  site.

 Sampling precision may be determined by collecting and analyzing collocated or field replicate samples
 and  then creating and analyzing laboratory replicates from one or more of the field samples.  The
 analytical results from the collocated  or field replicate samples provide data on overall measurement
 precision; analysis results from the laboratory replicates  provide data on analytical precision.
 Subtracting the analytical precision from the measurement precision defines the sampling precision.

 4.6.2    ACCURACY

 Accuracy measures the bias in a  measurement system; it is difficult to measure for the entire data
 collection activity.  Sources of error are the sampling process, field contamination, preservation,                     ',
 handling, sample matrix, sample preparation and analysis techniques.  Sampling accuracy may  be assessed           I
 by evaluating the results of field/trip blanks, analytical accuracy may be assessed through use of known              i
 and  unknown QC samples and matrix spikes.                                                                    f

 As an  example of how the sampling process can affect accuracy, consider  the collection of ground water             '
 samples for volatile organic analysis.   In the actual sampling, some portion of the volatile components
 may be lost.   There is no way to measure this loss  easily.  The sample could also be subjected  to
 contamination from a wide range of sources in the field and laboratory. To check the system for
 contamination, trip and field blanks can be used.

 4.6.3    REPRESENTATIVENESS

 Representativeness expresses the degree to which sample data accurately and precisely represent a
 characteristic  of a population,  parameter variations at a sampling point, or an environmental condition.
 Representativeness is a qualitative parameter which  is most concerned with the proper design of the
 sampling program. The representativeness criterion is best satisfied by making certain that sampling
 locations are selected properly and a sufficient number of samples are collected.

 Representativeness is addressed by describing sampling techniques and the rationale used to select
 sampling locations.  Sampling locations can  be biased (based on existing data,  instrument surveys,
 observations, etc.) or unbiased (completely random  or stratified-random approaches).  Either way, the
 rationale used to determine sampling locations must be explicitly explained.  If a sampling grid  is being
 utilized, it should be shown on a  map of the site. The type of sample, such as a grab or composite
 sample, as well as the relevant standard operating procedure (SOP) for sample collection, should be
 specified.

An example of the way representativeness  is  ensured in a sampling program  is the use of proper ground
water sampling techniques.  The SOPs for ground water  sampling require  that a well be purged a certain
 number of well volumes prior  to sampling, to be certain that the sample is representative of the
 underlying aquifer at a point in time.

Representativeness can  be assessed by the use of collocated samples.  By definition, collocated samples
are collected so that they are equally representative of a given point in space  and time.  In  this way.
they  provide both precision and representativeness information.


                                              4-18

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              4.6.4    COMPLETENESS

              Completeness is defined as the percentage of measurements made which are judged to be valid measurements.
              The completeness goal is essentially the same for all data uses:  that a sufficient amount of valid data
              be generated. It is  important that critical samples are identified and plans made to achieve valid data
              for them.

              Almost no  historical data on the completeness achieved by individual  methods exists.  However, the  CLP
f             data has  been found to be 80-85 percent complete on a nationwide basis.  This can be extrapolated to
|              indicate that Level III. IV and V analytical techniques will generate data that are approximately 80
i              percent complete.  Levels I and II would be expected to have lower completeness  levels.  However,  since
|              they are  on-site  measurement techniques providing results in  real-time or after minimal delay, invalid
i              measurements can be repeated easily.  Thus, a high degree of compieteness can be achieved with these
I              analytical levels.

'              4.6.5    COMPARABILITY
i
•'              Comparability is a qualitative parameter expressing the confidence with which one data set can be
              compared with another.  Sample data should be comparable with  other measurement  data for similar samples
              and sample conditions.  This goal is achieved through using standard techniques to collect and analyze
J              representative samples and reporting analytical results in appropriate units. Comparability is limited
j              to the other PARCC parameters because only when precision and accuracy are known can data sets  be
i              compared with confidence.

i              4.7   UTILIZING PARCC PARAMETER INFORMATION

              In Stage  2  of the DQO process, the PARCC  parameters should be considered in  evaluating sampling and
              analysis  options.  To the extent possible, they should be defined as goals in the Stage 3 Data Collection
              Program.

              Whenever  measurement data are reviewed (in Stage  1 of the DQO process), the PARCC  parameters which
              were achieved should be included in the review.  The laboratory  should provide numerical  precision
              and accuracy data;  Level II field analyses may also generate precison and accuracy data.  Precision
              and accuracy data may be expressed in several ways and are best  evaluated by an  analytical  chemist
              or a statistician. Since the precision data quantify the scatter of results about a mean value, a
              lower precison value means less scatter. Accuracy is most frequently reported as percent recovery,
              or percent  bias. A 100 percent recovery indicates a completely accurate measurement; the  closer the
              recovery is to 100 percent, the more accurate the measurement.  Percent bias reports the difference
              of the result  from the true value.  A completely accurate measurement would have zero percent bias;
              the lower the percent bias, the more accurate the measurement.

              The data user must keep the level of concern and the end use of  the data in mind when  reviewing
              precision and accuracy information.  In some cases, even data of poor precision and/or accuracy may
              be useful.  For  example,  if all the results are far above the level  of concern,  the precision and
              accuracy are much less important. However, close to the level of concern, precision and accuracy
              are quite important and should be carefully reviewed.  If results have very good precision but poor
              accuracy, it may be acceptable to correct the reported results using the percent recovery or  percent
              bias data.  This judgment should  be made by a data user with appropriate technical expertise.
                                                            4-19

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4.8   REFERENCES

U.S. Environmental Protection Agency (EPA).   1987.  Compendium of Field Operations Methods. Office
   of Emergency and Remedial Response, Washington. D.C. June.

	.   1983.  Characterization of Hazardous Waste Sites - A Methods Manual. Volume 1  - Site
   Investigations.  NTIS PB84-126929.  EPA/600/4-84/075                                                  '

	.   1985.  Guidance on Remedial Investigations Under CERCLA.  Office of Emergency and Remedial           }
   Response, Office of Waste Programs Enforcement,  Office of Solid Waste and Emergency Response,
   Washington,  D.C. Office of Research and Development, Cincinnati, Ohio. EPA/540/G85/002.  June.           *

	.   1985.  Guidance on Feasibility Studies Under CERCLA.  Office of Emergency and Remedial               j
   Response, Office of Waste Programs Enforcement,  Office of Solid Waste and Emergency Response,             j
   Washington,  D.C. Office of Research and Development, Cincinnati, Ohio. EPA/540/G85/003.  June.           1
                                                                                                      5
	.   1985.  Superfund Exposure Assessment Manual. Office of Solid Waste and Emergency Response.           ^
   OSWER Directive 9285.5-1                                                      "                      J
                                                                                                      j
	.   1985.  Sediment Sampling Quality Assurance User's  Guide.  EPA 600/4-85-048                           j
                                                                                                      i
	.   1984.  Superfund Public Health Evaluation Manual.  Office of Solid Waste and Emergency
   Response, OSWER Directive 9285.4-1
                                          4-20

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            5.0  RI/FS DQO STAGE 3  DESIGN DATA COLLECTION  PROGRAM

Stage 3 of the DQO process entails design of the detailed data collection program for the remedial action
project.  Through the process of addressing the elements identified in Stages 1  and 2. all the components
required for completion of Stage 3 should be available.  Stage 3 is outlined in Figure 5-1.

5.1    ASSEMBLE DATA  COLLECTION COMPONENTS

During Stage 2, specific DQOs were developed by media or sampling activity.  The intent of Stage 3 is to
compile the information and DQOs developed for specific tasks into a comprehensive data collection
program.  A detailed list of all samples to be obtained should be assembled in a format which includes
phase, media, sample type,  number  of samples,  sample location, analytical methods, and QA/QC samples
(type and number).  In  addition, a schedule for all sampling activities should be developed in bar chart
or critical path method format.

5.2    DEVELOP DATA COLLECTION DOCUMENTATION

The output of the DQO process is a well defined sampling and analysis (S&A)  plan with summary information
provided in the work plan.

Data collection documentation requirements vary on a regional basis within the EPA. The DQO guidance
provided in this document does  not require the submittal of deliverables in addition to those  already
established in  the regions.  Rather, the DQO process provides a framework to ensure that all the
pertinent issues related to the collection of data with known quality are addressed.

5.2.1    SAMPLING AND ANALYSIS PLANS

A written quality assurance/site sampling plan must be prepared for all  remedial investigation activities
which  involve sampling. These plans should include the following:

     •  Description  of the objectives of the sampling efforts, including the phase of the sampling
         and ultimate use of the data

     •  Specification of sampling protocol  and  procedures

     •  Specification of the types, locations, and frequency of samples to be taken

The S&A plan identifies the individuals responsible and the procedures  for field activities and sample
analyses.  Quality assurance project plan (QAPjP) elements should be addressed in the S&A plan. The
standard elements of a QAPjP are listed in Table 5-1.  Details on preparation of QAPjPs are contained in
Interim Guidelines and  Specification for Preparing Quality Assurance Project Plans (EPA 1980).

The 16 points required  in a QAPjP may be  incorporated by reference if the information has been documented
elsewhere.  For example, if a project description  (Item 3) is available in the work plan, it is
acceptable to refer to this document  rather than repeat the information.  Quality assurance issues which
are program wide in nature, such as internal quality control checks  (Section II), performance and system
audits  (Section 12), corrective action (Section 15) and quality assurance reports to management (Section
16), are generally specified in the quality assurance program plan (QAPP) and can be  included in the
QAPjP by reference.

Field investigation activities can be  undertaken in a  phased approach. Separate sampling/analysis plans
may be prepared for the separate phases of a remedial investigation. For example, geophysical
investigations  may be performed to select locations for  monitoring \vells.  In such  a case, a sampling
plan should be prepared for the geophysical investigations and. following  evaluation of the data,  a

                                              5-1

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              ASSEMBLE
            DATA COLLECTION
             COMPONENTS
  DEVELOP DATA COLLECTION DOCUMENTATION

      •  WORK PLAN
      •  SAMPLING &  ANALYSIS PLAN
           Include QAPjP Elements
      •  WORK PLAN
             FIGURE 5-1
         STAGE 3 ELEMENTS
DESIGN DATA COLLECTION PROGRAM
                5-2

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                     TABLE 5-1

QUALITY ASSURANCE PROJECT PLAN  ELEMENTS


           1          Title Page
                      Introduction

           2          Table of Contents

           3          Project Description

           4          Project Organization

           5          Quality Assurance Objectives
                      for Data  Measurement

           6          Sampling Procedure

           7          Sample and Document Custody
                      Procedures

           8          Calibration Procedures and Frequency

           9          Analytical Procedures

           10         Data Reduction, Validation
                      and Reporting

           11         Internal Quality Control Checks

           12         Performance and System Audits

           13         Preventive Maintenance

           14         Data Measurement Assessment
                      Procedures

           15         Corrective Action

           16         Quality Assurance Reports to
                      Management
                    5-3

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 separate plan should be developed for installation of the wells.  Additional plans for the subsequent
 phases of a remedial investigation may be prepared at any time during the course of the project as the
 need for additional field  investigation is identified.

 5.2.2    WORK PLANS

 Work plans define the scope of services, level-of-effort. costs, and schedule for performing the RI/FS;
 in general, the work plan describes what will be done,  while the S&A plan and QAPjP describe how each
 task will be done.  The scope of the sampling effort depends on the quality of existing data, an
 understanding of the site problems, identification and evaluation of feasible remedial actions, and
 enforcement needs.

 The work  plan provides the general description  of the activities to be performed as  part of the RI/FS.
 However,  it does not contain the detailed description of how a sample is obtained or an analysis
 performed. This type of information is presented in the S&A plan.  The level  of detail to be included in
 the work plan for the RI phase is outlined below:

      •   How site mapping will be performed including survey limits,  the scale of the plan to be
          produced, the horizontal and vertical control,  and significant site features

      •   Number of individuals to be involved in each  field sampling task and estimated duration  in
          days

      •   Identification of geophysical survey areas  or transects, soil boring and test pit locations
          on  the  map provided in the draft work plan

      •  Number of samples to be obtained  in the  field including blanks and duplicates and the
          location from which the samples will be obtained illustrated on a map included in the draft
         work plan

      •  List of  analyses to be performed

      •  A general discussion of DQOs

      •  Identification of pilot or bench-scale studies that will be performed

This information is required  as part of the work plan in order to establish a basis for the schedule and
cost estimate.  Work plans prepared  for a phased RI approach should be specific for the initial phase,
and general for subsequent phases, with subsequent phases well defined when the previous phase is
completed.

5.2.3    ENFORCEMENT CONCERNS

All RI/FS activities should be conducted and  documented such that sufficient data are collected to make
sound decisions concerning remedial  action selection.  This applies to fund-lead, and potentially
responsible party lead projects.   The data collection and documentation activities should be similar for
all types of RI/FSs.  In other words,  if enough data are collected using  appropriate  protocols,  and  the
data are sufficiently valid upon which to base a remedial action decision, then the procedures and
documentation should be sufficient to be admissible as evidence in litigation.

The guidelines outlined below should be followed to assure that data quality objectives are met:

     •   Appropriate plans (i.e.. work plans, sampling  and analysis plans. QAPjP) should be developed
         to document intentions.

                                              5-4

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     •   Field notebooks should be maintained to keep accurate records of sampling activities.

     •   Personnel should have appropriate experience or training.

     •   Chain of custody records must be kept for samples.

     •   Methods used for sampling and analysis should be valid  from an engineering/scientific
         standpoint and be consistent with standard analytical procedures.

     •   Documentation should be sufficient to allow the persons  involved  in the site studies to
         reconstruct the work if necessary.

     •   EPA's or the  state's responsibility from a QA/QC standpoint is to audit randomly some RI/FS
         field sampling, analysis (QA/QC) and data validation to confirm that procedures utilized were
         sufficient.

The above requirements pertain to civil cases only. Criminal cases will require additional documentation
and/or materials.  EPA counsel should  be consulted in these cases.

5.3   REFERENCES

American Chemical Society.  1983. Principles of Environmental Analyses. Analytical Chemistry
   55:2210-2218.

ASTM. 1985. Quality  Assurance for Environmental Measurements.  ASTM Special Tech Pub 867.  J.K.
   Taylor and T.W.  Stanley eds.

Taylor, J.U.  1981. Quality Assurance of Chemical Measurements Analytical Chemistry.  Volume 53,
   No. 14.  December.

U.S. EPA.  1980. Standard Operating Procedures and Quality Assurance Manual-Draft.  Region IV
   Surveillance and Analysis  Division-Water  Surveillance Branch.   Athens, Georgia.

	.  1980.  Interim Guidelines and Specifications for Preparing Quality Assurance Project Plans.
   QAMS.  EPA-600/4-83-004.  NTIS PB83-170514.

	.  1981.  Work Plan Development Technical Methods for Investigating Sites Containing Hazardous
   Substances.  Technical Monograph No. 6.

	.  1984.  Soil Sampling Quality Assurance User's Guide.  EPA 600/4-84-043.

	.  1984.  Memorandum from Stanley Blacker, about QAMS Checklist for DQO Review.

	.  1984.  Quality Assurance Management and Special Studies Staff. Calculation of Precision. Bias.
   and Method Detection Limit for Chemical and Physical Measurements.  March  30.

	.  1984.  Soil Sampling Quality Assurance User's Guide.  EMSL-LV 600/4-84-043.  May.

	.  1985.  Guidance on Remedial Investigations Under CERCLA Hazardous Waste Engineering Research
   Laboratory Office of Emergency and Remedial Response and Office of Waste Programs Enforcement.

   .  1985.  Sediment Sampling Quality Assurance User's Guide.  Environmental Research Center
   Cooperative Agreement CR 810550.01

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	.  1985. Construction Quality Assurance for Hazardous Waste Land Disposal Facilities Public
   Comment Draft. EPA/530-SW-85-021

	.  1985. Protection of Public Water Supplies from Groundwater Contamination. EPA/625/4-85/016.

	.  1985. RCRA. Groundwater Monitoring Technical Enforcement Guidance Document. Office of Waste
   Programs Enforcement, Office of Solid Waste and Emergency Response.

	.  1985. Draft Superfund Public Health Evaluation Manual. OSWER Directive 9285.4-1. December.
                                           5-b

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        APPENDIX A
STATISTICAL CONSIDERATIONS

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                                          APPENDIX  A
                                STATISTICAL CONSIDERATIONS

Statistical techniques should be used to evaluate environmental data and to assist in designing
appropriate sampling plans based on the data.  Statistical techniques should be applied during PA/SI,
RI/FS, RD. and RA activities.

Statistical considerations come into play in Stages  1, 2. and 3 of the DQO process.  In Stage 1 the
existing data are compiled and evaluated and statistical techniques can  be used to evaluate the
comparability  of different sets of existing data and to evaluate the need to obtain  additional data. In
Stage 2 data quality and quantity needs can be stated in terms of confidence limits  or within other
statistical  framework.  After Stage 3,  statistics can be used to evaluate  newly acquired data and to
assess uncertainty in various decisions.

This appendix provides discussions of various statistical approaches which may be  appropriate for
remedial action programs.  The discussions are based upon hypothetical scenarios which  have or might
occur at hazardous waste sites and  links available statistical methods to potential  applications.  The
scenarios  presented are not the only situations in which statistics can be applied, but they provide  an
indication of the information that can be obtained from statistical methods.

The scenarios will be discussed  in  an  intuitive fashion keeping the use of equations and rigorous
statistical  formalism to a minimum. Hopefully this approach will allow the reader without a strong
background in mathematics  to follow the discussion and grasp the important role which statistical  methods
can play during site investigation and remediation.  Because of the decision to present this material in
a somewhat simplified form, readers with advanced knowledge of statistics may believe that the topics are
not treated in  sufficient detail.  These  readers and others who wish to  obtain additional  information on
the methods presented are referred to the list of references provided at the end of this section.

A.I    CLASSICAL STATISTICS VERSUS GEOSTATISTICS

When applying statistical procedures there are essentially  two possible families of procedures which  can
be applied.  Classical statistical techniques, based on the concept of the random variable,  have been
applied with success for well over 100 years.  Geostatistical techniques, based on the concept of the
random function, were developed in the 1960's, but have been applied very successfully to data from such
diverse fields as mining engineering, petroleum engineering, hydrogeology, soil science,  and, recently,
hazardous waste.  The property  of geostatistical  procedures which makes them applicable  in such a wide
variety of fields is that geostatistical techniques utilize the  location of the data and the size of the
site in all calculations, whereas  classical techniques ignore both of these important parameters.
Because classical techniques ignore data location,  the decision of which set of procedures should be
applied  to a data set is straightforward. If the locations of the data and the size of the site can be
ignored, then  classical techniques can be accurately applied:  otherwise, geostatistical techniques should
be applied.

In the following sections, applications of both classical and geostatistical procedures  will be  provided.
These sections will provide a clear distinction between these methods and  will indicate when each  are
appropriate.

A.2   ACCURACY AND PRECISION OF ANALYTICAL PROCEDURES

The type of statistical information which most readers are likely to  encounter is precision and accuracy
data.  This  data accompanies the results from each case of samples sent to the CLP  and most non CLP
laboratories.  Interpreting accuracy and precision  information can be key  in understanding the
significance of the reported  values and assessing the confidence associated with any  RI decision.
                                               A-l

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Because of the importance of this information, detailed definitions of both accuracy and precision are
provided. A short example is  provided to illustrate the use of these parameters.

If analytical procedures were perfect, the reported anaiyte concentrations would always exactly equal the
actual concentrations present in the sample. In reality, analytical procedures are not perfect, so the
reported and actual concentrations are commonly not identical.  The  difference between the reported
concentration and the actual concentration of a sample is the analytical error.  Without knowledge of the
potential magnitude of the analytical error it is impossible to judge the significance of a reported
concentration.  An example where knowledge of the analytical error is crucial  is the decision to shut
down a drinking water well when the reported concentration is below the action level. Although, in this
case, the reported value is  below the action level, the actual concentration might exceed the action
level due to analytical error.  Without knowledge of the likely magnitude of the analytical errors the
decision maker has insufficient information to make a decision.   If the likely magnitude of errors were
known, the decision maker could examine the impacts and likelihood of an incorrect decision and could
reach an informed, correct, decision.  In this section a procedure for examining analytical errors and
judging the significance of reported values will be discussed.  These  procedures are based solely on
classical statistics.

A.2.1    DEFINITION OF ANALYTICAL ERROR

There are many sources of error which can be introduced when  obtaining a sample.  Some of these sources
are improper sampling procedures, contaminated sample containers and use of improperly decontaminated
sampling equipment.  These types of errors are separate from analytical error and are not considered
here. Analytical error is taken as the error due solely to the analytical procedure. This  error is
measured by laboratory spikes and duplicate samples.

A.2.2    DEFINITION OF ACCURACY AND PRECISION

Analytical procedures can introduce errors  due to a wide  variety of causes, some of which are described
in Appendix B.  It is impossible to deterministically predict the magnitude of each error, so accuracy
and precision have been introduced to summarize the errors of an analytical procedure.   An example will
provide a means of introducing accuracy and precision.  Suppose that a standard sample containing a known
amount of an anaiyte is submitted to four different laboratories (Lab A, Lab B, Lab C, and Lab D), each
using the same analytical procedure.  Each laboratory analyzes ten replicates of the sample. The
following results are obtained.

                           Reported  Concentration  of Standard (ppm)
                                                      Laboratory
               Replicate               A           B             C            D
                  1                     10           13           10          4
                  2                     10           12           14         14
                  3                     10           12            6         15
                  4                     12           11            8           I
                  5                      9           14           12            I
                  6                      8           12            7          6
                  7                     13           II           11            I
                  8                     11            12            5         14
                  9                     12           13           15          II
                 10                     10           12           10          15
  Ten replicates were chosen only to illustrate the concepts of accuracy and precision.  There is no
  implicit or explicit recommendation that each sample be analyzed  10 times to determine accuracy and
  precision.

                                              A-2

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The actual concentration of the standard is 10 ppm.  The majority of people examining these results would
conclude that laboratory A provides the "best" results.  This conclusion is reached because laboratory A
either reports 10 ppm or a value very close to 10 ppm for each replicate.

Presented with results for replicate analyses,  the average person could qualitatively rank a set of
laboratories; however, such a comparison is  time consuming and ultimately not useful, since it is not
quantitative.  To make sense of replicate data it must be summarized in a  meaningful way.  Accuracy and
precision provide a method of summarizing replicate data which allows different analytical procedures and
different laboratories  to be compared.  Accuracy and precision also allow  a determination of the
significance of individual reported values.  The accuracy and precision of common analytical methods are
presented in  Appendix C.

A. 2.3    ACCURACY

Intuitively it  is desirable that, on average, the reported concentration equal the actual concentration
present in a sample.  That is, ideally the analytical method should not have any systematic errors.
Accuracy measures the average or systematic error of a method.  In the example of the four laboratories,
accuracy can be defined as the difference between the average of the 10 reported values and the actual
value (10 ppm).  Performing this calculation, the following  results are obtained:

     Lab         Average of 10 Replicates  (ppm)                 Average Error (ppm)

       A                         10.5                                      0.5
       B                         12.2                                      2.2
       C                          9.8                                    -0.2
       D                          8.2                                    -1.8

These results show that, on average, laboratories A and C yield reported values which are very close to
the actual or spiked value.  Thus, laboratories A and C are  more accurate than laboratories B and D.

Accuracy values can be presented in a variety of ways.  The average error shown  above is one way of
presenting this information; however, more commonly accuracy is presented as percent bias or percent
recovery.  Percent bias is a standardized average error; that is, the average error divided by the actual
or spiked concentration and converted to a  percentage. For Lab A in the  previous example, the percent
bias is .5/10 = .05 or 5 percent since the  actual concentration is 10 ppm. Percent bias is unitless so
it allows the accuracy of analytical procedures to be compared easily.

