United States Office of Water EPA 841-B-95-004
Environmental Protection S4503F) July 1995
Agency
EPA Generic Quality Assurance
Project Plan Guidance for
Programs Using
Community Level
Biological Assessment in
Wadable Streams and
Rivers
Recycled/Recyclable Printed with Vegetable Based Inks on Recycled Paper (20% Postconsumer)
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ACKNOWLEDGMENT
This document was developed by the U.S. Environmental Protection Agency
through contract no. 68-C3-0303 with Tetra Tech, Inc. The project managers
were Martin W. irossman and Chris K. Faulkner, U.S. EPA Office of Wetlands,
Oceans, and Watersheds. Principal authors include Dr. James B. Stribling and Ms.
Christiana Gerardi, Tetra Tech, Inc., Owings Mills, Maryland. The document was
improved with substantial input by the reviewers listed on page ix and x.
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FOREWORD
In order to help ensure that environmental monitoring data are of known quality, U.S.
Environmental Protection Agency (USEPA) has established specific requirements for
development of Quality Assurance Project Plans (QAPPs). These QAPPs are
required for environmental monitoring tasks accomplished within USEPA by its
contractors and its grantees.
Since 1980, the standard guidance for developing QAPPs has been the Quality
Assurance Management Division's (QAMD) 005/80 "Interim Guidelines and
Specifications for Preparing Quality Assurance Project Plans". This guidance has now
been replaced by EPA QA/R-5 "EPA Requirements for Quality Assurance Project
Plans for Environmental Data Operations," Draft Interim Final August 1994.
The new QAPP guidance provides considerable versatility in preparation of QAPPs for
particular data needs. Among the new materials and approaches introduced by EPA
QA/R-5;are inclusion of the Data Quality Objectives process in the QAPP; an
expansion of additional elements to be addressed in QAPP; and an approach that
permits "tailoring" the comprehensiveness of the QAPP to the nature of work being
performed and the particular use of the data.
For many years the major water monitoring efforts of USEPA have focused on
chemical/physical measurements. Accordingly, guidance documents, such as those
for developing QAPPs, have tended to utilize terminology and examples relevant to
these monitoring measurements.
The recent expansion of biological monitoring has brought new terminology and
approaches which do not fit "comfortably" in the past chemical/physical descriptions.
Within the USEPA, Office of Water it has become apparent that some means must be
found to ensure effective control of data quality for these measurements. Accordingly,
it was decided that a generic QAPP for biological measurements, following the
structure of the QAPP which had evolved from chemical/physical measurements,
would be of considerable value; hence, this document.
This guidance is based upon EPA QA/R-5. However, wherever appropriate, biological
terminology and examples are given to facilitate use in the discipline of biological
monitoring. In addition, "element" descriptions have been expanded to facilitate use
by biologists and others who may not be familiar with the terminology and approaches
typical of chemical/physical monitoring and laboratory analysis.
Development of this guidance has involved extensive inputs, reviews, and
recommendations of a wide community of biologists expert in various areas of
biological monitoring and analysis. USEPA Quality Assurance Officers well-versed in
the use of QAPPs in more typical chemical/physical measurement and analysis have
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also reviewed this document. The Quality Assurance Management Division of
USEPA, responsible for the USEPA QA program and its guidance documents, has
provided assistance in this adaptation of EPA/QA/R-5 to biological monitoring.
As in the case of all new guidance, however, considerable insight for improvement will
be gained from its use. Hence, the users of this document are urged to send
comments on utility and suggestions for improvement/expansion to USEPA 4503F,
Assessment and Watershed Protection Division, Monitoring Branch, Washington, D.C.
20460, Attention: Biological Monitoring Coordinator. As experience is gained and use
expands, revised editions of the document will be considered.
