United States
Environmental Protection
Agency
Office of
Solid Waste and
Emergency Response
Publication 9200.2-16FS
February 1993
v>EPA Quality Assurance for
Superfund Environmental Data
Collection Activities
Office of Emergency and Remedial Response
5203G
Quick Reference Fact Sheet
Many Superfund decisions (both Fund-financed and enforcement) require the collection and evaluation of site-specific
environmental data. Major activities associated with acquiring these data include planning, sample collection and analysis, and
data quality assessment. EPA policy requires the development and implementation of quality assurance (QA) programs to ensure
that these activities generate data of known quality. The overall goal of a QA program is to measure and minimize systematic
sources of error and to monitor conditions of sampling, storage and transport.
The Office of Emergency and Remedial Response (OERR) developed this fact sheet to promote a common understanding of
Superfund QA requirements for site-specific environmental data collection activities. The fact sheet focuses on the preparation
and implementation of sampling and analysis plans (SAPs). Requirements for planning and design, sampling, analysis, quality
control (QC), and data assessment are discussed. The process described is consistent with the integrated site assessment and
accelerated response objectives of the Superfund Accelerated Cleanup Model (SACM). Conforming to these requirements will
help ensure that site managers generate data of known quality.
INTRODUCTION
This fact sheet provides Superfund program participants with an
overview of Superfund QA requirements for data collection
activities. The information is pertinent to all Superfund site
managers, including remedial project managers (RPMs), site
assessment managers (SAMs), and on-scene coordinators
(OSCs). The information also applies to Agency contractors,
states, and potentially responsible parties (PRPs) and their
contractors.
The fafct sheet addresses three primary areas: (1) the mandatory
QA requirements specified in Agency policy documents; (2) Q A
management for Superfund; and (3) the process for developing
SAPs for Superfund activities. The relationship between these
primary areas is depicted in Exhibit 1. References are
identified after each primary section to provide additional
information on discussion topics. These reference materials
contain guidance on the appropriate quality control (QC)
considerations sife rhanagers should include as part of the QA
program.
AGENCY QA POLICY
EPA Order 5360.1 establishes mandatory QA
requirements for Agency environmental data collection
activities. The National Oil and Hazardous Substances
Pollution Contingency Plan (NCP) mandates specific
Superfund QA requirements.
EPA Order 5360.1 and the NCP collectively define Superfund
QA policy for environmental data collection. Both documents
emphasize two requirements. The first is that Superfund
environmental data must be of known quality. The quality of
data is known when all components associated with its
derivation are thoroughly documented and the documentation
has been reviewed by a qualified reviewer. Second, QA plans
are required to attain the first objective. These may be based
on generic or site-specific procedures depending on project
requirements. This section summarizes the QA requirements
contained in each document.
EPA Order 5360.1, entitled, Policy and Program Requirements
to Implement the Mandatory Quality Assurance Program.
describes two major EPA requirements related to
environmental data collection activities. The first is
participation by all EPA organizations in a central QA
program. The goal of the QA program is to ensure the
generation of data of known quality. Basic Agency QA
implementation requirements are summarized in Exhibit 2.
The second major requirement is the development of QA
project plans for all environmental data collection activities.
These plans specify data quality goals acceptable to data users,
and they assign responsibility for achieving these goals.
The NCP establishes the specific requirements used in the
Superfund program to comply with EPA's overall QA policy.
The NCP requires site managers to develop SAPs for the
following Superfund hazardous substance response activities:
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EXHIBIT 1 - SUPERFUND QA OVERVIEW
AGENCY QA POUCY
EPA Order 5360.1
NCP
QA MANAGEMENT
OERR QA Program
Regional QA Programs
Contractor QA Programs
SITE-SPECIFIC QA
Sampling & Analysis
Plan (SAP) Development
• FSP
- QAPP
Project
Objectives
Sampling Sampling Sample
Design Execution Analysis
Assessment
of
Data Quality
• Remedial site inspections
• Removal site evaluations
• Remedial investigation/feasibility studies
These plans document the process for obtaining data of
sufficient quality and quantity to satisfy data users' needs. The
NCP further states that the SAP shall include a field sampling
plan (FSP) and a QA project plan (QAPP). The FSP defines
the number of samples, sample type (matrix), sampling location,
and required analyses. The QAPP describes the policy,
organization, functional activities, and data quality objectives
(DQOs) that site managers need to establish and document prior
to performing any site-specific work. The SAP is a single
document with two separable components - the FSP and QAPP
- allowing for separate submissions consistent with Regional
guidance.
REFERENCE BOX 1
Environmental Protection Agency (EPA). 1984. EPA
Order 5360.1 - Policy and Program Requirements to
Implement the Mandatory Quality Assurance
Program. Office of Research and Development.
Environmental Protection Agency (EPA). 1988. National
Oil and Hazardous Substances Pollution Contingency
Plan (NCP). 40 CFR 300.
