EPA/540/G-90/004
OSWER Directive 9360.4-01
April 1990
QUALITY ASSURANCE/QUALITY CONTROL GUIDANCE
FOR REMOVAL ACTIVITIES
SAMPLING QA/QC PLAN
and
DATA VALIDATION PROCEDURES
Interim Final
Environmental Response Team
Emergency Response Division
Office of Emergency and Remedial Response
U.S. Environmental Protection Agency
Washington, DC 20460
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Disclaimer
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 endorsement
or recommendation for use.
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Preface
the QA/QC Guidance
Removal
Mr. William A. Coakley
Removal Program QA Coordinator
U.S. EPA - ERT
Raritan Depot - Building 18, MS-101
2890 Woodbridge Avenue
Edison, NJ 08837-3679
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Quality Assurance/Quality Control Guidance for
Removal Activities
EPA Work Group
EPA Headquarters
Office of Emergency and Remedial Response
EPA Regional
Region 6:
Region 8:
Region 10:
ICF Kaiser Engineers
Roy F. Weston
Ecology and Environment
Other Organizations
William Coakley
Duane'Geuder
Tom Kady
Bob Forrest
Rick Edmonds
John Geidt
Peter jstevenson
Jim Everts
Peter Mavraganis
Regina Prevosto
Joannj Sabak
Joseph Tang
Christine Andreas
Kenda Layne
John Matep
Michael Bray
Bill CJarberry
Bob Marguccio
Glenn Scherb
In addition to the work group members, helpful suggestions and comments on the draft
provided by the following as well as other EPA and contractor staff.
Carla Dempsey (Hazardous Response Support Division, OERR)
Anibal Diaz (Region 2 TAT, Roy F. Weston)
Owen Douglass (Zone 1 ZPMO, Roy F. Weston)
Donnissa Duvic (Region 4 TAT, Roy F. Weston)
David Friedman (Office of Solid Waste)
John Geidt (Region 8, Environmental Services Division)
Jerry McKenna (Region 2, Environmental Services Division)
Tun Ott (Region 1 TAT, Roy F. Weston)
Diane Terry (ZPMO, Roy F. Weston)
Donald Zelazny (Zone II ZPMO, Ecology and Environment)
document were
IV
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TABLE OF CONTENTS
PARTI: SAMPLING QA/QC PLAN
1.0
Introduction
1.1
1.2
1.3
Purpose
Background
Analytical Methods and Data Quality
2.0 Elements of a Sampling QA/QC Plan
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
Title Page
Background
Data Use Objectives
Quality Assurance Objectives
2.4.1 Methods
Approach and Sampling Methodologies
Project Organization and Responsibilities
Quality Assurance Requirements
Error Determination (Analytical and Total Error)
2.8.1 Matrix Spike Samples
2.8.2 Site Background Samples
2.8.3 Site Action Level Samples (Total Error)
Deliverables
Data Validation
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LIST OF TABLES
1 Example Proposed Schedule of Work
2 Field Sampling Summary
3 QA/QC Analysis and Objectives Summary
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TABLE OF CONTENTS
PART II: DATA VALIDATION PROCEDURES
gection
1.0 Introduction
2.0 Data Validation Qualifiers
3.0 Metallic Inorganic Parameters
3.1 Sample Holding Times .
3.2 Initial and Continuing Calibration Verification
3.3 Blanks
3.4 ICP Interference Check Sample
3.5 Error Determination
3.5.1 Determination of Bias
3.5.1.1 Percent Recovery _ .
3.5.1.2 Adjustment of Sample Value for Bias
3.5.2 Determination of Precision
3.5.2.1 Replicate Analysis
3.5.2.2 Coefficient of Variation
3.6 Performance Evaluation Samples
3.7 Optional Additional Instrument QC (for elevated concentrations)
3.7.1 ICP Serial Dilution
3.7.2 Atomic Absorption Analysis Specific QC
3.8 Overall Assessment of Data
4.0 BNAs by GC/MS Analysis
4.1 Sample Holding Times
42 GC/MS Tuning Criteria . .
4.3 Initial and Continuing Calibration Verification
4.3.1 Internal Standards ,
4.4 Error Determination
4.4.1 Determination of Bias
4.4.1.1 Percent Recovery
4.4.1.2 Adjustment of Sample Value for Bias
4.4.2 Determination of Precision
4.4.2.1 Replicate Analysis
4.4.2.2 Coefficient of Variation
4.5 Blanks
4.6 Compound Identification
461 Tentatively Identified Compounds
4.7 Compound Quantitation and Reported Detection Limits
4.8 Performance Evaluation Samples
4.9 Overall Assessment of Data
4.10 Optional QC Checks
4.10.1 Surrogate Recovery
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TABLE OF CONTENTS (continued)
Section
5.0 VOAs
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
Sample Holding Times
GC/MS Tuning Criteria
Initial and Continuing Calibration Verification
5.3.1 Internal Standards
Error Determination
5.4.1 Determination of Bias
5.4.1.1 Percent Recovery
5.4.1.2 Adjustment of Sample Value for Bias
5.4.2 Determination of Precision
5.4.2.1 Replicate Analysis
5.4.2.2 Coefficient of Variation
Blanks
Compound Identification
5.6.1 Tentatively Identified Compounds
Compound Quantitation and Reported Detection Limits
Performance Evaluation Samples
Overall Assessment of Data
Optional QC Checks
5.10.1 Surrogate Recovery
6.0 Pesticides/PCBs
6.1
6.2
6.3
6.4
6.4.2
6.5
6.6
6.7
6.8
6.9
6.10
Sample Holding Times
Instrument Performance
Initial and Continuing Calibration Verification
Error Determination
6.4.1 Determination of Bias
6.4.1.1 Percent Recovery
6.4.1.2 Adjustment of Sample Value for Bias
Determination of Precision
6.4.2.1 Replicate Analysis
6.4.2.2 Coefficient of Variation
Blanks
Compound Identification
Compound Quantitation and Reported Detection Limits
Performance Evaluation Samples
Overall Assessment of Data
Optional QC Checks
6.10.1 Surrogate Recovery
7.0 PCBs
7.1 Sample Holding Times
7.2 Instrument Performance
7.3 Initial and Continuing Calibration Verification
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TABLE OF CONTENTS (continued)
Section
7.0 PCBs (continued)
7.4 Error Determination
7.4.1 Determination of Bias
7.4.1.1 Percent Recovery
7.4.1.2 Adjustment of Sample Value for Bias
7.4.2 Determination of Precision
7.4.2.1 Replicate Analysis
7.4.2.2 Coefficient of Variation
7.5 Blanks .
7.6 Compound Identification . .
7.7 Compound Quantitation and Reported Detection Limits
7.8 Performance Evaluation Samples
7.9 Overall Assessment of Data
7.10 Optional QC Checks
7.10.1 Surrogate Recovery
8.0 2,3,7,8-TCDD
8.1 Sample Holding Times
8.2 Instrument Performance
8.3 Initial Calibration
8.4 Continuing Calibration
8.5 Error Determination
8.5.1 Determination of Bias
8.5.1.1 Percent Recovery
8.5.1.2 Adjustment of Sample Value for Bias
85.2 Determination of Precision
8.5.2.1 Replicate Analysis
8.5.2.2 Coefficient of Variation
8.6 Blanks
8.7 Internal Standard Requirements
8.8 Identification of 2,3,7,8-TCDD
8.9 Performance Evaluation Samples
8.10 Overall Assessment of Data
8.11 Optional QC Checks
8.11.1 Surrogate Recovery
9.0 Generic Data Validation Procedures
9.1 GC Analyses (i.e., Herbicides, Organophosphate,
Pesticides)
9.1.1 Sample Holding Times
9.1.2 Instrument Performance
9.1.3 Initial and Continuing Calibration Verification
9.1.4 Error Determination
9.1.4.1 Determination of Bias
9.1.4.1.1 Percent Recovery
9.1.4.1.2 Adjustment of Sample Value for Bias
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TABLE OF CONTENTS (continued)
9.2
9.0 Generic Data Validation Procedures (continued)
9.1.4.2 Determination of Precision
9.1.42.1 Replicate Analysis
9.1.4.2.2 Coefficient of Variation
Blanks
Compound Identification
Compound Quantitation and Reported Detection Limits
Performance Evaluation Samples
Overall Assessment of Data
Non-Metallic Inorganic Parameters (i.e., anions, pH, TOC, nutrients)
9.2.1 Sample Holding Times
9.2.2 Initial and Continuing Calibration Verification
Error Determination
9.2.3.1 Determination of Bias
9.2.3.1.1 Percent Recovery
9.2.3.1.2 Adjustment of Sample Value for Bias
Determination of Precision
9.2.3.2.1 Replicate Analysis
9.2.3.2.2 Coefficient of Variation
Blanks
Compound Quantitation and Reported Detection Limits
Performance Evaluation Samples
Overall Assessment of Data
9.1.5
9.1.6
9.1.7
9.1.8
9.1.9
9.2.3
9.2.3.2
9.2.4
9.2.5
9.2.6
9.2.7
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IX
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PARTI
SAMPLING QA/QC PLAN
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1.0 Introduction
Part I provides a detailed description of each section to be
contained in a "Sampling QA/QC Plan." The development
of the Sampling QA/QC Plan is the responsibility of the
On-Scene Coordinator (OSC). The OSC reviews and
approves the site-specific plan and may obtain assistance
from the Regional QA Officer. This guidance will help
ensure that reliable, accurate, and quality data are
obtained through field sampling efforts as well as field and
laboratory analytical services. The document to be
produced from this guidance is neither intended to
supersede nor replace the QA Project Plan; however, it is
intended to augment the project plan by detailing site-
specific information regarding sampling, analysis, and QA
protocols.
Note: QA/QC and QA are interchangeable terms used
throughout the guidance document.
' '- ? ' '"
1.1 Purpose
The purpose of this document is to provide guidance in
establishing, implementing, and using quality
assurance/quality control (QA/QC) protocols for data
collection activities performed under the Removal
Program.
1.2 Background
Agency policy requires that all EPA organizational units,
including program offices, EPA regional offices, and EPA
laboratories, that perform environmentally related
measurements, participate hi a centrally managed quality
assurance (QA) program, as stated in the Administrator's
Memorandum of May 30, 1979. This requirement applies
to all environmental monitoring and measurement efforts
mandated or supported by EPA through regulations, grants,
contracts, or other formal means not currently covered by
regulation. The responsibility for developing, coordinating,
and directing the implementation of this program has been
delegated to the Office of Research and Development
(ORD), which has established the Quality Assurance
Management Staff (QAMS) for this purpose. As stated in
EPA Executive Order 5360.1, "Policy and Program
Requirements to Implement the Mandatory Quality
Assurance Program," the primary goal of the QA program is
to ensure that all environmentally related measurements
performed or supported by EPA produce data of known
quality. The quality of data is known when all
components associated with its derivation are
thoroughly documented, with,such documentation being
verifiable and defensible^
As part of their participation in the Agency-wide QA
program, program offices are required to establish their own
"QA ^Program Plan." ? This .plan is to be prepared and
annually updated based on guidelines established by QAMS.
It specifies the quality of data required from environmentally
related measurements and provides sufficient resources to
assure that an adequate level of QA is performed. The
program plan is established at the Headquarters EPA level.
For the Removal Program, the responsibility for the program
plan lies with the Office of Emergency and Remedial
Response (OERR). In addition to program plans, plans
need to be developed for each regional office. These plans
are similar to the program plans, but are tailored to the
specific operational needs of the regional office. The
program and regional plans are both broad in scope and
merely provide the objectives and resources for undertaking
environmentally-related measurements.
The most specific element of QA documentation is the QA
Project Plan (see Figure I). A QA Project Plan specifies
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the policies, organization (where applicable), objectives,
functional activities, and specific QA and QC activities
designed to achieve the data quality goals of a specific
projects) or continuing operation(s). The QA Project
Plan is required for each specific project or continuing
operation (or group of similar projects or continuing
operation(s)). Guidance for preparing such plans is
contained in "Guidelines and Specifications for Preparing
Quality Assurance Project Plans" (also known as QAMS-
005), which was developed by QAMS. This document
describes sixteen elements that must be considered for
inclusion in all QA Project Plans.
To meet the requirement for a QA Project Plan in the
removal program, the Emergency Response Division of
OERR established a QA Workgroup to provide guidance.
The workgroup decided that the QA Project Plan would
be divided into two functional documents: a generic
"Branch QA Project Plan," and a site-specific "Sampling
QA/QC Plan." When combined, both documents address
the sixteen elements described in QAMS-005. The Branch
QA Project Plan will be prepared by each regional
removal branch and will address only those elements
generic to all activities occurring within the Region; the.:
Sampling QA/QC Plan will be prepared for each site
where sampling will be performed and address those
Clements specific to the site, such as sample collection and
analysis. The Branch Plan should be updated periodically
to reflect any operational changes in the Region. The
Sampling QA/QC Plan should be prepared for each site
and updated (amended) when the scope of work changes
significantly from the scope of work described in any
previous plan. Elements that are not addressed in the
Sampling QA/QC Plan are included in the Branch Plan.
For emergency responses, a Sampling QA/QC Plan is
required to be submitted no later than 30 days after the
response date for documentation purposes.
i
The intent of this document is to jprovide guidance on
developing a site specific "Sampling! QA/QC Plan" and
assessing and substantiating data for various data users. The
guidance is not intended to addres|s field and lab QC
practices. It is assumed and expected that field samplers
and analytical labs will follow approved methods (with their
inherent QC checks) and adhere to generally accepted "good
laboratory practices."
QA Program Plan (HQ
AS per QAMS-004/80 G
Regional QA Progra
(Regional Leve
As per QAMS-004/80
Generic QA Projec
(Branch Level
As per QAMS -005/80
Sampling QA/QC
(Site-Specif:
As per OSWER Dire
9360.4-01 Guid<
Level)
uidance
m Plan
1)
Guidance
,t Plan
)
Guidance
.Plan
-cj
active
ince
Figure 1: EPA Quality Assurance Documentation
This guidance has been designed to allow for the greatest
possible variation in monitoring str itegies. However, it is
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recognized that occasionally certain quality assurance
requirements cannot be met. In such cases, the reason for
the deviation should be stated in the Sampling QA/QC
Plan along with the expected or observed impact on the
data.
13 Analytical Methods and Data Qualify
The quality of data is determined by its accuracy and
precision against prescribed requirements or specifications,
and by its usefulness in assisting the user to make a
decision or answer a question with confidence. The use of
any one particular analytical method. or instrument,
therefore, cannot determine the quality of data obtained
without an evaluation of the analytical accuracy (qualita-
tive and quantitative) of the data and of the relevance
(representativeness) of the data to user needs. Likewise,
certain analytical methods may provide more information
than other, methods, but not necessarily better quality data.