Percent recovery provides the same information as percent bias. Since accuracy is often determined from
spiked samples, laboratories commonly  report accuracy in this form.  Percent recovery is defined as:

         % Recovery =   R X 100
                          S
           where   S = spiked concentration
                   R = reported concentration


Given this  definition it can be shown that


                   % bias =  % recovery -  100



                                               A-.l

-------
For this example, the observed  % bias and %  recovery are:


       Lab                          Percent Recovery                       Percent Bias

        A                                 105                                       5
        B                                 122                                      22
        C                                  98                                      -2
        D                                  82                                    -18

A. 2.4    PRECISION

Whereas accuracy measures the average properties of an analytical method, precision examines the spread
of the reported values about their mean.  The spread of reported values refers to how different the
individual reported values are from the average reported value.  Precision can thus be seen as a measure
of the magnitude of the errors.

Precision  can be measured in a variety of ways, each of which has its merits. A simple measure of
precision is the variance.  The sample variances, calculated using  the standard formula for the sample
variance, for the 10 replicate samples sent to the 4 previously discussed labs are as follows:

         Lab                    Variance of the replicates

           A                                  2.3
           B                                  0.8
           C                                  11.1
           D                                 38.4

These results  indicate that Lab B  is the most precise.  This could  be determined by examining the ten
individual values reported by Lab B which are ail extremely similar.  The Lab D reported values are  very
dissimilar.  This feature is expressed by a large variance of the replicates.

Laboratories commonly determine precision  from duplicate samples; thus precision is usually expressed as
relative percent difference (%RPD) or relative  standard deviation (%RSD).  These quantities are defined
as follows.

         % RPD =  100 x 2   ]X± - X, |
                              (Xx + X2)


         where X  and X.  are the reported concentrations for each duplicate sample


         % RSD =  (IOO//2) x [2|X1 -  X2 j/(X1  + X2)]


A.2.5    SUMMARY OF ACCURACY AND  PRECISION

Based on the definitions of accuracy and precision, the performance of each of the four laboratories can
be summarized in relative terms.
                                              A-4

-------
                  Lab            Accuracy               Precision


                   A               High                   High

                   B               Low                  Very High

                   C               High                   Low

                   D               Low                   Low
From this summary, it appears that Lab A provides the most reliable values. Notice however, that
although Lab B has low accuracy, its precision is very high.  Thus, if the reported values are corrected
for the systematic error introduced by the laboratory, Lab B is superior to Lab A.  In other words,  if
2.2 ppm (which is the absolute average error, as calculated previously) is added to each of the values
reported by Lab B. the reported values will have both very high accuracy and precision.  This example
demonstrates that if the bias of an analytical method is known, it can be easily be removed;  however, it
is  not possible to correct  for low precision.

A.2.6    USING ACCURACY AND PRECISION INFORMATION

The accuracy and precision of four laboratories have been  determined for a  specific analyte.  This
information can now be used in the DQO process. For  the purpose of this  example, assume that a  drinking
water sample is sent to a  single laboratory.  The sample  will be analyzed for four suspected
contaminants. The historical accuracy and precision of the analytical procedures are known for these
four analytes.  The action levels for the four contaminants  are:

                         Contaminant                    Action  Level

                              A                               12

                              B                               10

                              C                               15
                              D                               15
The lab reported the following concentrations for the sample:
                  Contaminant                    Reported Concentration
                         A                                    9.0
                         B                                    9.99
                         C                                    7.0
                         D                                    8.0


All analytes  except contaminant B are reported at concentrations below the action levels.  The reported
concentration for contaminant B is almost exactly at the action level. Based on these results, the well
water might be considered to be safe for drinking.

Accuracy and precision information, as found in Appendix F. can be used to determine the  safety of the
drinking water by determining the probability that the actual concentration of each analyte present in
the sample exceeds the appropriate action level.
                                               A-5

-------
 The first step is to correct for the bias of each analytical procedure.  To correct for bias,  divide the
 reported concentration by the average percent recovery which is determined from spiked samples analyzed
 with the present sample, or historical information.  Note that systematic correction of reported values
 for bias is  not recommended; however, it is performed in this example  because it is assumed that the bias
 is well known  .  The corrected values are presented below.                                                      >

                                Reported                  Percent            Corrected                        ;
     Contaminant               Concentration              Recovery            Value                          .

            A                        9.0                     105                8.6                          I
            B                        9.99                    122                8.2                          \

            C                        7.0                      98                7.2                          I

            D                        8.0                      82                9.8                          1
                                                                                                              I

  The standard deviation, S, for the analytical procedures can  be calculated from the percent                        j
  relative standard deviation, percent RSD. The standard deviation (S) is calculated in  the                          ?
  following table by multiplying the reported value by the percent RSD.                                            j
                                                                                                              i
                                 Reported                                                                     \
     Contaminant               Concentration                    %RSD              S
                                                                                                              i
            A                      9.0                           14.5              1.3                         :

            B                       9.99                          7.5               .75                        J

            C                       7.0                           34.0              2.4                         !

            D                      8.0                           75.6              6.0


A simple technique for presenting the uncertainty in analytic results  is to present the probable range of
values which might be expected from the analytical procedure. In a quality control chart, the probable
range is usually _+3 standard deviations about the expected value, in our case the corrected value.


    Contaminant

         A
         B
         C
         D


The upper  limit  of the probable range is
                  Corrected Value  + 3 x Standard Deviation.
Reported
Concentration
9.0
9.99
7.0
8.0
Corrected
Value
8.6
8.2
7.2
9.8
S
1.3
.75
2.4
6.0
Action
Level
12
10
15
15
Probable
Range
(4.7,12.5)
(6.0,10.5)
(0 ,14.4)
(0 ,27.8)
   Correcting for bias in an analytical procedure should be done on a contaminant by contaminant
 basis, taking into account the nature of the media and the matrix  being analyzed.
                                               A-6

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Note that only contaminant C has an upper limit which does not exceed the action levels.  The upper limit
for contaminants A and B just exceeds the action level. (12.5 vs.  12 and 10.5 vs. 10).  Contaminant D's
upper limit is well above its action level, even though  its reported value is only 8.0.

If the distribution of reported values is assumed to be  normal, the probability that the actual sample
concentration exceeds the corresponding action level can be calculated.

Tables of the normal distribution are available in all statistics books.  These tables give the
probability of exceeding a series of standardized variables.  To utilize these tables, the reported
analytical values must be standardized.  The standardization is

                   Z = X-Xr
                       ~T

      where   Z  = standardized value

              X  = action  levei

              Xc = corrected reported concentration

              S =  standard deviation of the analytic test.


The values X, Xc ,  and S are known in this example and can be used  to determine the
probability of exceeding the action levels, Pr[Xc )  Action  Level].

                        Reported              Action
    Contaminant      Concentration           Level             Z

         A                 9.0                   12             2.6           .005

         B                 9.99                 10             2.4           .008

         C                 7.0                   15             3.3           .001

         D                 8.0                   15             0.87          .19


These probability values indicate that by  utilizing accuracy and precision information, the significance
of the reported values can be assessed.  Even though all the reported concentrations were below the
action levels,  further analysis demonstrates that contaminant D has a 19 percent chance of being greater
than the action level of 15.  Contaminant B, with a reported  value at the action level, 9.99 vs.  10.0,
has in actuality less than a  1 percent chance of exceeding the action level of 10.

The precision of the analytical procedure for analyte D is poor as expressed by its high percent RSD of
75.6  percent.  Precision can be improved by analyzing sample replicates or splits.  If the lab analyzed
three splits, the percent RSD and standard deviation would have been reduced by 58 percent (1//3).  The
new S would have been 3.5 (6x.58), and the new Z value  1.48.  Thus, the new probability that contaminant
D would exceed the action  level of 15 is  only 7 percent which could be an acceptable risk depending on
the toxicity and health effects of contaminant D.

This simple example demonstrates the importance of accuracy and precision information and indicates the
possible consequences of ignoring these data.  Because of the importance of accuracy and precision
information. Appendix F, which gives accuracy and precision data for many common analytical  techniques,
has been compiled.  Decision makers are urged to examine this appendix and to utilize the information
prior to reaching a  decision.
                                               A-7

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 A.3   PROBABILITY OF LOCATING A CONTAMINATED ZONE

 At sites or portions of sites where soil contamination is suspected but no definite sources have been
 identified, an objective of the remedial  investigation might be to determine if soil contamination is
 present.  Important decisions facing the site  manager are how many samples must be taken to investigate
 the potentially contaminated area and where  the samples will  be located.

 In certain situations geophysical surveys can be utilized  in determining the location of contaminated
 zones.  Geophysics can effectively be used to determine  the locations of certain ground water plumes
 (such as hydrocarbon plumes) and concentrations of buried metallic objects (drums and tanks).  The
 following discussion concerning the probability of locating a contaminated zone  is applicable to
 geophysical methods  as well as to standard sampling techniques.

 The decision  maker must determine, in Stages 1  and 2 of the DQO process, the acceptable probability of
 not finding an existing contaminated zone in the suspected area.  For instance, it might be determined
 that a 20  percent chance of missing a 100-ft-by-lOO-ft contaminated zone is acceptable  but only a 5
 percent chance of missing a 200-ft-by-200-ft zone is acceptable.  This probability value provides the
 basis for  using statistics to determine how  many samples are required.  Statistical methods can be used
 to determine the number and  location of data required to lower the probability of missing an existing
 contaminated  zone to a value less than the acceptable predefined value.  The acceptable probability of
 missing a contaminated zone must be established by the  decision maker working in concert with the data
 users.  Individuals involved in developing risk assessments may provide meaningful inputs into
 determination of the appropriate probability values to be  utilized.

 The statistical method applied in this instance involves geometric probabilities.  That is. the
 probability of not  identifying a contaminated  zone is related to the area or volume of the contaminated
 zone and  the spatial  location of the samples.  This method is not clearly a classical statistical or
 geostatistical  procedure, it  will be considered as a hybrid statistical method.

 To apply this  method, the following assumptions are required:

      •    The shape and size of the contaminated zone must be known at least approximately.  This known
          shape will be termed the target.

      •   Any sample located within the contaminated zone will identify the contamination.

 These assumptions are not severe and should be met in practice.

 If,  in addition to the above assumptions, data are located on a perfectly regular grid and the target is
 circular, the probability of hitting the target for a given grid size  is given by the following (Gilbert
 1982):

           Probability of a Hit                      G/A


                 0.8                               1.13
                 0.9                               1.01
                 0.95                              0.94

                 0.99                              0.86
where A is the diameter of the target and G is the linear grid spacms.

-------
If data are not regularly located or the target is not circular, a simulation procedure is used.

The procedure used is hit or miss simulation involving the following steps:

     •   Simulate a contaminated zone or target.

     t   Randomly locate the  target within the site.

     •   Determine if any sample locations fall within the boundaries of the target. If so score a
         hit, otherwise a miss.

     •   Simulate and randomly locate several hundred targets using a computer program and record the
         number of hits and misses.

The probability of locating the contaminated zone is equal to the total number of hits divided by the
total number of simulations.

Figure A-l illustrates  the hit or miss approach  for two simulated contaminated zones.  The  method is
flexible so various different sample configurations and various different target sizes can be quickly
examined.  By varying the number of samples for a fixed  target, the number of samples required to lower
the  risk of missing the contamination to an acceptable level can be determined.  Thus, this method allows
determination of both  the number and location  of samples necessary to satisfy DQOs.

A.4   CONFIDENCE LIMITS ON ESTIMATES OF MEAN CONTAMINATION

At sites where contamination is known to exist, a parameter of interest  is the mean contaminant
concentration over the contaminated area.  Mean contaminant concentrations are important when evaluating
contaminants contained within  a confined area such as a lagoon.  In this case, the  mean contaminant
concentration determines the total amount of contaminants contained in the lagoon.  To assess various
remedial alternatives it is important to know the maximum quantity of contaminants present in the lagoon.
Confidence limits can  be used  to state the probable  range of total contaminants contained in the lagoon.

Confidence limits can, theoretically, be placed on any quantity calculated from a data set. Perhaps the
most useful quantity is the sample mean.  When the sample mean is calculated from a set of data, it is
unlikely that the actual or population  mean will equal the sample mean. The sample mean for a fixed
number of data is a random variable whose value will fluctuate depending on the specific data collected.
Confidence intervals are a method of quantifying the likely range of fluctuation of the sample mean.
Confidence intervals are defined as follows; if the 95  percent  confidence interval is set for the sample
mean after each  repetition  of an experiment and the experiment is performed 100 times, the population
mean is expected to fall between confidence limits 95  times.

For example, 20 soil samples are collected at a site with known soil contamination.  The sample mean is
calculated from these  samples and is determined to  be 14 mg/kg of an analyte of interest. Furthermore,
it is determined that the 95 percent confidence limits  for this sample mean are  12 and 17 mg/kg.  In this
example, there is a 95 percent chance that the  actual  mean soil concentration falls  between 12 and 17
mg/kg.

To determine a confidence interval the distribution of the sample mean  must be known.  To determine the
distribution at least three quantities are required.  These quantities are the estimated sample mean, the
variance of the sample mean and the  shape of its distribution.  Both classical and geostatistical
approaches can be used to determine  these quantities.  Each of these methods will be discussed
individually; however, before proceeding it must be noted  that neither of these methods can be applied
without site-specific information.


                                              A-9

-------
                                            B
NOTE:  This figure illustrates two possible simulations of a circular target for a fixed
   set  of data locations.  The upper figure  (a) illustrates a hit while the lower
   figure  (b)  illustrates  a miss
                                        A-1
                           HIT &  MISS  EXAMPLE
                                       A-IO

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A.4.1    THE CLASSICAL STATISTICAL APPROACH

Classical statistical approaches assume that the distribution of the sample mean follows Student's t or,
if more than 30 data are available, a normal distribution.  This assumption is considered valid because
of the power of the central limit theorem which states that regardless of the distribution of the data,
the sample mean follows a normal distribution when sufficient data are obtained.  The drawback of the
classical statistics approach is that the variance of the sample mean is taken as the variance of the
data divided by the number of the data available regardless of the location of the data or the size of
the site.

Thus, when using classical statistics to determine a confidence interval for a sample mean based on  10
data, it  does not matter whether the data are spread uniformly over the site or clustered in one corner;
nor does it matter if the site covers  1/4 acre or 20 square miles.  The confidence  intervals in  each of
these cases will be identical.

Since all scientists and  engineers working on  hazardous waste sites realize that both data location and
the size of the site are crucial factors in analyzing the significance of data, it is not logical to
apply a  procedure which does not account for these important factors. However, if in some specialized
instance it is deemed that sample locations are not important, the classical  statistical procedures based
on the t statistic yield simple formulas for determining confidence intervals and data requirements.
These formulas are provided in  many references  including EPA 1984 and  EPA  1985.

A.4.2    GEOSTATISTICAL APPROACH

Geostatistics, or more  formally, the theory of regionalized variables, is similar to classical statistics
in many ways.  Most importantly, it differs  with respect to basic assumptions regarding independence of
the data. Classical statistics assumes that data are mutually independent, that is, that one data point
is not related to another.  Geostatistics recognizes that observed concentrations are governed by
physical processes; thus, one particular point  in space yields information concerning the expected
contaminant level at a  location 5 or 10 ft away from the sampled  point. In statistical terms, these data
are correlated in space.  Geostatistical tools  measure and exploit the correlation between data to
estimate contaminant concentrations  and determine the uncertainty associated with the estimate. In other
words,  geostatistical  methods consider the location of data and the size of the site in any calculation.

Geostatistics can be used to determine the variance of errors associated with any weighted estimate of
the sample mean.  In particular, geostatistics can be used to determine the variance of errors  associated
with estimating the true mean contaminant concentration by the average of the available data.   The
detailed derivation of the methodfor determining confidence intervals is given by Journel and Huijbregts
(1978).   A brief discussion of the method is provided here.

An estimate of the true mean site contamination can be determined from an average of the available data.
The estimate is  not,  in  general,  equal to the true mean so an error is made. The error of estimation is
defined  as the estimated mean less the true  mean. The particular error observed is one realization of
the error random variable.  The variance of the error variable is  unknown, but it  is known that the mean
of the error distribution is zero  since only unbiased estimators will be used.  The  variance of the error
distribution  can be determined  using geostatistics.

The variance estimate requires knowledge of the average correlation between the data and the average
correlation between the data and the volume defining the site. Determination of these quantities
requires a model of correlation at the site.   This correlation model is provided by  the experimental
variogram determined  from the data.  The experimental variogram  is defined as follows:
                                               A-ll

-------
          g(h)  =J Z(z(x.  + h) -z(x.))2

                n(h)


            Where:  n(h) is the number of data separated by distance h

                     z(x. ) is the contamination observed at location x£

                     z(x^ + h) is the contamination observed at location XA +h

                     g(h) is the experimental variogram for distance h


By varying h, a model of the variogram versus h can be developed and applied to determine the variance of
errors.  An example of a variogram is provided  in Section 5.5.3.2 of the DQO example document.

To this point, the mean and variance of the distribution of errors  have been discussed.  The remaining
parameter of interest is the shape of the distribution of errors.  As the number of data used to estimate
the true mean increases, the distribution of errors becomes more  and  more like a normal distribution.
This is  not a theoretical result but an observation from practical applications.  Given that the errors
follow an approximately normal distribution, the confidence limits can be determined by the following
procedure.

     •    Define the level of confidence required.

     •    Find the standard normal variate corresponding to this probability in a  normal table.

     •    Apply the following formula:

          Z-ys
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A.5    LOCAL ESTIMATION OF CONTAMINATION

In many instances, the contamination at a particular point within the site is of interest.  Determination
of contaminant concentrations at unsampled locations is termed local estimation.  For example, consider a
site with a known source of contamination.  Available information indicates that contaminants are
migrating toward the western edge of the site.  An objective of the RJ might be to determine the western
extent of contaminant migration.  Geostatistics can be used to determine the likely extent of
contamination.  This information will greatly aid in choosing  data locations.

A second example where local estimation is important is in determining optimal contours  for a variable.
For instance,  in many enforcement cases an accurate determination  of the ground water gradient is
required to correctly identify potentially responsible parties.  Water  levels are measured in wells which
are separated by varying distances. The heads between wells  must be obtained.  To ensure that the
estimated heads and associated contour lines are as accurate as possible  the heads at unsampled points
should be estimated optimally using geostatistics.

Geostatistics can  be used to address problems presented in the previous scenarios.  The geostatistical
technique which will be applied  is known as kriging.  Kriging, which is similar to multiple regression,
determines an optimal estimate of a variable at any particular  location in space.  Associated with this
estimate is a measure of uncertainty known as the kriging variance.   To apply kriging, a model of the
correlation between data is required.  This  model is obtained  by modeling the experimental variogram of
the data.

An example of the  use of kriging to optimally estimate  the concentration of lead in soil surrounding a
smelter is shown in Figure A-2.

A.6    LOCAL ESTIMATION OF PROBABILITY

At soil contamination sites  where a fixed cleanup criterion has been set, geostatistics can be used to
estimate the risk  associated with not removing any particular quantity of soil. Geostatistics can be
used to quantify the probability of exceeding this criteria and  to develop  probability contour maps.
This map may be used in conjunction with the acceptable  uncertainty determined during Stages 1 and 2  of
the DQO process to define what volume of soil must be removed.

To determine  the probability of exceeding a given value at an  unsampled point it is necessary to estimate
the entire contaminant distribution at that point.  Given this distribution, the probability that the
contaminant concentration exceeds any value of interest can be determined.

An example of a probability map is provided in Figure A-3.  In this example. lead contamination has been
found in soil surrounding a lead smelter.  It has been determined that all soil in excess of 1000 ppm
will be  removed as part of the remedial action.  The probability map gives the likelihood of exceeding
1000 ppm at  each  point in the site.  If, through the DQO process,  30 percent had been  determined  as an
acceptable probability of exceeding 1000 ppm. then all soil within the 30 percent contour would  be
removed.  The remaining soil would have,  at most, a 30 percent chance of exceeding  1000 ppm.  If  a
different acceptable probability was defined, the volume of soil removed would be defined by the
particular contour.  This method provides an objective method for determining the volume of soil to be
removed.

Techniques for estimating local probability distributions include indicator kriging. probability kriging.
and multivariate gaussian kriging.  (Journel 1983: Sullivan 1984: Verly 1983: and Isaakes  1983).  These
techniques are known as non-linear estimators and are related to but are more complex than kriging.
These estimators require an accurate and detailed model of the correlation structure of the data to be
effective.

-------
N
          1000
            (ft.)
           750 *
           500 •
           250 •
                         250
500
750
1000('t-)
               Nolt:
                    Contour map of lead concentration in soil surrounding a smtlttr. Contours
                    ara bis«d on tjtimam of soil Itid concamration (in ppm) dttarmmcd by
                    kriging.
                                           A-2
                              EXAMPLE  OF  KRIGING
                                       A-14

-------
N
           1000
            (ft.)
            750 -
            500 -
            250 -
                          250
500
750
1000<«.)
              NOTE: Probabilities of exceeding 1000 ppm soil lead concentration near a smelter.

                   Material with probabilities exceeding the acceptable risk defined in the DQO

                   process will be removed as part of the remedial action.
                                              A-3

                                   PROBABILITY  MAP
                                        A-15

-------
 An important feature of non-linear estimators is that any uncertainty in the data values stemming from
 laboratory or sampling errors can easily be incorporated into the estimate.  Since non-linear estimators
 can be used  to  estimate the mean or variance at a point  or over a region, these techniques provide a
 means of including uncertainty in any regional or local  estimate of the mean.  The uncertainty associated
 with these estimates will include the uncertainty present in the data.

 A.6   REFERENCES

 Addiscott, T.M., and J.R. Wagenet.  1985. A Simple Method for Combining Soil  Properties that Show            '
   Variability. Soil Science Society of America  Journel.  49:  1365-1369.

 Box, G.E.P., W. Hunter, and J.S. Hunter. 1978. Statistics  for Experimenters:  An Introduction to
   Design, Data Analysis, and Model Building.  John Wiley and Sons. New York.                                |
                                                                                                           I
 Camp Dresser & McKee Inc. (CDM).  1986. Statistics for Contaminated Zones at the North Cavalcade             j
   Site, CDM Internal Correspondence, J. Sullivan.                                                            !

 EPA. 1984. A Soil Sampling Quality Assurance User's  Guide  EPA 600/4-84-043                                j

 EPA. 1985.  Sediment Sampling Quality Assurance User's Guide EPA  600/4-85-048                            j

 Flatman,  G.T.  1985 Design of Soil Sampling Program:  Statistical Considerations Draft.                            '

 Flatman,  G.T.  and A.A. Yfantis. 1984. Geostatistical Strategy  for Soil Sampling: The Survey and the              •
   Census. Environmental Mentoring and Assessment 4:335-349.                                               \

 Gilbert, 1982.  Some Statistical Aspects of Finding Hot  Spots and Buried Radioactivity.  TRAN-STAT              j
   Statistics for Environmental Studies,  Batelle Institute, Richland Washington, No. 19.

 Isaaks, E., 1984. Risk Qualified Mappings for  Hazardous Waste Sites:  A case study  in distribution
   free geostatistics, unpublished master's thesis, Stanford University.

 Journel, A.G.,  1983.  Non Parametric Estimation of Spatial Distributions, Journal of Mathematical
   Geology, Vol. 15, No. 3, pp. 445-468.

 Journel, A.G. and Ch.J. Huijbregts.  1978. Mining Geostatistics. Academic Press, London.

 Klusman, R.W. 1985. Sample Design and Analysis for  Regional Geochemical Studies. Journal of
   Environmental Quality. 14:369-375

 Ripley, B. 1982. Spatial Statistics. John Wiley & Sons, New  York.

 Russo, D. 1984. Design of an Optimal Sampling Newtwork for Estimating the Variogram.  Soil Science
   Society of  America Journal 48 (4): 708-716

 Sullivan, J. 1984.  Conditional Recovery Estimation  through Probability Kriging - Theory and
   Practice, in Geostatistics for Natural  Resource Characterization.  Reidel,  Dordrecht, Holland.