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CONTENTS
Acknowledgment ii
Foreword iii
Contents v
List of Tables vii
List of Figures viii
List of Reviewers ix
Introduction 1
1. Title and Approval Sheet 6
2. Contents 8
3. Distribution List . . . 9
4. Project/Task Organization 10
5. Problem Definition/Background; Project Description . 14
6. Quality Objectives for Measurement Data 16
7. Project Narrative 24
8. Special Training Requirements/Certification 25
9. Documentation and Records 26
10. Sampling Process Design {Experimental Design}/
Sampling Methods Requirements 27
11. Sample Handling and Custody Requirements 37
12. Analytical Methods Requirements 42
13. Quality Control Requirements 48
14. Instrument/Equipment Testing, Inspection, and Maintenance Requirements 50
15. Instrument Calibration and Frequency 53
16. Inspection/Acceptance Requirements for Supplies and Consumables .... 55
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17. Data Acquisition Requirements {non-direct measurements) 56
18. Data Management . 57
19. Assessments and Response Actions 58
20. Reports to Management 61
21. Data Review, Validation, and Verification Requirements 64
22. Validation and Verification Methods . 65
23. Reconciliation with Data Quality Objectives 67
Literature Cited 68
Appendix A Abbreviated QAPP Form
Appendix B QAPP Glossary of Terms
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LIST OF TABLES
4-1 A list of key positions or areas of responsibility often included in a project
organization framework.
6-1 Summary of some measurement (indicator) selection criteria.
6-2 Example summary table of some hypothetical data quality requirements.
10-1 Rule-of-thumb for number of replicate QC samples based on numbers of
sites.
12-1 Comparison of reference and voucher collections.
14-1 Example of equipment and supply list for benthic macroinvertebrate
sampling.
14-2 Example of equipment list for fish sampling in wadable streams.
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LIST OF FIGURES
1 Generalized flow diagram for the preparation, approval, and implementation
process of QAPPs.
1-1 Example of title page format for QAPPs.
I-2 Example of a document control header.
4-1 Organizational chart illustrating project organization and lines of
communication.
6-1 The seven step DQO process.
10-1 Example of sample label information.
10-2 Alternative examples of sample identification numbering.
II-1 Chain-of-custody record.
12-1 Macroinvertebrate laboratory bench sheet.
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LIST OF REVIEWERS
The following individuals have provided useful reviews and comment on previous
drafts of this document.