To illustrate, a gas chromatograph/mass spectrometer
method provides more information than a gas
chromatograph method, which in turn provides more
information than a spectrophotometermethod. ^However
more information's not synonymous with accurate or
useful data. Analytical quality is dependent on analytical
accuracy; that is to say that there is a degree of confidence
associated with the data. The term accuracy refers to both
the correctness of the concentration value and the
qualitative certainty that an analyte is present.
This guidance is based on the idea that the use of any one
particular analytical method or instrument does not
determine the quality of data obtained. This guidance
prompts the data collector to define the data quality within
a framework that also incorporates the intended use of the
data.
The guidance is structured around three quality assurance
objectives. Each quality assurance objective is associated
with a list of minimum requirements. Therefore, any
method or analytical instrument that can meet the quality
requirements can be used for any one of the objectives.
For example, if a spot test method was able to meet the
requirements for QA3 (i.e., identify the specific analyte,
determine the true concentration, and determine the error),
then the spot test would not only be a valid method but it
would give the same quality of data of a mass spectrometer
(assuming the mass spectrometer method met all the QA3
requirements). It is anticipated that QA1 and QA2 will
satisfy most data quality requirements for the Removal
Program. QA3 is expected to be used only in those cases
where an error determination is needed to identify false
negative or false positive values for critical decision level
concentrations.
2.0 Elements of a Sampling QA/QC Plan
The,Sampling QA/QC:Plan/should contain.the following
sections: , , , .
Title page ,
Background
Data Use Objectives
Quality assurance objectives
Approach and sampling methodologies
Project organization and responsibilities
Quality assurance requirements
Deliverables
Data validation
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2.1 Title Page
The title page should include the name of the site/project,
the contract and work order numbers (if the plan is being
prepared by contractors), the contractor name, the date,
key project personnel, and the approval signatures of the
OSC and other appropriate persons. (Although it is
recommended that the QA Sampling Plan be reviewed by
Regional QA staff, it is not necessary that the plan be
approved by the Regional QA officer.)
22 Background
This section should provide a brief description of the
events or occurrences that led to the initiation of the
sampling activity. This section may list chemicals which
possibly contributed to the suspected contamination,
including the suspected range of contamination, the
sampling area size and proximity to local residents, or any
other information that may be useful in an assessment of
the situation and determination of QA, sampling, or
analytical needs, possible contacts and existence of access
agreements. Sources of such data include inventories,
manifests, or other records; prior sampling data, such as
that generated by an RI/FS; geological surveys; and
incidents of exposure.
23 Data Use Objectives
Before any sampling activity is conducted, the intended use
of the data must be determined. Careful consideration of
intended data use is critical because it will affect the QA
objective chosen and thereby maximize the probability of
making a correct decision based on the analytical results.
The decisions to be made, questions to be addressed, or
both, should be listed in this section.
2.4 Quality Assurance Objectives
For each data collection activity, the QJA/QC objective must
be specified to correspond to the data ^ise objectives. Three
equally important QA/QC objectives have been defined for
assessing and substantiating the collection of data to support
its intended use. The three QA/QG objectives, hereafter
referred to as QA1, QA2, and QA3, are described below.
Evaluate the characteristics of the following QA objectives
to determine which one or combination fits your data usage.
All three objectives provide useful
enforcement purposes, disposal
responsible party identification, and
The QA characteristics are based
and valid data for
and/or treatment,
cleanup verification.
on thp Agency QA
objectives for precision, accuracy (both quantitative and
qualitative), representativeness, compl ;teness, comparability,
and detection level.
QA1: Rationale for OA1 objective:
QA1 is a screening objective to afford a quick,
preliminary assessment of site contamination. This
objective for data quality is available for data
collection.activities that involve rapid, non-rigorous
methods of analysis and quality assurance. These
methods are used to makje quick, preliminary
assessments of types and levels of pollutants. The
primary reason for this objeclve is to allow for the
collection of the greatest amount of data with the
least expenditure of time and money. The user
should be aware that data collected for this objective
have neither definitive identification of pollutants nor
definitive quantitation of their concentration level.
Although there is no quality assurance data collected
with the data at this objective, a calibration or
performance check of the method is required along
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with verification of the detection level. Methods will
be applied as per standard operating procedures and
squipment manufacturer's specifications.
'' , );
The QA1 objective does not preclude the adherence
to prescribed quality control checks given in EPA
methods and SOPs or the manufacturer's
recommendations. The QA1 objective is generally
applied to but not limited to the following activities:
physical and/or chemical properties of samples;
extent and degree of contamination relative to
concentration differences; delineation of pollutant
plume in ground water (head space or soil gas
analysis techniques); monitor well placement; waste
compatibility; preliminary health and safety
assessment; hazardous categorization; and preliminary
identification and quantitation of pollutants
(determination of PH, flammability, chlorine
presence, etc.).
QA1 .Characteristics!
Non-analyte or analyte specific (may also
be specific for a chemical class, i.e., PCBs,
total hydrocarbons, total organic halides,
total ionizable organics, radiation).
Non-definitive (i.e., unconfirmed)
identification; non-qualitative to semi-
qualitative.
Non-definitive quantitation; no error
determination (no precision and
accuracy determination).
Representative, comparable, complete1.
QA requirements for objective "QA1"
. are specified in Section 2.7, "Quality
Assurance Requirements."
QA2: Rationale for OA2 obiecrivp.-
QA2 is a verification obiective used to verify analytical
(field or lab) results. A minimum of 10% verification
of results is required. This objective for data quality is
available for data collection activities that require
qualitative and/or quantitative verification of a "select
portion of sample findings" (10% or more) that were
acquired using non-rigorous methods of analysis and
quality assurance. This quality objective is intended to
give the decision-maker (OSC) a level of confidence for
a select portion of preliminary data. This objective
allows the OSC to focus on specific pollutants and
specific levels of concentration quickly, by using field
screening methods and verifying at least 10% by more
rigorous analytical methods and quality assurance, The
results of the 10% of substantiated data gives an
associated sense of confidence for the remaining 90%.
However, QA2 is not limited to only verifying screened
data. The QA2 objective is also applicable to data that
are generated by any method which satisfies all the QA2
requirements and thereby incorporates any one or a
combination of the three verification requirements.
Generally the methods used for verification are more
rigorous, as to analytical methodology and quality
assurance. Only those verification methods that are
analyte specific can be considered for this quality
objective. When required, the analytical error is
determined for all analytes that are of interest to the
decision-maker (OSC) on at least 10% of samples.
Representative: The degree to which sample data accurately and
precisely represent the characteristic of'the population. 'Comparable-
An evaluation of the similarity of conditions under which different set
of data are produced. Complete: The percentage of measurements
made which are judged to be valid.
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The QA2 objective is generally applied, but not
limited to the following activities: physical and/or
chemical properties of samples; extent and degree
of contamination; verification of pollutant plume
definition in ground water; verification of health
and safety assessment; verification of pollutant
identification; and verification of cleanup.
OA2 Characteristics:
Analyte specific (i.e., benzene, cyanide,
2,3,7,8-TCDD, chromium).
VERIFICATION of analyte identity
and/or concentration. Choose any one or
any combination of the following three:
1. Definitive identification (choose one):
Note:
Except for X-ray fluorescence
(XRF), confirmation of identity
applies to organic analytes only.
Confirm XRF determined analytes
by an EPA-approved method.
a. Screened data - confirm analyte
identification by an EPA-approved
method, different from the screening
method, on at least 10% of
preliminary screened samples.
b. Unscreened data - confirm analyte
identification by an EPA-approved
method on all unscreened
environmental samples (field or lab).
2. Non-definitive quantitation (choose one):
a. Screened data - verify analyte
concentration on at least 10% of
preliminary screened samples (field
or lab) using an EPA-approved
method, different from the screening
method.
b. Unscreened data - determine analyte
concentration on all unscreened
environmental samples (field or lab)
using an EPA-approved method.
3. Definitive quantitation/analytical error
(choose one): Also, see Section 2.8 -
Part I and Error Determination - Part
II.
Note: Error determination is advised if data are
fr^ng pvalnafp.H against k critical action level.
a. Screened data - determine the analytical
error by calculating the precision,
accuracy, and coefficient of variation for
a subset (at least [lO%) of the verified
data using an EPA-approved method.
b. Unscreened data - determine the
analytical error I by calculating the
precision, accuracy, and coefficient of
variation for allj.of the quantitative
results using an EPA-approved method.
Note: If definitive quantitation is chosen along
with definitive identification for all the data,
then your data meet tjie QA3 objective.
Representative, comparable, complete.
QA requirements for objective "QA2
are
»*:- T.
specified in Section 2.7,
Requirements."
QA3: Rationale for OA3 objective:
QA3 is a definitive objective
accuracy of the concentration
"Quality Assurance
used to assess the
level as well as the
identity of the analyte(s) of interest. This objective
for data quality is available for data collection
activities that require a high degree of qualitative and
quantitative accuracy of all findings using rigorous
methods of analysis and quality assurance for "critical
samples" (i.e., those samples for which the data are
considered essential in makirg a decision). This
quality objective is intended to give the decision
maker (OSC) a level of confidence for a select group
of "critical samples" so he/she can make a decision
based on an action level with regard to: treatment;
disposal; site remediation j and/or removal of
pollutants; health risk or environmental impact;
cleanup verification; pollutant
delineation of contaminants;
source identification;
and other significant
decisions where an action level is of concern. Only
those methods that are analyte specific can be used
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for this quality objective. Error determinations
are made for. all analytes that are of interest to
the decision maker (OSC) for each critical
sample that is of interest.
OA3 Characteristics:
Analyte specific.
Definitive identification - confirm
analyte identification by a second
method, such as mass spectroscopy, on
100% of the "critical samples" collected;
applies only to organic analytes.
Note: Except for X-ray fluorescence
(XRF), confirmation of identity
applies to organic analytes only.
Confirm XRF determined analytes
by an EPA-approved method.
Non-definitive quantitation (choose one):
a. Screened data - verify analyte
concentration on at least 10% of
preliminary screened samples (field or
lab) using an EPA-approved method,
different from the screening method.
b. Unscreened data - determine analyte
concentration on all - unscreened
environmental samples (field or lab) using
an EPA-approved method.
Definitive quantitation/analytical error
- (determine the analytical error by
calculating the precision, accuracy, and
coefficient of variation) on 100% of the
"critical samples" collected using an
EPA-approved method.
Representative, comparable, complete.
QA requirements for objective "QA3" are
specified in Section 2.7, "Quality
Assurance Requirements."
provides. Quality is a matter of degree and can only be
assessed against specific criteria. Therefore, one can choose
any analytical method to use for any one of the three quality
assurance objectives in Section 2.4, provided all of the quality
assurance requirements are met for that objective as
specified in Section 2.7. The methods that can be used for
any of these three objectives include, but are not limited to,
spot tests; paper strip tests; indicator tubes; chemical
reactions producing colors, gases, or precipitates; electronic
meters such as Geiger counters, pH meters, conductivity
meters; electronic detectors such as photoionization, electron
capture, flame ionization, flame photometric, electrolytic, and
infrared; gas chromatography; mass spectroscopy; atomic
absorption; inductively coupled plasma (ICP), and X-ray
fluorescence. These methods may respond to either groups
of analytes or specific analytes or both.
2.5 Approach and Sampling Methodologies
This section should provide a description of the possible
sample matrices, required equipment and fabrication,
sampling design (reference SOPs and EPA procedures used
for collecting samples), sample documentation, corrective
action, sample analyses, and a schedule of work (see Table
1). Procedures for decontamination of equipment and
materials should be outlined in this section. In addition, a
field sampling summary table (see Table 2) should be
completed. In this table, specify the number of samples
required per parameter per matrix, the number of QA
samples, the required preservatives, appropriate sample
containers and sample volumes.
2.4.1 Methods
It should not be assumed that an analytical method
imparts a certain degree of quality to the results it
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2.6 Project Organization and Responsibilities
This section should list the managers, coordinators, and
field sampling personnel, along with their project duties
and responsibilities. The name and type of the laboratory
performing the analysis, if appropriate, should also be
included in this section. In addition, the parameters of
interest (BNAs, VOAs, metals) should be detailed.
2.7 Qualify Assurance Requirements
This section should describe the appropriate data quality
indicators and QA/QC protocols, based on the QA/QC
objective determined in Section 3.0, which will be followed
in the evaluation of lab data packages. A QA/QC
Analysis and Objectives Summary, including references to
analytical methods (see Table 3), should be completed.
The data quality indicators of concern for each QA/QC
objective are listed below.
OA1
The following requirements apply:
A. Sample documentation.
B. Instrument calibration data or a performance
check of a test method (i.e., Draeger tubes,
test strips, spot tests).
C. Detection limit" should be determined, unless
inappropriate.
Note: QC procedures prescribed in SOPs and
methods must be followed.
OA2
The following requirements apply:
A. Sample documentation.
B. Chain of custody (optional for field screening
locations).
C. Sample holding times (document sample
collection and analysis dates).
D. Initial and continuing instrument calibration data.
E. Method blank, rinsate blank, trip blank data
(refer to Table 2, footnotes 2 and 3).
F. Choose any one "or any combination of the
following three:
1. Definitive identification (chaose one):
a. Screened data - confirm the identification
of analytes via an EPA-approved method
different from the screening method (field
or lab) on at least 10^> of the preliminary
screened samples collected; provide
documentation such as gas chromatograms,
mass spectra, etc. |
b. Unscreened data - confirm the identification
of analytes via an EPAJapproved method on
all unscreened environmental samples;
provide documentation such as gas
chromatograms, mass spectra, etc.
2. Non-definitive auantitaticn
(choose one):
a. Screened data - provi ie documentation of
quantitative results from both the screening
method and the EPA-approved verification
method.
b. Unscreened data - provide documentation
of quantitative results}
(Documentation includes informjation and/or evidence
on calculation procedures, calibration data, sample
weight or volume, dilution factor, etc.)
3. Definitive quantitation/analytical error
(choose one):
a. Screened data - determine the analytical
error by calculating trie precision, accuracy,
and coefficient of variation* by preparing
and analyzing eight (8) QA replicates from
the subset of samples used to verify
screening results usiiig an EPA-approved
method. (See error determination Section
2.8.)
b. Unscreened data - determine the analytical
error by calculating the precision, accuracy,
and coefficient of vajriation* by preparing
and analyzing eight (8) QA replicates from
all of the samples anjalyzed using an EPA-
approved method.
G. Performance Evaluation Sample (optional) and
where available.