QA MANAGEMENT FOR
SUPERFUND ACTIVITIES
OERR, Regional Offices, and contractors participate in
Super/and QA management activities.
To conform to the requirements specified in Order 5360.1 and
the NCP, Superfund follows a well-defined management
structure operated by the Office of Solid Waste and Emergency
Response (OSWER). Within OSWER, OERR establishes and
oversees QA procedures, performed in support of Superfund
data collection activities. Regions perform most data collection
activities and implement the associated QA program. Regions
achieve QA goals by using qualified personnel and well-
defined procedures (including the development of DQOs) and
performing or requiring the performance of precise data
collection and accurate interpretation of data results.
OERR Quality Assurance Program
The OERR QA program applies to all Superfund site-specific
data collection activities. This program has been developed to
establish national consistency in the implementation of the
Superfund QA program. Agency and Superfund policy is set
forth in the OERR Quality Management Plan to provide site
managers with information on program requirements for
generating data of known quality.
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EXHIBIT 2 - EPA ORDER 5360.1 BASIC
QA REQUIREMENTS
Preparation and annual update of a Quality Management
Plan
Development of QA project plans for all major contracts
involving environmental measurements
Implementation of QA procedures for all contracts
involving environmental data collection activities as
specified in applicable Agency regulations, subcontracts,
and subagreements
Conduct audits on a scheduled basis
Development and adoption of technical guidelines for
assessing data quality
Establishment of achievable data quality limits for
methods cited in regulations
Implementation of a corrective action program
Provisions for appropriate training as required for all
levels of QA management
Regional Quality Assurance Programs
Regional Administrators are responsible for implementing EPA
Order 5360.1 and for tailoring the OERR QA program to
Region-specific operations. Regional Quality Management
Plans contain Region-specific policies, procedures, and
organizational structures necessary for generating data of known
quality.
Contractor Quality Assurance Programs
Each Superfund contractor performing data collection activities
must also establish a QA program to generate data of known
quality and to meet other Agency policies. Specific
requirements for contractor QA programs are defined in the
OERR and Regional Quality Management Plans.
The contractor QA program must be documented through a
Quality Management Plan that describes the corporate QA
policies and general requirements for all environmental data
collection activities. In addition, the contractor must develop
project-specific QA plans and SAPs that are presented for
review and approval as delineated in each Region's Quality
Management Plan.
Superfund Quality Assurance Program Assessment
EPA Headquarters and the Regions continually monitor the
effectiveness of the Superfund QA program through the use of
management and technical systems reviews. EPA Regional and
Headquarters staff review the performance of each contractor to
ensure conformance to technical and contractual requirements.
The frequency of these reviews will be determined by contract
requirements or as specified in the Region's Quality
Management Plan.
Exhibit 3 presents a brief description of the systems reviews.
These reviews assist OERR and Regional QA staff in assessing
the implementation and adequacy of Superfund QA at the
program and project management levels. Project reviews, a
type of management systems review, evaluate the integral
components associated with data collection activities. The
results of these reviews assist site managers and other data
users to verify the quality of sampling and analytical
operations.
EXHIBIT 3 - SYSTEMS REVIEWS
Management Systems Reviews assess the effectiveness of
the implementation of the approved QA program. These
reviews consider linkages across organizational lines and can
be used to discern areas requiring improved guidance.
Technical Systems Reviews assess project QC activities and
environmental data collection systems. Areas typically
examined during this review include: sampling/measurement
systems; equipment/facility maintenance records; and control
charts.
Audits of Data Quality address whether or not sufficient
information exists for data sets to support data quality
assessment. This type of audit may also be used to determine
if the organization collecting or using the data performed a data
quality assessment.
Performance Evaluation Reviews evaluate the laboratory
and/or field analytical personnel's performance and the
instrumentation or analytical systems used.
Superfund Evidentiary Concerns
The National Enforcement Investigations Center (NEIC) is
responsible for providing a range of technical, investigative,
and litigation expertise for the Agency's enforcement cases.
NEIC is granted statutory authority under CERCLA for
inspecting, record-keeping, and compiling confidential
information. Applicable NEIC evidentiary requirements for
site-specific field activities must be included in the project
SAP.
NEIC has prepared guidance pertaining to evidentiary
requirements for Superfund in the NEIC Policies and
Procedures Manual. Examples of evidentiary requirements
include:
Sample identification
Chain-of-custody procedures
Sample receiving procedures
Sample tracking procedures
Document control procedures
Standard operating procedures
3
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Additional information on evidentiary policies for the Contract
Laboratory Program (CLP) can be found in Exhibit F of the
current CLP Statements of Work. Exhibit F describes the
chain-of-custody, document control, and related standard
operating procedures for the CLP. Individual laboratories are
expected to incorporate Agency evidentiary requirements in
their own standing procedures.
REFERENCE BOX 2
Environmental Protection Agency (EPA). 1985. Interim Policy
and Guidance for Management Systems Audits of
National Program Offices. Quality Assurance
Management Staff (QAMS).