Verly, G.  1983. The Multigaussian Approach and its Application to the Estimation of Local Reserves,
   Journal of Mathematical Geology, Vol. 15. No.  2 pp. 263-290.

Yost, R.S.. G. Uehara and R.L. Fox. 1982. Geostatistical Analysis of Soil  Chemical  Properties of
   Large Land Areas.  I. Semi-variograms. Soil  Science Society of America Journal 46(5): 1028-1032
                                             A-16

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Yost.  R.S.. G. Uehara and R.L. Fox.  1982. Geostatistical Analysis of Soil Chemical Properties of
   Large Land Areas. II. Kriging. Soil Science Society of America Journal 46(5): 1033-1037

Zirchky. J., Deary. G.P., Gilbert. R.O.. Middlebrooks. EJ. 1985.  Spatial Estimation of Hazardous
   Waste Site Data. In: Journal of Environmental Engineering. Vol.III. No.6 pp. 777-787.
                                             A-17

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       APPENDIX B
ANALYTICAL CONSIDERATIONS

-------

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                                         APPENDIX  B
                               ANALYTICAL CONSIDERATIONS

Analytical methods must be evaluated during the development of site specific data quality objectives.
The parameters for which the analytical method is valid, its limitations, and any special considerations
(such as sample preparation)  which will affect data quality must be understood in order to select
appropriate analytical methods for specific uses.

This section provides an overview of the analytical considerations which should be taken into account
during DQO development. Analytical considerations must be evaluated concurrently with statistical and
sampling considerations in order to ensure that established DQOs  can be attained.

B.I    ANALYTICAL SUPPORT LEVELS

The analytical options available to support data collection activities are presented in five general
levels.  These levels are distinguished by the types of technology and documentation used, and their
degree of sophistication as follows:

     •   LEVEL V - Non-standard methods. Analyses which may require method modification and/or
         development.  CLP Special Analytical  Services (SAS) are considered Level V.

     •   LEVEL IV - CLP Routine Analytical Services (RAS). This level is characterized by rigorous
         QA/QC protocols and documentation and provides qualitative and quantitative analytical data.
         Some regions have  obtained similar support via their own regional laboratories, university
         laboratories, or other  commercial laboratories.

     •   LEVEL III - Laboratory  analysis using methods other than the CLP RAS.  This level is used
         primarily in support of engineering studies using standard EPA approved procedures.  Some
         procedures may be  equivalent to CLP RAS. without the CLP requirements for documentation.

     •   LEVEL II - Field analysis. This level  is characterized by the use of portable analytical
         instruments which can be used on-site, or in  mobile laboratories  stationed near a site
         (close-support labs).  Depending upon the types of contaminants, sample matrix, and personnel
         skills, qualitative and  quantitative data can be  obtained.

     •   LEVEL I - Field screening.  This level is characterized by the use of portable instruments
         which can provide real-time data to assist in the optimization of sampling point locations
         and for health and safety  support. Data can be generated regarding the presence or absence
         of certain contaminants (especially volatiles) at sampling  locations.

Table B-l provides a summary  of the analytical levels, their applicability, and limitations.  Within each
level, different procedures may  be used to produce different quality data to  some extent. For example.
Level II  encompasses both mobile laboratory procedures and less sophisticated "tailgate" operations which
may produce data of different quality.

B. 1.1    LEVEL V ANALYTICAL SUPPORT  - NON-STANDARD METHODS

The objective of non-standard analytical support  is to provide the RI/FS  process with data that cannot be
obtained through standard avenues  of analytical support. Analytical support of this type may involve the
research, development and documentation of a method,  or more typically,  the modification of an existing
method.  EMSL-LV should be consulted for protocol availability,  modification,  or development.  Level  V
methods  are available through CLP Special Analytical Services  (SAS). university laboratories, commercial
laboratories. National Enforcement Investigation Center, and Environmental Services Division.  Not all
SAS analyses are non-standard;  they may just not be part of CLP  protocols.

                                              B-l

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                                            TABLE B-l   SUMMARY  OF  ANALYTICAL LEVELS FOR RI/FS
                Option
Type of Analysis
Uses
                                                                                          Limitations
CD
r-j
                Level  V       - Non-convential
                                parameters
                              - Method-specific
                                detection limits
                              - Modification  of
                                existing methods
                              - Appendix 8  parameters
                              - TIC
                Level  IV      - HSL organics/inorganics
                                by GC/MS;  AA;  ICP.
                Level III     - Organics/inorganics
                                using EPA procedures
                                other than RAS  can  be
                                analyte-speci fie
                              - RCRA characteristic
                                tests
                              - Confirmational
                              - Toxicology
                              - Site-specific
                                conditions/parameters
                              - RCRA compliance
                                Confirmational
                                Toxicology
                                All  other program
                                activities
                                Confirmational but with
                                less documentation
                                Presence or absence of
                                contaminants
                                Engineering uses
                                Screening
                              - Requires method
                                development/modi fi ca-
                                tion
                              - Mechanism  to obtain
                                services requires
                                special leadtime
                              - Calibration standards
                                may not be readily
                                available

                              - Tentative  identifica-
                                tion of non-HSL parameters
                              - Some time  is Required
                                for validation of
                                packages

                              - Methods may vary
                Level II
                Level I
  Variety of organics
  by GC; inorganics
  by AA; XRF
  Tentative ID;  analyte-
  specific
  Detection limits vary
  from low ppm to low ppb
  Portable/mobile
  instrumentation

  Total organic  vapor
  detection using
  portable instruments
  pH, conductivity,
  salinity, DO
  Presence or absence of
  contaminants
  Relative concentrations
  Engineering
  Screening
- Assist in identifying
  sample locations
- Field screening
- Health and safety
- Tentative ID
- Techniques/instruments
  1imited
- Instruments respond to
  naturally-occurring
  compounds

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                                       TABLE B-l  SUMMARY OF ANALYTICAL LEVELS FOR RI/FS
                                                          (continued)
                Option
Data Quality
Cost
Time
                Level V
- Method-specific
ca
                Level IV
                Level III
                Level II
                Level I
  Rigorous  QA/QC
  Standard  methods
  Similar detection
  limits  to  CLP
  Less  rigorous  QA/QC
  Dependent  on  QA/QC
  steps  employed
  Data  typically  reported
  in concentration  ranges

  If instruments  cali-
  brated and data
  interpreted correctly,
  can provide indication
  of contamination
- Initially high,
  if method development
  is required.
  $l,000/Sample for
  organics
  $200/Sample for
  metals
- $960/Sample for
  organics
- $200/Sample for
  metals

- $15-40/Sample
                                                             Negligible,  if
                                                             capital  costs
                                                             excluded
  Entries refer to
  all  types of
  analysis listed.
  No specific time/
  cost requirements
  can be specified.
  In general the
  time frame can
  range from a few
  weeks to signifi-
  cantly longer if
  method development
  is needed.

  Contractually,
  30-40 days
  Shorter turnaround
  time possible
  through SAS
  request

  14 days, but can
  vary based on
  contract require-
  ments.

  Real-time to
  several hours
                          - Real-time

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The analysis of samples for the RCRA modified Appendix VIII list of contaminants could currently be
considered a Level V application.  The modified Appendix VIII list contains 92 organic compounds that are
not a part of the Hazardous Substances List (HSL) and therefore are not normally tested for on samples
obtained from CERCLA sites.  Appendix D of this document contains tables from a preliminary feasibility
study performed to address the applicability of using or modifying existing analytical procedures for
Appendix VIII analysis.

Level V poses limitations to implementation because the amount of lead-time for start-up may be
significant, and the analyses may be "one-of-a-kind"  applications of the method, resulting in a lack of
comparability of the data.  The unit costs for Level V sample analysis are dependent on the analysis
requested.  Generally, initial unit costs will be high,  reflecting the costs of becoming familiar with
the method. If the method is  used for other projects or sites,  unit costs may decrease with  the demand,
and the method may become standard. The amount of documentation available for Level V analytical support
will vary depending on the sophistication of the technology used.  If method development is required,
this information should be requested and reviewed by the user.

Accuracy and precision information is generally not available for Level V.  Thus, when level  V methods
are used,  the number of duplicate and spiked samples must be increased to allow a determination  of the
accuracy and precision of the  method.

B. 1.2    LEVEL IV ANALYTICAL  SUPPORT - CONTRACT LABORATORY PROGRAM (CLP)
         ROUTINE ANALYTICAL  SERVICES  (RAS)

The CLP RAS provides for analyses of all types of media for Hazardous  Substance List (HSL) organic
compounds and priority pollutant inorganic compounds. (CLP RAS does not provide biota  and air media
[adsorption tube] analyses.) These services are available through  CLP RAS and regional EPA ESD
laboratories.  Level IV analyses are currently used for most RI/FS activities. However, the use of level
IV data may not be required for many RI/FS purposes.  Level  IV analyses are typically used  for
confirmation of lower level data, risk  assessment, and to obtain highly documented data.

CLP RAS generated data have the following properties:

     •   Confirmed identification and quantitation of compounds (for HSL parameters only unless
         otherwise specified)  to the detection specified in the IFB.

     •   Tentative identification of a  contractually-specified number (30) of non-HSL parameters.

     •   Sufficient documentation to  allow qualified personnel to  review and evaluate  data quality.

     •   Uniform methods of analysis activities.

     •   Detection limits may not be sufficient for toxicological evaluations

     •   CLP support is one  of the most expensive routine analytical services available to the
         Superfund program, (e.g., RAS for organics is about $1,000/sample.  RAS  for inorganic is
         about $200/sample).

     •   RAS is contractually operating on a 30-40 day turnaround  although delays can occur. Since
         demands fluctuate, space may be limited at times for  the Superfund program.  In  addition,
         data validation usually takes 3-4 weeks after data is received.

The CLP RAS is very specific concerning the documentation that  is supplied with every data package.   The
RAS deliverables package contains information on initial and continuing  calibration. GC/MS tuning,


                                              B-4

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 surrogate percent recovery, and matrix spike duplicates.  In addition, hard copies are provided of
 reconstruction ion chromatograms. GC chromatograms. and spectra  for every sample and every blank,
 standard, or spike run with a particular set of samples.  Documentation is also provided for blank
 analyses, internal chain  of custody and holding times.

 The bias and precision of CLP analytical procedures can be assessed by examining the performance of the
 laboratory in analyzing matrix spikes.  However, an indication of the performance of the laboratory is
 also provided by the results of quarterly laboratory performance evaluation samples.  These evaluation
 samples are submitted blind so the laboratory has no indication of the actual contaminant value.  In
 contrast, the laboratory knows the exact concentration of a matrix spike.

 Historical CLP precision and  accuracy data classified by media are presented in Appendix F as Level IV.
 Each table is footnoted to show the source of the precision and accuracy data and, to the extent
 possible, the type of QC samples used, the numbers of data points, etc. Contract required detection
 limits are presented in Appendix H.

 B. 1.3   LEVEL III ANALYTICAL SUPPORT - LABORATORY ANALYSIS

 Level  III analytical support is designed to provide laboratory analysis using standard EPA approved
 procedures  other than current CLP RAS.  This level is used to obtain similar analysis with less
 documentation.

 Generally the analyses performed using Level III techniques are designed  to provide confirmed
 identification and  quantification of organic and inorganic compounds  in water, sediment, and soil
 samples.  These analyses are available through commercial laboratories, ESD, CLP SAS, and the CLP
 screening service  (in  development).

 Level III provides data for site characterizations, environmental monitoring,  confirmation of field data
 and to support engineering studies (e.g., design, modeling, and pilot/bench studies).  In specific cases,
 Level III analyses can also provide data for risk  assessment requirements.

 Level III laboratory analysis provides the following:

      •  Data to support engineering design  parameters

      t  Data for use in evaluating the site for further action, e.g., to determine extent of
         environmental contamination

      •  Data for use in risk  assessments

     •  Rapid turnaround of data may be available

     •  Detection limits for presence or absence of compounds  comparable to Level IV

     •  Costs range from about $200/sample for inorganics  to $960/sample. for organics analysis.
         Turnaround time for Level III laboratory analysis for organics is expected to be about  14-21
         days.

Level III protocols all have built-in QA/QC, including calibration runs, surrogate standards, etc.
External QA, which is also used for the CLP. is  employed in  the form of trip blanks, replicate and
duplicate samples, and blind spikes submitted with the samples.

The type of  laboratory support available under Level III ranges in sophistication from GC/MS
instrumentation to the measurement of water quality parameters.  The type and amount of documentation

                                              B-5

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available depends on the type of analysis requested.  Data users should review a sample report issued by
the laboratory for the analysis requested to determine if the degree of documentation supplied is
adequate or whether additional information must be requested.   If the documentation is sufficient, Level
III could save time and cost.

Accuracy,  precision and MDL information that  is  considered representative of this level of analytical
support was compiled from SW-846.  This information is provided  in Appendix F.  These procedures are
applicable  for all sample matrices; however, the SW 846 information presented was derived from the
analysis of water and  wastewater samples and  performance evaluation standards.  Therefore, the criteria
specified in this table should be considered as "best case" information when non-aqueous media samples
are analyzed.  Also, these data are presented irrespective of the  sample pretreatment or preconcentration
techniques used. These techniques may include liquid-liquid extraction (3520), acid/base-neutral
clean-up extraction (3530), soxhlet extraction  (3540), sonication extraction (3550), headspace (5020),
and purge  and trap (5030).  They are used in conjunction  with  the  analytical procedures presented in SW
846.

B. 1.4    LEVEL II ANALYTICAL SUPPORT -  FIELD ANALYSIS

Level II analytical support is designed to provide real-time  data for ongoing field activities or when
initial data will provide the basis for seeking laboratory analytical support.  Level II analysis can
also be utilized effectively when a phased approach is used for field sampling.  In a phased  sampling
effort,  the  results of the first phase guide the development of subsequent phases, and thus, real-time
data are important.

Field analysis involves the use  of portable or transportable  instruments which are based at or near a
sampling site.  Field analysis should not be confused  with the process  of obtaining total organic
readings using portable meters.

Field analysis can  provide data from the analysis of air, soil and water samples for many Hazardous
Substance  List (HSL) organic compounds, including volatiles, base neutral acid (BNA) extractable
organics, and pesticides/PCBs. Inorganic analysis can also be conducted using portable atomic adsorption
(AA) or other instruments.

Level II analyses is used for onsite,  real-time  screening, baseline data development, extent of
contamination, and on-site remedial activities.

Level II -  field analytical techniques provide the following:

     •  Rapidly available data for a variety of activities, including hydrogeologic investigations;
         cleanup operations; and health and safety.

     •  Detection limits for volatiles range from  0.5 ppb  in air, 2-3 ppb in water, and 10 ppb for
         soil.  Detection limits for PCBs  in soil are about 1.0 ppm.  Detection  limits  for extractable
         organic compounds analyzed in mobile labs are in the vicinity of 10 ppb.

     •  Special applications - e.g., vadose zone monitoring.

     •  Volatile organic data  can be used as early indicators or tracers of off-site contaminant
         migration.  Volatiles  are the most mobile of organic contaminants in all media,  and are
         typically found at some concentration at  virtually  all  sites.

The ability to assess data quality for  field activities is  dependent upon the QA/QC steps taken in the
process (e.g., documentation of blank injections, calibration standard runs, runs of qualitative
standards between samples,  etc.).

                                                B-b

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If capital expenditures are excluded, the costs of field analysis are in terms of personnel time in
performing analyses, preparation/maintenance of equipment,  etc.  Per sample costs for mobilizing and
staffing a field laboratory will decrease as the number of samples increases. Based on limited data from
Region I FIT experience, per-sample costs for volatile and inorganic analyses are approximately $15.
Per-sample costs for mobile laboratory analyses may approach $100.  Depending on the type of analysis,
time requirements per analysis  range from 10 minutes to  1-2 hours.

Since Level II analyses are performed in the field, the amount and type of documentation available will
vary with the type of analysis and the standard operating procedures used.  Typically,  a gas
chromatograph operated in the  field provides the bulk of the analytical support at this level.  The
documentation available utilizing this level of analytical support would consist of the output of an
integrator or strip chart recorder for all samples, standards, and blanks analyzed. Field and analysis
log books would also be a source of additional documentation.

Data generated by Level II analysis are typically confirmed by submitting some duplicate samples to CLP
and/or a local laboratory. Factors to consider in choosing the number (or subset) of samples to be
submitted for confirmational purposes include the following:

     •  Total number of samples taken (i.e., when only a few samples are taken, 100 percent
         confirmational analyses may be appropriate)

     •  Objective of sampling

     •  Data uses

     •  Method of analyses used

In general, confirmational samples should include a subset (or all) of designated critical samples, a
subset of samples covering the entire  range of identified concentrations, and a subset of samples near
the (preliminary) action level and near the "0" concentration  or not detectable range.

An additional factor to consider is the measured precision  of the field instrument in use. When
precision is high, fewer samples need to be confirmed: if precision is low, analysis should be suspended
until the reason for the low precision  is  determined.  A qualified chemist should be contacted for input
on instrument calibration, and the utility of the analysis  method with the specific field  conditions.

The data base for documenting  accuracy, precision and MDL information for Level II analyses is sparse.  A
number of factors  have recently stimulated an interest in the development of Level II methods. This
activity is centered primarily in  various EPA Environmental Service Divisions (ESD) and remedial
contractors.  There are two ongoing projects expected to contribute significantly to the Level  II data
quality criteria data base.  These projects are an  EPA Headquarters-directed compilation of all Level II
analytical methods currently used by Field Investigation Teams (FITs) and the operation of a mobile field
analytical laboratory being directed by EPA/ESD in Region IV. The Region IV project, in particular,
holds the promise of a significant contribution, since virtually all organic  Hazardous Substance List
(HSL) parameters  are being analyzed  for.  As these data become available they will be incorporated into
this document.

B. 1.5    LEVEL I ANALYTICAL SUPPORT - FIELD SCREENING

The objective of Level  I analysis is to generate data which  are generally used in refining sampling plans
and determining the extent of contamination at this  site.  This type of support also provides real time
data for health and safety purposes.  Additional data which can effectively be obtained by Level I
analyses include pH. conductivity, temperature, salinity, and dissolved oxygen.

                                              B-7

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Level I analyses are generally effective for total  vapor readings using portable plioto-
ionization or flame ionization meters which respond to a variety of volatile inorganic and organic
compounds.  These analyses are available through ESD or remedial contractors.

Level I analysis provides data for onsite, real-time total vapor measurement, evaluation of existing
conditions, samples location  optimization, extent of contamination, and health and safety evaluations.
Data generated from Level I support are generally considered qualitative in nature, although limited
quantitative data can also be  generated. Data generated from this type of analysis provide the
following:

     •  Identification of soil,  water, air and waste  locations which  have a high  likelihood of
         showing contamination through subsequent analysis.

     •  Real-time data to be used for health and safety consideration during site reconnaissance and
         subsequent intrusive activities.

     •  Quantitative data if a  contaminant is known and the instrument is calibrated to that
         substance.

     •  Presence or absence of contamination.

The most available form of documentation  for this support level is the field operator log book.  Sample
identification, location, instrument reading, calibration and blank information is  usually contained in
the field log book.  A hardcopy stripchart recorder output can be used with these instruments,  but this
is  not common practice.

There  are no data quality criteria specified for Level I, Field Screening Support, because this level is
characterized by the use of hand-held instrumentation (PID, FID) which  in general measure total organic
vapor concentrations only, and as such, is  not conducive to the generation of quantitative data.  In
specialized applications, FIDs can  be calibrated  to a specific compound and quantitative data can be
obtained. Specific information regarding individual  compound sensitivities and response factors can be
obtained in the manufacturer's manual for specific instruments.

B.2    ANALYTICAL FACTORS

Other  factors which may affect development of DQOs include the following:

     •  Analytical quality control

     •  Instrumentation options

     •  Media variability

     •  Method detection limit

     •  Matrix effects

     •  Tentatively identified  organic compounds

     •  Data qualifiers
                                                B-8

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 B.2.1     ANALYTICAL QUALITY CONTROL

 The classification of analytical support into broad levels takes into account internal laboratory quality
 assurance/quality control (QA/QC) in a general manner only.  Internal QA/QC refers to the surrogate and
 matrix spikes, method blanks, and duplicate/replicate runs, among other laboratory or field operation
 quality control.  Within  a given level of analytical support, there may be differences in the way
 individual laboratories or field operations approach  internal QA/QC.  For CLP Invitation for Bid (IFB)
 RAS analytical support,  the procedures are standardized and contract-specified.

 When evaluating laboratory QA/QC. it is important for the reviewer to keep the level of analytical
 support in perspective.  These levels produce data of different quality and documentation and should be
 reviewed with this in mind.  For example,  it would be inappropriate to hold a screening laboratory to  CLP
 RAS standards,  or expect a field screening  operation to have as rigorous QA/QC as a laboratory.
 Expectations such as  these would be inconsistent with the  concept of classifying analytical support by
 the quality of the data needed.

 B.2.2     INSTRUMENTATION OPTIONS

 In some cases, the decision maker may have the option of choosing between similar analytical  procedures
 for the analysis  of a given  parameter. Although each procedure is an EPA approved method, the reason for
 the equivalent procedures is that different analytical instrumentation is used for each method.  Although
 the results obtained are equivalent, there can be subtle differences in the types of data produced by
 different instrumentation.  When choosing  analytical procedures, consideration should always be given to
 the instrumentation used in order to select the method that will  best satisfy the stated analytical
 requirements.  One of the many examples of equivalent procedures  using different instrumentation  for the
 analysis of the same parameters is the gas chromatography (GC) and gas chromatography/mass spectrometry
 (GC/MS) procedures used for the analysis of organic compounds.  An analytical chemist should be consulted
 to select the appropriate  procedures for the specific  problems  encountered at the site.

 B. 2.3    MEDIA VARIABILITY

 Decision makers and data users should be aware that  variability is introduced by the response of a given
 analytical technique or method  to a given sample medium.  Most of the analytical methods utilized  in
 support of RI/FS activities were developed,  at least originally, for aqueous samples and modified for use
 with other media with varying results.  Also, the quality control data published for most analytical
 methods (concerning accuracy and precision information)  were developed using aqueous samples.   The
 performance criteria published  may not totally apply to the use of the method with other sample media.
 When considering the analysis of source materials,  leachate or other complex matrices, qualified
 analytical support personnel should be consulted to determine the most appropriate analytical approach.

 B.2.4    METHOD DETECTION LIMIT

 Regardless of the specified method detection limit, the actual detection limit reported may be sample
 specific.  This is especially true of samples having complex sample matrices (i.e., samples containing
 numerous analytes at widely-different concentration  ranges).  If the concentration of a particular sample
constituent is so high that it requires dilution prior to  analysis, the resulting detection limit for
that sample will  be raised by the dilution factor. For  example, consider a sample being analyzed by
GC/MS for volatile organics. The laboratory's normal detection limit for benzene by this method  is 4.0
 ug/1. but the sample  may contain a high concentration of  volatiles, and  have to be diluted (say by at
 least a 1:10  ratio).  The  resulting detection  limit for benzene will be 50.0 ug/l.  In some cases, the
laboratory can analyze the same sample twice to obtain the specified detection limit but this is  not
always possible, is not considered standard  practice, and would have to be specified  prior to sample
submittal.

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It is important to recognize that quantitative results reported at the detection limit may not be
reliable.  If the action  level of a contaminant is 5.0 ug/1. an analytical method with a detection limit
of 5.0 ug/1 may not provide suitable data to meet the criteria.  For example, the action level for
trichloroethene (TCE)  as defined  the the Safe Drinking Water  Act as a proposed Maximum Contaminant Level
(MCL) is 5.0 ug/1.  Analytical method 624 for volatile organics by GC/MS has a detection limit of 5.0
ug/1.  Thus, method 624 may not be acceptable for this application. The magnitude of the action level
and the detection limit must be considered  in selecting a procedure.