H. Detection limit should be determined, unless
inappropriate.
-------
OA3:
Note:
The following requirements apply:
A. Sample documentation.
B. Chain of custody.
C. Sample holding times (document sample
collection and analysis dates).
D. Initial and continuing instrument
calibration data.
E. Definitive identification:
Confirm the identification of analytes by
an EPA-apprqVed method on 100% of the
"critical" samples collected; and provide
documentation such as gas
chromatograms, mass spectra, etc.
F. Non-definitive quantitation (choose one):
a. Screened data - provide
documentation of quantitative results
from both the screening method and
the EPA-approved verification
method.
b. Unscreened data - provide
documentation of quantitative results.
(Documentation includes information and/or
evidence on calculation procedures, calibration
data, sample weight or volume, dilution factor,
etc.)
G. Definitive quantitation/analytical error
Determine the analytical error by an
EPA-approved method on 100% of the
"critical" samples collected. Calculate the
precision, accuracy, and coefficient of
variation* by preparing and analyzing
eight (8) QA replicates from the critical
samples collected. (See error
determination Section 2.8.)
H. Method blank, rinsate blank, and trip
blank data (refer to Table 2, Footnotes 2
and 3).
See data validation protocols for determining
precision, accuracy, and coefficient of variation.
I. Performance Evaluation Samples, where
available.
J. Detection limit should be determined, unless
inappropriate.
Reference must be made to standard QA/QC protocols (i.e.,
SOPs, EPA reference procedures) for generating the above
data quality indicator information.
2.8 Error Determination (Analytical and Total Error)
Any one of the following options can be used when
determing error for QA2 or QA3:
2.8.1 Matrix Spike Samples
Spike and analyze at least eight (8) replicate samples with
a concentration level equal to the level of interest. Use
samples whose unspiked concentrations are less than or
equal to the level of interest. Samples should be
homogeneous. Determine bias (percent recovery) and
precision (coefficient of variation) according to Section 3.5
of Part II - Data Validation Procedures.
2.8.2 Site Background Samples
Spike and analyze at least eight (8) replicate samples with
a concentration level equal to the level of interest. These
samples are from the site of interest (or nearby proximity).
The analyte of interest is not detectable in the sample for
the method used. Samples should be made homogeneous.
Determine bias (percent recovery) and precision (coefficient
of variation) according to Section 3.5 of Part II - Data
Validation Procedures.
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2.83 Site Action Level Samples (Total Error)
Collect and analyze at least 8 replicate samples whose
analyte concentrations are equal to the level of interest.
(Do this by collecting one sample with sufficient material
to divide into the required number of replicates. Except
for VOA samples, homogenize the sample thoroughly
before dividing into replicates.) These samples are from
the selected site and contain the target analyte at or near
the level of interest. Determine bias (percent recovery)
and precision (coefficient of variation) according to
Section 3.5 of Part II - Data Validation Procedures. Bias
can not be determined unless these samples are spiked
first and percent recovery is calculated.
Note: This procedure (2.8.3) is useful in determining the
total (sampling and analytical) error as well as the
analytical error since it evaluates the sample collection,
sample preparation, and the analysis. Sampling error
determination is being addressed hi representative
sampling guidance documents for each media. These
documents are under development for removal activities.
2.9 Deliverables
This section should provide a description of the reports
and other deliverables (e.g., field activities, trip reports,
status reports, maps/figures, analysis, data review,
analytical reports, and draft final reports) to be generated
as a result of the sampling activity.
2.10 Data Validation
This section details the criteria used to ensure that the
analytical results received from a laboratory are valid and
accurate for the QA objective chosen. Consult the "Data
Validation Procedures" in this guidance document for the
appropriate evaluation criteria. These procedures have been
developed mainly from the "Laboratory Data Validation
Functional Guidelines for Evaluation ofj Organic, Inorganic,
and Dioxm Analyses" used in the
Laboratory Program.
QA1
Agency's Contract
QA1 data need only be evaluated for calibration and
detection limits criterion.
QA2
The results of 10% of the samples in the analytical data
packages should be evaluated forj all of the elements
listed in Section 2.7, "QA Requirements" of the
Sampling QA/QC Plan. The Holding times, blank
contamination, and detection capability will be reviewed
for all remaining samples.
OA3
This objective, the most stringent
of all the objectives,
requires that at least 10% of the Camples in a lab data
package be evaluated for all of the listed elements in
Section 2.7 "QA Requirements' of the Sampling
QA/QC Plan. Of the remaining samples, holding
times, blank contamination, precision, accuracy, error
determination, detection limitjs, and confirmed
identification data will be reviewed/This objective also
requires review of all elements for all samples in each
analyte category (i.e., VOAs and
data package received from an individual lab.
PCBs) in every 10th
10
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Table 1: Example Proposed Schedule of Work
Item
(time period)
J. /
1. Laboratory Procurement
2. Phase 1 Site Work
3. Drilling Subcontract Procurement
4. Phase 2 Site Work
5. Laboratory Analysis
6. Data Review
7. Draft Report
8. Final Report
11
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Table 2: Field Sampling Summary
Analytical
'arameter
VOA
VGA
BHA
BNA
PESTICIDE
PESTICIDE
PCB
PCB
P.P.
HETALS
P.P.
HETAUS
CYANIDE
CYANIDE
Level
of
ensiti-
vity'
*
Matrix
S
W
S
W
S
W
S
W
S
W
S
W
Container Type
and Volume
# container rq'd)
40ml vial
(1)
40ml vial
(3)
8oz glass
(1)
32oz amber glass
(2)
8oz glass
(1)
32oz amber glass
(2)
8oz glass
(1)
32oz amber glass
(2)
8oz glass
(1)
1 liter glass or
polyethylene
(1)
8oz glass
CD
1 liter
polyethylene
(1)
reserv-
ative
4°C
**
4°C
4°C
4°C
4°C
**
4°C
4°C
**
4°C
4°C
N03 ph<2
4«C
4°C
NaOH to
pH > 12
4°C
iolding
Times
7 day
7 day
7/40 d
7/40 d
7/40 d
7/40 d
7/40 d
7/40 d
6 mos
6 mos
14 day
14 day
ubtotal
amples
QC Extras
insate
lanks"
rip ,
tanks'1
VOAs)
H
H
SH
oc
ositiv*
s4
Matrix,-
Spikes3
Total
Field
Samples
*
**
Matrix: S-Soil, W-Water, 0-Oil, DS-Drum Solid, DL-Drum Liquid, TS-Tank Solid,' TL-Tank Liquid, X-Other, A-Air
If residual chlorine is present, preserve with 0.008%
1. The concentration level, specific or generic, that is ne
in order to make an evaluation.
provide a basis for determining the analytical method to be used.
onlv reouired if dedicated sampling tools are not used. For QA2 and QA3, one blank required
2. Only required if dedicated sampling
This level will
per parameter per 20
3 ForPGA2"and°QA3, one"trip blank required per cooler used to ship VOA samples. Each trip blank consists of two
40ml vials filled with distilled/deionized water. For QA1, enter "N/A". I
4. Performance check samples; optional for QA2, mandatory for QA3 at one per parameter per matrix. For QA1, enter
5 For QA2 (optional) and for QA3 (mandatory): Determine bias (% recovery) using a minimum of 2 matrix spikes.
" Determine precision using a minimum of 8 matrix spikes. Ensure that sufficient environmenta sample is collected
for lab spiking. For QA1, enter "N/A".
12
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Table 2: Field Sampling Summary (continued)
Analytical
Parameter
PHENOLS
PHENOLS
Level
of
Sensiti-
vity'
Matrix
S
W
Container Type
and Volume
(# container rq'd)
8oz glass
(1)
1 liter amber
glass
(1)
Preserv-
ative ;
4°C
H2SO, to
pH 5 2
4«C
Holding
Times
28 day
28 day
Subtota
Samples
QC Extras
Rinsate
Blanks
Trip ,
Blanks
(VOAs)
QC
Positives
Matrix-
Spikes
Total
Field
Samples
3.
** tf nXL S',=< r.,,tt;~:» .- , -r .'
is
13
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TableS: OA/OC Analysis and Objectives Summary
Analytical
Parameter
VGA
VOA
BHA
BHA
PESTICIDE
PESTICIDE
PCS
PCB
P.P.
METALS
P.P.
METALS
CYANIDE
CYAHIDE
Matrix
S
W
S
W
S
W
S
W
S
W
S
W
Analytical
Method Ref.
8240/SW-846
624/CLP
8250 or 8270/
SW-846
625/CLP
8080/SW-846
608
8080/SW-846
608
SW-846
EPA-600/CFR 40
SW-846
SW-846
Spil
Matrix1
ces
Surrogate
*
B^S8PSpii|Biffi$S||iji|
election
Limits3
A Objective4
* Matrix: S-Soil, W-Water, 0-0! I, DS-Drum Solid, DL-Drun Liquid, TS-Tank Solid, TL-Tank Liquid, X-Other
' ef 'SSSS!
2. iF^U«58SSSt£rS;«f ^K^lvlr. "VSTrunT each sa^ie; therefore, enter -y... For
3. ?o"b4 detlrminedby the person arranging the analysis. Should be equal to or less than the | level of
sensitivity.
4. Enter the QA Objective desired: QA1, QAZ, or QA3.
14
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Table 3: QA/QC Analysis and Objectives Summary (continued)
Analytical
Parameter
PHENOLS
PHENOLS
Matrix
S
W
Analytical
Method Ref .
8040/SW-846
604/CFR 40
Spikes
Matrix1
Surrogate
in
QA/QC
Limits'5
QA Objective
Matrix: S-Soil, W-Water, 0-Oil, DS-Drum Solid, DL-Drum Liquid, TS-Tank Solid, TL-Tank Liquid, X-Other,
A-Air
1. For QA2, (optional) and for QA3 (mandatory): Determine bias (% recovery) using a minimum of 2 matrix
spikes. Determine precision using a minimum of 8 matrix spikes. Ensure that sufficient environmental
sample is collected for lab spiking. For QA1, enter "N/A".
2. For QA2 and QA3, surrogate spike analysis is to be run for each sample; therefore enter "yes" For
QA-1: enter "N/A".
3. To be determined by the person arranging the analysis. Should be equal to or less than the level of
sensitivity.
4. Enter the QA Objective desired: QA1, QA2, or QA3.
15
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PARTH
DATA VALIDATION PROCEDURES
16
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1.0 Introduction
Part II provides guidance in the validation of laboratory
data packages, according to the guidelines established by
the Sampling QA/QC Plan. It is a compilation of those
procedures used hi the Contract Laboratory Program
(CLP) and those found in the "Laboratory Data Validation
Functional Guidelines for Evaluating Organic, Inorganics,
Pesticides, and Dioxin Analysis." This guidance was
developed for the Emergency Response Divisions' (ERD)
use and is not intended to supercede the guidance
documents developed for CLP data validation used for
Remedial activities.
Items reviewed during the data validation process are
dependent upon the QA objectives previously established
by the data user in the Sampling QA/QC Plan. According
to the tiered approach implemented in the Sampling
QA/QC Plan each QA objective requires the following
review:
QA3 - This objective, the most stringent of all the
objectives, requires 'that at least 10% of the
samples in a lab data package be reviewed for all
of the elements. Of the remaining samples,
holding times, blank contamination, precision,
accuracy, error determination, detection limits, and
confirmed identification data will be reviewed.
This level also requires the review of all the
elements for all samples in each analyte category
in every 10th data package received from an
individual lab.
QA2 - This objective requires that the results of
10% of the samples reported in the analytical data
package should be evaluated for all of the elements
listed in Section 7, QA Requirements, of the
Sampling QA/QC Plan. The holding times, blank
contamination, and detection limits will be
reviewed for the remaining.
QA1 - Thjs objective requires review of only the
calibration and detection limits for all data.
Included hi the section on Matrix Spike/Matrix Spike
Duplicates are formulas for calculating confidence limits and
the coefficient of variation. Confidence limits should be
determined for all data generated under QA3 and may be
calculated for QA2 if, a sufficient number of spiked samples
are collected. Although not stated in the following data
validation procedures, the reviewer must examine the data
packages for transcription/calculation errors that may have
been overlooked by the lab.
2.0 Data Validation Qualifiers
J The associated numerical value is an estimated
quantity because the reported concentrations were
less than the required detection limits or quality
- control criteria were not met.
N Presumptive evidence of presence of material.
NJ Presumptive evidence of the presence of the material
at an estimated quantity.
PND Precision Not Determined. -
R
The sample results arc rejected (analyte may or may
not be present) due to gross deficiencies is quality
control criteria. Any reported value is unusable.
Resampling and/or reanalysis is necessary for
verification.
RND Recovery Not Determined.
U
UJ
The material was analyzed for, but not detected.
The associated numerical value is the sample
detection limit or adjusted sample detection limit.
The material was analyzed for, but not detected.
The reported detection limit is estimated because
Quality Control criteria were not met.
17
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3.0 Metallic Inorganic Parameters
3.1 Sample Holding Times
1. Were any of the sample holding tunes
exceeded?*
Sample Holding Times:
Metals - 6 months
Cyanide - 14 days
Mercury - 28 days
Chromium4"6 - 24 hours
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL).
Values .that are less than the IDL can be flagged
as estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
3.2 Initial and Continuing Calibration Verification
1. Are values outside the range of 90% to 110% of
the mean value, except for tin and mercury, for
which the range is 80% to 120%, and cyanide,
for which the range is 85% to 115%?
ACTION: If values are between 75-89% or 112-
125% (65-79% and 121-135% for Hg and Sn, 70-
84% and 116-130% for cyanide), flag as estimated
(J).
If values are outside of the above windows, reject
(R) as unacceptable data between calibration
standard outside of above windows and nearest
adjacent acceptable calibration standard(s).
2. Was a calibration standard and blank analyzed at
the beginning of the analysis and after every 10
samples?
ACTION: If no, flag as estimated (J) all values not
analyzed within 5 samples of a calibration standard
or blank.
3. Were any sample results greater than 110% of the
highest calibration standard?
ACTION: If yes, flag result reported as estimated
(J).
1. Do the concentrations oi; all blanks fall below the
IDL for all parameters?
ACTION: If no, flag as undetected (U) all reported
positive data that has a concentration less than 5
times the blank value. |
I
NOTE: In instances where more than one blank is
associated with a given sample, qualification should
be based upon a comparison with the associated
blank having the highest concentration of a
contaminant. The results must not be corrected by
subtracting any blank value.
2. Was one method blank analyzed for each 20
samples?
ACTION: If no, flag as estimated (J) all data for
which a method blank was not analyzed. If only one
blank was analyzed for more than 20 samples, the
first 20 samples analyzed do not have to be flagged
as estimated (J).