Environmental Protection Agency (EPA). 1987. Guidelines
and Specifications for Preparing Quality Assurance
Program Plans (QAPPs) and Quality Assurance Annual
Report and Workplans (QAARWs) for EPA National
Program Offices and the Office of Research and
Development (ORD). Quality Assurance Management
Staff. EPAQA/G-2.
Environmental Protection Agency (EPA). 1992. Quality
Assurance Management Plan for the Office of
Emergency and Remedial Response.
Environmental Protection Agency (EPA). 1980 Guidelines
and Specifications for Preparing Quality Assurance
Program Plans, QAMS-004/80. QAMS is currently
developing an update to this guidance entitled, Guidance
for Preparing, Reviewing, and Implementing Quality
Assurance Management Plans, EPAQA/G-2.
SAMPLING AND ANALYSIS PLAN
DEVELOPMENT
Sampling and analysis plans are site-specific
documents that contain sampling objectives, strategies,
and the appropriate QA procedures necessary to meet
project objectives. SAPs should incorporate or build
upon generic plans and standard operating
procedures, when available. Major activities
associated with the development of the plans are
presented in Exhibit 4.
The effective and efficient development and implementation of
SAPs is essential to obtaining data of sufficient quantity and
quality to support program decisions. As defined in the NCP,
SAPs consist of two integral components, the FSP and the
QAPP. Exhibit 5 presents the minimum requirements for each
component. When preparing SAPs, care should be taken to
streamline the process and avoid duplication between the two
components. Also SAPs. should incorporate or reference
generic plans and Regional standard operating procedures, as
appropriate.
The SAP should describe each project objective in detail.
Usually this is done by describing the specific decisions to be
made with the data and involving the decision maker from the
beginning. The plan should describe how each data value will
be used to make a decision. It should include a description of
the monitoring network, the location of each place samples will
be collected, the sampling frequency, the types of analyses for
each sample, the target precision at the concentration of
interest and the rationale for the design. All factors that will
influence the eventual decision should, to the extent practical,
be evaluated and specified at the beginning of the process.
The plan should balance the need for an appropriate level of
QA (commensurate with project needs) with timeliness and
cost considerations. Finally, the plan should include the
organization's functional activities and the names of all key
people. The remaining sections of this document discuss the
SAP development process, from definition of the project
objectives to generation and evaluation of the environmental
data.
Project Objectives
Project and data quality objectives must be developed
to assist in assuring the generation of useable data.
The first stage in developing the SAP is to determine overall
project objectives and DQOs. Project objectives define the
type and extent of investigations required at a given site.
DQOs specify the level of uncertainty that site managers are
willing to accept in results or decisions derived from
environmental data. Site managers should develop project
objectives and DQOs in accordance with data useability
requirements for project activities. For example, the technical
requirements for scoring a site using the Hazard Ranking
System (HRS) may be less stringent than those required for a
risk assessment.
Because DQOs are developed before the data are collected, this
process can improve the efficiency of data gathering by
defining the number and type of samples and level of QA.
Since these factors are determined based on project need,
DQOs assist in stream lining the process and ensuring cost
effectiveness.
Exhibit 6 illustrates the DQO process as it is defined in
Guidance for Data Useability in Site Assessment. Additional
references on the DQO process can be found in Reference Box
3.
Once these objectives have been defined, the site manager
must identify the procedures required to achieve these
objectives and the acceptable degree of uncertainty. Chemists,
geologists, biologists, ecologists, risk assessors, computer
modelers, statisticians, QA staff, and Regional Counsel should
be invited to participate in this process, as appropriate.
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EXHIBIT 4 - SAMPLING AND ANALYSIS PLAN DEVELOPMENT
ASSESSMENT
OF
DATA QUALITY
SAMPLING & ANALYSIS
PLAN
DEVELOPMENT
Sampling Design
Effective sampling designs are dependent upon project
and data quality objectives. It Is important to avoid
collecting more samples than required to support
project decisions.
During sampling design, site managers develop project
objectives into specific operational parameters. The design
identifies the number, type, and location of samples. Effective
sampling designs result in the_generation of data that meet the
project objectives and DQOs. The sampling design should also
generate data that are representative of the conditions at the site
within resource limitations.
Examples of the types of site-specific factors that should be
considered when designing a sampling plan include: site
accessibility, climate, potential hazards, media of concern, and
site heterogeneity. Information that can be used to support the
design often includes site maps, geological information, disposal
records, and historical data. Standard Operating Procedures
(SOPs) for the most common sampling techniques and field
procedures should be used consistent with Regional guidance.
Sampling designs may be statistical, judgmental, or a
combination of both. Statistical sampling designs entail
selecting sampling locations using a probability based scheme.