B.2.5     MATRIX EFFECTS

A matrix  effect is a phenomenon  that occurs when the sample  composition interferes with the  analysis of
the analyte(s) of interest.  This can bias the sample result either in a positive or in a negative  way,
with the negative bias being the most common.

The magnitude of a matrix effect  is best assessed by the use of matrix spikes. Matrix spikes supply
percentage recovery information which addresses the amount of bias present in the measurement system.
This information can be used to adjust reported concentrations by the application of a correction factor
based on  percentage recovery.  It is not recommended that  sample values actually  be adjusted  for percent
recovery unless a worst-case scenario is being developed.

B.2.6     TENTATIVELY IDENTIFIED ORGANIC COMPOUND (TIC)

Under the CLP RAS procedures,  30 non-HSL peaks present in the  reconstructed ion chromatogram are
identified  as tentatively identified compounds (TICs).  Other laboratories may not address TICs or have
different reporting criteria. If compounds of interest are tentatively  identified by GC/MS and  are high
in spectra matching criteria (above 90 percent match)  and above action levels, samples may be re-run
against a standard in order to verify the compound's identity.   Chromatographic retention time
consideration is an important factor in assessing the probability of tentative identification
reliability. Approaches for providing more reliable tentative identifications are under development.

B.2.7     DATA QUALIFIERS

When  analytical data are validated, the analytical results and the associated QA/QC information are
reviewed using criteria specific to the analysis performed.  This review can range  from superficial to
very rigorous, depending on the level of analytical  support utilized and  the type of technical review
requested  by the data user.

Data qualifiers are commonly used during the data validation process to classify sample data as to its
conformance to QC requirements. The most common qualifiers are listed below:

     •   A - Acceptable

     •  J - Estimate,  qualitatively correct  but quantitatively suspect

     •   R - Reject, data  not suitable for any purpose

     •  U - Not detected at a specified detection limit (e.g.. 10U)

Sample data can be qualified with a "J" or  "R" for many different reasons.  Poor  surrogate recovery,
blank contamination, or calibration problems,  among other  things,  can cause sample data to be qualified.
Whenever sample data are qualified, the reasons for the qualification are stated in  the  data validation
report.  Data users are reminded  that data validation is generally performed using  strict analytical
criteria which do not take  the sampling activity's DQOs into account.  Data users  should request that the


                                              B-10

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technical staff interpret the validation report according to the sampling activity's objectives and data
uses.  For example, data qualified with a "J" may be perfectly suitable for some data uses.

B.3   ANALYTICAL ERROR

Analytical errors can be estimated for each compound or element of interest by method.  Analytical error
should be calculated for non-standard (Level V) or field (Level I) methods when possible.

In order to determine potential analytical errors, the accuracy and precision of the method must be
known.  The information  required to develop meaningful calculations of analytical error  include
interlaboratory information for matrix spikes, surrogate recoveries, duplicated and blind  performance
evaluation standards for each compound analyzed for each  analytical procedures as follows:

      •    Statistical Information - N, bias, RSD of percent recovery, concentration of spike, and
          concentration of analyte

      •    Matrix - air. aqueous, soil/sediment, leachate or source material

      •    Concentration Range - Liquids:  0-10 jjg/l;  10-100 jig/1;  100-1000 ^g/1 or > 1000 jug/1.  Solids:
          <1  jig/kg; 10-1000 jig/kg; or  >1000^ig/kg

If the above listed information is available, analytical errors could be predicted for the majority of
analyses conducted in support of remedial actions.

For example: based on interlaboratory spike recoveries for benzene in ground water in the 0 to 10 ug/1
concentration range using Method 624, the confidence interval at the 95 percent confidence level  can be
stated.  This statement would be further qualified based on the number and types of laboratories,  other
types of performance evaluation criteria, matrix strength, and other pertinent analytical information.
The detailed statistical information described above is not presently available. The accuracy and
precision information that is available is given  in Appendix F.

B.3.1    LEVEL IV

Precision and bias data provided by the CLP RAS to be used in the estimation of analytical confidence
limits include:

      •   Interlaboratory  volatile organic matrix spike  duplicate data for water and soil samples (N,
          percent RSD. percent RSD at 85th Percentile)

      •   "Interlaboratory" surrogate recovery data of generated volatile compounds from water and  soil
          material (N. bias percent, percent RSD)

      •   Interlaboratory performance evaluation standard data for volatile and semi-volatile organic
          compounds in water and soil  (N, bias percent,  percent RSD).

 In all cases, the data base has been sanitized,  i.e.. outliers have been removed.   In the case of
 "interlaboratory" surrogate recoveries the data base should be considered interlaboratory in the classic
 sense -  same sample submitted to a number of laboratories - but it is actually a close approximation.
 The  same chemical surrogates are added to samples in individual laboratories but the laboratories are not
 recovering the  surrogate  from the same matrix.  In addition, recovery data should be provided for the
 air, leachate  and source  material.

 All of this information can still be used intliNiiiuallv or in concert  to de\elop uncertainty statements
 but with some  inherent limitations.

                                                n-n

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     •   The interlaboratory matrix spike data as provided do not stratify the data with respect to
         concentration.  Using these data requires the assumption that matrix recovery is a linear
         function of concentration.

     •   The "interlaboratory" surrogate recovery data are generally for one concentration range and
         as a result do not account for variability of accuracy as a function of concentration;  assume
         that all analytes act as surrogate during the analytical process: and do not account for
         interlaboratory variations associated with different matrices.

     •   Interlaboratory performance evaluation standard data can probably be considered a "best case"
         for the development of uncertainty  statements (actual samples would have a greater degree of
         uncertainty).  The uncertainty associated with these data do not account for true sample
         matrix effects, or a wide range of analyte concentrations and as a result, the actual
         analytical uncertainty could only be worse than that estimated using this data set.  It does
         have the advantage of being truly interlaboratory and blind (sample concentrations not known
         by  participating laboratories) and should be a true measure of analytical uncertainty for the
         concentration range and matrix analyzed.

The best estimate of analytical  uncertainty would be a composite of the uncertainty associated  with
matrix spike  with the uncertainty associated with performance evaluation standards (interlaboratory
performance only).

B.3.2    LEVEL III

The available information to estimate uncertainty for Level III is  the accuracy and precision statements
included with the individual EPA approved procedures  in SW-846. While this information is rarely
stratified as to matrix and concentration, it could serve as a starting point (best case)  from  which the
uncertainty associated with the  actual analytical conditions could be estimated.

B.3.3    LEVEL II

The most important factor that  influences the uncertainty associated with Level II analyses is the skill
of the analyst doing the work.  Because the procedures are not formalized,  a great deal of improvisation
usually takes place.  The  inherent variability of the procedures themselves  would make the development of
a centralized quality assurance  data base tenuous. The same  reasoning would  apply to making uncertainty
predictions based on a centralized data base.

B.3.4    LEVEL I

Level I analyses are qualitative, and therefore it is not possible to quantify the  uncertainty in these
methods.

B.4   REFERENCES

The following references can be consulted for further information on analytical considerations.  This
list present a  representative sample of documents.

Aleckson,  K.A.. J.W. Fowler and Y.T. Lee. 1986.  Inorganic Analytical Methods Performance and
   Quality  Control Considerations. In:  Quality Control in Remedial Site Investigation:  Hazardous
   Industrial  Solid Waste  Testing Fifth  Volume  ASTM STP 925.

American Public Health Association. American Water  Works Association. Water Pollution Control
   Federation. 1975. Standard  Methods for Examination of Water and Wastewater. 14th Ed.

                                               B-12

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American Society for Testing Materials.  1976. Annual Book of ASTM Standards, Part 31, "Water",
   Standard D3223-73, p. 343

Anderson. D.C., K.W. Brown and J. Green. 1981. Organic Leachate Effects on the permeability of clay
   liners.  National Conference on Management of Uncontrolled Hazardous Waste Sites,  pp 223-229.
   October 28-30, 1981  Washington, DC.

Bishop, J.N.  1971. Mercury in Sediments, Ontario Water Resources Comm., Toronto, Ontario, Canada.

Boston Society of Civil Engineerrs. 1985. Controlling Hazardous Wastes. Lecture Series.

Brandenberger, H. and H. Bader, 1967. The Determination of Nanogram Levels of Mercury in Solution by
   a Flameless Atomic Absorption Technique, Atomic Absorption Newsletter (6),  101

CDM. 1986 Draft Memorandum re:  XRF Field Analysis of Smuggler Mountain Soil Samples from R. Chapp
   R. Olsen to J. Hillman January 13, 1986 EPA Contract No. 68-01-6939 Document No.  149-EP-CCCU-l.

Federal Register, Organochlorine Pesticides and PCBs, Method 608; 2,3.7,8-TCDD, Method 613;
   Purgeables (Volatiles), Method 6224;  Base/Neutrals, Acids and Pesticides, Method 625;  Vol. 44, No.
   233, Monday, December 3, 1979, pp. 69501, 69526, 69532 and 69540.

Flotard, R.D., M.T.  Homshen, J.S.  Wolff and J.M. Moore. 1986.  Volatile Organic Analytical Methods
   Performance and Quality Control  Considerations.  In:  Quality Control in Remedial Site
   Investigation:  Hazardous and  Industrial Solid Waste Testing, Fifth Volume ASTM STP 925.

Garbarino, J.R. and H.E. Taylor, 1979. An Inductively-Coupled Plasma Atomic Emission Spectrometric
   Method for Routine Water Quality Testing. Applied Spectroscopy 33, (3)

Garner, F.C., M.T. Homsher and J.G. Pearson.  1986.  Performance of USEPA Method of Analysis of 2, 3,
   7. 8 - Tetrachlorodibenzo-P Dioxin in Soils and Sediments by Contractor Laboratories.  In:
   Quality Control in  Remedial Site Investigation:  Hazardous and Industrial Solid Waste Testing
   Fifth Volume TSTM STP 925.

Goulden, P.D. and B.K. Aighan. 1970.  An Automated Method for Determining Mercury in Water.
   Technicon, Adv. in Auto. Analy.  2 317

Hatch, W.R. and W.L. Ott, 1968. Determination of Sub-Microgram Quantities of Mercury by  Atomic
   Absorption Spectrophotometry  Analytical Chemistry 40: 2085.

Kopp, J.F., Longbottom, M.C. and Lobring, L.B. 1972.  Cold Vapor Method for Determining  Mercury.
   AWWA, 64:20.

Martin, T.D.  (EMSL/Cincinnati). Inductively Coupled Plasma - Atomic Emission Spectrometric Method of
   Trace Elements Analysis of Water  and Waste, Method 200.7, Modified by CLP Inorganic Data/Protocol
   Review Committee.

Martin. T.D., J.F. Kopp, and R.D.  Ediger, 1975. Determining Selenium in Water, Wastewater, Sediment
   and  Sludge by Flameless Atomic Absorption Spectroscopy. Atomic Absorption Newsletter 14: 109

Shackelford.  W.M.. D.M. Cline. L.  Faas.  and  G. Kurth. 1983. An Evaluation of Automated Spectrum
   Matching for Survey Identification  of Wastewater  Components by  Gas Chromatography - Mass
   Spectrometry.  Analytica Chimica Acta.


                                            B-13

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Technicon Industrial Systems. 1980.  Operation Manual for Technicon Auto Analyzer 11C System.
   Technical Pub. #TA9-0460-00, Tarrytown, New York.

U.S. EPA. 197 Handbook for Analytical Quality Control in Water and Wastewater Laboratories,
   USEPA-600/4-79-019.

	.  1973. Handbook for Monitoring Industrial Wastewater, USEPA Technology Transfer.

	.  1974. Methods for Chemical Analysis of Water and Wastewater, USEPA Technology Transfer.

	.  1979. Methods for Chemical Analysis of Water and Wastes. 600/4-79-20.

	.  1981. EMSL, Users Guide for the Continuous Flow Analyzer Automation System. Cincinnati, Ohio.

	.  1982.  Test Method for Evaluating Solid Waste.   Physical/Chemical Methods.  SW-846. 2nd
   Edition.

	.  1982. Office  of Solid Waste and Emergency Response, Modification (By Committee) of Method
   3050. SW9846, 2nd Ed., Test Methods  for Evaluating Solid Waste. July.

	.  1984.  Soil Properties, Classification,  and Hydraulic Conductivity Testing.  Draft-Technical
   Resource Document for  Public Comment.  SW-925.

	.  1984.  Solid  Waste  Leading Procedure.  Draft-Technical  Resource Document for Public Comment.
   SW-924.

	.  1984.  Toxiciry Characteristic Leaching Procedure (TCLP) - Draft Method 13XX.  TK0703.

	.  1984.  OERR. User's Guide to the Contract Laboratory Program.

	.  1984.  Test Methods for Evaluating Solid Waste, Physical/Chemical Methods.  SW-846.

	.  1984.  Calculation of Precision,  Bias and Method Detection Limit for
   Chemical and Physical Measurements.  (QAMS Chapter 5.)

	.  1986.  Demonstration of a Technique for Estimating Detection Limits with Specified Assurance
   Probabilities. Contract No. 68-01-6939.

Winefordner, J. D., Trace Analysis:  Spectroscopic Methods for Elements. Chemical Analysis, Vol. 46:
   41-42.

Winge, R.K., V.J. Peterson, and V.A.  Fassel,  1970  Inductively Coupled Plasma - Atomic Emission
   Spectroscopy Prominent Lines. EPA-600/4-79-017.

Wolff, J.S., M.T. Homsher, R.D. Flotard and J.G. Pearson.  1986.  Semi-volatile Organic Analytical
   Considerations. In:  Quality Control  in Remedial Site Investigation.  Hazardous and Industrial
   Solid Waste Testing, Fifth Volume, ASTM STP 925.
                                            B-14

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      APPENDIX C
SAMPLING CONSIDERATIONS

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                                         APPENDIX C
                                SAMPLING CONSIDERATIONS

The error introduced by sampling procedures must be considered during the development of DQOs.  Factors
that can introduce sampling error include sampling/handling variability and the variability of
contaminants as a function of location and time. The magnitude of each  of these factors is largely site
specific.  The site specific nature of sampling errors distinguishes sampling from analytical errors,
which are largely independent of site conditions.

This section focuses on factors that influence sampling errors and provides general guidance on sampling
considerations to be evaluated during DQO development.  It does not discuss specific sampling methods or
provide strict guidelines for sampling design.  The RPM and  the site manager are responsible for ensuring
that the appropriate technical experts are involved in development of the site-specific S&A plan.

C.I    SAMPLING STRATEGY

In designing a sampling plan there  are many factors which must be considered.  Some of these  factors  such
as the physical characteristics of the site  (geology,  hydrogeology, physiography) are unique to each
site. However, there also are several general factors which must be considered for all sites.  The
general factors include decisions addressed during  the DQO process such as:

     •   Will a phased approach be used?

     •   Will samples be collected for site characterization?

     •   Will samples be collected for confirmation purposes?

     •   Will grab or composite samples be collected?

     •   Will a grid system be  used?

The importance of each of these factors varies from site to site, and therefore must be analyzed
individually.

For sites at which a significant amount of data  have been generated during preliminary assessments and
site investigations, a focused approach to the RI can be  developed.   For sites for which little  or  no
data are available or data are inconclusive, a broader approach to site investigations  must be
implemented.  Data which are inconclusive or  unvalidated may be  appropriate for data uses requiring lower
data quality (e.g.. as indicators of areas requiring  further study or confirmation).

C.2   SAMPLING PROGRESSION

In  the DQO process  it may be necessary to identify a sampling approach before sufficient information has
been gathered to use the statistical methods discussed in Appendix  A. In these cases, it may be
beneficial to use a phased data collection approach.  In  a phased approach, samples are collected in a
series of independent sampling events.  The first phase  may be undertaken for site characterization
purposes while subsequent phases use the information generated by earlier phases to fill in data gaps.
If a mobile lab is utilized, phases may be continuous as results are analyzed and data gaps are
identified and filled.  The DQO process  applies to each phase of an RI and for each sampling task.
Initial sampling undertaken during  the first phase  may not yield specific information since little  or no
site specific data may be available.  However, in subsequent phases of the RI more data will be available
for decision making.
                                               C-l

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 A phased approach to sampling is. in  most cases, a cost effective method since areas of concern are
 identified in the early phases and  are then targeted for additional sampling.  When sampling is  performed
 in only one phase, every conceivable target  must be completely  sampled.  If one or several of the targets
 prove to be uncontaminated. a large number of unnecessary samples will have been taken.

 Phased approaches must be developed on a site specific basis but generally will follow  sequentially from
 general to progressively more detailed and sophisticated field sampling and analysis programs.  The steps
 likely to be included in a phased sampling approach include the following:

      •   Review of existing information/data

      •   Remote sensing/geophysical  techniques

      •   Field screening

      •   Intrusive sampling

      •   Pilot studies

 C. 2.1    RE VIE W OF EXISTING INFORMATION/DATA

 All sources of available information should be obtained and reviewed during the initial stages of RI/FS
 project planning. It is especially important to obtain and  review data from  any previous investigations
 gathered in the National Priorities List (NPL) ranking process,  FIT and/or TAT team  investigations, and
 other data gathering activities conducted  by the state or other parties.  Detailed discussions of the
 various data sources which should be accessed during  review of existing information are contained in the
 Remedial Investigation Guidance Document (EPA 1985).

 C.I.2    REMOTE SENSING/GEOPHYSICAL TECHNIQUES

 Remote  sensing is a term applied to methods used for the detection, recognition, or evaluation of objects
 or conditions by means of distant sensing or recording devices,  including,  but not limited  to, aerial
 photography or satellite imagery.  Kroeck and Shelton  (1981) discuss the application of aerial
 photography to investigation of waste sites. Geophysical techniques are remote sensing methods used to
 characterize subsurface conditions without excavation.  Information on the  application of geophysical
 techniques to hazardous  waste sites can be found in Benson, et.  al. (1983).

 Remote sensing/geophysical investigations should be used in the initial stages of RIs to provide  an
 overall sense of the site environs (aerial  photographs) and subsurface conditions.  These techniques may
 also be used at later stages to provide a means for extrapolation of data obtained from disruptive
 techniques.  For example, soil  borings installed at a site may reveal the presence  of a clay lens over a
 portion of a site which could affect ground water migration.  Geophysical techniques could be used to
 provide information on subsurface conditions between the soil borings.  In  the absence of this
 information, an extrapolation of the soil strata between the borings may result in an erroneous
 interpretation of subsurface conditions.

C.2.3     FIELD SCREENING

Proper field screening techniques can be instrumental in reducing the time it takes to perform an RI/FS.
reduce costs, reduce "intrusive" sampling locations,  and.  in general, lead to more effective use of Level
HI and  IV analyses.

Field  screening  is primarily used to provide  indications of contamination at analytical Levels I and II.
Thus, the decisions that will be based on the results of this type  of sampling are in many cases yes/no


                                               C-2
                                                                                                                 i

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type decisions. For instance, on the basis of soil gas screening it may be determined that contamination
of a particular unconfined aquifer is indicated and further direct sampling is warranted.

C.2.4    INTRUSIVE SAMPLING

Intrusive sampling includes all  methods in which a physical sample from the media of concern is obtained.
Intrusively obtained  samples are used to obtain  a numerical value for a physical or chemical measurement
at a particular point.  Intrusive  sampling provides much more exact information concerning the
concentration  of contaminants or physical features than non-intrusive remote sensing or field screening
techniques.

C.2.5    PILOT STUDIES

Pilot studies are undertaken to obtain data to assess the applicability of various proposed alternatives
for site remediation in a controlled  manner. Pilot studies can also be undertaken to evaluate the
effectiveness of various unit processes for treatment of a contaminant source at a site or for developing
data needed to optimize system  design and operation.  The results of pilot treatability studies are used
to develop design criteria: develop cost estimates; and to identify any special management or operational
constraints which must be implemented in order to utilize the system under study.  Analytical Levels II,
HI, IV, or V  may apply to pilot studies.

C.3  SOURCES OF VARIABILITY

To determine  the uncertainty associated with a decision, all sources of variability must be taken into
consideration.  Important sources of variability  are sampling/handling variability and the variability of
contaminants as a function  of location and time.  Of these three sources, the variability of the
contaminants as a function  of location is expected to be the largest.

C. 3.1    SAMPLING/HANDLING VARIABILITY

Sampling/handling variability is defined as any  variability  introduced by the sampling and/or handling
procedures, resulting in a contaminant concentration in  the sample that is different than the
concentration  in the original media. Causes of sampling variability include incorrect sampling
procedures and cross contamination.  Since most of the causes of sampling/handling variability are
related to errors in  procedures, measurement of sampling variability is difficult.  The magnitude of
sampling variability can range from small to very large; however, if correct sampling and handling
procedures are followed, sampling variability should be  small compared to laboratory variability.

Sampling/handling variability can be reduced by training sampling personnel and performing all sampling
activities in accordance with standard operating procedures (SOPs). SOPs are developed to ensure that
any samples collected are representative of the undisturbed media of interest. By adhering to the SOPs,
intra- and intersite variability for a  given sampling method are greatly reduced or eliminated.

C. 3.2    TEMPORAL VARIABILITY

Many observed contaminant concentrations are dependent on time related variables such as the time of day
or season of the year. The important variable linking concentration and time is often climatological
(i.e.. temperature or rainfall).  Since the linking variables (temperature, for instance)  follow cyclical
patterns over a day or year, time dependent  contaminant levels are also expected to follow cyclical
patterns.  To obtain representative samples of time related variables, it is important to  identify  the
cyclical nature of the contaminant concentrations and to sample at various phases of the cycle to obtain
a representative sample.

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C. 3.3    SPATIAL VARIABILITY

Spatial variability describes the manner in which contaminants vary as a function of location. Although
this source of variability is normally not considered explicitly, it is implicitly expected.  The
magnitude of the difference in contaminant concentrations in samples separated by a fixed distance is a
measure of spatial variability.  The level of spatial  variability is site and contaminant specific. When
spatial variability is high, a single sample  is likely  to be unrepresentative  of the average contaminant
concentration  in the media surrounding the sample.  Although it is important to recognize the nature of
spatial variability at all times, it is crucial  when the properties observed in a single sample will be
extrapolated to the surrounding volume.  Thus, when analyzing the results from a single ground water
sample, spatial variability is not important; however, when attempting to determine the mean contaminant
concentration  over a portion of a  site, or attempting to extrapolate or interpolate concentrations,
spatial variability is important.  Analysis of spatial  variability is accomplished using geostatistics.

C.4   SAMPLE TYPES

During the DQO development process the decision maker and data users must determine which types of
samples should be obtained during the RI. The types of samples required to characterize a site may
differ from those required to perform a pilot study. An evaluation of the intended use of the data must
be  undertaken in order  to ensure that the type of sample obtained provides the necessary information to
address the issues of concern. In determining the types of samples which should be obtained the
following issues should  be considered:

     •   Media vs. waste samples

     •   Grab vs. composite samples

     •   Filtered vs.  unfiltered samples

     0   Biased vs. unbiased sampling

C.4.1    MEDIA VS. WASTE SAMPLES                                                                       j
                                                                                                               i

Media or environmental samples refer to sampling of air. water, soils, and other environmental media to             ;
determine the  extent of  contamination. Waste samples refer to the sampling of the actual wastes.
Typically this  will mean drums, impoundments, tanks, or other waste disposal areas.                                 i

Sampling will  typically involve both investigation of general environmental media and specific waste
accumulation areas.  General questions regarding environmental media include:

     •   Which media  are contaminated?  (air. water, soil, ground water,  biota)

     •   What is the  average contamination?                                                                     ,
                                                                                                               !
     •   What is the  total contamination?  (mass, volume)                                                         i
                                                                                                               \
     •   What is the  maximum contamination? (concentration)                                                    J
                                                                                                               ;
     •   What area of the site is  contaminated?                                                                   j
                                                                                                               i
     •   What is the  vertical and horizontal extent of contamination?