18
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3.4 ICP Interference Check Sample
1. If all ICP Interference Check Sample (ICS)
results are not inside of control limits (± 20%
of mean value), are concentrations of Al, Ca,
Fe, or Mg lower in the sample than in the ICS?
ACTION: If no, flag as estimated (J) those sample
results for which ICS recovery is between +. 50%
of mean value. For those sample results in which
ICS recovery is above 150% or 50%, reject (R) all
results.
2. Was ICS analyzed at the beginning and end of
each run or at least twice every 8 hours,
whichever is more frequent?
ACTION: If no, flag as estimated (J) all samples
for which Al, Ca, Fe, or Mg concentration is higher
than in ICS.
3.5 Error Determination
See Part I - Section 2.8 for QA Samples to be used
for error determination.
3.5.1 Determination of Bias (% Recovery - Optional for
QA-2; Mandatory for QA-3)
3.5.1.1 Percent Recovery
1. Were at least eight spiked sample
replicates for the matrix of interest
analyzed at the required frequency?
ACTION: If no, flag as recovery not
determined fRND) all data for which
spiked samples were not analyzed.
2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable control
limits (80% to 120%)?
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x IQQ
Spike cone, added
ACTION: If recoveries are within applicable
control limits, no bias is considered. If %
Recovery is less than 80% or greater than
120%, the sample data should be flagged with
a (J) estimate and a corresponding (-) or ( + )
sign to show direction of the bias.
Adjustment of sample values should be
considered whenever there, is consistent
evidence of bias.
3.5.1.2 Adjustment of Sample Value for Bias
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to or from the sample
values. % bias is the reciprocal value of
% recovery (i.e., for 70% recovery you
have a negative 30% bias). Use the
average % recovery from the total number
of matrix spikes analyzed. This adjustment
approach assumes a spiking concentration
.equal to the concentration found in the
sample.
3.5.2 Determination of Precision (Optional for QA-2;
Mandatory for QA-3)
3.5.2.1 Replicate Analysis
1. Was a minimum of eight replicates
analyzed? If yes, determine coefficient of
variation. If no, flag data with precision
not determined (PNDV for which replicate
samples were not analyzed.
19
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3.5.2.2 Coefficient of Variation (Percent Relative
Standard Deviation)
1. The coefficient of variation (CV) is
used in determining the precision or
standard deviation. The CV expresses
the standard deviation as a percentage
of the mean (average) value of the
replicate values. The CV is used to
determine a false positive or false
negative value for results that are
respectively greater than or less than
a decision level concentration.
Determine the coefficient of variation
using the following equation:
CV = s x 100
where:
XDL = the decision level concentration
s = the sample standard deviation
given by the equation:
s* = [fc-xf/Oi-l)]*4
*Note: When using a programmable calculator
or computer statistics software, be sure the
above equation with (n - 1) is used and not (n)
by itself. The equation using (n) is to determine
the population standard deviation (a) rather
than the sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as
follows:
False positive value = Decision level value +
(CV x decision level)
3.6
False negative value = Decision level value - (CV
x decision level)
Example:
For an decision level = 50 ppm and CV = 20%
False positive value = 50 ppm + (20%
50 ppm)
= 50 ppm + (10 ppm)
= 60 ppm i
False negative value = 50 ppm - (20% x
50 ppm)
= 50 ppm - (10 pbm)
= 40 ppm
For the above false positive example, any value
between 50 ppm and 60 ppm are considered suspect
and should be reanalyzed. Values above 60 ppm are
considered actionable. In many cases, false positives
have been considered actionable by the Agency for
safety reasons. However, depending on the action to
be taken, this can be costly and unjustifiable.
Consult the QA plan for intended use of data and
data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered suspect
and should be reanalyzed. Values below 40 ppm are
considered non-actionable. . In most cases, the
decision maker will be using the false negative value
as his decision level and not
be concerned about the
false positive value. Whenever sample values need
to be corrected for both bias and precision, first
correct the value for bias, [then correct the biased
value for precision.
Performance Evaluation Samples
I;
1. Were recovery limits within those set by the
EMSL lab?
20
-------
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
3-7 Optional Additional Instrument QC (for elevafprf
concentrations)
3-7.1 ICP Serial Dilution (if recovery is outside
acceptable range)
1. Was serial dilution performed on one of each 20
samples of similar matrix where concentrations
exceed 50 times IDL?
ACTION: If no, flag associated data as estimated
(J).
2. If analyte concentration after a five fold dilution
is greater than 10 times IDL, did analysis of
diluted sample agree to within 10% of original
determination for each parameter?
ACTION: If no, flag associated data as estimated
(J).
3-7.2 Atomic Absorption Analysis Specific OC
1. Is any furnace result flagged with an (E) by the
laboratory to indicate interference?
If yes, is any associated post-digestion spike
recovery less than 10% for any result flagged
with an (E).
ACTION: If yes, reject (R) affected data.
4.0
4.1
2. When the method of standard addition was
required, is the coefficient of correlation less than
0.995 for any sample?
ACTION: If yes, flag the associated data as
estimated (J).
Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data package
for a case. This is particularly appropriate for cases
in which there are several QC criteria out of
specification. The additive nature of QC factors
which are out of specification is difficult to assess in
an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer would
be greatly assisted in this endeavor if the data quality
objectives were provided.
BNAs by GC/MS Analysis
Sample Holding Times
1. Were any of the sample holding times exceeded?*
Sample Holding Times from date of sample
collection:
Water - 7 days to extract
Soil, sediment, sludges - 14 days to extract
Water/soil - analyze within 40 days after extraction
21
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ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL).
Values that are less than the IDL can be flagged
as estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
42 GC/MS Tuning Criteria
1. Has decafluorotriphenylphosphine (DFTPP)
been run for every 12 hours of sample analysis
per instrument?
ACTION: If no, reject (R) all associated data for
that instrument which fall outside an acceptable
12-hour time interval.
2. Have the DFTPP ion abundance criteria been
met for each instrument used?
m/z Ion abundance criteria
51 30-60% of mass 198
68 Less than 2% of mass 69
69 (reference only)
70 Less than 2% of mass 69
127 40-60% of mass 198
197 Less than 1% of mass 198
198 Base peak, 100% relative abundance
199 5-9% of mass 198
275 10-30% of mass 198
365 Greater than 1% of mass 198
441 Less than mass 443
442 Greater than 40% of mass 198
443 17-23% of mass 442
ACTION: If no, evaluate against expanded ion
abundance criteria.
3. Have the appropriate expanded ion abundance
criteria been met for each instrument used?
m/z Expanded ion abundance criteria
51 22-75% of mass 1R8
68 Less than 2% of mass 69
69 (reference only) :
70 Less than 2% of mass 69
127 30-75% of mass 198
197 Less than 1% of mass 198
198 Base peak, 100% [relative abundance
199 5-9% of mass 198J:
275 7-37% of mass 198
365 Greater than 0.75;% of mass 198
441 Present, but lessjhan mass 443
442 Greater than 30% of mass 198
443 17-23% of mass 4*2
ACTION: It is up to the reviewer's discretion, based
on professional judgement, to flag data associated
with tunes meeting expanded criteria, but not basic
criteria. If only one element falls within the
expanded criteria, no qualification may be needed.
On the other hand, if several data elements are in
the expanded windows, all associated data may merit
an estimated flag (J). Note that the data reviewer
may still choose to flag all data associated with a
tune not meeting contract criteria as rejected (R) if
it is deemed appropriate.
i
The most critical factors in! the DFTPP criteria are
the non-instrument specific requirements that are
also not unduly affected by the location of the
spectrum on the chromatographic profile. The m/z
198/199 and 442/443 ratios jare critical. These ratios
are based on the natural abundances of Carbon 12
and Carbon 13 and should always be met. Similarly,
the m/z 68, 70, 197 and ml relative abundances
indicate the condition of the instrument and the
suitability of the resolution! adjustment and are very
important. Note that all of the foregoing abundances
relate to adjacent ions; thej are relatively insensitive
to differences in instrument design and position of
the spectrum on the chromatographic profile. For
the ions at m/z 51, 127, arid 275, the actual relative
abundance is not critical. For instance, if m/z 275
has a 40% relative abundance (criteria 10-30%) and
other criteria are met, the deficiency is minor. The
22
-------
relative abundance of m/z 365 is an indicator of
suitable instrument zero adjustment. If m/z 365
relative abundance is zero, minimum detection
limits may be affected. On the other hand, if m/z
365 is present, but less than the 1% minimum
abundance criteria, the deficiency is not as serious.
4-3 Initial and Continuing Calibration Verification
1. Do any compounds have an average response
factor equal to zero?
ACTION: If yes, reject (R) sample data for
associated compounds.
2. Verify that all BNA compounds have Relative
Response Factors of at least 0.05.
ACTION: If any BNA compound has a Relative
Response Factor of less than 0.05, flag positive
results for that compound as estimated (J). Flag
non-detects for that compound as rejected (R).
3. Verify that all BNA compounds have a percent
Relative Standard Deviation (%RSD) of <. 30%
for the initial calibration.
%RSD = s x 100
x
where:
s = standard deviation of 5 response factors
x = mean of 5 response factors
ACTION: If any BNA compound has a %RSD of
greater than 30%, flag positive results for that
compound as estimated (J). Non-detects may be
qualified (J) using the reviewer's professional
judgement.
4. Verify that the percent difference (%D) is <. 25%
for all BNA compounds in the continuing
calibration.
ACTION: If any BNA compound has a %D between
the initial and continuing calibration of greater than
25%, flag all positive results for the compound as
estimated (J). Non-detects maybe qualified (J) using
the reviewer's professional judgement.
4.3.1 Internal Standards
1. Verify that all retention times and Internal
Standard (IS) areas are acceptable.
ACTION: If an IS area is outside -50% or +100%
of the associated standard, flag the positive results as
estimated (J) for that sample fraction. Non-detects
for compounds quantitated using that IS are flagged
with the sample quantitation limit flagged as
estimated (J) for that sample fraction. If extremely
low area counts are reported, or if performance
exhibits a major abrupt drop-off, then a severe loss
of sensitivity is indicated. Non-detects should then be
flagged as rejected (R).
If an IS retention time varies by more than 30
seconds, the chromatographic profile for that sample
must be examined to determine if any false positives
or negatives exist. For shifts of a large magnitude,
the reviewer may consider partial or total rejection
(R) of the data for that sample fraction.
4.4 Error Determination
See Part I - Section 2.8 for QA samples to be used
for error determination.
23
-------
4.4.1 Determination of Bias (% Recovery- Optional for
QA-2; Mandatory for QA-3)
4.4.1.1 Percent Recovery
1. Were at least eight spiked sample
replicates for the matrix of interest
analyzed at the required frequency?
ACTION: If no, flag as recovery not
determined (RND) all data for which
spiked samples were not analyzed.
2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable control
limits (80% to 120%)?
Cf,
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x
Spike cone, added
ACTION: If recoveries are within
applicable control limits, no bias is
considered. If % Recovery is less than
80% or greater than 120%, the sample
data should be flagged with a (J) estimate
and a corresponding (-) or (+) sign to
show direction of the bias. Adjustment of
sample values should be considered
whenever there is consistent evidence of
bias.
4.4.1.2 Adjustment of Sample Values for Bias
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to or from the sample
values. % bias is the reciprocal value
of % recovery (i.e., for 70% recovery
you have a negative 30% bias). Use
the average % recovery from the total
number of matrix spikes analyzed.
This adjustment approach assumes a spiking
concentration equal
found in the sample.
4.4.2 Determination of Precision
Mandatory for QA-3)
to the concentration
(Optional for QA-2;
4.4.2.1 Replicate Analysis
1. Was a minimum of eight replicates
analyzed? If yes, determine coefficient of
variation. If no, flag data with precision
not determined CPNDX for which replicate
samples were not
analyzed.
4.4.2.2 Coefficient of Variation (Percent Relative
Standard Deviation)
1. The coefficient o£ variation (CV) is used
in determining the precision of standard
deviation. The CV expresses the standard
deviation as a percentage of the mean
(average) value of the, replicate values.
The CV is used to determine a false
positive or false rlegative value for results
that are respectively greater than or less
than a decision level concentration.
Determine the coe fficient of variation using
the following equation:
CV = s * 100
where:
= the decision level concentration
s = the sample standard deviation given by
the equation: |
|
s* = [(x, - x)2/(n - l)f2
24
-------
*Note: When using a programmable calculator
or computer statistics software, be sure the
above equation with (n - 1) is used and not (n)
by itself. The equation using (n) is to determine
the population standard deviation (o) rather
than the sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as
follows:
False positive value = Decision level value +
(CV x decision level)
False negative value = Decision level value -
(CV x decision level)
Example:
For an decision level = 50 ppm and CV = 20%
False positive value = 50 ppm + (20% x
50 ppm)
= 50 ppm + (10 ppm)
= 60 ppm
False negative value = 50 ppm -
50 ppm)
= 50 ppm - (10 ppm)
= 40 ppm
(20% x
For the above false positive example, any value
between 50 ppm and 60 ppm are considered
suspect and should be reanalyzed. Values above
60 ppm are considered actionable. In many cases,
false positives have been considered actionable by
the Agency for safety reasons. However,
depending on the action to be taken, this can be
costly and unjustifiable. Consult the QA plan for
intended use of data and data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below 40
ppm are considered non-actionable. In most cases,
the decision maker will be using the false negative
value as his decision level and not be concerned
about the false positive value. Whenever sample
values need to be corrected for both bias and
precision, first correct the value for bias, then correct
the biased value for precision.
4.5 Blanks
1. Was a method blank extracted and analyzed for
each set of samples or every 20 samples of similar
matrix and similar extraction technique?
ACTION: If no, flag as estimated (J) all data for
which a method blank was not analyzed. NOTE:
If only one blank was analyzed for more than 20
samples, the first 20 samples analyzed do not have
to be flagged as estimated (J).
2. Has the method blank for BNAs been run on the
same GC/MS or GC system as the sample?
ACTION: If no, flag as estimated (J) all results that
do not have an associated blank.
3. Are the concentrations of blank contaminants for
BNAs greater than the Required Detection Limit
(RDL) of any BNA compound?
ACTION: For sample values reported at less than
10 times the blank contamination level for common
phthalate esters and 5 times the blank contamination
level for other BNA compounds, flag as undetected
(U).
NOTE: In instances where more than one blank is
associated with a given sample, quantification should
be based upon a comparison with the associated
blank having the highest concentration of a
25
-------
contaminant. The results must not be corrected by
subtracting any blank value.