Judgmental sampling designs focus the sampling location
specifically in the area of concern. HRS scoring is an example
of when high bias is acceptable, therefore, the use of non-
statistical or judgmental sampling is appropriate. To determine
which design is appropriate, practical trade-offs between
response time, analytical costs, number of samples, sampling
costs, and level of uncertainty should be weighed by the site
manager. A combination of statistical and judgmental
sampling can often be used to maximize available resources,
but a statistician should be consulted.
Because site conditions may change, sampling designs should
be flexible enough to allow for modifications during sampling
execution. However, deviations from the original design
should be approved in advance by the site manager.
Field analyses can also be an important component of the
overall sampling design. These analyses can be used to
provide threshold indications of contamination and may be
helpful in revising and refining the sampling strategy.
Analytical field methods also can be useful in directing
sampling into areas of greatest contamination or "hot spots."
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EXHIBIT 5 - SAMPLING AND ANALYSIS PLAN
COMPONENTS
Field Sampling Plan: Specifies field activities
necessary to obtain environmental data and contains
the following elements:
Site background
Sampling objectives and rationale
Sampling matrix/location/frequency
Sample identification/documentation
Sampling equipment/procedures/decontamination/
disposal
Sample handling/packaging/analysis
Quality Assurance Project Plan: Describes the
policy, organization, and DQOs for decision-making
purposes. Key elements include:
Project description
Project organization/responsibilities
QA objectives for measurement
Sampling procedures and QC
Sample custody
Calibration procedures
Analytical procedures with detection
limits/quantitation limits
Data reduction/validation and reporting
Internal quality control
Performance and systems reviews
Preventive maintenance
Data assessment procedures
Corrective actions
QA reports
Finally, field analytical methods should be used to accelerate
the site assessment process and reduce costs when their use is
consistent with site conditions (e.g. contaminants, media) and
the DQO's established for the site.
Sampling Execution
Representative samples are collected through the use
of well-defined sampling practices.
Sampling execution involves the collection and documentation
of samples identified by site managers during the sampling
design phase. The goal of sampling execution is to collect
samples representative of site conditions to fulfill project
requirements and DQOs.
In order to collect representative samples, the number, location,
sampling methods, equipment, and containers must be
appropriate for the sample matrix and the contaminants of
concern. Collection procedures should not alter the matrix
sampled. In addition, samples should be preserved in a manner
that minimizes potential chemical and biological degradation.
Site managers are responsible for identifying background and
QC samples during the sampling design stage. Background
samples are .collected in conjunction with environmental
samples and are evaluated to establish baseline values for the
contaminants of concern. These samples are collected at or
near the hazardous waste site in areas not influenced by site
contamination. Background samples should be collected at
EXHIBIT 6 - THE DQO PROCESS
STEPS IN THE DQO PROCESS
State Problem - Describe the problem, possible
resolutions, and data collection constraints.
Identify Problem - State the question that will be
answered using environmental data.
Identify Input Affecting Decision - List the variables to
be measured and other information needed to make the
decision. List procedures for assessing the precision
and accuracy of the data at the concentration of interest.
Specify Domain Of Decision - Specify the locations of
concern within the site and describe the different
pathways.
Develop Logic Statement - Develop a quantitative
statement defining how the data will be summarized and
used to answer each question.
Establish Constraints On Uncertainty - Define the
procedures for determining total uncertainty in the data,
and develop data acceptance criteria.
Optimize Design For Obtaining Data - Develop a
practical design for the study that is expected to produce
the necessary data.
approximately the same time and under the same conditions as
the test samples, and they should be collected for each matrix.
QC samples assist in assessing the data quality. Field QC
samples are collected on-site in conjunction with environmental
samples and are used to gather information on the precision
and bias of the sampling process. Types of field QC samples
include double-blind samples (e.g., field evaluation samples,
field matrix spikes, and field duplicates), single-blind samples
(e.g., trip blanks, rinsate blanks), and non-blind samples (e.g.,
laboratory control samples as used in the CLP).
The precise composition and frequency of QC samples is
dependent on the objectives for the sampling event and existing
Regional guidelines. All field QC samples should be stored,
transported, and analyzed using the same procedures used for
site samples.
Site managers should assess sampling execution by evaluating
the data from field QC samples and observing field activities.
Field duplicate sample results can provide useful QC
information. However, this field assessment will not provide
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real time data on the precision of the sampling event since it is
assessed during the data review.
On-site observations of field activities are conducted to verify
that the SAP is being followed. The SAP should specify areas
where flexibility in procedures and/or criteria may be acceptable
and should specify procedures for documenting these changes.
Field documentation is critical to a successful QA program.
The field log book should be legally defensible and all entries
should be complete and accurate enough to permit
reconstruction of field activities.
Sample Analysis
Contract Laboratory Program (CLP) or non-CLP
analytical services may be procured in support of
Superfund activities. Laboratories which can
demonstrate a successful history of independent audits
should be selected for use.