Waste samples are those collected from drums, tanks, lagoons, pits, uaste piles, fresh  spills, and other
areas of waste  accumulation.  The specific area or  container being sampled differs from the media samples           i

                                              C-4

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in two ways: (1) the questions asked of the data and (2) the general characteristics of the materials
being sampled.  The most common questions are concerned with waste characterization:

     •   What compounds are present?

     •   Do these contaminants  exceed any criteria or standards?

C.4.2    COMPOSITE VS. GRAB SAMPLES

Grab samples are discrete aliquots which are representative of a specific location at a specific point in
time. Composite samples represent the mixing of a number of grab samples and represent an average value.
In the most common case, two or more grabs are added to the same container, mixed, and then a single
aliquot is taken from the mixture. However, other forms of composite  sampling might be from radiation
badges or body samples for lead  readings.  In both these cases, the measurements would be over a number
of hours and would not represent a single sampling location or time.

When developing or reviewing a sampling plan,  it is important to consider the uses of grab and composite
samples.  Grab samples offer the most information regarding contaminant variability. Since compositing
involves combining several grab  samples, estimation of overall site  properties using composites is less
expensive  than using grabs due to reduced analytical costs. However, compositing does not allow the
spatial variability of data to be determined, so the confidence  in a composite value may be impossible to
determine.  Composite samples should not be used when there is a potential risk of dangerous chemical
reaction or when a measure of spatial variability is important.

C.4.3    BIASED VS. UNBIASED SAMPLING

Biased sampling refers to a sampling scheme whose resulting data places emphasis on a single
characteristic or factor of the  problem.  Unbiased sampling refers to sampling methods which allow for
estimates to be drawn from the data which are representative  of the population at large.  These terms
usually can be considered to be synonymous with random and non-random sampling.

Biased sampling is most common during the site  investigation (SI)  process.  The purpose of the SI is to
find out whether any contamination is present. Thus,  these studies are typically conducted in ways that
maximize  the chance of analyzing samples which have  contamination above a particular criteria. The use
of direct reading instruments  to screen samples is a good example of biased sampling.  The samples which
are finally analyzed using, for example, GC/MS will represent higher contamination than might exist
overall at  the site. This type  of sampling is typically acceptable for the SI.  In the RI/FS, this type
of sampling may be acceptable in cases where design of a treatment system is dependent on the maximum
treated load.

Unbiased  sampling is performed by sampling on  a regular grid.  This type of sampling is unbiased because
each sample is representative  of an identical volume of the medium being sampled.  This type of sampling
is best for predicting overall site  properties.

C.5   SAMPLING PATTERNS

When acquiring data which will  be used to  make general inferences concerning site characteristics, it is
important that samples provide complete coverage of the area of interest and that sample locations do not
introduce bias.  Complete coverage is necessary to ensure that no areas of contamination are missed.
Bias in a  data set causes the mean of the data to be systematically different from the true mean.  Bias
is caused  by any systematic error in data location, such as clustering of data.  When data are clustered
(located close together) some  small portions of the site are sampled more densely than the remainder of
the site.  The particular contaminant value observed in the densely sampled area will be over represented
                                              C-5

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 iii the estimate of the sample mean.  If. as is often the case, samples are clustered in highly
 contaminated areas, the mean site contaminant concentration will be overestimated.

 Sampling patterns should be designed to minimize bias  and provide complete site coverage.  The best
 sampling pattern for accomplishing both of these goals is a regular grid.  It can be shown theoretically
 (Ripley 1982). that data taken on a regular grid will yield a more precise estimate of the mean site
 contamination than data located according to any other procedure.  This fact combined with the  superior
 coverage and non-biased property of regular sampling make it the preferred sampling pattern when
 statistics will be applied.

 The use of an unbiased approach during the initial sampling phases is recommended in order to ensure that
 no area of the site is overlooked in sampling. Subsequent sampling phases should incorporate the
 information  resulting from the unbiased sampling which occurred during the initial phases.  The data
 should be used to identify areas in which additional samples should be obtained and areas where no
 additional samples are required.  Introduction of bias during subsequent phases may be justified in these
 instances.

 C.5.1    GRID SYSTEMS

 Grid systems are used in developing systematic non-biased sampling plans in which samples are located at
 consistent distances  from one another.  The most elementary grid system is a straight line between two
 points on which regularly spaced sampling locations are noted.  This type of one-dimensional sampling
 grid may be  useful for  sampling along a straight drainage ditch or other man-made feature.  The majority
 of environmental sampling, however, requires a two-dimensional approach to sample location
 identification.

 Figure C-l presents a two dimensional square grid system for locating sampling points.  The grid is
 comprised of equidistant parallel lines at right angles to each other.  Figure C-2 presents a two
 dimensional  triangular grid  system comprised of equidistant parallel lines intersected by lines drawn at
 60  from vertical  in both directions.  Sampling  generally is undertaken at the intersection of the
 parallel lines which  compose a grid, although other approaches such as sampling in  the center of each
 grid box or obtaining a composite of samples within a grid box are also acceptable.  It may be
 appropriate to modify the grid system to account for variations in concentration gradients as illustrated
 in Figure C-3.

 C.5.2    STRATIFICATION

 Stratification  refers to the process of locating samples within distinct populations or strata. Commonly
 occurring strata are  geological formations, soil horizons, and visually different areas of contamination.
 Typically, the number of samples taken within strata varied.  For instance, initially,  more samples
 should be taken from a  visibly contaminated soil horizon than from a soil horizon which is not visibly
 contaminated.  This  approach needs to be used with caution and by experienced field personnel,  as soil
 (and other media)  which is not visibly contaminated could well  be contaminated.  By varying the number  of
 samples in each strata based on  existing information or information obtained in the field, the sampling
 program can  concentrate on the most important aspects of the site.  Stratification is thus a valuable
 method for conserving  resources.

C.5.3    GRID SPACING

Spacings of grids are usually established to allow for sampling at each grid intersection.  These
alternative sampling  approaches  can be  used when a low intensity investigation is used preliminary to
more intensive sampling to be performed following review of the data.  For example, a grid system may be
placed  over a site with grid lines spaced at 10-ft  intervals.  During the preliminary investigation
samples may  be obtained at every tenth  intersection on every tenth  grid line, thereby resulting in

                                               C-6

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     SITE BOUNDARY
        C-1
SQUARE GRID SYSTEM
       (A=B)
    C-7

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          C-2
TRIANGULAR GRID SYSTEM
    (A=B
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                                           O
                                           I—

                                           cc
                                           O
                                        O
                                        O
                                         T
                 C-3
       MODIFIED GRID SYSTEM TO
ACCOUNT FOR DIRECTIONAL CORRELATION
                  C-9

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 samples being obtained at 100-ft spacings.  Following review of the preliminary data, intenshe sampling
 may be warranted in a number of discrete locations on the site.  This intensive sampling may then be
 performed at the previously established  10-ft grid intersections.

 The distance between the grid lines will determine the number of intersections and hence the number of
 potential sampling points within a specified area.  As the grid line spacings increase, the number of
 potential sampling points will decrease for any given sampling area.  Confidence interval methodology can
 be used to select optimal grid spacing.

 C.6   QUALITY CONTROL SAMPLES

 Various types of samples may be obtained during a  remedial investigation in order to provide quality
 control information for interpretation of data including:

      0  Background samples

      •  Critical samples

      •  Collocated and replicate samples

      •  Split samples

      •  Field and trip blanks

      •  Matrix  spikes

 In all cases QC samples must be submitted to the laboratory as blind samples.

 C.6.1    BACKGROUND SAMPLES

 Inclusion of background samples in an RI sampling task must be taken into consideration during the DQO
 process. A background sample is one taken from media characteristic of the site but outside the  zone of
 contamination. Monitoring data as well as available literature on natural background concentrations of
 chemicals in the area should be collected, reviewed and/or verified to determine background conditions.
 Background data  should be defined as either natural or anthropogenic chemical  contamination resulting
 from a source or sources other  than the site undergoing assessment.

 C.6.2    CRITICAL SAMPLES

 Critical data points are  sample locations  for which valid data must be obtained in order for the sampling
 event to be considered complete. An example of a critical data point may be an upgradient well  in a
 ground water contamination study or any other data  point considered vital to the decision making process.
 Critical data points should be carefully considered in the sampling plan design.  In some cases, taking
 critical data point samples in duplicate is appropriate. A common problem of any  sample design  is the
 loss of data during implementation of the design.  Care must be taken to determine the set of points for
 which data  must be collected in  order to analyze the results accurately.  The set of points which  must be
 collected are called the  "critical  points."  Critical points may be defined  in terms of the minimum
 number of data points which must be collected  and analyzed.  Critical data points should be identified in
every completeness statement developed during  the DQO process.

C.6.3    COLLOCATED AND REPLICATE SAMPLES

Collocated samples are  independent samples collected in such a manner that they are equally
representative of the parameter(s) of interest at  a given  point  in space and time.  Examples of


                                              C-10

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collocated samples include:  samples from two air quality analyzers sampling from a common sample
manifold, two water samples collected at essentially  the same time and from the same point in a lake, or
side-by-side soil core samples.

Collocated samples,  when collected, processed, and  analyzed by the same organization, provide
intralaboratory precision information for the entire measurement system including sample acquisition,
homogeneity,  handling, shipping, storage, preparation and analysis.  Collocated samples, when collected.
processed and analyzed by different organizations, provide interlaboratory precision information for the
entire measurement  system.

Replicate samples are samples that have been divided into two or more  portions at some step  in the
measurement process. Each portion is then carried through the remaining steps in the measurement
process.  A sample may be replicated in the field or at different points in the analytical process.  For
field replicated samples, precision information would be gained on homogeneity (to a lesser extent than
for collocated  samples), handling, shipping, storage, preparation,  and analysis.  For analytical
replicates, precision information would be gained on preparation and analysis.  Examples of  field
replicated samples include a soil core sample that has been collected and poured  into a common container
for mixing before being split and placed in individual sample containers.

Collocated samples  can be used to estimate the overall precision of a data collection activity.  Sampling
error can be estimated by the inclusion of collocated and replicated versions of the same sample. If a
significant difference in precision between the two subsets  is found, it may be attributed to sampling
error.  As a data base on  field sampling error is accumulated, the magnitude of sampling error can be
determined.

The following are suggested guidelines for the inclusion of collocated and replicated samples  in field
programs:

     •   Ground and surface water - one out of every 20  investigative samples should be collocated.
         Replicated samples could be substituted where  appropriate.  These samples should be spread
         out over the sampling event, preferably at  least one for each day of sampling.

     •   Soil, sediments and solids - one out of every 20 investigative samples should be field
         replicated or collocated.  To estimate sampling error, collocated and field replicated
         samples should be of the same investigative sample.  These samples should be spread out over
         the sampling event, preferably one per each day  of sampling.

C.6.4    SPLIT SAMPLES

Split samples are replicate samples divided into two  portions, sent to different laboratories, and
subjected  to the same environmental conditions and  steps in  the measurement process.  They serve as an
oversight function in assessing the analytical portion of the measurement system.

C.6.5    TRIP AND FIELD BLANKS

Trip blanks generally pertain to volatile organic samples only. Trip blanks are prepared prior to the
sampling event in the actual sample containers and are kept  with the investigative samples throughout the
sampling event. They are then packaged for  shipment with the other samples and sent for analysis.  There
should be one trip blank included in each sample shipping container.  At no time after their  preparation
are the sample containers opened before they reach  the laboratory.

Field blanks are defined as samples which are obtained by running anahte-free deionized water through
sample collection equipment (bailer,  pump, auger, etc.)  after decontamination, and placing it in the
appropriate  sample  containers for analysis.  These samples will be used to determine if decontamination

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 procedures have been  sufficient.  Using the above definition, soil field blanks could be called rinsate
 samples.  These should be included in a sampling program as appropriate.

 The following guidelines for including blanks in sampling programs are suggested.

      •   Ground and  surface water - Field blanks should be submitted at the rate of one field
          blank/matrix/per day or one for every 20 investigation samples,  whichever results in fewer
          samples.  Trip blanks should be  included at a frequency of one  per day of sampling or as
          appropriate.

      •   Soil sediments and solids  - Rinsate samples should be submitted at the rate of one for every
          20 investigative samples for each matrix being sampled or as appropriate.

 Guidelines for blank, duplicate, and background samples are provided  in Table C-l.  These guidelines
 serve as a starting point from which to develop site-specific sampling plan QC sample numbers.  In
 certain instances, it may be appropriate to  utilize known reference materials when available for QC
 checking.  The  numbers and sources of reference  materials which would  provide meaningful comparison and
 checks for media obtained from hazardous waste sites are limited.  Analytical chemists should be
 consulted regarding the appropriateness of use of reference materials as a  QC check.

 C.6.6    MATRIX SPIKES

 Many samples exhibit  matrix effects, in which other sample components interfere with the analysis of
 contaminants of interest. Matrix  spikes provide the best measurement of this effect.  When done  in the
 field, immediately after collection, they also provide a measurement of sampling,  handling and
 preservation error.  The field matrix spike does provide the best overall assessment of accuracy for the
 entire measurement system, as collocated samples do for precision assessment. However, there are some
 serious issues regarding the field  spiking of environmental samples that must be considered.  Field
 matrix spikes are generally not recommended because of the high level of technical expertise required for
 proper use and their sensitivity to environmental variables.

 The major problems associated with field matrix spikes are due to the fact that all spike recovery data
 must be interpreted very carefully.  Spike recoveries are subject to many competing factors, such  as
 analyte stability, holding time, and the sample matrix.  Because of the inherent variability associated
 with spike recoveries, the additional variability introduced by spiking samples in the field can  increase
 the overall uncertainty associated  with a data set rather than decrease it.

 The two most important issues to  address when considering field spiking as an option  are the  source  of
 the spiking material and the technical capability of the person doing the spiking.  Spiking materials
 that can be used are Standard Reference Materials (SRMs), EPA quality control ampules, or
 laboratory-prepared solutions made  from pure compounds.  SRMs are stand-alone standards prepared by NBS
 that can be placed  in the appropriate sample containers and sent to the  laboratory  to be analyzed.  The
 use of certified standards such as  SRMs solves the  "traceability"  issue concerning the  integrity of the
 blind standard and also does not require a skilled technician to prepare the standard.  However, because
 the SRM is a stand-alone sample, it provides no information on the  impact of the  sample  matrix on the
 measurement system.   An aliquot of an SRM can be used to spike an environmental sample, but it would no
 longer be traceable and would require a person skilled  in the appropriate analytical techniques, just as
the use of quality control ampules or laboratory-prepared spikes do.   The competence of the person doing
the spiking is critical.  The exact  amount of spiking material must be recorded for future  use in
assessing recoveries.  Errors in measurement of the spike or use of the  wrong spiking material will cause
serious problems in interpreting the usability of the data.
                                              C-12

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                                                 TABLE  C-l

                      GUIDELINES FOR  MINIMUM  QA/QC  SAMPLES  FOR  FIELD SAMPLING PROGRAMS
                         DUPLICATES
n
MEDIA
Aqueous
Soil,
sediment
Air
Source
material
FIELD
COLLOCATED OR REPLICATE
one in twenty
one in twenty
one in twenty
one in twenty
FIELD
BLANK
one in
twenty
one in
twenty
not
available
not usually
required
TRIP
BLANK
one per
day of
sampling

one per
day of
sampl ing

BACKGROUND
SAMPLE
min. of two
per sampling
event-media
min. of two
per sampling
event-media
min. of two
per sampling
event-media

INTER-LAB
SPLIT SAMPLE
when required
to meet
objectives
when required
to meet
objectives
when required
to meet
objectives
when required
to meet
                                                                                       objectives
    NOTE:  This table is provided to serve  as  a  guideline  only;  QA/QC sample requirements must be
           developed on a site-specific basis.  Laboratory blanks and spikes are method specific and are
           not included in this table.

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 In summary, field matrix spikes are not recommended unless the appropriate technical support is
 available.  Absolute attention to all details is required to obtain  useful information from the
 procedure.  If field matrix spikes are used, the results should be compared with laboratory matrix spike
 results.

 C.I   REFERENCES

 Benson, R.C.; R.A. Glaccum, M.R. Noel.  1984. Geophysical  Techniques for Screening Buried Wastes and
   Waste Migration, U.S. EPA Environmental Monitoring Systems Laboratory-Las Vegas.

 Charlie, W.A., R.E. Wardwell, and O.B. Andersland. 1979.  Leachate Generation from sludge disposal
   area.  ASCE Journal of Environmental Engineering Division  Vol. 10S EES:947-960.

 Claassen. H.C. 1982.  Guidelines and  Techniques for Obtaining Water Samples that Accurately
   Represent the Water Chemistry of an Aquifer.  USGS Open  File Report 82-1024.  Lakewood, CO.

 Clay, P.P. and T.M. Spittler. 1982. The Use of Portable Instruments in Hazardous Waste Site
   Characterizations, Proceedings of the Third National Conference on Management of Uncontrolled
   Hazardous Waste Sites, Washington, D.C.

 Ecology  and Environment, Inc. 1982. FIT Operations and Field Manual, HNu  Systems PI 101
   Photoionization Detector and Century Systems Model OVA-128 Organic Vapor Analyzer, prepared for
   U.S. EPA, Washington, D.C.

 Elans, R.B. 1986. Groundwater Monitoring Data Quality Objectives for Remedial Site Investigations.
   In:  Quality Control in Remedial Site Investigations:  Hazardous and Industrial Solid Waste
   Testing Fifth Volume ASTM STP 925.

 Fuller, W.H., A. Amoozeyar-Fard, E.F. Niebla,  as M. Boyle.  1980. Influence of Leachate Quality on
   Soil Attenuation of Metals.  In: Disposal of Hazardous Wastes pp 108-117.  EPA-600-9-80-010.

 Jacot, Brian. 1983. OVA Field Screening at a Hazardous Waste Site. Proceedings of the Fourth
   National Conference on Management of Uncontrolled Hazardous Waste Sites. Washington, D.C.

 Kroeck.  R.M.; and G.A. Shelton. 1982.  Overhead Remote Sensing for Assessment of Hazardous Waste
   Sites.  U.S. EPA Environmental Monitoring Systems-Las Vegas. 600/X-82-019

 Mehran, M. et al.  1983. Delineation of Underground Hydrocarbon Leaks by Organic Vapor Detection.
   Proceedings of the Fourth National Conference on Management of Uncontrolled Hazardous Waste Sites.
   Washington, D.C.

 Quimby, J.M. et al. 1982. Evaluation and Use of a Portable Gas Chromatograph for Monitoring
   Hazardous Waste Sites. Proceedings  of the Third National Conference on Management of Uncontrolled
   Hazardous Waste Sites. Washington, D.C.

Raveh, A. and Y. Avnimelech.  1979. Leaching of pollutants from sanitary landfill models.  Journal
   WPCF 51(11):2705-2716.

Ripley, B. 1982.  Spatial Statistics. John Wiley & Sons. New York.

Spittler. T.M. 1980. Use of Portable Organic Vapor Detectors for Hazardous Waste Site
   Investigations.  Second Oil and  Hazardous  Materials Spill Conference and  Exhibition. Philadelphia,
   Pennsylvania.
                                             C-14

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Spittler, T.M. et al. 1981. Ambient Monitoring for Specific Volatile Organics Using a Sensitive
   Portable PID GC. Proceedings of the Second National Conference on Management of Uncontrolled
   Hazardous Waste Sites. Washington, D.C.

Spittler, T.M. 1983. Field Measurement of PCBs in Soil and Sediment Using a Portable Gas
   Cliromatograph. Proceedings of the Fourth National Conference on Management of Uncontrolled
   Hazardous Waste Sites. Washington, D.C.

U.S. EPA. 1977. Procedures Manual for Groundwater Monitoring at Solid Waste Disposal Facilities. EPA
   530/SW-611.

	.  1980. Environmental Monitoring and Support Laboratory. Cincinnati. Ohio, Interim Methods for
   the Sampling and Analysis of Priority  Pollutants in Sediments and  Fish Tissue. October.

	.  1981. Soil  and Sediment Sampling  Methods.  Technical Methods for Investigating Sites
   Containing Hazardous Substances.  Technical Monograph No. 17.

	.  Handbook for Sampling and Sample Preservation of Water and Wastewater. EPA 600/4-82-029.

	.  1983. Characterization of Hazardous Waste Sites~A Methods Manual, Volume II,  Available
   Sampling Methods.  NTIS PB84-126929.

	.  1983. Preparation of Soil Sampling Protocol: Techniques and Strategies NTIS PB83-20-6979
   EPA 600/4-83-020.

	.  1984. Soil  Sampling Quality Assurance User's Guide. EPA 600/4-84-043

	.  1984. Quality Assurance  Handbook for Air Pollution Measurement Systems.  Volumes I and 2,  EPA
   - 600/9-76-005. January 19.

	.  1984. Standard Operating Safety Guides. Office of Emergency and Remedial Response.

	.  1985. Characterization of Hazardous Waste Sites - A Methods Manual.  Volume  1 - Site
   investigation.  EPA/600/4-84/075.
                                             C-15

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       APPENDIX D
REVIEW OF QAMS CHECKLIST

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                                        APPENDIX  D

                            REVIEW OF QAMS DQO CHECKLIST
In a memorandum dated April 3, 1983, Mr. Stanley Blacker. Director of the Quality Assurance
Management Staff (QAMS) issued a checklist to be used by QAMS staff during their review of
DQOs.  The purpose of this appendix is to review the QAMS checklist with respect to this
RI/FS DQO guidance.

The QAMS checklist is designed for use in reviewing specific DQOs rather than an approach to
DQO development for a complex process such as an RI/FS.  This appendix presents a review of
the checklist items, along with a reference to the section where the item  is addressed and/or
a comment regarding the applicability of the item to the RI/FS DQO process.

The RI/FS process involves multiple levels of data and data uses, and culminates in a
decision regarding the degree of remedial  response to be implemented for a site.  Decisions
are based  on analytical and other measurement  data which are often integrated to interpret
various aspects of a site's characteristics.  Thus, many different sets of DQOs may be
required for a given RI/FS.
                                              D-!

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                                         APPENDIX D

               SUMMARY  OF  DQO CHECKLIST  ITEMS WITH  RESPECT TO
                                 RI/FS  DQO APPLICABILITY
      DQO CHECKLIST ITEM
A-l.  The decision maker and
associated users are clearly
identified.
COMMENT RE: RI/FS DQO APPLICABILITY

The key RI/FS decision is remedy
selection (i.e., ROD/EDD
signature).  For  the majority of
RI/FS projects, remedy selection is
delegated to the Regional
Administrator  (RA).  Program
Management responsibilities are
delegated to the Waste Management
Division Director and Managers,
with project specific management
and oversight assigned  to Remedial
Project Managers (RPM). In this
role the regional EPA RPM, is
responsible for coordinating the
DQO development process, and
overseeing remedial contractors,
state officials,  or private parties
conducting the RI/FS.  Associated
data users include primary,
secondary and technical support and
project review/audit personnel.
A-2.  The decision maker and
associated data users have been
involved in the development of
DQOs.

B-la. A statement of the
decision(s) that depend(s) on
the results of this data
collection  activity.
See Section 2.0. Stage 1 - Identify
& Involve Data Users.
The decision(s) that result from
the RI/FS process involve multiple
levels of data for multiple
purposes.  See Section 3.0.  Stage 1
- Specify RI/FS Objectives.
B-lb. If the data collection
activity is of an exploratory
nature and  not formally linked
with a regulatory decision, then
the document should include a
clear explanation of the purpose
for which the environmental data
are intended.
See Section 4.0. Stage 2 Identify
Data Uses/Needs.
                                              D-2

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                                        APPENDIX  D
                                          (continued)

               SUMMARY OF  DQO  CHECKLIST ITEMS WITH  RESPECT TO
                           RI/FS  DQO APPLICABILITY  (continued)
     DQO CHECKLIST ITEM
B-2.  Statements of each specific
question that will be addressed
in the data collection activity
and the type of conclusion that
is anticipated as an appropriate
answer to each question.  The
conclusions  should depend only on
measurement data.