4.6 Compound Identification
1. Verify the following:
-the Relative Retention Time (RRT) of
reported compounds is within 0.06 RRT units of
the standard RRT.
-all ions present in the standard mass spectrum
at a relative intensity greater than 10% are also
present in the sample mass spectrum.
-all ions present in the sample, but not present
in the standard are accounted for.
-relative intensities of the ions specified above
as present in the sample and at a relative
intensity greater than 10% hi the standard,
agree within 20% between the sample and the
standard spectra.
ACTION: Use professional judgement to
determine acceptability of the data if the above
criteria were not all met. If it is determined that
incorrect identifications were made, all such data
should be reported as not detected with an
estimated (J) quantisation limit. Ions greater than
10% in the sample spectrum but not present in the
standard spectrum must be considered and
accounted for.
4.6.1 Tentatively Identified Compounds
1. Verify the following:
-all ions presenting the reference mass spectrum
with a relative intensity greater than 10% are
present in the sample mass spectrum.
-relative intensities specified above agree within
20% between the
spectra.
sample and the reference
-molecular ions present in the reference spectrum
are present in the sample spectrum.
-all tentatively identified compounds are reported
with estimated quantitation and detection limits.
ACTION: Use professional judgement to determine
acceptability of the data if the above criteria are not
all met. If data are considered to be unacceptable,
the tentative ID should be changed to "unknown".
4.7 Compound Ouantitation and Reported Detection
Limits j
1. Verify that the reported values, both positives and
non-detects, have been correctly adjusted to reflect
all dilutions, concentrations, splits, cleanup
procedures, dry weight factors, an any other
adjustments that have nc t been accounted for by
the method.
BNA for waters: ug/L = (AjfUfV,)
BNA for soils: ug/kg =
A;,. =
Is =
RF =
V, =
V; =
Vs =
Ws =
D =
area of characteristic ion for compound being
measured
area of characteristic ion for the internal
standard j
amount of internal standard added (ng)
daily response factor for the compound being
measured
volume of total extrjact (ul)
volume injected (ul)
volume of sample (jnl)
weight of sample extracted (g)
(100 - % moisture) /.ICO
ACTION: If incorrect valubs have been reported, it
is essential that the correct values be determined.
26
-------
The reviewer should contact the laboratory to
verify any corrections made to the data.
4.8 Performance Evaluation Samples
1. Were recovery limits within those set by the
. EMSL lab?
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
4-9 Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data
package for a case. This is particularly appropriate
for cases in which there are several QC criteria out
of specification. The additive nature of QC factors
which are out of specification is difficult to assess
in an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer
would be greatly assisted in this endeavor if the
data quality objectives were provided.
4.10 Optional QC Checks
4.10.1 Surrogate Recovery
1. If either two or more base neutral or acid
surrogates were outside of specifications for any
sample or blank, were the appropriate samples
reanalyzed?
5.0
5.1
ACTION: If initial analysis and reanalysis both
have two or more surrogates putside of specifications
for samples or blanks, estimate (J) all quantitation
results, including detection limits.
2. Does any one surrogate have less than 10%
recovery?
ACTION: If yes, flag as estimated (J) positive results
for that fraction; flag negative results as rejected (R).
VOAs by GC/MS Analysis
Sample Holding Times
1. Were any of the sample holding times exceeded?*
Sample Holding Times from date of sample
collection:
Aromatic (for water) - 7 days (unpreserved), 14
days (preserved)
All other compounds - 14 days
Soil, sludge, sediments - 14 days
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL). Values
that are less than the IDL can be flagged as
estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
27
-------
52 GC/MS Tuning Criteria
1. Has bromofluorobenzene (BFB) been run for
every 12 hours of sample analysis per
instrument?
ACTION: If no, reject (R) all associated data for
that instrument which fall outside an acceptable
12-hour time interval.
2. Have the BFB ion abundance criteria been met
for each instrument used?
m/z
50
75
95
96
173
174
175
176
177
Ion abundance criteria
15-40% of mass 95
30-60% of mass 95
Base peak, 100% relative
abundance
5-9% of mass 95
Less than 2% of mass 174
Greater than 50% of mass 95
5-9'% of mass 174
95-101% of mass 174
5-9% of mass 176
ACTION: If no, evaluate against expanded ion
abundance criteria.
53
but not basic criteria. If anly one element falls
within the expanded criteria, no qualification may be
needed. On the other hand,
are in the expanded windows
if several data elements
all associated data may
merit an estimated flag (J). Note that the data
reviewer may still choose toj flag all data associated
with a tune not meeting contract criteria as rejected
(R) if it is deemed appropriate.
For BFB, the most important factors to consider are
the empirical results that are1 relatively insensitive to
location on the chromatographic profile and the type
of instrumentation. Therefore, the critical ion
abundance criteria for BFB are the m/z 95/96 ratio,
the 174/175 ratio, the 176/177 and the 174/176 ratio.
The relative abundances oil m/z 50 and 75 are of
lower importance.
Initial and Continuing Calibration Verification
I
1. Do any compounds have an average response
factor equal to zero?
j"
ACTION: If yes, reject j (R) sample data for
associated compounds. j
3. Have the appropriate expanded ion abundance
criteria been met for each instrument used?
m/z
50
75
95
96
173
174
175
176
177
Ion abundance criteria
11-50% of mass 95
22-75% of mass 95
Base peak, 100% relative
abundance
5-9% of mass 95
Less than 2% of mass 95
Greater than 50% of mass 95
5-9% of mass 174
95-101% of mass 174
5-9% of mass 176
ACTION: It is up to the reviewer's discretion,
based on professional judgement, to flag data
associated with tunes meeting expanded criteria,
2. Verify that all VOA compounds have Relative
Response Factors of at least 0.05.
j-
ACTION: If any VOA compound has a Relative
Response Factor of less than 0.05, flag positive
results for that compound as estimated (J). Flag
non-detects for that compound as rejected (R).
3. Verify that all VOA compounds have a percent
Relative Standard Deviation (%RSD) of <. 30%
for the initial calibration.:
%RSD = s x 100 '
28
-------
where:
5.4 Error Determination
s = standard deviation of 5 response
factors
x = mean of 5 response factors
ACTION: If any VGA compound has a %RSD of
greater than 30%, flag positive results for that
compound as estimated (J). Non-detects may be
qualified (J) using the reviewer's professional
judgement.
4. Verify that the percent difference (%D) is <_
25% for all VOA compounds hi the continuing
calibration.
ACTION: If any VOA compound has a %D
between the initial and continuing calibration of
greater than 25%, flag all positive results for the
compound as estimated (J). Non-detects may be
qualified (J) using the reviewer's professional
judgement.
5.3.1 Internal Standards
1. Verify that all retention times and Internal
Standard (IS) areas are acceptable.
ACTION: If an IS area is outside -50% or +100%
of the associated standard, flag the positive results
as estimated (J) for that sample fraction. Non-
detects for compounds quantitated using that IS
are flagged with the sample quantitation limit
flagged as estimated (J) for that sample fraction.
If extremely low area counts are reported, or if
performance exhibits a major abrupt drop-off, then
a severe loss of sensitivity is indicated. Non-detects
should then be flagged as rejected (R). If an IS
retention time varies by more than 30 seconds, the
chromatographic profile for that sample must be
examined to determine if any false positives or
negatives exist. For shifts of a large magnitude, the
reviewer may consider partial or total rejection (R)
of the data for that sample fraction.
See Part I - Section 2.8 for QA samples to be used
for error determination.
5-4-! Determination of Bias (% Recovery - Optional for
QA-2; Mandatory for QA-3)
5.4.1.1 Percent Recovery
1. Were at least eight spiked sample
replicates for the matrix of interest
analyzed at the required frequency?
ACTION: If no, flag as recovery not
determined CRND^ all data for which spiked
samples were not analyzed.
2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable control
limits (80% to 120%)?
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x ^QQ
Spike cone, added
ACTION: If recoveries are within applicable
control limits, no bias is considered. If %
Recovery is less than 80% or greater than
120%, the sample data should be flagged with
a (J) estimate and a corresponding (-) or ( + )
sign to show direction of the bias.
Adjustment of sample values should be
considered whenever there is consistent
evidence of bias.
5.4.1.2 Adjustment of Sample Values for Bias
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to or from the sample
29
-------
values. % bias is the reciprocal value of %
recovery (i.e., for 70% recovery you have a
negative 30% bias). Use the average % recovery
from the total number of matrix spikes analyzed.
This adjustment approach assumes a spiking
concentration equal to the concentration found in
the sample.
5.4.2 Determination of Precision (Optional for QA-2;
Mandatory for QA-3)
5.4.2.1 Replicate Analysis
1. Was a minimum of eight replicates
analyzed? If yes, determine coefficient
of variation. If no, flag data with
precision not determined CPND1 for
which replicate samples were not
analyzed.
5.4.2.2 Coefficient of Variation (Percent Relative
Standard Deviation)
1. The coefficient of variation (CV) is
used in determining the precision of
standard deviation. The CV expresses
the standard deviation as a percentage
of the mean (average) value of the
replicate values. The CV is used to
determine a false positive or false
negative value for results that are
respectively greater than or less than
a decision level concentration.
Determine the coefficient of variation
using the following equation:
CV =
s x 100
XDL
where:
s = the sample standard deviation given by
the equation:
s =
- x)2|/(n -
*Note: When using a programmable calculator
or computer statistics software, be sure the above
equation with (n -1) is used and not (n) by itself.
The equation using (n) is to determine the
population standard deviation (
-------
Consult the QA plan for intended use of data and
data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below
40 ppm are considered non-actionable. In most
cases, the decision maker will be using the false
negative value as his decision level and not be
concerned about the false positive value.
Whenever sample values need to be corrected for
both bias and precision, first correct the value for
bias, then correct the biased value for precision.
5.5 Blanks
1. Was a method blank prepared and analyzed for
each set of samples or every 20 samples of
similar matrix and similar preparation
technique?
ACTION: If no, flag as estimated (J) all data for
which a method blank was not analyzed. NOTE:
If only one blank was analyzed for more than 20
samples, the first 20 samples analyzed do not have
to be flagged as estimated (J).
2. Has the method blank for VOAs been run on
the same GC/MS or GC system as the sample?
ACTION: If no, flag as estimated (J) all results
that do not have an associated blank.
3. Are the concentrations of any blank
contaminants for VOAs greater than the RDL
of any VOA compound?
ACTION: For sample values reported at less than
10 times the blank contamination level for
methylene chloride, acetone, toluene and 2-
butanone and 5 times the blank contamination
level for other VOA compounds, flag as undetected
(U).
NOTE: In instances where more than one blank is
associated with a given sample, quantification should
be based upon a comparison with the associated
blank having the highest concentration of a
contaminant. The results must not be corrected by
subtracting any blank value.
5.6 Compound Identification
1. Verify the following:
-the Relative Retention Time (RRT) of reported
compounds is within 0.06 RRT units of the
standard RRT.
-all ions present in the standard mass spectrum at
a relative intensity greater than 10% are also
present in the sample mass spectrum.
-all ions present in the sample, but not present in
the standard are accounted for.
-relative intensities of the ions specified above as
present in the sample and at a relative intensity
greater than 10% in the standard, agree within
20% between the sample and the standard spectra.
ACTION: Use professional judgement to determine
acceptability of the data if the above criteria were
not all met. If it is determined that incorrect
identifications were made, all such data should be
reported as not detected with an estimated (J)
quantitation limit. Ions greater than 10% in the
sample spectrum but not present in the standard
spectrum must be considered and accounted for.
31
-------
5.6.1 Tentatively Identified Compounds
1. Verify the following:
-all ions presenting the reference mass spectrum
with a relative intensity greater than 10% are
present in the sample spectrum.
-relative intensities specified above agree within
20% between the sample and the reference
spectra.
-molecular ions present hi the reference
spectrum are present in the sample spectrum.
-all tentatively identified compounds are
reported with estimated quantitation and
detection limits.
ACTION: Use professional judgement to
determine acceptability of the data if the above
criteria are not all met. If data are considered to
be unacceptable, the tentative ID should be
changed to "unknown".
5.7 Compound Ouantitation and Reported Detection
Jrnit
1. Verify that the reported values, both positives
and non-detects, have been correctly adjusted to
reflect all dilutions, concentrations, splits,
cleanup procedures, dry weight factors, an any 5.9
other adjustments that have not been accounted
for by the method.
VGA for waters: ug/L =
.
(Ais)(RF)(V0)
Low level VOA for soils:
ug/kg =
(Ais)(RF)(W5)(D)
High level VOA for soils:
ug/kg = {
(Ais)(RF)(Ws)(D)(Vi)
Au =
Is =
RF =
V0 =
Ws =
D =
Vt =
area of characteristic ion for compound being
measured
area of characteristic; ion for the internal
standard ,|
amount of internal standard added (ng)
daily response factor . for compound being
measured j
volume of water purged (ml)
weight of sample extracted (g)
(100 - % moisture)/]100 or 1 on wet weight
basis ;
volume of total extract (ul)
= volume of extract added (ul) for purging
ACTION: If incorrect values
is essential that the correct
The reviewer should contact
have been reported, it
values be determined.
:he laboratory to verify
any corrections made to the data.
5.8 Performance Evaluation Samples
1. Were recovery limits within those set by the
EMSL lab? i
1
I
I
ACTION: If outside the! limits, review on a
compound by compound b'asis. If 50% of the
compounds are outside of confidence limits or were
misidentified, all sample results should be rejected
(R).
Overall Assessment of Data I
[
It is appropriate for the Idata reviewer to use
professional judgment and i express concerns and
comments on the validity of the overall data package
for a case. This is particularly appropriate for cases
in which there are several QC criteria out of
specification. The additive nature of QC factors
which are out of specificatioh is difficult to assess in
an objective manner, but| the reviewer has a
responsibility to inform the user about data quality
32
-------
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer
would be greatly assisted in this endeavor if the
data quality objectives were provided.
5.10 Optional PC Checks
5.10.1 Surrogate Recovery
1. If either one or more VOA surrogates were
outside of specifications for any sample or
blank, were the appropriate samples
reanalyzed?
ACTION: If initial analysis and reanalysis both
have two or more surrogates outside of
specifications for samples or blanks, estimate (J)
all quantitation results, including detection limits.
2. Does any surrogate have less than 10%
recovery?
ACTION: If yes, flag as estimated (J) positive
results for that fraction; flag negative results as
rejected (R).
6.0 Pesticides/PCBs
6.1 Sample Holding Times
1. Were any of the sample holding times
exceeded?