Project DQOs and analytical factors dictate the selection of
analytical methods. The analytical method and associated QC
should provide data' of known quality for the contaminants of
concern. Data users should consider the following factors when
selecting analytical methods:
Quantitation limit
Detectable constituents
Qualitative confidence
Method precision and bias
Turnaround time
Analytical cost
Once the site manager has evaluated these factors, analytical
services may be procured through either CLP or non-CLP
services. The site manager or other data user is responsible for
planning, monitoring, and assessing the quality of data
regardless of the analytical service procured.
The CLP is a national program of commercial laboratories
under contract to EPA that provides routine or specialized
analytical services. Routine analytical services (RAS) use a
limited number of standardized methods and are designed to
generate consistent results. Specialized analytical services
(SAS) provide non-standardized analyses or faster turnaround
time and require the data user to specify the necessary
analytical methods and QC requirements. The CLP adheres to
specific data acceptance criteria that result in data of known and
documented quality. However, it cannot be assumed that CLP
data achieve the DQO requirements established for the project.
Data quality assessment is still required.
Analytical , services procured outside of the CLP are
characterized as non-CLP. These can be provided by
laboratories that participate in the CLP, use CLP methods,
.generate CLP-type data packages, or by laboratories that have
Jiever participated in a national analytical program. It is
recommended that non-CLP laboratories be audited to assure
the validity and defensibility of any data generated.
Non-CLP data are generated by laboratories whose proficiency
in the methods of interest may or may not be known. It is the
responsibility of the data generator and user to select the
method and data acceptance criteria that will verify method
and laboratory performance.
Two categories of non-CLP services are available: fixed
laboratory and field analyses. Fixed laboratory analyses are
performed by commercial laboratories selected by the data
user. Field analyses are commonly performed in mobile
laboratories or with fieldable or portable analytical instruments.
In addition to quick turnaround and lower cost, field analyses
can: (1) focus sampling efforts; (2) provide qualitative
information; (3) provide quantitative results when
supplemented by confirmatory samples sent to a fixed
laboratory; and (4) potentially satisfy project needs.
Analytical QC is comprised of a planned system of routine
activities for obtaining prescribed standards of performance.
QC frequency, type, and acceptance criteria should correlate
with the study objectives. The type, frequency, sequence, and
control criteria for analytical QC samples are predetermined for
CLP RAS. Site managers specify the control criteria for both
CLP SAS and non-CLP analyses.
Assessment of Data Quality
Site managers and other data users assess data quality
to determine if the data are consistent with project
objectives and are appropriate for supporting a specific
decision.
Steps in assessing data quality may include data review,
uncertainty determination, and data useability assessment.
Benefits data users can obtain from proper assessment of data
quality include: (1) establishment of data useability; (2)
determination of sufficient data quantity; and (3) improvement
of future data collection efforts by identifying major sources of
error in the data.
Data Review/Validation: The first step in assessing data
quality is data review, also known as data validation. Data
review/validation is the technical examination of environmental
data and the associated QC data to determine the limitations of
the data. During this process, the reviewer applies analytical
criteria to determine if analyses were performed under
controlled conditions and whether or not the data should be
qualified. Because data review/validation criteria are based on
the methods used to generate the data, the results of a data
review/validation are frequently independent of the intended
use of the data. The data review/validation process establishes
the quality of the data. Data review must be consistent with
the project DQQs and QAPP.
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CLP data review is performed by technical personnel who have
been trained by Regional staff or follow Agency guidance. The
data package is reviewed using EPA's functional guidelines for
evaluating organic and inorganic laboratory data (see Reference
Box 3) and Regional SOPs that comprise standardized
procedures and criteria based on the associated analytical
methods. Non-CLP data are reviewed based on available
information including the sample matrix and analytical method
used and in accordance with the procedures and criteria
specified in the DQOs. Data validation procedures must avoid
conflict of interest problems.
Determination of Total Uncertainty: Each step of the data
acquisition process has an inherent uncertainty associated with
it. The uncertainty acceptance level depends on the purpose for
which the data are being collected. Total error is comprised of
two types of error: sampling variability and measurement error.
Sampling variability is the variation between true sample values
and is a function of the spatial variation in the pollutant
concentrations. Measurement error represents the difference
between the true sample value and the reported value.
Examples of these types of errors are provided in Exhibit 7.
Factors that can influence sampling and measurement errors
include:
Instrument capabilities
Variability (media, spatial, temporal, operational)
Incorrect sample collection coordinates
Improper decontamination procedures
Improper sample preservation
Inadequate storage procedures
Inappropriate sample preparation analysis
Exceeded holding times
EXHIBIT 7 - TOTAL ERROR COMPONENTS
Sampling variability is a function of the spatial variation
in pollutant concentrations. For example, landfills may
have "hot spots" with non-representative concentrations.
Measurement error, which has components from both
sampling and analysis, is estimated using the results of
QC samples. For example, sample results may be biased
low due to the holding time being exceeded.