B-3.  A clear statement of the
way in which each conclusion of
the study will be represented, in
terms of the results of
statistical calculations  made
with the data.
B-4.  Statements of the
acceptable levels of precision
and accuracy associated with each
of the conclusions depend on
measurement data.

B-5.  A definition of the
population to which each of the
conclusions  apply, including
definitions of all subpopulations
or strata.

B-6.  Definitions of the
variables that will  be measured.

B-7.  The acceptable levels of
precision and accuracy for the
measurements to be made.

B-8.  A flow chart or spread
sheet illustrating the
relationship between the measure-
ment data and each conclusion
that will be  made with  the data.
COMMENT RE: RI/FS DQO APPLICABILITY

See Section 4.0, Stage 2
See Section 4.0, Stage 2 - The
conclusions of an RI/FS study are
highly interdependent.  The format
for data presentation will vary,
based upon data quantity.  A
statistical approach may not be
feasible.

See Section 4.0, Stage 2.
See Section 4.0, Stage 2.
See Section 4.0, Stage 2.
See Section 4.0, Stage 2.
See Section 4.0. Stage 2.
                                              D-3

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       APPENDIX E

  POTENTIALLY APPLICABLE
     OR RELEVANT AND
APPROPRIATE REQUIREMENTS

       (50 FR 47948)

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                                        APPENDIX  E

    POTENTIALLY APPLICABLE OR RELEVANT AND APPROPRIATE REQUIREMENTS
1.     EPA's Office of Solid Waste administers, inter alia, the Resource Conservation and Recovery Act
      of 1976, as amended (Pub.  L. 94-580, 90 Stat 95, 42 U.S.C. 6901 et seq.).  Potentially
      applicable or relevant requirements pursuant to that Act are:

      a.   Open Dump Criteria - Pursuant to RCRA Subtitle D criteria for classification of solid waste
           disposal facilities (40 CFR Part 257).
           Note:  Only relevant to nonhazardous wastes.

      b.   In most situations Superfund wastes will be handled in accordance with RCRA Subtitle C
           requirements governing standards for owners and operators of hazardous waste treatment,
           storage, and disposal facilities:  40 CFR Part 264, for permitted facilities, and 40 CFR
           Part 265,  for interim status facilities.

     •    Ground Water Protection (40 CFR 264.90-264.109).

     •    Ground Water Monitoring (40 CFR 265.90-265.94).

     •    Closure and Post Closure (40 CFR 264.110-264.120, 265.110-265.112).

     •    Containers  (40 CFR 264.170-264.178.  265.170-265.177).

     •    Tanks (40 CFR 264.190-264.200, 265.190-265.199).

     •    Surface Impoundments (40 CFR 264.220-264.249, 265.220-265.230).

     •    Waste Piles (40 CFR 264.250-264.269, 265.250-265.258)

     •    Land Treatment (40 CFR 264.270-264.299, 265.270-265.282).

     •    Landfills (40 CFR 264.300-264.339, 265.300-265.316).

     •    Incinerators (40 CFR 264.340-264.999, 265.340-265.369).

     •    Dioxin-containing Wastes, (50 FR 1978).  Includes the final rule for the listing of dioxin
          containing waste.

2.    EPA's Office of Water administers several potentially applicable or relevant and appropriate
     statutes and regulations issued thereunder:

     a.    Section  14.2 of the Public Health Service Act as amended by the Safe Drinking Water Act as
          amended (Pub. L. 93-523, 88 Stat 1660. 42  U.S.C. 300f et sec.)

     •    Maximum Contaminant  Levels (for all sources of drinking water exposure).  (40 CFR
          141.11-141.16)

     •    Underground Injection Control Regulations.  (40 CFR Parts 144.  145. 146.  and 147)
                                             E-l

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     b.   Clean Water Act as amended (Pub. L. 92-500.  86 Stat 816, 33 U.S.C.  1251 et. seq.)

     •    Requirements established pursuant to sections 301. 302, 303 (including State water quality
          standards), 306. 307. (including Federal Pretreatment requirements for  discharge  into a
          publicly owned  treatment works), and 403 of the Clean Water Act.  (40 CFR Parts 131,
          400-469)

     c.   Marine Protection, Research, and  Sanctuaries Act (33 U.S.C. 1401).

     •    Incineration at sea requirements.   (40 CFR Part 220-225, 227, 228.  See also  40  CFR
          125.120-125.124)

3.  EPA's Office of Pesticides and Toxic Substances

     •    Toxic Substances Control Act (15 U.S.C. 2601).

     •    PCB Requirements General ly:  40 CFR Part 761; Manufacturing Processing,  Distribution  in
          Commerce, and Use of PCBs and  PCB Items (40 CFR 761.20-761.30); Markings of PCBs  and PCB
          Items (40 CFR  761.40-761.45); Storage and Disposal (40 CFR 761.60-761.79).  Records and
          Reports (40 CFR 761.180-761.185).  See also 40 CFR 129.105, 750.

     •    Disposal  of Waste Material Containing TCDD.  (40 CFR Parts 775.180-775.197).

4.   EPA's  Office of External Affairs

     •    Section 404(b)(l) Guidelines for Specification of Disposal Sites for Dredged or Fill
          Material  (40 CFR Part 230).

     •    Procedures for denial or Restriction of Disposal Sites for Dredged Material (Section 404(c)
          Procedures, 40  CFR Part 231).

5.   EPA's  Office of Air and Radiation administers several potentially applicable or relevant and
     appropriate statutes and regulations issued thereunder:

     a.    The Uranium Mill Tailings Radiation Control Act of 1978 (42 U.S.C. 2022).

     •    Uranium mill tailing rules - Health and Environmental Protection Standards for Uranium and
          Thorium Mill Tailings
          (40 CFR Part 192).

     b.    Clean Air Act (42 U.S.C. 7401).

     •    National Ambient Air Quality Standards for total suspended particulates (40 CFR Part 50.6-
          50.7)

     •    National Ambient Air Quality Standards for ozone (40 CFR 50.9).

     •    Standards  for Protection  Against Radiation - high and low level radioative waste rule.  (10
          CFR Part  20).   See also  10 CFR Parts 10. 40. 60. 61. 72. 960. 961.

     •    National Emission Standard for Hazardous Air Pollutants for Asbestos. (40 CFR
          61.140-61.156).  See also 40 CFR 427.110-427.116. 763. National Emission  Standard  for
          Hazardous Air Pollutants for Radionuclides  (40  CFR Part 61.  10 CFR 20.101-20.108).
                                             E-:

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6.   Other Federal Requirements

     a.    OSHA requirements for workers engaged in response activities are codified under the
          Occupational Safety and Health Act of 1970 (29 U.S.C. 651),  The relevant regulatory
          requirements are included under:

     •    Occupational Safety and Health Standards (General industry Standards) (29 CFR Part 1910).

     •    The Safety and Health Standards for Federal Service Contracts (29 CFR Part  1926).

     •    The Shipyard and Longshore Standards (29 CFR Parts 1915,  1918).

     •    Recordkeeping,  reporting, and related regulations (29 CFR Part 1904).

     b.    Historic Sites, Buildings, and Antiquities Act (16 U.S.C. 461).

     c.    National Historic Preservation Act, 16 U.S.C. 470.  Compliance with NEPA required pursuant
          to 7 CFR Part 650,  Protection of Archaelogical Resources:  Uniform Regulations --
          Department of Defense (32 CFR Part 229, 229.4), Department of the Interior (43 CFR Part 7,
          7.4).

     d.    D.O.T. Rules for the Transportation of Hazardous Materials, 49 CFR Parts 107, 171.1-171.500.
          Regulation of activities in or affecting waters of the United States pursuant to  33 CFR
          Parts 320-329.  The following requirements are also triggered by Fund-financed actions:

     •    Endangered  Species Act of 1973, 16 U.S.C. 1531.  (Generally, 50 CFR Parts 81, 225, 402).
          Wild and Scenic Rivers Act, 16 U.S.C. 1271.

     •    Fish and Wildlife Coordination Act, 16 U.S.C. 661 note.

     0    Fish and Wildlife Improvement Act of 1978, and Fish and Wildlife Act of 1956, 16 U.S.C. 742a
          note.

     •    Fish and Wildlife Conservation Act of 1980, 16 U.S.C. 2901. (Generally, 50 CFR Part 83).

     •    Coastal Zone Management Act of 1972, 16 U.S.C.  1451.  (Generally,  15  CFR Part 930 and 15
          CFR 923.45 for Air and Water Pollution Control Requirements).


                 OTHER FEDERAL CRITERIA.  ADVISORIES.  GUIDANCES.
                      AND  STATE STANDARDS TO  BE  CONSIDERED
1.    Federal Criteria, Advisories and Procedures

     •    Health Effects Assessments (HEAs).

     •    Recommended Maximum Concentration Limits (RMCLs).

     •    Federal Water Quality Criteria (1976. 1980.  1984).  Note:  Federal Water Quality Criteria
          are  not legally enforceable.  State water quality standards are legally enforceable, and are
          developed  using appropriate  aspects of Federal Water Quality Criteria.  In many cases. State
                                             n-3

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           water quality standards do not include specific numerical limitations on a large number of
           priority pollutants. When neither State standards nor MCLs exist for a given pollutant.
           Federal Water Quality Criteria are pertinent and therefore are to be considered.

      •    Pesticide registrations.

      •    Pesticide and food additive tolerances and action levels.  Note:  Germane portions of
           tolerances and action levels may be pertinent and therefore are to be considered in certain
           situations.

      •    Waste load allocation procedures,  EPA Office of Water.

      •    Federal sole source aquifer requirements.

      •    Public health basis for the decision to list pollutants as hazardous under section 112 of
           the Clean Air Act.

      •    EPA's Ground-water Protection Strategy.

      •    New Source Performance Standards for Storage Vessels for  Petroleum Liquids.

      »    TSCA health data.

      •    Pesticide registration data.

      •    TSCA chemical advisories (2 or 3 issued to date).

      •    Advisories issued  by FWS and NWFS under the Fish and Wildlife Coordination Act.

      •    Executive Orders  related to Floodplains (11988) and Wetlands (11990) as implemented by EPA's
           August 6, 1985, Policy on Floodplains and Wetlands Assessments for CERCLA Actions.

      •    TSCA Compliance Program Policy.

      •    OSHA health and  safety standards that may be used to
           protect public health (non-workplace).

      •    Health Advisories. EPA Office of Water

2.    State  Standards

      •    State Requirements on Disposal and Transport of Radioactive wastes.

      •    State Approval  of Water Supply System Additions or Developments.

     •    State Ground Water Withdrawal Approvals.  Requirements of authorized (Subtitle C of RCRA)
           State hazardous waste programs.

     •     State Implementation Plans and Delegated Programs Under Clean Air Act.

     •    All other State  requirements, not delegated through EPA authority.

     •   Approved State NPDES programs under the Clean Water Act.
                                              E-4

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     o    Approved State UIC programs under the Safe Drinking Water Act.

          Note:  Many other State and local requirements could be pertinent.  Forthcoming guidance
          will include a more comprehensive list.

3.  USEPA RCRA Guidance Documents

     o    Draft Alternate Concentration Limits (ACL) Guidance

A.  EPA's RCRA Design Guidelines

     1.   Surface Impoundments, Liners Systems, Final Cover and Freeboard Control.

     2.   Waste Pile  Design - Liner Systems.

     3.   Land Treatment Units.

     4.   Landfill Design -  Liner Systems and Final Cover.

B.   Permitting Guidance Manuals

     1.   Permit Applicant's Guidance Manual for Hazardous Waste Land Treatment, Storage, Disposal
          Facilities.

     2.   Permit Writer's Guidance Manual for Hazardous Waste Land Treatment, Storage, and Disposal
          Facilities.

     3.   Permit Writer's Guidance Manual for Subpart F.

     4.   Permit Applicants Guidance Manual for the General Facility Standards.

     5.   Waste Analysis Plan Guidance Manual.

     6.   Permit Writer's Guidance Manual for Hazardous Waste Tanks.

     7.   Model  Permit Application for Existing Incinerators.

     8.   Guidance Manual for Evaluating Permit Applications for the Operation of Hazardous Waste
          Incinerator Units.

     9.   A guide for Preparing RCRA Permit Applications for Existing Storage Facilities.

    10.   Guidance Manual on Closure and Post-Closure Interim Status Standards.

C.  Technical Resource Documents (TRDs)

     1)   Evaluating  Cover Systems for Solid and Hazardous Waste.

     2)   Hydrologic Simulation of Solid Waste Disposal Sites.

     3)   Landfill and Surface Impoundment Performance Evaluation.

     4)   Lining of Water Impoundment and Disposal Facilities.
                                             E-5

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      5)    Management of Hazardous Waste Leachate.

      6)    Guide to the Disposal of Chemically Stabilized and Solidified Waste.

      7)    Closure of Hazardous Waste Surface Impoundments.

      8)    Hazardous Waste Land Treatment.

      9)    Soil Properties, Classification, and Hydraulic  Conductivity Testing.

D.   Test Methods for Evaluating  Solid Waste

      1)    Solid Waste Leaching Procedure Manual.

      2)    Methods for the Prediction of Leachate Plume Migration
           and Mixing.

      3)    Hydrologic Evaluation of Landfill Performance (HELP) Model Hydrologic Simulation on Solid
           Waste Disposal Sites.

      4)    Procedures for Modeling Flow Through Clay  Liners to
           Determine Required Liner Thickness.

      5)    Test Methods for  Evaluating Solid Wastes.

      6)    A Method for  Determining the Compatibility of Hazardous
           Wastes.

      7)    Guidance  Manual on Hazardous Waste Compatibility.

4.  USEPA Office of Water Guidance Documents

A.  Pretreatment Guidance Documents

      I)    304(g) Guidance Document Revised Pretreatment Guidelines (3) Volumes

B.    Water Quality Guidance Documents

      1)    Ecological Evaluation of Proposed Discharge of Dredged Material into Ocean Waters (1977)

      2)    Technical  Support Manual:  Waterbody Surveys and Assessments for Conducting Use
           Attainability Analyses (1983)

      3)    Water-Related  Environmental Fate of 129 Priority Pollutants (1979)

      4)    Water Quality  Standards Handbook (1983)

      5)    Technical  Support Document for Water Quality-based Toxics Control.

C.    NPDES Guidance Documents

      1)   NPDES Best Management Practices Guidance Manual (June 1981)

     2)   Case studies on toxicity  reduction evaluation (May  1983).


                                             E-6

-------
D.  Ground Water/UIC Guidance Document

     1)   Designation of a USDW

     2)   Elements of Aquifer Identification

     3)   Interim guidance for public participation

     4)   Definition of major facilities

     5)   Corrective action requirements

     6)   Requirements applicable to wells injecting into, through or above an aquifer which has been
          exempted pursuant to Section  146.104(b)(4).

     7)   Guidance for UIC  implementation on Indian lands.

5. USEPA Manuals  from the Office of Research and Development

     1)   EW 846 methods - laboratory analytic methods.

     2)   Lab protocols developed pursuant to Clean Water Act
          Section 304(h).
                                              n-7

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           APPENDIX F

HISTORICAL PRECISION AND ACCURACY
     DATA CLASSIFIED BY MEDIA
       BY ANALYTICAL LEVEL

-------
          APPENDIX  F CONTENTS
HISTORICAL PRECISION AND ACCURACY TABLES
            Introduction

            Water:  Level III
            Water:  Level IV

            Soil:  Level  I
            Soil:  Level  II
            Soil:  Level  III
            Soil:  Level  IV

            Air:  Level I
            Air:  Level II
            Air:  Level III

            Other Media: Level III
              F-l

-------
                                INTRODUCTION
The data in this Appendix have been compiled to assist the reader in
selecting an analytical method appropriate for each data use.  The methods
are classified by media and by analtyical levels defined as follows:


    •  Level I - field screening or analysis using portable instruments.
       Results are often not compound specific and not quantitative but
       results are available in real-time.

    •  Level II - field analysis using more sophisticated portable
       analytical instruments; in some cases, the instruments may be set up
       in a mobile or onsite laboratory.  There is a wide range in the
       quality of data that can be generated.  Quality depends on the use
       of suitable calibration standards, reference materials, and sample
       preparation equipment; and the training of the operator.  Results
       are available in real-time or several hours.

    •  Level III - all analyses performed in an offsite analytical
       laboratory using standard, documented procedures.  The laboratory
       may or may not be a CLP laboratory.

    •  Level IV - CLP routine analytical services (RAS).  All analyses are
       performed in an offsite CLP analytical laboratory following CLP
       protocols.
Precision and accuracy data are presented in tabular fashion.  Footnotes to
each table cite the sources of the data and the concentration or
concentration range at which the precision and accuracy were determined.
When no concentration is cited no concentration information was available

in the source material.


Precision is a measure of the variability in repeated measurements of the
same sample compared to the average value.  Precision is reported as %
Relative Standard Deviation (RSD).  The lower the % RSD, the more precise
the data.
                              F-2

-------
RSD is calculated  for  a pair of  replicates using  the  following  formula:

                     %RSD = [ 2 | Xj_ -X2 |/( Xt +X2 ) ]  (100//2)
                 where X^^  is measurement #1 of a  replicate

                       X2  is measurement #2 of a  replicate

Accuracy is  reported as %  Bias; as % Bias approaches  zero, accuracy
increases.   Bias is calculated by the following formula:

                             %  Bias = X-Y (100)
                                       Y
              where Y is  the known concentration or true value
                    X  is the reported concentration

Bias measures the  systematic error within an analytical  technique.
                              F-3

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                         HISTORICAL PRECISION AND ACCURACY DATA/WATER a
           LYTICAL TECHh
I THAN CLP I
ANALYTES
BRCIMODICHLOROMFTHANF
BROMOFORM
METHOD CONCENTRATION
(TECHNIQUE) RANGE
624
(GC/MS)
8240
(GC/MS)
624
(GC/MS)
501.1
(PURGE & TRAP GC/MS)
501.2
(EXTRACTION GC/MS)
624
(GC/MS)
501.1
(PURGE & TRAP GC/MS)
501.2
(EXTRACTION GC/MS)
11 ug/l
480 ug/l
5-100 ug/l
8 ug/l
480 ug/l
0.9 ug/l
550 ug/l
1.8 ug/l
170 ug/l
9 ug/l
400 ug/l
4.8 ug/l
550 ug/l
6 ug/l
170 ug/l
PRECISION
% RSD
16
21
21
28
18
66
34
61
23
32
30
44
41
14
15
ACCURACY
% BIAS
0
-16
12
-8.8
-6.7
0
-3.8
33
-19
-23
10
-27
7.5
-23
1.8

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                                           HISTORICAL PRECISION AND ACCURACY DATA/WATER a
                                                       (continued)

                   LEVEL III ANALYTICAL TECHNIQUES - METHODS OTHER THAN CLP RAS METHODS
                  ANALYTES


                    CHLOROFORM
Tl
I
                   DIBROMOCHLOROMETHANE
                   DIOXIN
METHOD CONCENTRATION
(TECHNIQUE) RANGE
624
(GC/MS)
501.1
(PURGE & TRAP GC/MS)
501.2
(EXTRACTION GC/MS)
624
(GC/MS)
501.1
(PURGE & TRAP GC/MS)
501.2
(EXTRACTION GC/MS)
613
(GC/MS)
4.5 ug/l
300 ug/l
0.9 ug/l
550 ug/l
1.8 ug/l
170 ug/l
8.1 ug/l
360 ug/l
0.8 ug/l
550 ug/l
1.8 ug/l
170 ug/l
21 ng/l
202 ng/l
PRECISION
% RSD
31
14
64
14
68
26
13
19
35
36
37
13
25
21
ACCURACY
% BIAS
2.2
-0.6
44
-0.02
-39
-1.2
-3.1
10
-12.5
4.7
0
0.02
N.A.
N.A.

-------
                            HISTORICAL PRECISION AND ACCURACY DATA/WATER a
                                         (continued)
LEVEL III ANALYTICAL TECHNIQUES - METHODS OTHER THAN CLP RAS METHODS
ANALYTES
METHYLENE CHLORIDE
JjQUJELE

TRICHLOROETHENE

LEAD


METHOD
(TECHNIQUE)
624
(GC/MS)
624
(GC/MS)
8240
(GC/MS)
624
(GC/MS)
8240
(GC/MS)
200.7
(ICP)
239.1
(FLAME AA)
239.2
(FURNACE AA)
CONCENTRATION
RANGE
7.2 ug/l
480 ug/l
13.5 ug/l
600 ug/l
25 ug/l
75 ug/l
5.4 ug/l
360 ug/l
25 ug/l
75 ug/l
42 ug/l
47.7 ug/l
12 ug/l
105 ug/l
10 ug/l
234 ug/l
PRECISION
% RSD
78
52
19
31
19
48
39
24
34
5
5.9
6.7
53
19
ACCURANCY
% BIAS
-17
-25
15
-14
-10
44
-2.3
5
31
4.4
17
-1.9
-22
-3.1
 a.  Source:  Draft Compendium of Information and Performance Data on Routinely Used Measurement Methods (RUMM) - Pilot Phase,
            RTI/3087/03, prepared for EPA Quality Assurance Management Staff, January 1986. This document should be
            consulted for more information on individual analytes.

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         HISTORICAL PRECISION AND ACCURACY DATA/WATER
                   (Continued)

LEVEL III ANALYTICAL TECHNIQUES - SW-846 METHODS
Method
Number
ORGANICS
8010
8020
8030

8040
8060
8080

8090
8100

8120
8140
8150
8240
8250

8040

Method Name
•
•
Halogenated Volatile Organics
Aromatic Volatile Oranics
Acrolein, Acrylonitrile,
Acetonitrile
Phenols
Esters
Organochlorine Pesticides
and PCBs
Nitroaromatics and Cyclic
Ketones
Polynuclear Aromatic
Hydrocarbons
Chlorinated Hydrocarbons
Organophosphorous Pesticides
Chlorinated Herbicides
Volatile Organics
GC/MS Semivolatiles (Packed
Column)
GC/MS Semivolatiles
(Capillary)
Data
Source
SW 846
SW 846
SW 846

SW 846
EPA 606
SW 846

SW 846


SW 846
SW 846
SW 846
SW 846




Range of
Recovery (%)
75.1 - 106.1
77.0 - 120
96 - 107

41 - 86
82 - 94
86 - 97

63 - 71
NAb

76 - 99
56.5 - 120.7
NA
95 - 107
41 - 143

NA

Precision
(%)
2.
9.
5.

7.
1.
1.

3.
0 - 25.1
4 - 27.7
6 - 11.6

9 - 16.5
3 - 6.5
3 - 6.5

1 - 5.9
NA

10
5.
NA
9
20

NA


- 25
3 - 19.9

- 28
-145



MDL
(mg/1)
0.03 - 0.
0.2 - 0.4
0.5 - 0.6

058 - 2.2
0.29 - 3.
0.29 - 3.