Sample Holding Times from date of sample
collection:
Water - 7 days to extraction
Soil, sludge, sediment - 14 days to extract
All - analyze within 40 days after extraction
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL), based
on the reviewers professional judgement and the
nature of the sample and analyte. Values that are
less than the IDL can be flagged as estimated (UJ)
or rejected (R) based on the reviewers professional
judgement and the nature of the sample and analyte.
Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
6-2 Instrument Performance
1. Check the raw data to verify that DDT retention
time is greater than 12 minutes on the standard
chromatogram and that there is adequate
resolution (> 25%) between peaks of other
pesticide standard compounds.
ACTION: If the retention time of DDT is less than
12 minutes (except on OV-1 and OV-101), a close
examination of the chromatography is necessary to
ensure that adequate separation of individual
components is achieved. If adequate separation is
not achieved, flag all affected compound data as
rejected (R).
2. Check raw data to verify that retention time
windows are reported and that all pesticide
standards are within the established retention time
windows.
ACTION: If the standards do not i'all within the
retention time windows, professional judgement
should be used in the evaluation of associated sample
results.
3. Check the raw data to verify that the percent
breakdown for endrin and 4,4'-DDT, or the
33
-------
combined percent breakdown, does not exceed
20% in all Evaluation Standard Mix B analysis.
ACTION: If the DDT breakdown is greater than
20%, beginning with the last in-control standard,
flag all results for DDT as estimated (J). If DDT
was not detected,but DDD and DDE are positive,
then flag the quantitation limit for DDT as rejected
(R). Flag results for DDD and/or DDE as
presumptively present at an estimated quantity
(NJ).
If the endrin breakdown is greater than 20%, flag
all quantitative results for endrin as estimated (J).
If endrin was not detected, but endrin aldehyde
and endrin ketone are positive, then flag the
quantitation limit for endrin as rejected (R). Flag
results for endrin ketone as presumptively present
at an estimated quantity (NJ).
4. Check the raw data to verify that the percent
difference in retention time for the surrogate
dibutylchlorendate (DEC) in all standards and
samples in <. 2.0% for packed column analysis,
<. 0.3% for capillary column analysis, and .<.
1.5% for wide-bore capillary column analysis.
ACTION: If any of the percentages are greater
than indicated, the analysis may be flagged as
rejected (R) for that sample. Qualification of the
data is left up to the professional judgement of the
reviewer.
63 Initial and Continuing Calibration Verification
1. Verify that the %RSD of the calibration factor
for aldrin, endrin, DEC and DDT are less than
or equal to 10% for the initial calibration
linearity check.
ACTION: If criteria for linearity is not met, flag
all associated quantitative results as estimated (J).
%RSD = s x 100
x
where:
s = standard deviation of 5 response factors
x = mean of 5 response factors
2. If toxaphene or DDT series was identified and
quantitated, verify that a three-point calibration
was established.
ACTION: If no, flag as estimated (J) positive results
for toxaphene or DDT. ;
I'
3. Verify the proper 72-hour analytical sequence as
follows: |
|
Standard Mix A, Standard Mix B, Standard Mix
C (individual standard mix A, individual standard
mix B, may be one mix), Toxaphene, Aroclors
1016/1260, (Aroclor 1221, Aroclor 1232, once per
month), Aroclor 1242, Aroclor 1248, Aroclor 1254,
5 samples, Standard Mix B,i5 samples, Individual
Standard Mix A or B, 5 samples, repeat starting
from Standard Mix B, must end with individual
Standard Mix A and B. !
[
I
ACTION: If the proper standards have not been
analyzed and the sequence followed, use professional
judgement to determine the severity of the effect and
qualify the data accordingly. |
i
4. Review the pesticide sample1 data to verify whether
the standard was used as a' quantitation standard
or as a confirmation standard.
i
ACTION: If the %D for standard analysis is greater
than 15% on the quantitation column or greater than
20% on the confirmation column, flag all associated
positive sample results as estimated (J).
34
-------
6.4 Error Determination
See Part I - Section 2.8 for QA samples to be used
for error determination.
6.4.1 Determination of Bias (% Recovery - Optional for
QA-2; Mandatory for QA-3)
6.4.1.1 Percent Recovery
1. Were at least eight spiked sample
replicates for the matrix of Interest
analyzed at the required frequency?
ACTION: If no, flag as recovery not
determined TRND) all data for which
spiked samples were not analyzed.
2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable control
limits (80% to 120%)?
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x ^QQ
Spike cone, added
ACTION: If recoveries are within
applicable control limits, no bias is
considered. If % Recovery is less than
80% or greater than 120%, the sample
data should be flagged with a (J) estimate
and a corresponding (-) or (+) sign to
show direction of the bias. Adjustment of
sample values should be considered
whenever there is consistent evidence of
bias.
6.4.1.2 Adjustment of Sample Values for Bias
values. % bias is the reciprocal value of
% recovery (i.e., for 70% recovery you
have a negative 30% bias). Use the
average % recovery from the total number
of matrix spikes analyzed. This
adjustment approach assumes a spiking
concentration equal to the concentration
found in the sample.
6.4.2 Determination of Precision (Optional for QA-2;
Mandatory for QA-3)
6.4.2.1 Replicate Analysis
1. Was a minimum of eight replicates
analyzed? If yes, determine coefficient of
variation. If no, flag data with precision
not determined (PND}. for which replicate
samples were not analyzed.
6.4.2.2 Coefficient of Variation (Percent Relative Standard
Deviation)
1. The coefficient of variation (CV) is used
in determining the precision of standard
deviation. The CV expresses the standard
deviation as a percentage of the mean
(average) value of the replicate values.
The CV is used to determine a false
positive or false negative value for results
that are respectively greater than or less
than a decision level concentration.
Determine the coefficient of variation
using the following equation:
CV = s x 100
XDL
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to or from the sample
35
-------
where:
X = the decision level concentration
DL
s = the sample standard deviation
given by the equation:
*Note: When using a programmable calculator
or computer statistics software, be sure the
above equation with (n - 1) is used and not (n)
by itself. The equation using (n) is to determine
the population standard deviation (a) rather
than the sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as
follows:
False positive value = Decision level value +
(CV x decision level)
False negative value = Decision level value -
(CV x decision level)
Example:
For an decision level = 50 ppm and CV = 20%
False positive value = 50 ppm + (20% x
50 ppm)
= 50 ppm + (10 ppm)
= 60 ppm
False negative value = 50 ppm - (20% x
50 ppm)
= 50 ppm - (10 ppm)
= 40 ppm
For the above false positive example, any value
between 50 ppm and 60 ppm are considered
suspect and should be reanalyzed. Values above 60
ppm are considered actionable. In many cases, false
positives have been considered actionable by the
Agency for safety reasons. Hoiwever, depending on
the action to be taken, this] can be costly and
unjustifiable. Consult the QA |plan for intended use
of data and data quality objectives.
i.
For the above false negative j example, any values
between 40 ppm and 50 ppm are considered suspect
and should be reanalyzed. Values below 40 ppm are
considered non-actionable. |In most cases, the
decision maker will be using th'e false negative value
as his decision level and not be concerned about the
false positive value. Whenever sample values need
to be corrected for both bias and precision, first
correct the value for bias, then correct the biased
value for precision. |
6.5 Blanks |
!
1. Verify that method blank analysis has been
reported per matrix, per concentration level, at
the proper frequency, for each GC system used
to analyze samples, for each extraction batch.
ACTION: If the proper type and frequency of
method blank have not , been analyzed, use
professional judgement to determine the effect on
the data. - I
2. Verify that all blank analyses contain less than the
Required Detection Limits (RDL) of any pesticide
or interfering peak.
ACTION: Any pesticide detected in the sample and
also detected in any associated blank, must be
qualified as non-detect (U) when the sample
concentration is less than 5X the blank
concentration.
NOTE: In instances where more than one blank is
associated with a given sample, quantification should
36
-------
be based upon a comparison with the associated
blank having the highest concentration of a
contaminant. The results must not be corrected by
subtracting any blank value.
6.6 Compound Identification
1. Verify that positive identifications have GC/MS
confirmation or dissimilar column analysis (the
3% OV-1 column cannot be used for
confirmation if both dieldrin and DDE are
identified).
ACTION: If the qualitative criteria for dual
column or GC/MS confirmation were not met, all
reported positive results should be flagged as
presumptively present at an estimated quantity
(NJ).
2. If multipeak pesticides (chlordane and
toxaphene)/PCBs were reported, were the
retention times and relative peak height ratios
of major component peaks compared against
the appropriate standard chromatograms.
ACTION: If multipeak pesticides/PCBs exhibit
marginal pattern-matching quality professional
judgement should be used to establish whether the
differences are attributable to environmental
"weathering". If the presence of a multipeak
pesticide/PCB is strongly suggested, results should
be reported as presumptively present (N).
3. Verify that the sample chromatogram agree
with the correct daily standard chromatogram,
and that the retention time windows match.
ACTION: If the chromatograms do not agree, and
the retention time windows vary significantly, the
reviewer must use professional judgement to
determine the flags that should be applied and the
usefulness of the data.
6-7 Compound Ouantitation and Reported Detection
1. .Verify that the reported values, both positives and
non-detects, have been correctly adjusted to reflect
all dilutions, concentrations, splits, cleanup
procedures, dry weight factors, an any other
adjustments that have not been accounted for by
the method.
Pesticide/PCBs for waters: ug/L =
Pesticide/PCBs for soils: ug/kg =
(As)(Ws)(D)(Vi)
AX = area of quantitation peak(s)
Is = amount of standard injected (ng)
Vt< = volume of total extract (ul)
Vj = volume injected (ul)
Vs = volume of sample (ml)
Ws = weight of sample extracted (g)
D = (100 - % moisture)/100 or 1 for wet weight
basis
As = Area of external standard
ACTION: If incorrect values have been reported, it
is essential that the correct values be determined.
The reviewer should contact the laboratory to verify
any corrections made to the data.
37
-------
6.8 Performance Evaluation Samples
1. Were recovery limits within those set by the
EMSL lab?
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
6.9 Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data
package for a case. This is particularly appropriate
for cases in which there are several QC criteria out
of specification. The additive nature of QC factors
which are out of specification is difficult to assess
in an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer
would be greatly assisted in this endeavor if the
data quality objectives were provided.
6.10 Optional OA Checks
6.10.1 Snrrnorata Recovery
1. Verify that the recoveries are within the control
limits.
ACTION: If not, check the raw data for possible
interferences.
2. If recoveries are out of control limits, use
professional judgement
appropriate action.
to determine the
ACTION: If zero surrogate, pesticide recovery is
reported, determine whether tjhe surrogate is outside
its retention time window, it yes, use professional
judgement in the evaluation1 of this data. If the
surrogate is not present, flag; all negative results as
rejected (R).
7.0 PCBs
7.1 Sample Holding Times
1. Were any of the sample holding times exceeded?
I
Sample Holding Times ifrom date of sample
collection: !
Water - 7 days to extract [,
Soil, sediment, sludges - l|4 days to extract
i
All - analyze within 40 days after extraction
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL), based
on the reviewers professional judgement and the
nature of the sample and arialyte. Values that are
less than the IDL can be flagged as estimated (UJ)
or rejected (R) based on thp reviewers professional
judgement and the nature ofj the sample and analyte.
i
l
Because of their long sh'elf lives, performance
evaluation samples do no|t have any associated
holding times.
1.1 Instrument Performance !
1. Examine standard chromatograms to assure
adequate quantitation pe'ak resolution.
38
-------
ACTION: If there is inadequate peak separation
(<25% quantitation peak resolution), flag the data
as rejected (R).
2. Examine raw data and spot check the surrogate
compound retention times.
ACTION: If the retention time shift for the
surrogate compound exceeds 2.0% for packed
columns, 0.3% for capillary columns, 1.5% for
wide-bore capillary columns, the data may be
rejected (R), but the qualification is left up to the
professional judgement of,the reviewer.
73 Initial and Continuing Calibration Verification
1. Verify that the Aroclors of interest have been
analyzed at a minimum of three different
concentrations (e.g., Aroclor 1260 analyzed at
1.0, 5.0 and 10.0 ppm).
ACTION: If no, flag data as estimated (J).
2. Verify that the %RSD of the calibration factor
for all Aroclors is less than or equal to 10% for
the initial linearity check.
%RSD = sx 100
x
where:
s = standard deviation of 5 response factors
x = mean of 5 response factors
ACTION: If criteria for linearity is not met, flag
all associated quantitative results as estimated (J).
3. Verify that the continuing calibration for each
Aroclor of interest was analyzed daily.
ACTION: If no, flag all associated sample results
as estimated (J).
4. Verify %D between calibration factors.
ACTION: If the %D for standard analysis is greater
than 15% on the quantitation column or greater than
20% on the confirmation column, flag all associated
positive sample results as estimated (J).
7.4 Error Determination
See Part I - Section 2.8 for QA samples to be used
for error determination.
7.4.1 Determination of Bias (% Recovery - Optional for
QA-2; Mandatory for QA-3)
7.4.1.1 Percent Recovery
1. Were at least eight spiked sample
replicates for the matrix of interest
analyzed at the required frequency?
ACTION: If no, flag as recovery not determined
(RND) all data for which spiked samples were not
analyzed.
. .'. . 2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable conlrol
limits (80% to 120%)?
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x ^QQ
Spike cone, added
ACTION: If recoveries are within applicable
control limits, no bias is considered. If %
Recovery is less than 80% or greater than
120%,the sample data should be flagged with a
(J) estimate and a corresponding (-) or ( + ) sign
to show direction of the bias. Adjustment of
sample values should be considered whenever
there is consistent evidence of bias.
39
-------
r
7.4.1.2 Adjustment of Sample Values for Bias
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to or from the sample
values. % bias is the reciprocal value
of % recovery (i.e., for 70% recovery
you have a negative 30% bias). Use
the average % recovery for the total
number of matrix spikes analyzed.
This adjustment approach assumes a
spiking concentration equal to the
concentration found in the sample.
7.4,2 Determination of Precision (Optional for QA-2;
Mandatory for QA-3)
7.4.2.1 Replicate Analysis
1. Was a minimum of eight replicates
analyzed? If yes, determine coefficient
of variation. If no, flag data with
precision not determined fPNDV for
which replicate samples were not
analyzed.
7.4.2.2 Coefficient of Variation (Percent Relative
Standard Deviation)
1. The coefficient of variation (CV) is
used in determining the precision of
standard deviation. The CV expresses
the standard deviation as a percentage
of the mean (average) value of the
replicate values. The CV is used to
determine a false positive or false
negative value for results that are
respectively greater than or less than
a decision level concentration.