Site managers and other data users should establish procedures
for estimating total uncertainty and data acceptance criteria
during the DQO development stage. EPA currently is
developing procedures for determining total error for soil
analyses. The Environmental Monitoring Systems Laboratory
in Las Vegas (EMSL/LV) has developed a guidance, A_
Rationale for the Assessment of Errors in the Sampling of Soils,
to serve this purpose.
Data Useability Assessment: After the data have been
reviewed and the total uncertainty estimated, the data must be
examined in the context of the DQOs to determine whether
they are valid for their intended use.
Site managers or other data users assess data useability by
evaluating the sampling and analytical performance against the
quality indicators specified in the DQOs. Quality indicators
consist of quantitative statistics and qualitative descriptors and
are used to interpret the degree of acceptability of data to the
user. The data quality indicators are:
Bias/Accuracy
Precision
Comparability
Completeness
Representativeness
Site managers may be required to implement corrective action
in the event the system fails to achieve the established
performance criteria.
EPA has established a Data Useability Workgroup to develop
national guidance for minimum data quality requirements to
increase the useability of environmental data in support of
Superfund. Within this workgroup, the risk assessment
subgroup has developed minimum requirements for risk
assessments (see Guidance for Data Useability in Risk
Assessment: Final). The site assessment subgroup has
developed similar guidance for site assessments.
Automated Computer Systems
Automated computer systems are useful tools in
supporting data collection activities.
Several automated computer
systems are being developed that
can assist site managers in
performing various aspects of
data collection, including
developing SAPs, developing
and evaluating sampling
strategies, and performing
automated data review. This
section describes some of the systems that are in the prototype
stage of development. Because these systems have not been
finalized, their current useability cannot be guaranteed. Exhibit
8 provides EPA contacts for further information on each of
these systems.
Sampling and Analysis Plan Development. The Quality
Assurance Sampling Plan for Emergency Response (QASPER)
was created to automate the development of a site-specific
SAP for the Removal program. The system is implemented
using WordPerfect software. QASPER includes step-by-step
procedures for developing a SAP, from development of DQOs
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through data validation. The system significantly expedites the
SAP development process.
Development and Evaluation of Sampling Strategies: Several
automated systems have been produced to develop and evaluate
sampling strategies. Each automated system has specific data
requirements and is based on specific site assumptions.
The DQO Expert System was developed by the Quality
Assurance Management Staff (QAMS). It is a training system
that assists in planning environmental investigations based on
the DQO process. The four systems that follow were developed
by EMSL/LV. The Environmental Sampling Expert System
(ESES) assists in planning sample collection. It includes
models that address statistical design, QC, sampling procedures,
sample handling, budget, and documentation. The current
system addresses metal contaminants in a soil matrix.
Expanded application of this system is under development. The
Geostatistical Environmental Assessment Software (GEO-EAS)
is a collection of software tools for two-dimensional
geostatistical analysis of spatially distributed data points.
Programs include file management, contour mapping, variogram
analysis, and kriging. SCOUT Multivariate Statistical Analysis
Package is a collection of statistical programs that accept GEO-
EAS files for multivariate analysis. The Assessment of Errors
in Sampling of Soils (ASSESS) system estimates measurement
error variance components. It presents scatter plots of QC data
and error plots to assist in determining the appropriate number
of QC samples required.
Automated Data Review: Automated data evaluation systems
Inave been developed to reduce the resources and the amount of
time required to review data. The Computer-Aided Data
Review and Evaluation (CADRE) system developed by
EMSL/LV is an automated evaluation system that assists in the
review of CLP organic RAS data. CADRE evaluates data
quality according to the QC criteria defined in the EPA's
functional guidelines for evaluating inorganic and organic data.
The system is being modified to accept manual entry of other
date sets and to accept user-defined criteria to meet specific
information needs.
The Electronic Data Transfer and Validation System (eDATA)
developed by the Removal program assists in rapid evaluation
of data in emergency responses. This system may be applicable
for both CLP and non-CLP data. The system combines DQOs,
pre-established site specifications, QC criteria, and sample
collection data with laboratory results to determine useability.
This software consists of three separate and distinct modules
that reflect the needs of the site manager, the laboratory, and
the data validator. In using eDATA, the site manager specifies
the DQOs associated with any given batch of samples, the
choice of the pre-established QA/QC criteria, and the limits for
volatile, semivolatile, PCB and pesticide, and metal constituents.
The site manager can also create sets of user-defined criteria to
meet project-specific needs.
EXHIBIT 8 - COMPUTER SYSTEM CONTACTS
ASSESS: Jeff Van Ee, Exposure Assessment Division,
USEPA EMSL/LV, (702) 798-2367.
CADRE: John Nocerino, Quality Assurance
Division, USEPA EMSL/LV, (702) 798-2110.
DQO Expert System: John Warren, USEPA Quality
Assurance Management Staff, (202) 260-9464.
eDATA: William Coakley, USEPA Environmental
Response Team, (908) 906-6921.