0.06/ND
NA

0.03 - 1.
0.1 - 5.0
0.1 - 200
1.6 - 6.9
0.9 - 44

NA


52




0
0




34








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                                              HISTORICAL PRECISION AND ACCURACY DATA/WATER
                                                        (Continued)
                                     LEVEL III ANALYTICAL TECHNIQUES - SW-846 METHODS
Tl
CD
Method
Number
Method Name
Data
Source
Range of
Recovery (%)
Precision
(%)
MDL
(mg/l)
        8310
INORGANICS;

7000 Series
7470
9010
9030
Polynuclear Aromalic           SW 846       78 - 116
Hydrocarbons (HPLC)
(Capillary)
Metals (ICAP)                  EPA 200.7    MA
Metals (FLAME) 7000 Series     EPA 200      NA
Metals (FLAME LESS/GF)         EPA 200      NA
Metals (MERCURY)               EPA 245.2    87 - 125
Cyanides                       EPA 335.2    85 - 102
Sulfides                       EPA 376.1    NA
                                                                         7.3 - 12.9
3 - 21.9 (RSD)
NA
NA
0.9 - 4.0
0.2 - 15.2
NA
                 0.03 - 2.3
1.3 - 75 Mg/l
0.01 - 5
0.001 - 0.2 Mg/l
0.0002
0.02 Mg/l
1 Mg/l
        a.  For water only
        b.  NA Not Available
        NOTES:  Method Detection Limit (MDL)  as listed on this table is the minimum concentration of a substance
                that can be measured and reported with 99% confidence that the value is above zero.
                Accuracy, presented as an average percent recovery,  was determined from replicate (10-25) analyses
                of water and wastewater samples fortified with known concentrations of the analyte of interest at
                or near the detection limit.   In most cases this was less than 10 times the MDL.
                Precision data are used to measure the variability of these repetitive analyses reported as a
                single standard deviation or, as a percentage of the recovery measurements.  For presentation
                purposes accuracy, precision and MDL information is presented as an average range of individual
                values for every analyte covered by the procedure.  If specific information on a particular
                compound is required, the specific analytical method cited should be consulted.

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                                                   HISTORICAL PRECISION 2ND ACCURACY DATA/WATER'
  LEVEL  IV ANALYTICAL TECHNIQUES - CLP RAS METHODS
•n
I
ANALYTES

Volatilesb
   Methylene chloride
   1,1-Dichloroethene
   1,1-Dichloroethane
   Trans-1,2-Dichloroethene
   Chloroform
   1,2-Dichloroethane
   1,1,1-Trichloroethane
   Carbon Tetrachloride
   1,1,2,2-Tetrachloroethane
   Bromodichloromethane
   1,2-Dichloropropane
   Trans-1,3-Dichloropropene
   Trichloroethene
   Dibromochlorome thane
   1,1,2-Trichloroethane
   Benzene
   Cis-1,3-Dichloropropene
   Bromoform
   Tetrachloroethene
   Toluene
   Chlorobenzene
   Ethyl Benzene

Semiyolatiles
   bis(2-Chloroethyl)ether
   2-Chlorophenol
   1,3-Dichlorobenzene
   1,4-Dichlorobenzene
   1,2-Dichlorobenzene
   2-Methylphenol
   bis( 2-Chloroisopropyl )ether
                                   TECHNIQUE

                                   Purge & Trap GC/MS
CONCENTRATION
   RANGE

    N.A.C
                                   GC/MS
    N.A.
PRECISION
 % RSD
                              56
                              20
                              13
                              31
                              12
                              13
                              19
                              12
                              11
                              19
                              18
                              31
                              17
                              14
                              11
                              12
                              22
                              16
                              13
                              14
                              14
                               4
                                                                                        24
                                                                                        29
                                                                                        24
                                                                                        21
                                                                                        29
                                                                                        29
                                                                                        25
ACCURACY
% Bias
                    +36.6
                    -26.3
                    -46.4
                    -21.7
                    -21.1
                     +2.4
                    -41.0
                    -32.1
                     -5.8
                    -13.0
                    -12.9
                    -41.2
                    -22.8
                     -3.3
                     -7.0
                     -3.3
                    -35.5
                     +6.5
                    -42.5
                    -23.3
                    -15.9
                    -31.9
                                                -16
                                                -21
                                                -48
                                                -25
                                                -28
                                                -30
                                                -22

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                                                 HISTORICAL PRECISION AND ACCURACY DATA/WATER
LEVEL IV  ANALYTICAL TECHNIQUES - CLP RAS METHODS
ANALYTES

Semivolatiles
   4-Methylphenol
   N-Ni troso-di-n-propylamine
   Nitrobenzene
   Isophorone
   2-Nitrophenol
   bis (2-Chloroethoxy)me thane
^  2,4-Dichlorophenol
g  1,2,4-Trichlorobenzene
   Naphthalene
   4-Chloro-3-methylphenol
   2,4,6-Trichlorophenol
   2-Chloronapthalene
   Acenapthene
   2,4-Dinitrophenol
   2,4-Dini trotoluene
   2,6-Dinitrotoluene
   4-Chlorophenyl-phenylether
   Fluorene
   4,6-Dinitro-2-methylphenol
   4-B romopheny 1-phenyle the r
   Hexachlorobenzene
   Pentachlorophenol
   Phenanthrene
   Fluoranthene
   Benzo(b)fluoranthene
   Benzo(a)pyrene
                                  TECHNIQUE

                                   GC/MS
CONCENTRATION
   RANGE

      N.A.C
PRECISION
  % RSD
                                                                                       33
                                                                                       31
                                                                                       32
                                                                                       23
                                                                                       30
                                                                                       34
                                                                                       29
                                                                                       30
                                                                                       44
                                                                                       26
                                                                                       25
                                                                                       24
                                                                                       28
                                                                                       24
                                                                                       34
                                                                                       25
                                                                                       34
                                                                                       25
                                                                                       30
                                                                                       32
                                                                                       36
                                                                                       31
                                                                                       21
                                                                                       42
                                                                                       39
                                                                                       42
ACCURACY
 % Bias
                                               -36
                                              +0.3
                                               -23
                                                -8
                                               -21
                                              -2.6
                                               -20
                                               -47
                                               -38
                                               -32
                                               -17
                                              +3.4
                                               -12
                                               -23
                                               -33
                                               -48
                                               +12
                                               -24
                                               -13
                                              -0.1
                                               -42
                                               -24
                                               -28
                                               -15
                                               -10
                                               -29

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                                                  HISTORICAL PRECISION AND ACCURACY DATA/WATER
                                                            (continued)
LEVEL IV  ANALYTICAL TECHNIQUES - CLP HAS METHODS

ANALYTES
Metals6
Aluminum
Antimony
Arsenic
Barium
Beryllium
Pflrfrnjiim
Calcium
Chromium
Cobalt
"• Copper
: Iron
Lead
Magnesium
Manganese
Mercury
Nickel
Potassium
Selenium
Sodium
Thallium
Tin
Vanadium
Zinc

TECHNIQUE

ICP
ICP
Furnace AA
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
Furnace AA
ICP
ICP
Cold Vapor
ICP
ICP
Furnace AA
ICP
Furnace AA
ICP
ICP
ICP
CONCENTRATION
RANGE

1000-3000 ug/1
180-600 ug/1
50-150
800-1500
30-45
25-50
1000-30000
50-150
200-1000
125-250
200-800
30
10000-40000
30-150
5-20
160
10000-20000
50
10000-45000
80-100
160
60-200
50-800
                                                                                 PRECISION
                                                                                   % RSD
                                                                                      9.1
                                                                                       11
                                                                                      9.4
                                                                                      6.8
                                                                                       15
                                                                                       12
                                                                                      6.0
                                                                                      9.8
                                                                                      6.7
                                                                                      6.7
                                                                                     10.4
                                                                                       32
                                                                                      6.6
                                                                                      6.2
                                                                                     18.8
                                                                                      9.0
                                                                                     16.
                                                                                      8.
                                                                                      8.

                                                                                     17-Z
                                                                                   N.A.
                                                                                      7.6
                                                                                      9.1
.2
.7
.7
                ACCURACY
                 % Bias
 -4.3
 -9.2
 -8.3
 -3.9
 +3.7
 -3.3
 -1.6
 -2.6
 -2.9
 -1.1
 +6.5
 -0.7
 -2.5
 -1.0
-14.4
 -2.5
-12.1
 -5.7
 -2.8
 -4-.2
 -2.5
-0.46
 +3.0
a.  Source:  Quality Control in Remedial Site Investigation:   Hazardous  and  Industrial Solid Waste Testing,  Fifth Volume,
    ASTM STP 925, C.L. Perket, Ed., American Society for Testing Materials,  Philadelphia, 1986.

b.  Volatile precision and accuracy data from 26-34  laboratories' results on quarterly blind performance evaluation
    samples; 29-152 data points for each compound.

c.  N.A. - Not Available.

d.  Semi volatile precision and accuracy data from 1985 preaward program  data; 22-227 data points for each compound.
e.  Metals precision and accuracy data is based on performance  evaluation sample results from 18 laboratories; number
    of data points is not given.

-------
                                        HISTORICAL PRECISION AND ACCURACY DATA/SOILS
LEVEL I FIELD SCREENING TECHNIQUES
MEASUREMENT
RESISTIVITY
TERRAIN
CONDUCTANCE

TERRAIN
CONDUCTANCE

Magnetic Field
    Intensity

Subsurface
Lithology
Changes

Subsurface
Lithology
Changes
INSTRUMENT
(TECHNIQUE)

Bison 2390 T/R
(Resistivity meter)

EM 31
(conductivity)

EM 34-3
(conductivity)

EDA - Omni IV
(Magnetometer)

SIR-8
(Ground Penetrating
   Radar)
EG+G 1225
(Seismograph)
INSTRUMENT
  RANGE

0-1999
millivolts

0-1000
mi11imhos/mete r

0-300
mi11imhos/mete r

18000-110000
gammas
1-81 dielectric
constant
0-2000
milliseconds
INSTRUMENT .
PRECISION

at 1% range setting,
0-5% of full scale

2% of full scale


2% of full scale


0.02 gamma



N/Ad



N/Ad
INSTRUMENT
ACCURACY

2% of measured
value

5% at 20 millimhos/taeter
5% at 20 millimhos/meter
1 gamma at 50000 gammas
at 23oC
N/A
0.01%

-------
                                           HISTORICAL PRECISION AND ACCURACY DATA/SOIL
                                                     (continued)
   LEVEL I FIELD SCREENING TECHNIQUES
I
I—•
UJ

MEASUREMENT
TOTAL
VOLATILE
ORGANICS







INSTRUMENT FIELD SCREENING
(TECHNIQUE) RESULTS in ppm (X)
PHOTO VAC 11.4
(GC/Photoionization) 22.0
56.0
139
70.0
24.9
60.0
6.6
12.1
8.7
CLP
RESULTS in ppm (Y)
26.9
32.8
129.7
228.0 & 258.0
126.7
2823.0
53.3
0.056
0.032
0.024
ACCURACY6
(% Bias)
-57.6
-32.9
-56.8
-42.8
-44.8
+99.1
+12.6
+116.9
+377.1
+361.5
   a.  Source:  Manufacturers' manuals unless otherwise cited.  Mention of specific models does not constitute
       and endorsement of these instrument.

   b.  Precision refers to reproducibility of meter or instrument reading as cited in instrument specifications,

   c.  Accuracy refers to instrument specifications unless otherwise cited.

   d.  N.A. » not available.

   e.  Accuracy of PhotoVac field screening results calculated by assuming that CLP results on the same samples
       were completely accurate.   % Bias - 100 (X-Y).  Source of these data is CDM project files.
              "*                                 —

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                                                      HISTORICAL PRECISION AND ACCURACY DATA/SOIL"
                LEVEL II FIELD TECHNIQUES
I
J>
ANALYTES INSTRUMENT FIELD RESULTS
fTECHNIQUE) IN ppm (x)
PCBs HNu 301 6.0
(GC/ELECTRON 6.0
CAPTURE) 6.0
9.0
13.0
14.0
14.0
21.0
35.0
41.0
48.0
50.0
65.0
67.0
92.0
95.0
11
202
269
286
1215
1647
3054
CLP RESULTS
IN ppm (y)
22.0
6.1
510.0
3.9
3.0
3.1
23.5
8.1
7.7
2.1
11.0
460.0
23.1
18.7
75.0
30.0
12.3
99.0
370.0
80.5
640.0
1040.0
9,300
ACCURACY b
% BIAS
-72.7
-1.6
-98.8
+56.7
+333.3
+351.6
-40.4
+ 159.3
354.5
+1,852
+336.3
-89.1
+181.4
+258.3
22.7
+216.7
-10.6
+ 104.0
-27.3
+255.3
+90.0
+58.4
-67.2.
               a.  Source:  COM Project files.


               b. Source: Accuracy calculated by assuming that CLP results on the same samples were completely accurate. % Bias = 100   (X'V)
                                                                                                                             y

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                            HISTORICAL PRECISION AND ACCURACY DATA/SOIL3
 I FVFI  III ANALYTICAL TECHNIQUES - METHODS OTHER THAN CLP RAS METHODS

 ANALYTE                       METHOD          CONCENTRATION      PRECISION       ACCURACY
                             (TECHNIQUE)            RANGE            % RSD           % BIAS

 DIOXINS                          8280               5 ppb             6-30             N.A.
                             (HPLC/LRMS)         125 ppb             3-10             N.A.

                         JAR EXTRACTION GC/MS      1 ppb               20               0
                                                   10 ppb               10             -18
a.  Source:  Draft Compendium of Information and Performance Data on Routinely Used Measurement Methods (RUMM) - Pilot Phase,
           RTI/3087/03, prepared for EPA Quality Assurance Management Staff, January 1986.  This document should be
           consulted for more information on individual analytes.

-------
                                                  HISTORICAL PRECISION AND ACCURACY DATA/SOILS3

LEVEL  IV ANALYTICAL TECHNIQUES - CLP RAS METHODS

                                                         CONCENTRATION            PRECISION          ACCURACY
                                  TECHNIQUE                 RANGE                 % RSD             %  Bias

Volatiles                         Purge & Trap GC/MS           N.A.C
   Chloroform                                                                         g Q              -01
   1,2-Dichloroethane                                                                 i3'±             +11 1
   Dibromochlorometnane                                                               35*0             -12 0
   Benzene                                                                            32^             _1(J;3
   Bromoform                                                                          16>6             _121
   2-Hexanone                                                                        16 6             _45 5

   SJUenL                                                                           13-8             +13.7
   Chlorobenzene                                                                      21.2             +13.2

Semivolatiles                    GC/MS                          N.A.C
   1,4-Dichlorobenzene                                                                  27               -51
   Nitrobenzene                                                                         21               -48
   Isophorone                                                                          24               -47
   2-Nitrophenol                                                                        35               _^g
   2,4-Dichlorophenol                                                                   31               _gg
   1,2,4-Trichlorobenzene                                                               28               -43
   Penta Chlorophenol                                                                   17               _4g
   Pyrene                                                                               25               -15
   2-Methylnaphthalene                                                                  26               -42
   bis-(2-Ethylhexyl)phthalate                                                          33                _2
   Phenol                                                                               38               _27
   Acenaphthylene                                                                       26               -27
   Diethyphthalate                                                                      16               _2Q

Dioxin
   2T1,7,8-TCCD                                          1-10     ugAg                   15             -11.5

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                                                  HISTORICAL PRECISION AND ACCURACY DATA/SOILS3
                                                            (continued)

LEVEL IV ANALYTICAL TECHNIQUES - CLP RAS METHODS

                                                         CONCENTRATION                PRECISION           ACCURACY
ANALYTES                          TECHNIQUE                 RANGE                      % RSD              I Bias


                                                                                           14.4              -78.8
                                                                                           33.3               +2.9
                                                                                          N.A.                -4.2
                                                                                            7.8               -6.1
                                                                                           11.2               -2.5
                                                                                           10.7              -27.0
                                                                                            9.2               -2.2
                                                                                            7.5              -10.6
                                                                                            9.4              -15.1
                                                                                           25.0               -9.1
                                                                                           15.0              -17.Q
                                                                                           44.1              N.A.
                                                                                            5.8               -6.2


a.  Source:  Quality  Control  in Remedial Site Investigation:  Hazardous and Industrial Solid Waste Testing, Fifth Volume,
    ASTM STP 925,  C.L.  Perket, Ed., American Society for Testing Materials, Philadelphia, 1986.

b.  Volatiles  precision and accuracy data  is based on 1985 preaward analysis results from laboratories awarded
    contracts;  6-14 data points for each compound.

c.  N.A. - Not Available.

d.  Semivolatiles precision and accuracy data is based on 1985 preaward analysis results; 9-20 data points
    for each compound.

e.  Dioxin  precision  and accuracy data is  based on results of four performance evaluation samples including
    120 data points.

f.  Metals  precision  and accuracy data is  based on performance evaluation sample results from 18 laboratories;
    number  of  data points is  not given.
Metalsb
Aluminum
Cadmium
Calcium
Chromium
Copper
Iron
^ Lead
^ Magnesium
-j Manganese
Mercury
Nickel
Tin
Zinc

ICP
ICP
ICP
ICP
ICP
ICP
Furnace AA
ICP
ICP
Cold Vapor
ICP
ICP
ICP

2-22600 ugAg
5.5-20
2664-29000
8.5-29600
33-109
5028-113000
11.5-714
2428-7799
73.5-785
1.1-26.5
44-67
N.A.
19-1720

-------
                                          HISTORICAL PRECISION AND ACCURACY DATA/AIRa
      LEVEL I FIELD SCREENING TECHNIQUES
•n
i
ANALYTES
Organics
Organics
Organics
Organics
INSTRUMENT
(TECHNIQUE)
Century OVA-128
(Flame lonization)
HNu PI-101
(Photoionization)
AID - 710
(Flame lonization)
PhotoVac
(GC-Photoion-
ization)
INSTRUMENT
RANGE
0.1 - 1000 ppm
Methane
0.1 - 2000 ppm
Benzene
0.1 - 2000 ppm
Methane
N.A.
INSTRUMENT
SENSITIVITY
0.1 ppm Methane
0.1 ppm Benzene
0.1 ppm Methane
0.001 ppm
Benzene
                                                                                        INSTRUMENT
                                                                                        PRECISION
                                                                                        N.A.
                                                                                        + 1% of full scale
                                                                                        deflection

                                                                                        N.A.d
                                                                                        N.A.
      a.  Source:  Manufacturers' manuals unless otherwise cited.  Mention of specific models
          does not constitute an endorsement of these instruments.

      b.  It is difficult to differentiate between Level I and Level II techniques and
          instrumentation.  Several instruments may be used at both levels.

      c.  Sensitivity and precision refer to instrument specifications.

      d.  N.A. = Not Available.

-------
                                           HISTORICAL PRECISION AND ACCURACY DATA/AIR
       LEVEL II FIELD TECHNIQUES
TJ
I
ANALYTES
Organics
Compound—
Specific
Organics,
Compound-
Specific
Organics,
Compound-
Specific
Organics,
Compound-
Specific
Mercury
INSTRUMENT
(TECHNIQUE)
Mi ran IB
(Infrared)
Century OVA-128
(GC/Flame
lonization)
PhotoVac
(GC-Photo-
ionization)
SCENTOR
(Argon lonization
or Electron Capture)
Gold film Mercury
Analyzer
INSTRUMENT
RANGE
Compound Dependent,
0-2000 ppra
1-1000 ppm
Methane
N.A.
N.A.
N.A.
INSTRUMENT
SENSITIVITY0
N.A.d
N.A.
0.001 ppm
Benzene
0.001 ppm
Benzene
less than
0.01 ppm
INSTRUMENT
PRECISION
N.A.d
N.A.
N.A.
N.A.
N.A.
       a.  Source:  Manufacturers' manuals.  Mention of specific models does not constitute an
           endorsement of these instruments.

       b.  It is difficult to differentiate between Level I and Level II techniques and
           instrumentation.  Several instruments may be used at both levels.

       c.  Sensitivity and precision refer to instrument specifications.

       d.  N.A. = Not Available.

-------
                                     HISTORICAL PRECISION AND ACCURACY DATA/AIR'
        LEVEL III ANALYTICAL TECHNIQUES - METHODS OTHER THAN CLP RAS METHODS
         ANALYTES
        BENZENE
     METHOD
    (TECHNIQUE)

 CRYOGENIC TRAP/GC
                                      TENAX GC/MS
10
O
CONCENTRATION
    RANGE

   3.9 ppb
    93 ppb

   7.8 ug/m3
   4.5 ug/m3
PRECISION
 % RSD

   4.0
   5.1

    11
    21
ACCURACY
 % BIAS

    N.A.
    N.A.

    N.A.
    N.A.
        TOLUENE
                        10.8 ppb
                      5.11
                   N.A.
        TRICHLORQETHENE
                         3.5 ppb
                         84 ppb
                       4.1
                       3.7
                   N.A.
                   N.A.
        VINYL CHLORIDE
                         7.8 ppb
                      6.37
                   N.A.
        LEAD
40 CFR 50, APP G
  (FLAME AA)
   0.6 ug/m3
  8.01  ug/m3
   8.6
   3.9
      0
   -3.6
       a. Source:  Draft Compendium of Information and Performance Data on Routinely Used Measurement Methods (RUMM) - Pilot Phase,
                  RTI/3087/03, prepared for EPA Quality Assurance Management Staff, January 1986.  This document should be
                  consulted for more information on individual analytes.

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                                  HISTORICAL PRECISION AND ACCURACY DATAADTHER MEDIA3
 LEVEL III ANALYTICAL TECHNIQUES - METHODS OTHER THAN CLP RAS METHODS

 ANALYTE                    METHOD                             CONCENTRATION      PRECISION      ACCURACY
                           (TECHNIQUE)             MEDIUM             RANGE           % RSD        % BIAS

LEAD                           6010             OIL WASTE                 1.0  mg/kg     3.1             -10
                               (ICP)                                      -2.5  mg/kg     22             -20

                                                SOLID WASTE                 50  mg/kg      10            3.4
                                                                            75  mg/kg     3.7           -0.8



                               SOLID              SLUDGE                     5  mg/kg       2              0
                                                                            20  mg/kg      11             55
 a.  Source:  Draft Compendium of Information and Performance Data on Routinely Used Measurement Methods (RUMM) - Pilot Phase,
           RTI/3087/03, prepared for EPA Quality Assurance Management Staff, January 1986.  This document should be
           consulted for more information on individual analytes.