Determine the coefficient of variation
using the following equation:
CV = s x.lOO
where:
XDL =
decision level concentration
s = the sample standard deviation given
by the equation: |
,14
- x)f/(n , 1)]
*Nqte: When using a programmable
calculator or computer statistics software, be
sure the above equation with (n - 1) is used
and not (n) by itself. I The equation using (n)
is to determine the population standard
deviation (a) rather than the sample standard
deviation (s).
Apply the CV to jthe decision level to
determine the false negative or false positive
value as follows: j
. [
False positive value = Decision level value +
r" * (CV x decision level)
f - ; . . - . - . . ,\: . .-
False negative value = Decision level value -
-. (CV x decision level)
.,--.... . |:' - .
Example: !
r
i
For an decision level = 50 ppm and CV =
. 20% j:
|
False positive value = 50 ppm + (20% x 50 ppm)
= 50 ppm 4- (10 ppm)
= 60 ppm !
False negative value = 50 ppm - (20% x 50 ppm)
= 50 ppm -j (10 ppm)
= 40 ppm |.
I.
For the above false positive example, any value
between 50 ppm and 60 ppm fare considered suspect
40
-------
,7.5
and should be reanalyzed. Values above 60 ppm
are considered actionable. In many cases, false
positives have been considered actionable by the
Agency for safety reasons. However, depending on
the action to be taken, this can be costly and
unjustifiable. Consult the QA plan for intended
use of data and data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below
40 ppm are considered non-actionable. In most
cases, the decision maker will be using the false
negative value as his decision level and not be
concerned about the false positive value.
Whenever sample values need to be corrected for
both bias and precision, first correct the value for
bias, then correct the biased value for precision.
Blanks
1. Verify that method blank analysis has been
, , reported per matrix, per concentration level, at
the proper frequency, for each GC system used
to analyze samples, for each extraction batch.
ACTION: If the proper type and frequency of
method blank have not been analyzed, use
professional judgement to determine the effect on
the data.
NOTE: In instances where more than one blank
is associated with a given sample, quantification
should -be based upon a comparison with the
associated blank having the highest concentration of
a contaminant. The results must not be corrected by
subtracting any blank value.
7.6 Compound Identification
1. Review the data to confirm that positive results
were identified using the correct retention time
window, peak height ratio, and "fingerprint"
pattern. Determine which peak(s) were used to
quantitate each Arbclor and verify that the finger-
print pattern matches the standard chromatogram.
ACTION: If the reported positive results were not
identified correctly, professional judgement should
be used to qualify the data.
2. Verify that dual column confirmation of positive
results identify the same Aroclor or that the lab
performed GC/MS confirmation of PCB results
that were greater than 10 ng/ul.
ACTION: If the .qualitative criteria for dual column
or GC/MS confirmation were not met, all reported
positive results should be flagged as presumptively
present at an estimated quantity (NJ).
2. Verify that all blank analyses contain less than
the Required Detection Limits (RDL) of any
PCB or interfering peak.
ACTION: Any PCB detected in the sample and
also detected in any associated blank, must be
qualified as non-detect (U) when the sample
concentration is less than 5 times the blank
concentration.
41
-------
7.7 Compound Ouantitation and Reported Detection
Limits
1. Verify that the reported values, both positives
and non-detects, have been correctly adjusted to
reflect all dilutions, concentrations, splits,
cleanup procedures, dry weight factors, and any
other adjustments that have not been accounted
for by the method.
PCBs for waters: ug/L = fA..₯LW,1
PCBs for soils: ug/kg = (AjfLVV,)
(As)(Ws)(D)(Vi)
7.9 Overall Assessment of Data
I
It is appropriate for tie I data reviewer to use
professional judgment and! express concerns and
comments on the validity of the overall data package
for a case. This is particularly appropriate for cases
in which there are several QC criteria out of
specification. The additive, nature of QC factors
which are out of specification is difficult to assess in
an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer would
i.
be greatly assisted in this endeavor if the data quality
objectives were provided. !
Ax = area of quantitatibn peak(s)
I, = amount of standard injected (ng)
V, « volume of total extract (ul)
Vj = volume injected (ul)
V, = volume of sample (ml)
W, = weight of sample extracted (g)
D = (100 - % moisture)/100
A, = Area of external standard
ACTION: If incorrect values have been reported,
it is essential that the correct values be determined.
The reviewer should contact the laboratory to
verify any corrections made to the data.
7.8 Performance Evaluation Samples
1. Were recovery limits within those set by the
EMSL lab?
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
7.10 Optional PC Checks j
1
7.10.1 Surrogate Recovery !
i
1. Verify that the recoveries are within the control
limits. !
i
' i
. ACTION: If not, check the raw data for possible
;-.,. . interferences.
2. If recoveries are out of control limits, use
professional judgement to determine the
appropriate action. |
ACTION: If zero surrogate pesticide recovery is
reported, determine whetherithe surrogate is outside
its retention time window. If yes, use professional
judgement in the evaluation of this data. If the
surrogate is not present, flag all negative results as
rejected (R). !
42
-------
8.0 2,3,7,8-TCDD
m/z
Ion abundance criteria (continued)
8.1 Sample Holding Times
1. Were any of the sample holding times
exceeded?*
To extract - 6 months from sample collection
To analysis - 40 days from extraction
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL).
Values that are less than the IDL can be flagged
as estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
82 Instrument Performance
1.'Verify that a performance'check "solution was
run at the beginning of each 8-hour shift and at .
the end of the final 8-hour period.
ACTION: If no, use professional judgement to
qualify data.
2. Have the ion abundance criteria been met for
each instrument used?
m/z
51
68
69
70
127
Ion abundance criteria
30-60% of mass 198
Less than 2% of mass 69
(reference only)
Less than 2% of mass 69
40-60% of mass 198
197
198
199
275
365
441
442
443
Less than 1% of mass 198
Base peak, 100% relative abundance
5-9% of mass 198
10-30% of mass 198
Greater than 1% of mass 198
Present but less than mass 443
Greater than 40% of mass 198
17-23% of mass 442
ACTION: If no, use professional judgement to flag
all associated data.
3. Is the resolution of the valley between 2,3,7,8-
TCDD and the peak representing all other TCDD
isomers <. 25%? (where, Valley (%) = X/Y x
100 and X is measured from the valley of the least
resolved adjacent isomer to the baseline, Y =
peak height of 2,3,7,8-TCDD).
ACTION: If no, use professional judgement to
qualify all positive sample data associated with the
standard.
Initial Calibration
1. Verify the following:
-the five 2,3,7,8-TCDD standards have been run.
-the ratios of ions 320 to 322 for 2,3,7,8-TCDD
and 332 to 334 for 13C!2-2,3,7,8-TCDD is >. 0.67
and <_ 0.87.
-signal-to-noise ratios for ions 257, 320, 322 and
328 is >_ 2,5 and the signal to noise ratios for ions
332 and 334 is >. 10.
-the ions 257, 320, 322 for 2,3,7,8-TCDD reached
a maximum within three seconds of 13C12-TCDD
ions 332 and 334.
43
-------
-during the unlabeled 2,3,7,8-TCDD calibration
the percent Relative Standard Deviation
(%RSD) of relative response factors for the five
calibration concentrations is less than or equal
to 15%.
-during the ^Cl^SJ.S-TCDD calibration the
%RSD of relative response factors for the three
calibration concentrations is less than or equal
to 15%.
ACTION: If the calibration curve standards fail
the acceptance criteria, use professional judgement
to qualify associated data.
8.4 Continuing Calibration
1. Verify the following:
-the calibration standard has been run for every
eight hour shift.
-the ratios of ions are 320 to 322 for 2,3,7,8-
TCDD and 332 to 334 for 13C12-2,3,7,8-TCDD >.
0.67 and <. 0.87.
-the signal to noise ratios for ions are 257, 320,
322 and 328 >. 25 and the noise ratios for ions
332 and 334 >. 10.
-the ions are 257, 320, 322 for 2,3,7,8-TCDD
reached a maximum within three seconds of
13C,2-TCDD ions 332 and 334.
-the percent difference of the relative response
factor is ±. 30% of the initial calibration.
ACTION: If the calibration standard fails the
above acceptance criteria, use professional
judgement to qualify associated data.
8.5 Error Determination i
t
See Part I - Section 2.8 for QA samples to be used
for error determination. J
= i.
8.5.1 Determination of Bias (% Recovery - Optional for
QA-2; Mandatory for QA-3)
I
8.5.1.1 Percent Recovery |
j
1. Were at least ; eight spiked sample
replicates for the matrix of interest
analyzed at the required frequency?
ACTION: If no, [flag as recovery not
determined (RND^ all data for which spiked
samples were not analyzed.
\
I
2. Determine the average recovery of the
eight spiked replicates. Is the average
recovery within the applicable control
limits (80% to 120%)?
i -
% recovery for a single spiked sample =
- i'
Spiked sample cone. - Sample cone. x JQQ
Spike cone, added
i
, ACTION: If recoveries are within applicable
control limits, no bias is considered. If %
Recovery is less than 80% or greater than
120%, the sample data should be flagged with
a (J) estimate and a corresponding (-) or (+)
sign to show direction of the bias.
Adjustment of sample values should be
considered whenever there is consistent
evidence of bias. '
i
i
8.5.1.2 Adjustment of Sample Values for Bias
I
1. Depending on bias direction, add or
subtract the value (% Bias x spike
concentration) to, or from the sample
values. % bias is the reciprocal value of
44
-------
% recovery (i.e., for 70% recovery you
have a negative 30% bias). Use the
average % recovery from the total
number of matrix spikes analyzed.
This adjustment approach assumes a
, spiking concentration equal to the
concentration found in the sample.
8.5.2 Determination of Precision (Optional for QA-2;
Mandatory for QA-3)
8.5.2.1 Replicate Analysis
1. Was a minimum of eight replicates
, analyzed? If yes, determine
' , coefficient of variation. If no, flag
i data with, precision not determined
CPND). for which replicate samples
.were not analyzed.
8.5.2.2 Coefficient of Variation (Percent
Relative Standard Deviation)
1. The coefficient of variation (CV) is
used in determining the precision of
standard .deviation. The CV expresses
the standard deviation as a percentage
-'." of the mean {average) value of the
'.* replicate values. The CV is used to
determine a false positive or false
;, negative value for results that are
respectively greater than or less than
a decision level concentration.
Determine the coefficient of variation
using the following equation:
: CV = s .x-100
where: . ; '
XDL .= .the decision level concentration
s = the sample standard deviation given by
the equation:
' ' ' *
s* = [ft - x)2/(N - l)f2
*Note: When using a programmable calculator
or computer statistics software, be sure the above
equation with (n - .1) is used and not (n) by itself.
The equation using (n) is to determine the
population standard deviation (a) rather than the
sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as follows:
False positive value = Decision level value + (CV
x decision level)
False negative value = Decision level value - (CV
x decision level) ' ,,
.^Example:. .
For an decision level = 50 ppm and CV = 20%
False positive value = 50 ppm.+ (20% x 50 ppm)
=: 50 ppm + (10 ppm)
= 60 ppm
False negative value = 50 ppm - (20% x
nnm - (10 ppm)
50 ppm
= 40 ppm
50 ppm)
For the above..false positive example, any value
between 50 ppm and 60 ppm are considered suspect
and should be reanalyzed. Values above 60 ppm are
considered actionable. In many cases, false positives
45
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have been considered actionable by the Agency for
safety reasons. However, depending on the action
to be taken, this can be costly and unjustifiable.
Consult the QA plan for intended use of data and
data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below
40 ppm are considered non-actionable. In most
cases, the decision maker will be using the false
negative value as his decision level and not be
concerned about the false positive value.
Whenever sample values need to be corrected for
both bias and precision, first correct the value for
bias, then correct the biased value for precision.
8.6 Blanks
1. Has a method blank, spiked with the internal
standards, been analyzed with each case?
ACTION: If the method blank contains
contaminants at the method detection limit of the
matrix of interest, the blank must be reanalyzed.
If the contaminated method blank was extracted
along with a batch of samples the associated
positive samples must be reanalyzed. If the
samples were not reanalyzed or if contamination is
present in the second analysis, all positive sample
results less than 5 tunes the concentration in the
blank are flagged as non-detects (U).
2. Has a reagent blank been analyzed along with
each case?
ACTION: The reagent blank should be free of
contamination. If the level is > 0.10 ppb, use
professional judgement to qualify associated data.
8.7 Internal Standard Requirements
I
1. Did ion 332 or 334 fail the relative ion intensity
criteria (.> 0.67 and <. 0.87)? If yes, was the
sample reanalyzed? '
ACTION: If initial analysis and reanalysis both have
ions 332 or 334 outside the relative ion intensity
criteria, reject all quantitation results, including
detection limits. I
8.8 Identification of 23.7.8-TCDD
1. Verify the following:
-the retention time of the, sample component is
within three seconds of the retention tune of the
13C12 - 2,3,7,8-TCDD. j
I '
[
-the integrated ion currents detected for m/z 257,
320, and 322 maximize simultaneously.
i
i
-the ion ratio of 320 to 322| and 332 to 334 is >. .67
and < .87. !
: i
, .1.
. , - .;:!.', '
-the integrated ion current for each analyte and
surrogate compound (m/z; 257, 320, 322 and 328)
are at least 2.5 times background noise.
[
I
-internal standard ions are at least 10 times
background noise. (The integrated ion current or
the internal standard ions must not saturate the
i-
detector.)
i
-if the above requirements were not met, then
reanalyze the samples.
ACTION: If initial analysis aiid reanalysis both have
the sample outside the abovp limits, 2,3,7,8-TCDD
was not qualitatively identified, reject (R) all positive
results. i
46
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ACTION: If no, reject all quantitation results,
including detection limits.
8.9 Performance Evaluation Samples
1. Were recovery limits within those set by the
EMSLlab?
ACTION: For 37 Cl4-2,3,7,8-TCDD the ion 328
must have a signal to noise ratio of >_ 2.5. The
surrogate recovery must be >_ 60 and <_ 140 percent.
If the signal to noise ratio for ion 328 does not meet
acceptance criteria, reject positive and ND data. If
surrogate recovery is outside acceptance limits, use
professional judgement to qualify associated data.
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
8.10 Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data
package for a case. This is particularly appropriate
for cases in which there are several QC criteria out
of specification. The additive nature of QC factors
which are out of specification is difficult to assess
in an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer
would be greatly assisted in this endeavor if the
data quality objectives were provided.
8.11 Optional OC Checks
9.0 Generic Data Validation Procedures
9.1 GC Analyses (i.e., Herbicides, Organophosphate,
Pesticides)
9.1.1 Sample Holding Times
1. Were any of the sample holding times exceeded?*
Sample holding times can generally be found in
the analytical method, or in the appropriate
reference, such as the 40CFR Part 136, MCAWW,
or SW846.