ESES: Jeff Van Ee, Exposure Assessment Division,
USEPA EMSL/LV, (702) 798-2367.
GEO-EAS: Evan Englund, Exposure Assessment
Division, USEPA EMSL/LV, (702) 798-2248.
QASPER: William Coakley, USEPA Environmental
Response Team, (908) 906-6921.
SCOUT: Jeff Van Ee, Exposure Assessment Division,
USEPA EMSL/LV, (702) 798-2367.
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REFERENCE BOX 3
American Public Health Association. 1985. Standard Methods
for the Examination of Water and Wastewater. American
Public Health Association, 16th Edition, 1985.
Environmental Protection Agency (EPA). 1986. Test Methods for
Evaluating Solid Waste (SW-846): Physical/Chemical Methods.
Third Edition. Office of Solid Waste.
Environmental Protection Agency (EPA). 1987. A Compendium of
Superfund Field Operations Methods. Office of Emergency and
Remedial Response. EPA/540/P-87/001. •
Environmental Protection Agency (EPA). 1987. Data Quality
Objectives for Remedial Response Activities: Development
Process. EPA/540/G-87/003.
Environmental Protection Agency (EPA). 1987. Data Quality
Objectives for Remedial Response Activities, Example Scenario:
RI/FS Activities at a Site with Contaminated Soil and Ground
Water. Office of Emergency and Remedial Response.
EPA/540/G-87/004.
Environmental Protection Agency (EPA). 1988. Field Screening
Methods Catalog. Office of Emergency and Remedial Response.
EPA/540/2-88/005.
Environmental Protection Agency (EPA). 1988. Ground Water
Modeling: An Overview and Status Report. EPA/600/2-89/028.
Environmental Protection Agency (EPA). 1988. Laboratory Data
Validation Functional Guidelines for Evaluating Inorganics
Analysis. Office of Emergency and Remedial Response.
Environmental Protection Agency (EPA). 1988. Laboratory Data
Validation Functional Guidelines for Evaluating Organics
Analysis. Office of Emergency and Remedial Response.
Environmental Protection Agency (EPA). 1989. Determining Soil
Response Action Levels Based on Potential Contaminant
Migration to Ground Water: A Compendium of Examples.
EPA/540/2-89/057.
Environmental Protection Agency (EPA). 1989. Guidance on
Applying the Data Quality Objectives Process for Ambient Air
Monitoring Around Superfund Sites (Stages 1 and 2). Office of
Air Quality and Planning and Standards. EPA/450/4-89/015.
Environmental Protection Agency (EPA). 1989. Quality Assurance
Technical Information Bulletin, Vol. 1, No. 2, 11/13/89.
Environmental Protection Agency (EPA). 1989. Soil Sampling Quality
Assurance User's Guide. Environmental Monitoring Systems
Laboratory. Las Vegas, NV. EPA/600/8-89/046.
Environmental Protection Agency (EPA). 1989. Superfund Ground
Water Issue: Ground Water Sampling for Metals Analyses.
Office of Research and Development. EPA/540/4-89/001.
Environmental Protection Agency (EPA). 1990. QA/QC Guidance for
Remedial Activities. EPA 540/G-90/004.
Environmental Protection Agency (EPA). 1990. A Rationale for the
Assessment of Errors in the Sampling of Soils. Office of
Research and Development. EPA/600/4-90/013.
Environmental Protection Agency (EPA). 1991. CLP Analytical
Results Database (CARD) Quick Reference Fact Sheet.
Office of Emergency and Remedial Response.
Environmental Protection Agency (EPA). 1991. CLP
Statement of Work For Inorganics Analysis. Document
Number ILM02.0 (or most recent update).
Environmental Protection Agency (EPA). 1991. CLP
Statement of -Work for Organics Analysis. Document
Number OLM01.1 (or most recent update).
Environmental Protection Agency (EPA). 1991. Compendium
of ERT Ground Water Sampling Procedures. Emergency
Response Division Office of Emergency and Remedial
Response. EPA/540/P-91/007.
Environmental Protection Agency (EPA). 1991. Compendium
of ERT Soil Sampling and Surface Geophysics
Procedures. Emergency Response Division Office of
Emergency and Remedial Response. EPA/540/P-91/006.
Environmental Protection Agency (EPA). 1991. Compendium
of ERT Surface Water . and Sediment Sampling
Procedures. Emergency Response Division Office of
Emergency and Remedial Response. EPA/540/P-91/005.
Environmental Protection Agency (EPA). 1991. Compendium
of ERT Waste Sampling Procedures. Emergency
Response Division Office of Emergency and Remedial
Response. EPA/540/P-91/008.
Environmental Protection Agency (EPA). 1991. Management
of Investigation-Derived Wastes During Site Inspections.
EPA/540/G-91/009, May 1991.
Environmental Protection Agency (EPA). 1991. Practical
Guide for Groundwater Sampling. EPA 600/2-85/104,
September 1985.