-------
    APPENDIX G

 RCRA APPENDIX VIII
CLP HSL COMPARISON

-------
                       ORGANIC COMPOUNDS ON CLP/HSL
                 BUT NOT INCLUDED ON MODIFIED APPENDIX VIII
Common Name                                                CAS RN

Acetone                                                    67.64.1
Vinyl Acetate                                              108.05.4
2-Hexanone                                                 591.78.6
Ethylbenzene                                               100.41.4
Styrene                                                    100.42.5
Xylenes (Total)                                            1330-20-7
Benzyl Alcohol                                             100.51.6
Isophorone                                                 78.59.1
2-Nitrophenol                                              88.75.5
Benzole Acid                                               65.85.0
2-Methylnaphthalene                                        91.57.6
2-Nitroaniline                                             88.74.4
3-Nitroaniline                                             99.09.2
Dibenzofuran                                               132.64.9
4,Chlorophenyl-phenylether                                 7005.72.3
Endrin Ketone                                              53494.70.5
Endosulfan Sulfate                                         1031.07.8
                                G-l

-------
             ORGANIC COMPOUNDS ON MODIFIED APPENDIX VIII LIST
                        BUT NOT INCLUDED ON CLP/HSL
Common Name

Acetonitrile
Acetophenone
2-Acetylaminofluorine
Acrolein
Acrylonitrile
Allyl Alcohol
4-Aminobiphenyl
Aramite
Benzenethiol
p-Benzoquinone
Bromoacetone
2-sec-butyl-4,6-dinitrophenol
Chlorobenzilate
2-chloro-l,3-butadiene
3-chloropropene
3-chloropropionitrile
Diallate
Dibenzo [a,e] pyrene
Dibenzo [a,h] pyrene
Dibenzo [a,i] pyrene
1,2-dibromo-3-chloropropane
1,2-dibromoethane
Dibromomethane
1,4-dichloro-2-butene
Dichlorodifluoromethane
2,6 Dichlorophenol
1,3-Dichloropronene
0,0-Diethyl 0-2-pyrazinyl
phosphorothioate
3,3-Dimethoxybenzidine
p-Dimethylaminozobenzene
7,12-Dimethylbenz[a]anthracene
3,31-Dimethylbenzidine
alpha-Dimethylphenethylamine
1,4-Dioxane
Diphenylamine
1,2-Diphenylhydrazine
Di-n-propylnitrosamine
Disulfoton
Ethyl Cyanide
Ethylene Oxide
meta-dinitrobenzene
Silvex
1,2,3-trichloropronene
Tris (2,3-dibromopropyl) phosphate
CAS RN

75.05.8
98.86.2
53.96.3
107.02.8
107.13.1
107.18.6
92.67.1
140.57.8
108.98.5
106.51.4
598.31.2
88.85.7
510.15.6
126.99.8
107.05.1
542.76.7
2303.16.4
192.65.4
189.64.0
189.55.9
96.12.8
106.93.4
74.95.3
764.41.0
75.71.8
87.65.0
542.75.6
297.97.2

119.90.4
60.11.7
57.97.6
119.93.7
122.09.8
123.91.1
122.39.4
122.66.7
621.64.
298.04,
107.12.0
75.21.8
100.25.4
93.72.1
96.18.4
126.72.7
.7
.4
Class

CLP/VOA
CLP/BNA
CLP/BNA
CLP/VOA
CLP/VOA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/VOA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/VOA
CLP/VOA
CLP/VOA
CLP/VOA
CLP/VOA
CLP/BNA
CLP/VOA
NRA

CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/VOA
CLP/VOA
NRA
CLP/BNA
NRA
CLP/VOA
CLP/BNA
                               G-2

-------
Common Name
CAS RN
Class
Phenacetin
N-Phenylthiourea
Phorate
Famphur
2-Picoline
Propanamide
2-Propyn-l-ol
Pyridine
Resorcinol
Safrole
1,2,4,5-Tetrachlorobenzene
1,1,1,2-Tetrachloroethane
2-Naphthylamine
N-Nitrosodi-n-butylamine
N-Nitrosodiethylamine
N-Ni t rosomethylethylamine
N-Nitrosomorpholine
N-Nitrosopiperdine
5-Nitro-o-toluidine
Parathion
Pentachlorobenzene
Pentachlo roe thane
Pentachlo roni t robenzene
Kepone
Malonitrile
Methyacrylonitrile
Methapyrilene
3-Methylchloranthrene
4,4-Methylene-bis (2-chloroaniline)
Me thylmethac rylate
Methylmethanesulfonate
Aldicarb
Methyl parathion
1,4 Naphthoquinone
1-Naphthylamine
2,3,4,6-Tetrachlorophenol
Tetraethyldithiopyrophosphate
Trichloromethanethai
Trichloromonofluoromethane
2,4,5-T
Ethyl Methacrylate
Isodrin
Hexachlorophene
Hexachloropropene
lodomethane
Isobutylalcohol
Isosafrole
62.44.2
103.85.5
298.02.2
52.85.7
109.06.8
23950.58.5
107.19.7
110.86.1
108.46.3
44.59.7
95.94.3
630.20.6
91.59.8
924.16.3
55.18.5
10595.95.6
59.89.2
100.75.4
99.44.8
56.38.2
608.93.5
76.01.7
82.68.8
143.50.0
109.77.3
126.98.7
91.80.5
56.49.5
101.14.4
80.62.6
66.27.3
116.06.3
298.00.0
130.15.4
134.32.7
58.90.2
3689.24.5
75.70.7
75.69.4
93.76.5
97.63.2
465.73.6
70.30.4
1888.71.7
74.88.4
78.33.1
120.58.1
CLP/BNA
CLP/BNA
NRA
NRA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/VOA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/PCB-Pest
CLP/BNA
CLP/VOA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/BNA
CLP/BNA
NRA
CLP/BNA
CLP/VOA
NRA
CLP/BNA
CLP/PCB-Pest
CLP/BNA
CLP/VOA
CLP/VOA
CLP/VOA
NRA
                                G-3

-------
NOTES

aClass Abbreviations

 NBA - Not readily analyzable using current CLP Procedures

 CLP/VOA - Potentially analyzable using current CLP/HSL GC/MS Volatile
 Organics Procedure

 CLP/BNA - Potentially analyzable using current CLP/HSL Base/Neutral Acid
 Extractable GC/MS Procedure

 CLP/PCB-Pest - Potentially analyzable using current CLP/HSL PCB/Pesticide
 GC Procedure
                               G-4

-------
                       ORGANIC COMPOUNDS ON MODIFIED
                               APPENDIX VIII
              LIST THAT ARE NOT READILY ANALYZABLE BY CURRENT
                            CLP/HSL PROCEDURES
              Common Name                         CAS RN
Allyl alcohol                                    107.18.6
Bromoacetone                                     598.31.2
0,O-Diethyl-O-2-Pyrazinyl phosphorothioate       297.97.2
1,4 Dioxane                                      123.91.1
Ethylene Oxide                                   75.21.8
Silvex                                           93.72.1
Phorate                                          298.02.2
Famphur                                          52.85.7
2-Propyn-l-ol                                    107.19.7
Parathion                                        56.38.2
Methyl Parathion                                 298.00.0
Tetraethyldithiopyrophosphate                    3689.24.5
2,4,5-T                                          93.76.5
isosafrole                                       120.58.1
Class'
WS/NV
WR
OP
WS/NV
NR (VOA)
CH
OP
OP
WS/NV
OP
OP
WR
CH
D/H
NOTES
aClass Abbreviations
 WS/NV - Water soluble, nonvolatile compound probability not amenable to
 purge and trap or liquid/liquid extraction pretreatment.
 WR - Water reactive, unanalysable in aqueous matrix.
 OP - Organophosphorous pesticide best analyzed by a modified SW-846,
 Method 8140.
 NR (VOA) - Not recoverable at 200 PPB using standard  HSL/CLP volatile
 organics procedures.  May be more ameneable to head space analysis.
 CH - Chlorinate herbicide, must be derivatized prior  to analysis.   Best
 analyzed using modified SW-846 Method 8150.
 D/H - Decomposes at conventional GC temperatures HLPC procedure may be
 applicable.
                               G-5

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                            CLP VOLATILE ORGANIC CRDL
Target comoound name
£hloromethane
Bromomethane
Vinyl Chloride
Chi oroethane
Methyl ene Chloride
Acetone
Carbon D1sulf1de
1,1-01 chloroethene
1, 1-01 chl oroethane
Tran$-l, 2-01 chloroethene
Chloroform
1 ,2-01 chl oroethane
2-Butanone
1 ,1 ,l-Tr1 chl oroethane
Carbon Tetrachloride
Vinyl Acetate
Bromodl chl oromethant
1,1,2 ,2-Tetrachl oroethane
1 ,2-01 chl oropropane
Trans-1 ,3-01 chl oropropene
Tri chloroethene
01 bromochl oromethane
I,l,2-Tr1 chl oroethane
Benzene
C1 s-1 ,3-01 chl oropropene
2-Chloroethyl Vinyl Ether
Bromof orm
4-Methyl -2-pentanont
2-Hexanone
let ra chl oroethene
Toluene
Chlorobenzene
Ethyl Benzene
Styrene
Total Xylenes
SPCC&
cccc
SPCC

ccc




ccc
SPCC

ccc






SPCC
ccc







SPCC



ccc
SPCC
ccc


Low SOll
CRDL.
uq/kq
id
10
10
10
5
10
5
5
5
5
5
5
10
5
5
10
5
5
5
5
5
5
5
5
5
10
5
10
10
5
5
5
5
5
5
Low water
CRDL,
uq/L
id
10
10
10
5
10
5
5
5
5
5
5
10
5
5
10
s
5
5
5
5
5
5
5
5
10
5
10
10
5
5
5
5
5
5
CAS number
74-8J-*
74-83-9
75-01-4
75-00-3
75-99-2
67-64-1
75-15-0
75-35-4
75-35-3
156-60-5
67-66-3
107-06-2
78-93-3
71-55-6
56-23-5
108-05-4
75-27-4
79-34-5
78-87-5
10061-02-6
79-01-6
124-48-1
79-00-5
71-43-2
10061-01-5
110-75-8
75-25-2
108-10-1
591-78-6
127-18-4
108-88-3
108-90-7
100-41-4
100-42-5
N.A.
     dCRDL values obtained from the IFB  WA85-J664 [7J,
     bSystem Performance Check Compounds (SPCC) are used to check compound
      Instability and degradation 1n the GC/MS and to Insure minimum average
      response factors are met prior to  the use of the calibration curve.
     ^Column Check Compounds  (CCC)  are used to check the validity of the
      initial  calibration.
      Note:  Medium soil  and  water CROLs are 100 times the low level CROLs.
SOURCE:   Flotard, R.D.  et al 1986
                                 G-6

-------
                        CLP INORGANIC COMPOUND CRDL,
                 INSTRUMENT DETECTION LEVEL AND WAVELENGTH
Element
Al
Sb
As
Ba
Be
Cd
Ca
Cr
Co
Cu
Fe
Pb
Mg
Mn
Hg
N1
K
Se
Ag
Na
Tl
Sn
V
In
CRDL
200
60
10
200
5
5
5000
10
50
25
100
5
5000
15
0.2
40
5000
5
10
5000
10
40
50
20
Method
ICP
ICP
FAA
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
cv
ICP
ICP
FAA
ICP
ICP
ICP
ICP
ICP
ICP
N
7
5
18
5
10
5
7
9
11
11
10
12
11
10
12
9
8
18
10
9
18
7
10
0
IDL
Mean
70.7
42.3
4.6
22.1
2.3
4.0
529
5.8
11.4
9.7
27.4
2.3
385
5.2
0.2
17.8
668
2.8
5.4
756
4.3
23.8
13.1
8.3
IDL
Std Dev
59.3
11.3
2.3
31.7
1.7
1.1
472
2.9
8.5
6.5
20.9
1.2
449
4.6
0.1
10.1
444
1.3
2.7
864
2.4
8.4
10.0
6.3
Wave-
Length (ntn)
309. J
217.6
198.7
493.4
312.0
228.8
317.9
267.7
228.6
324.5
259.9
283.3
279.6
257.6
253.7
232.0
766.5
196.0
328.1
589.0
276.8
190.0
292.5
213.9
 IDL -Instrument Detection Limit U9/L).
   N - Number of laboratories using the most common wavelength.
CRDL - Contract Required Detection Limit (yg/L).
 SOURCE:   Aleckson,  K.A.  et al  1986.
                                G-7

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CLP SEMI-VOLATILE HSL COMPOUNDS AND CRDL
Compound name
Phenol
bls(Z-ChloPoethyl) ether
2-Chlorophenol
1 ,3-D1chl orobenzene
1,4-01 chl orobenzene
Benzyl alcohol
1 , 2-01 chl orobenzene
2-Methyl phenol
b1s(2-Chloro1sopropyl) ether
4-Methyl phenol
N-N1 troso-d1-n-propyl ami ne
Hexachloroe thane
Nitrobenzene
Isophorone
2-N1trophenol
2, 4 -01 methyl phenol
Benzole add
b1s(2-Chloroethoxy)me thane
2, 4-01 chl orophenol
1 , 2, 4-Tr1chl orobenzene
Naphthalene
4-Ch1oroan1l1ne
Hexachl orobutadlene
4-Chl oro-3-methyl phenol
2-Methyl naphthal ene
Hexachl orocycl opentadl ene
2, 4, 6-Tr1chl orophenol
2,4, 5-Tr1 chl orophenol
2-Chl oronaphthal ene
2-N1troan111ne
Dimethylphthalate
Acenaphthylene
3-N1troan1l1ne
Acenaphthene
2 ,4-01 nl trophenol
4-N1trophenol
Dlbenzofuran
2, 4-01 nltro toluene
2, 6-01 nltro toluene
01 ethyl phthal ate
4-Chl orophenyl -phenyl ether
Fluorene
4-N1troan1l1ne
4 , 6-0 1 n1 tro-2-«ethyl phenol
SPCCa
or CCCb
ccc



ccc





SPCC



ccc







ccc
ccc

SPCC
ccc






ccc
SPCC
SPCC








Low Soil
CRDL, tig/kg
330
330
330
330
330
330
330
330
330
330
330
330
330
330
330
330
1.600
330
330
330
330
330
330
330
330
330
330
1,600
330
1,600
330
330
1,600
330
1,600
1,600
330
330
330
330
330
330
1,600
1,600
Low Hater
CRDL, ug/L
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
50
10
10
10
10
10
10
10
10
10
10
50
10
50
10
10
50
10
50
50
10
10
10
10
10
10
50
50
CAS
Number
108-95-2
111-44-4
95-57-8
541-73-1
106-46-7
100-51-6
95-50-1
95-48-7
39638-32-9
106-44-5
621-64-7
67-72-1
98-95-3
78-59-1
88-75-5
105-67-9
65-85-0
111-91-1
120-83-2
120-82-1
91-20-3
106-47-8
87-68-3
59-50-7
91-57-6
77-47-4
88-06-2
95-95-4
91-58-7
88-74-4
131-11-3
208-96-8
99-09-2
83-32-9
51-28-5
100-02-7
132-64-9
121-14-2
606-20-2
84-66-2
7005-72-3
86-73-7
100-01-6
534-52-1
             G-8

-------
                 CLP SEMI-VOLATILE HSL COMPOUNDS AND CRDL
                               (continued)
Compound name
N-N1 trosodl phenyl ami ne
4-Bromophenyl -phenyl ether
Hexachl orobenzene
Pentachlorophenol
Phenanthrene
Anthracene
D1-n-buty1 phthal ate
Fl uoranthene
Pyrene
Butyl benzyl phthal ate
3,3'-D1ch1orobenz1d1ne
Benzot a) anthracene
b1s(2-Ethylhexyl) phthal ate
Chrysene
01 -n-octyl phthal ate
Benzo( b ) fl uoranthene
Benzol k ) fl uoranthene
Benzo(a)pyrene
Indeno(l,2,3-cd)pyrene
D1benz(a,h)anthracene
Benzo(g.h,1)perylene
SPCCa
or CCCb
CCC


CCC



CCC






CCC


CCC



Low Soil
CRDL, ug/kg
330
330
330
1.600
330
330
330
330
330
330
660
330
330
330
330
330
330
330
330
330
330
Low Water
CRDL, ug/L
10
10
10
50
10
10
10
10
10
10
20
10
10
10
10
10
10
10
10
10
10
CAS
Number
86-30-6
101-55-3
118-74-1
87-86-5
85-01-8
120-12-7
84-74-2
206-44-0
129-00-0
85-68-7
91-94-1
56-55-3
117-81-7
218-01-9
117-84-0
205-99-2
207-08-9
50-32-8
193-39-5
53-70-3
191-24-2
aCCC-Callbratlon Check compound
bSPCC-System Performance Check Compound
 Note:  Medium soil/sediment contract required detection limits are 60
        times the Individual low soil/sediment CRDL and medium water
        contract required detection limits are 100 times the Individual
        low water CRDL.
 SOURCE:  Wolf, J.S. et al 1986.


                                G-9

-------
         APPENDIX H

CONTRACT REQUIRED DETECTION
   LIMITS FOR HSL ANALYSES
  USING CLP IFB PROCEDURES

-------
                            CLP VOLATILE  ORGANIC CRDL
Target compound name
£hl oromethane
Bromomethane
Vinyl Chloride
Chloroethane
Methyl ene Chloride
Acetone
Carbon D1sulf1de
l,l-D1chloroethene
1,1-01 Chloroethane
Trans-1, 2-01 chl oroethene
Chloroform
1,2-01 Chloroethane
2-Butanone
1 ,1 ,l-Tr1 chl oroethane
Carbon Tetrachloride
Vinyl Acetate
Bromodi chl oromethane
1,1,2 ,2-Tetrachl oroethane
1 ,2-01 chl oropropane
Trans-1 ,3-01 chl oropropene
Tri chl oroethene
01 bromochl oromethane
1 ,1 ,2-Tr1 chl oroethane
Benzene
Ci s-1 ,3-01 chl oropropene
2-Chloroethyl Vinyl Ether
Bromoform
4-Methyl -2-pentanone
2-Hexanone
Tetrachl oroethene
Toluene
Chlorobenzene
Ethyl Benzene
Styrene
Total Xylenes
SPCC&
cccc
SPCC

ccc




ccc
SPCC

ccc






SPCC
ccc







SPCC



ccc
SPCC
ccc


CRDL,
pg/kg
10
10
10
10
5
10
5
5
5
5
5
5
10
5
5
10
5
5
5
5
5
5
5
5
5
10
5
10
10
5
5
5
5
5
5
Low water
CRDL,
uq/L
10
10
10
10
5
10
5
5
5
5
5
5
10
5
5
10
5
5
5
5
5
5
5
5
5
10
5
10
10
5
5
5
5
5
5
CAS number
74-87-3
74-83-9
75-01-4
75-00-3
75-09-2
67-64-1
75-15-0
75-35-4
75-35-3
156-60-5
67-66-3
107-06-2
78-93-3
71-55-6
56-23-5
108-05-4
75-27-4
79-34-5
78-87-5
10061-02-6
79-01-6
124-48-1
79-00-5
71-43-2
10061-01-5
110-75-8
75-25-2
108-10-1
591-78-6
127-18-4
108-88-3
108-90-7
100-41-4
100-42-5
N.A.
     aCRDL values ootained  from  the  IFB WA85-0654L/J.
     ^System Performance Check Compounds (SPCC) are used to check compound
      instability and degradation  in the GC/MS and to insure minimum average
      response factors are  met prior to the use of the calibration curve.
     ^Column Check Compounds  (CCC) are used to check the validity of the
      initial  calibration.
      Note:   Medium soil  and  water CROLs art 100 times the low level CROLs.
SOURCE:   Flotard, R.D. et  al 1986
                               H-L

-------
                        CLP INORGANIC COMPOUND CRDL,
                 INSTRUMENT DETECTION LEVEL AND WAVELENGTH
Element
Al
Sb
As
Ba
Be
Cd
Ca
Cr
Co
Cu
Fe
Pb
Mg
v
Mn
Hg
N1
K
Se
Ag
Na
Tl
Sn
V
Zn
CRDL
200
60
10
200
5
5
5000
10
50
25
100
5
5000
15
0.2
40
5000
5
10
5000
10
40
50
20
Method
ICP
1C?
FAA
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
ICP
CV
ICP
ICP
FAA
ICP
ICP
ICP
ICP
ICP
ICP
N
7
5
18
5
10
5
7
9
11
11
10
12
11
10
12
9
8
18
10
9
18
7
10
0
tDL
Mean
76.7 "
42.3
4.6
22.1
2.3
4.0
529
5.8
11.4
9.7
27 A
2.3
385
5.2
0.2
17.8
668
2.8
5.4
756
4.3
23.8
13.1
8.3
— me —
Std Dev
59.3
11.3
2.3
31.7
1.7
1.1
472
2.9
8.5
6.5
20.9
1.2
449
4.6
0.1
10.1
444
1.3
2.7
864
2.4
8.4
10.0
6.3
Wave-
length (nm)
309.3
217.6
198.7
493.4
312.0
228.8
317.9
267.7
228.6
324.5
259.9
283.3
279.6
257.6
253.7
232.0
766.5
196.0
328.1
589.0
276.8
190.0
292.5
213.9
 1WU ™ AM^vl IMII^II to W«*«*»vlWit  te •••• • v \ 9*yt M / v
   N - Number of laboratories  using  the most common wavelength.
CRDL - Contract Required  Detection Limit (ug/L).
 SOURCE:   Aleckson,  K.A. et al 1986.
                              H-2

-------
CLP SEMI-VOLATILE HSL COMPOUNDS AND CRDL
Compound name
Phenol
b1 s( 2-Chl oroethyl ) ether
2-Chlorophenol
l,3-D1ch1 orobenzene
1,4-01 chl orobenzene
Benzyl al cohol
1,2-01 chl orobenzene
2-Methyl phenol
b1s(2-Chloro1sopropyl) ether
4-Methyl phenol
N-N1troso-d1-n-propylam1ne
Hexachloroe thane
Nitrobenzene
Isophorone
2-N1trophenol
2,4-D1methyl phenol
Benzole add
b1 s( 2-Chl oroethoxy Jmethane
2 ,4-01 chl orophenol
1, 2, 4-Tr1 chl orobenzene
Naphthalene
4-Chl oroan H1ne
Hexachl orobutadiene
4-Chl oro-3-methyl phenol
2-Methyl naphtha! ene
Hexachl orocycl opentadl ene
2 ,4, 6-Tr1 chl orophenol
2, 4, 5-Tr1 chl orophenol
2-Chl oronaphthal ene
2-N1troan1l1ne
Dime thy! phthal ate
Acenaphthylene
3-N1troan1l1ne
Acenaphthene
2,4-01n1trophenol
4-N1trophenol
Dlbenzofuran
2, 4-01 nltro toluene
2, 6-01 nltro toluene
01 ethyl phthal ate
4-Chl orophenyl -phenyl ether
Fluorene
4-N1troan1l1ne
4 , 6-0 1 n1 tro-2-methyl phenol
5PCCa
or CCCb
ccc



ccc





SPCC



ccc







ccc
ccc

SPCC
ccc






ccc
SPCC
SPCC








LOW 5011
CRDL, tig/kg
330
330
330
330
330
330
330
330
330
330
330
330
330
330
330
330
1,600
330
330
330
330
330
330
330
330
330
330
1,600
330
1,600
330
330
1,600
330
1,600
1,600
330
330
330
330
330
330
1,600
1,600
Low Water
CRDL, ug/L
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
50
10
10
10
10
10
10
10
10
10
10
50
10
50
10
10
50
10
50
50
10
10
10
10
10
10
50
50
CAS
Number
108-95-2
111-44-4
95-57-8
541-73-1
106-46-7
100-51-6
95-50-1
95-48-7
39638-32-9
105-44-5
621-64-7
67-72-1
98-95-3
78-59-1
88-75-5
105-67-9
65-85-0
111-91-1
120-83-2
120-82-1
91-20-3
106-47-8
87-68-3
59-50-7
91-57-6
77-47-4
88-06-2
95-95-4
91-58-7
88-74-4
131-11-3
208-96-8
99-09-2
83-32-9
51-28-5
100-02-7
132-64-9
121-14-2
606-20-2
84-66-2
7005-72-3
86-73-7
100-01-6
534-52-1
              H-3

-------
                  CLP SEMI-VOLATILE HSL COMPOUNDS AND CRDL
                                (continued)
Compound name
N-N1 trosodl phenyl ami ne
4-Bromophenyl -phenyl ether
Hexachl orobenzene
Pentachl orophenol
Phenanthrene
Anthracene
Di-n-butyl phthal ate
FT uoranthene
Pyrene
Butyl benzyl phthal ate
3 , 3 ' -01 chl orobenz 1 d1 ne
Benzo( a) anthracene
b1s(2-Ethylhexyl) phthal ate
Chrysene
D1-n-octyl phthal ate
Benzo( b ) f! uoranthene
Benzol k ) fl uoranthene
Benzo(a)pyrene
Indeno(l,2,3-cd)pyrene
D1benz(a,h)anthracene
Benzo(g.h,1 )perylene
aCCC-Caflbratlon Check Comp
SPCCa
or CCCb
ccc


ccc



ccc






ccc


ccc



lound
Low Soil
CRDL, ug/kg
330
330
330
1.600
330
330
330
330
330
330
660
330
330
330
330
330
330
330
330
330
330

Low Water
CRDL, ug/L
10
10
10
50
10
10
10
10
10
10
20
10
10
10
10
10
10
10
10
10
10

CAS
Number
86-30-6
101-55-3
118-74-1
87-86-5
85-01-8
120-12-7
84-74-2
206-44-0
129-00-0
85-68-7
91-94-1
56-55-3
117-81-7
218-01-9
117-84-0
205-99-2
207-08-9
50-32-8
193-39-5
53-70-3
191-24-2

bSPCC-Syste» Performance  Check  Compound
 Note:  Medium soil/sediment contract required detection limits  are 60
        times the Individual  low soil/sediment CRDL and medium water
        contract required detection limits are 100 times the  Individual
        low water CRDL.
                                •ftU.S. GOVERNMENT PRINTING OFFICE: 1987 748-121/67042
SOURCE:  Wolf, J.S.  et  al  1986.


                               h-4

-------