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL). Values
that are' less than the IDL can be flagged as
estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
8.11.1 Surrogate Recovery
9.1.2 Instrument Performance
1. Was surrogate outside of specifications for any
samples? If yes, were the appropriate samples
reanalyzed?
1. Check the raw data to verify that there is adequate
resolution (> 25%) between peaks of the standard
compounds.
47
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ACTION: If adequate separation is not achieved,
flag all affected compound data as rejected (R).
2. Check raw data to verify that retention time
windows are reported and that all standard
compounds are within the established retention
time windows.
ACTION: If the standard compounds do not fall
within the retention time windows, professional
judgement should be used in the evaluation of
associated sample results.
9.1.3 Initial and Continuing Calibration Verification
1. Verify that the %RSD of the calibration factor
for the calibration compounds are less than or
equal to 10% for the initial calibration linearity
check.
ACTION: If criteria for. linearity is not met, flag
all associated quantitative results as estimated (J).
2. Verify the proper analytical sequence was run as
required. ,
ACTION: If the proper standards have not been
analyzed and the sequence followed, use
professional judgement to determine the severity
of the effect and qualify the data accordingly.
3. Review the sample data to verify whether a
standard was used as a quantitation standard or
as a confirmation standard.
ACTION: If the %D for standard analysis is
greater than 15% on the quantitation column or
greater than 20% on the confirmation column, flag
all associated positive sample results as estimated
(J).
9.1.4 Error Determination j
See Part I - Section 2.8 for QA samples to be used
for error determination. J
. - .'--. [
9.1.4.1 Determination of Bias (% Recovery -
Optional for QA-2; Mandatory for
QA-3) I
.... -, f
i
9.1.4.1.1 Percent Recovery
i
1. Were at least eight spiked sample
replicates for the matrix of
interest analyzed "at the required
frequency?
ACTION: if no, flag as recovery
not determined fRNDI all data for
which spiked samples were not
analyzed.
2. 'Determine the average recovery
of the eight spiked replicates. Is
the average recovery within the
* applicable control limits (80% to
120%)? i
% recovery for a single spiked sample =
Spiked sample cone. '<- Sample cone. x IQQ
Spike cone, added
; , I
ACTION: Jf recoveries are within
applicable control limits, no bias is
considered.^ If % Recovery is less
than 80% or greater than 120%, the
sample data should be flagged with
a (J) estimate and a corresponding
(-) or (+) s'ign to show direction of
the bias. [Adjustment of sample
values should be considered
48
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whenever there is consistent
evidence of bias.
9.1.4.1.2 Adjustment of Sample Values
for Bias
1. Depending on bias direction,
add or subtract the value (%
Bias x spike concentration)
to or from the sample
values. , This adjustment
approach assumes a spiking
concentration equal to the
concentration found in the
sample.
9.1.4.2 Determination of Precision (Optional for
QA-2; Mandatory for QA-3)
9.1.4.2.1 Replicate Analysis
1. Was a minimum of four
replicates for QA-2 or eight
replicates for QA-3
analyzed? If yes, determine
. .,,-;,; , coefficient of variation. If
no, flag data with precision
' , . . ' not determined (PND), for
which replicate samples were
not analyzed.
9.1.4.2.2 Coefficient of Variation
(Percent Relative
Deviation)
Standard
1. The coefficient of variation
(CV) is used in determining
the precision of standard
deviation. The CV expresses
the standard deviation as a
percentage of the mean
. (average) value of the
replicate values. The CV is
used to determine a false
positive or false negative value
for results that are respectively
greater than or less than a
decision level concentration.
Determine the coefficient of
variation using the following
equation:
CV = s x 100
X '
where:
XDL = the decision level
concentration
s = the sample standard
deviation given by the equation:
s* =
*Note: When using a programmable calculator
or computer statistics software, be sure the above
equation with (n -1) is used and not (n) by itself.
The equation using (n) is to determine the
population standard deviation (a) rather than the
sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as
follows:
False positive value = Decision level value +
(CV x decision level)
False negative value = Decision level value - (CV
x decision level)
Example:
For an decision level = 50 ppm and CV = 20%
49
-------
False positive value = 50 ppm + (20% x
50 ppm)
= 50 ppm + (10 ppm)
= 60 ppm
False negative value = 50 ppm - (20% x
50 ppm)
= 50 ppm - (10 ppm)
= 40 ppm
For the above false positive example, any value
between 50 ppm and 60 ppm are considered
suspect and should be reanalyzed. Values above 60
ppm are considered actionable. In many cases,
false positives have been considered actionable by
the Agency for safety reasons. However,
depending on the action to be taken, this can be
costly and unjustifiable. Consult the QA plan for
intended use of data and data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below
40 ppm are considered non-actionable. In most
cases, the decision maker will be using the false
negative value as his decision level and not be
concerned about the false positive value.
Whenever sample values need to be corrected for
both bias and precision, first correct the value for
bias, then correct the biased value for precision.
9.1.5 Blanks
1. Verify that method blank analysis has been
reported per matrix, per concentration level, at
the proper frequency, for each GC system used
to analyze samples, for each extraction batch.
ACTION: If the proper type and frequency of
method blank have not been analyzed, use
professional judgement to determine the effect on
the data.
2. Verify that all blank analyses contain less than the
Required Detection Limits (RDL) of any
compound or interfering peak.
ACTION: Any compound detected in the sample
and also detected in any associated blank, must be
qualified as non-detect (U) when the sample
concentration is less than 5X the blank concentration.
NOTE: In instances where more than one blank is
associated with a given sample, quantification should
be based upon a comparison with the associated
blank having the highest concentration of a
contaminant. The results must not be corrected by
subtracting any blank value.;
i
9.1.6 Compound Identification i
*^ i
i
r
1. Verify that positive identifications have dissimilar
column analysis.
ACTION: If the qualitative criteria for dual column
were not met, all reported positive results should be
flagged as presumptively present at an estimated
quantity (NJ).
:.'*'.., :'T' i;
\ (
2. If multipeak compounds wefe reported, were the
; retention times and relative peak height ratios of
major component peaks compared against the
appropriate standard chroniatograms.
ACTION: If multipeak compounds exhibit marginal
pattern-matching quality professional judgement
should be used to establish whether the differences
are attributable to environmental "weathering". If
the presence of a multipeak compound is strongly
suggested, results shoul(i be reported as
presumptively present (N). i
i,
3. Verify that the sample difpmatogram agree with
the correct daily standard chromatogram, and that
the retention time windows match.
50
-------
ACTION: If the chromatograms do not agree, and
the retention time windows vary significantly, the
reviewer must use professional judgement to
determine the flags that should be applied and the
usefulness of the data.
9.1.7 Compound Quantitation and Reported Detection
Limits
1. Verify that the reported values, both positives
and non-detects, have been correctly adjusted to
reflect all dilutions, concentrations, splits,
cleanup procedures, dry weight factors, an any
other adjustments that have not been accounted
for by the method.
For waters: ug/L = (AjfLW,)
For soils: ug/L = (AjfLW,)
(As)(Ws)(D)(Vi)
A< = area of quantitation peak(s)
I, = amount of standard injected (ng)
V, = volume of total extract (ul)
V; = volume injected (ul)
Vs = volume of sample (ml)
Ws = weight of sample extracted, (g),
D = (100 ,-,% moisture)/100; or 1 for wet weight
basis . .
AS = Area of external standard
ACTION: If incorrect values have been reported,
it is essential that the correct values be determined.
The reviewer should contact the laboratory to
verify any corrections made to the data.
9.1.8 Performance Evaluation Samples
1. Were recovery limits within those set by the
EMSL lab?
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or
were misidentified, all sample results should be
rejected (R).
9.1.9 Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data package
for a case. This is particularly appropriate for cases
in which there are several QC criteria out of
specification. The additive nature of QC factors
which are out of specification is difficult to assess in
an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer would
be greatly assisted in this endeavor if the data quality
objectives were provided.
92, Non-Metal Inorganic Parameters (i.e., anions, pH,
TOC, nutrients)
9.2.1 Sample Holding Times
1. Were any of the sample holding times exceeded?*
Sample Holding Times can generally be found in
the analytical method, or in the appropriate
reference, such as the 40CFR Part 136, MCAWW,
or SW846.
ACTION: If yes, flag as estimated (J) those values
above the Instrument Detection Limit (IDL). Values
that are less than the IDL can be flagged as
estimated (UJ) or rejected (R) based on the
reviewers professional judgement and the nature of
the sample and analyte.
51
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*Because of their long shelf lives, performance
evaluation samples do not have any associated
holding times.
9.2.2 Initial and Continuing Calibration Verification
1. Verify that the %RSD of the calibration factor
for the calibration compounds are less than or
equal to 10% for the initial calibration linearity
check.
ACTION: If criteria for linearity is not met, flag
all associated quantitative results as estimated (J).
2. Verify the proper analytical sequence was run
as required.
ACTION: If the proper standards have not been
analyzed and the sequence followed, use
professional judgement to determine the severity
of the effect and qualify the data accordingly.
9.2.3 Error Determination
See Part I - Section 2.8 for QA samples to be used
for error determination.
9.2.3.1 Determination of Bias (% Recovery -
Optional for QA-2; Mandatory for
QA-3)
9.2.3.1.1 Percent Recovery
1. Were at least eight spiked
sample replicates for the
matrix of interest analyzed at
the required frequency?
ACTION: If no. flag as recovery
not determined (RND) all data
for which spiked samples were
not analyzed.
2. Determine the average recovery
of the eight spiked replicates. Is
the- average recovery within the
applicable control limits (80% to
120%)? ;
% recovery for a single spiked sample =
Spiked sample cone. - Sample cone. x ^QQ
Spike cone, added
ACTION: If recoveries are within
applicable control limits, no bias is
considered. [If % Recovery is less
than 80% or greater than 120%, the
sample data should be flagged with
a (J) estimate and a corresponding
(-) or (+) sign to show direction of
the bias. Adjustment of sample
values should be considered
whenever there is consistent
evidence of bias.
9.2.3.1.2 Adjustment of Sample Values for
Bias ;'
1. Depending on bias direction, add
or subtract the value (% Bias x
spike concentration) to or from
the sample values. % bias is the
reciprocal value of % recovery
(i.e., for 70% recovery you have
a negative 30% bias). Use the
average % recovery from the
total number of matrix spikes
analyzed. This adjustment
approach i assumes a spiking
concentration equal to the
concentration found in the
sample.
52
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9.23.2 Determination of Precision (Optional for
QA-2; Mandatory for QA-3)
' 9.2.3.2.1 Replicate Analysis
s = the sample standard
deviation given by the equation:
1. Was a minimum of eight
replicates analyzed? If yes,
determine coefficient of
variation. If no, flag data
with precision not determined
(PND). for which replicate
samples were not analyzed.
9.2.3.2.2 Coefficient of Variation (Percent
Relative Standard Deviation)
1. The coefficient of variation
(CV) is used in determining
the precision of standard
deviation. The CV expresses
the standard deviation as a
percentage of the mean
(average) value of the
replicate values. The CV is
used to determine a false
positive or false negative
value for results that are
respectively greater than or
less than a decision level
concentration.
Determine the coefficient of
variation using the following
equation:
CV = s x 100
XDL
where:
XDL = tne decision level
concentration
*Note: When using a programmable calculator
or computer statistics software, be sure the above
equation with (n - 1) is used and not (n) by itself.
The equation using (n) is to determine the
population standard deviation (a) rather than the
sample standard deviation (s).
Apply the CV to the decision level to determine
the false negative or false positive value as follows:
False positive value = Decision level value + (CV
x decision level)
False negative value = Decision level value - (CV
x decision level)
Example:
For an decision level = 50 ppm and CV = 20%
False positive value = 50 ppm + (20% x
50 ppm)
= 50 ppm + (10 ppm)
= 60 ppm
False negative value = 50 ppm - (20% x
50 ppm)
= 50 ppm - (10 ppm)
= 40 ppm
For the above false positive example, any value
between 50 ppm and 60 ppm are considered suspect
and should be reanalyzed. Values above 60 ppm are
considered actionable. In many cases, false positives
have been considered actionable by the Agency for
safety reasons. However, depending on the action to
be taken, this can be costly and unjustifiable.
53
-------
Consult the QA plan for intended use of data and
data quality objectives.
For the above false negative example, any values
between 40 ppm and 50 ppm are considered
suspect and should be reanalyzed. Values below
40 ppm are considered non-actionable. In most
cases, the decision maker will be using the false
negative value as his decision level and not be
concerned about the false positive value.
Whenever sample values need to be corrected for
both bias and precision, first correct the value for
bias, then correct the biased value for precision.
9.2.4 Blanks
1. Verify that method blank analysis has been
reported per matrix, per concentration level, at
the proper frequency, for analytical system used
to analyze samples, for each extraction batch.
ACTION: If the proper type and frequency of
method blank have not been analyzed, use
professional judgement to determine the effect on
the data.
2. Verify that all blank analyses contain less than
the Required Detection Limits (RDL) of any
compound or interfering peak.
ACTION: Any compound detected in the sample
and also detected in any associated blank, must be
qualified as non-detect (U) when the sample
concentration is less than 5X the blank
concentration.
NOTE: In instances where more than one blank
is associated with a given sample, quantification
should be based upon a comparison with the
associated blank having the highest concentration
of a contaminant. The results must not be
corrected by subtracting any blank value.
9.2.5 Compound Quantitation and Reported Detection
Limits
1. Verify that the reported values, both positives and
non-detects, have been correctly adjusted to reflect
all dilutions, concentrations, splits, cleanup
procedures, dry weight i factors, and any other
adjustments that have not been accounted for by
the method. >
ACTION: If incorrect values have been reported, it
is essential that the correct values be determined.
The reviewer should contact the laboratory to verify
any corrections made to the data.
!
9.2.6 Performance Evaluation Samples
i
1. Were recovery limits within those set by the
EMSLlab? '.
ACTION: If outside the limits, review on a
compound by compound basis. If 50% of the
compounds are outside of confidence limits or were
misidentified, all sample results should be rejected
(R). I
i
9.2.7 Overall Assessment of Data
It is appropriate for the data reviewer to use
professional judgment and express concerns and
comments on the validity of the overall data package
for a case. This is particularly appropriate for cases
in which there are several QC criteria out of
specification. The additive nature of QC factors
which are out of specification is difficult to assess in
an objective manner, but the reviewer has a
responsibility to inform the user about data quality
and data limitations. This helps the user to avoid
using data inappropriately, while not precluding
consideration of the data. The data reviewer would
be greatly assisted in this endeavor if the data quality
objectives were provided. (
» U.S. Government Printino Otficat 1990-748-159/004S8
54
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