Environmental Protection Agency (EPA). 1991.
Representative Sampling Guidance, Vol. I, Soil.
OSWER Directive 9360.4-10.
Environmental Protection Agency (EPA). 1991. Sampler's
Guide to the Contract Laboratory Program. Office of
Emergency and Remedial Response. EPA/540/P-
90/006.
Environmental Protection Agency (EPA). 1991. User's
Guide to the Contract Laboratory Program. Hazardous
Site Evaluation Division Office of Emergency and
Remedial Response. EPA/540/P-91/002.
Environmental Protection Agency (EPA). 1992. Guidance
for Data Useability in Risk Assessment: Final. Office
of Emergency and Remedial Response. Part A:
9285.7-09A. Part B [radionuclides]: 9285.7-09B
Environmental Protection Agency (EPA). 1992. Preparation
of Soil Sampling Protocol: Sampling Techniques and
Strategies. EPA 600/R-92/128.
10
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GLOSSARY
Assessment - the evaluation process used to measure the
performance or effectiveness of a system and its elements.
Assessment is an all-inclusive term used to denote any of
the following: audit, performance evaluation, management
systems review, peer review, inspection or surveillance.
Audit - a planned and documented investigative evaluation
of an item or process to determine the adequacy and
effectiveness as well as compliance with established
procedures, instructions, drawings, QAPPs, and/or other
applicable documents.
Data Quality Objectives (DQOs) - a statement of the
precise data, the manner in which such data may be
combined, and the acceptable uncertainty in those data in
order to resolve an environmental problem or condition.
This may also include the criteria or specifications needed
to design a study that resolves the question or decision
addressed by the DQO process.
Data Quality Objectives Process - a Total Quality
Management (TQM) tool developed by the U.S.
Environmental Protection Agency to facilitate the planning
of environmental data collection activities. The DQO
process asks planners to focus their planning efforts by
specifying the use of the data (the decision), the decision
criteria, and their tolerance to accept an incorrect decision
based on the data. The products of the DQO process are
the DQOs.
Data Useability - the process of ensuring or determining
whether the quality of the data produced meets the
intended use of the data.
Detectable Constituent - a target analyte that can be
determined to be different from zero by a single
measurement at a stated level of probability.
Management Systems Review (MSR) - the qualitative
assessment of a data collection operation and/or
organization(s) to establish whether the prevailing quality
management structure, policies, practices, and procedures
are adequate for ensuring that the type and quality of data
needed are obtained.
Performance Evaluation (PE) - a type of audit in which
the quantitative data generated in a measurement system
are obtained independently and compared with routinely
obtained data to evaluate the proficiency of an analyst or
laboratory.
Quality - the sum of features and properties/characteristics
of a process, item, or service that bears on its ability to
meet the stated needs and expectations of the user.
Quality Assurance (QA) - an integrated system of
management activities involving planning, implementation,
assessment, reporting, and quality improvement to ensure
that a process, item, or service is of the type and quality
needed and expected by the customer.
Quality Assurance Project Plan (QAPP) - a formal
document describing in comprehensive detail the necessary
QA, QC, and other technical activities that must be
implemented to ensure that the results of the work
performed will satisfy the stated performance criteria:
Quality Control (QC) - the overall system of technical
activities that measure the attributes and performance of a
process, item, or service against defined standards to verify
that they meet the stated requirements established by the
customer.
Quality Management Plan (QMP) - a formal document that
describes the quality system in terms of the organizational
structure, functional responsibilities of management and
staff, lines of authority, and required interfaces for those
planning, implementing, and assessing all activities
conducted.
Quantitation Limit - the maximum or minimum level or
quantity of a target variable that can be quantified with the
certainty required by the data user.
Sampling and Analysis Plan (SAP) - a formal document
that provides a process for obtaining data of sufficient
quality and quantity to satisfy data needs. A sampling and
analysis plan consists of two parts:
(a) The field sampling plan, which describes the
number, type, and location of samples and the types of
analyses; and
(b) The quality assurance project plan, which
describes policy, organization, and functional activities
and the data quality objectives and measures
necessary to achieve adequate data for use in planning
and documenting the response action.
Technical Review - a documented critical review of work
that has been performed within the state of the art. The
review is accomplished by one or more qualified reviewers
who are independent of those who performed the work, but
are collectively equivalent in technical expertise to those
who performed the original work. The review is an in-depth
analysis and evaluation of documents, activities, material,
data, or items that require technical verification or validation
for applicability, correctness, adequacy, completeness, and
assurance that established requirements are satisfied.
Technical Systems Audit (TSA) - a thorough, systematic,
on-site, qualitative audit of facilities, equipment, personnel,
training, procedures, record keeping, data validation, data
management, and reporting aspects of a system.
Validation - an activity that demonstrates or confirms that
a process, item, data set, or service satisfies the
requirements defined by the user.
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