Dr. Loren Bahls
Dept. of Health and Environ. Sciences
Ecosystems Mgmt. Section
Cogswell Building
Room A-206
Helena, MT 59620
Ms. Celeste Barr
USEPA Region 1
60 West View St.
Lexington, MA 02173
Mr. Dan Boward
MD Department of the Environment
2500 Broening Hwy.
Baltimore, MD 21224
Mr. Bruce Duncan
USEPA Region 10
1200 Sixth Ave.
Seattle, WA 98101
Mr. Mark Gordon
Specialist-Watershed Management
Ministry of the Environment
Water Resources Branch
135 St. Clair Avenue West
Toronto, Ontario
Canada M4V 1P5
Mr. Donald Hart
Senior Environmental Biologist
Beak Consultants Limited
14 Abacus Road
Brampton, Ontario
Canada L6T 5B7
Ms. Gretchen Hayslip
USEPA Region 10
Mail Stop ES097
1200 Sixth Avenue
Seattle, WA 98101
Mr. Terry Hollister
USEPA Region 6, Houston Branch
10625 Fa 11 stone Rd.
Houston, TX 77099
Dr. Robert M. Hughes
Man-Tech Environmental
200 S.W. 35th Street
Corvallis, OR 97333
Mr. Phil Johnson
USEPA Region 8
999 18th Street, Suite 500
Denver, CO 80202-2466
Mr. Roy R. Jones
USEPA Region 10
1200 Sixth Avenue
Seattle, WA 98101
Dr. Donald J. Klemm
USEPA EMSL
Bioassess. and Biotox. Branch
3411 Church St.
Cincinnati, OH 45268
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Dr. James M. Lazorchak
USEPA EMSL
Bioassess. and Biotox. Branch
3411 Church St.
Cincinnati, OH 45268
Ms. Janice Smithson
Office of Water Resources
Division of Environmental Protection
1201 Greenbrier St.
Charleston, WV 25311-1088
Mr. Ed Liu
USEPA Region 9
75 Hawthorne St.
San Francisco, CA 94105
Dr. Jerry Smrchek
USEPA OPPT HERD
401 M St., SW #7403
Washington, DC 20460
Ms. Suzanne Lussier
USEPA/ORD
27 Tarzwell Dr.
Narragansett, Rl 02882
Mr. Michael Tucker
USEPA Region 7
25 Funston Rd.
Kansas City, KS 66115
Dr. Eugenia McNaughton
USEPA Region 9
75 Hawthorne St.
San Francisco, CA 94105
Mr. Mike Papp
USEPA GLNPO
230 South Dearborn Street
Chicago, IL 60605
Mr. David Peck
Lockheed Engineering and Sciences
Environ. Assessment Dept.
1050 E. Flamingo Road
Las Vegas, NV 89119
Dr. Donna Reed
USEPA/OWM
401 M St., SW #4203
Washington, DC 20460
Mr. George Schupp
c/o Ms. Denise Boone
USEPA Region 5
77 West Jackson Blvd.
Chicago, IL 60604-3590
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INTRODUCTION
Quality Assurance (QA) - an integrated system of activities involving
quality planning, quality control, quality assessment, quality reporting and
quality improvement to ensure that a product or service meets defined
standards of quality with a stated level of confidence.
Quality Control (QC) - the overall system of technical activities whereby
the purpose is to measure and control the quality of a procedure or
service so that it meets the needs of users. The aim is to provide quality
data that is satisfactory, adequate, dependable, and economical. One
example of a quality control element for biological sampling is taking
replicate samples to ensure consistency among and within sampling
crews.
Quality Assurance Project Plan (QAPP) - a formal document describing
the management policies, objectives, principles, organizational authority,
responsibilities, accountability, and implementation plan of an agency,
organization, or laboratory for ensuring quality in its products and utility
to its users.
A QAPP is a technical planning document that defines the objectives of a project or
continuing operation, as well as the methods, organization, analyses, and QA and QC
activities necessary to meet the goals of that project or operation. The EPA requires
that all monitoring and measurement projects carried out by or supported by USEPA
have written and approved Quality Assurance Project Plans (QAPPs). This document
represents generic guidance for development of QAPPs for specific bioassessment
projects or programs, This generic QAPP is based upon "EPA Requirements for
Quality Assurance Project Plans for Environmental Data Operations," EPA QA/R-5
(USEPA 1994, Draft Interim Final)1, The expanded descriptions and application
guidance have benefited from utilization of the Office of Water Quality Management
Plan and previous Office of Water QAPP guidance OWRS QA-1 "Combined Work/QA
Project Plans for Environmental Monitoring" (USEPA 1984). A variety of sources have
provided materials assisting in development of "biological" examples in the QAPP,
These include the work of the Environmental Monitoring Systems Laboratory
(Cincinnati, Ohio) to develop QA guidance for establishment of biological assessment
programs; technical QA literature (Smith et al. 1988); and selected bioassessment
documents (Karr et al. 1986; Ohio EPA 1987; Plafkin et al. 1989).
"'However, a slight modification in formal has been made. The "elements" of this QAPP
guidance are numbered sequentially instead of being broken down by sections A, B, C, and D, The
items covered have the same titles as in QA/R-5.
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This guidance does not promote one
bioassessment procedure over another;
it does provide a QA framework to which
different bioassessment programs may
be adapted. It is designed to allow
flexibility with regards to all components
in developing a bioassessment program.
It has been specifically designed for use
by states using bioassessment protocols
that focus on community-level responses
as indicated by a multimetric approach
and taxonomy to the genus/species
level.
Sampling gears should be appropriate
for the habitat and region being sampled
and may include active or passive collection devices such as square meter kicknets,
dipnets, square foot surber samplers, ponars, Hester-Dendy artificial sampler, basket
samplers for macroinvertebrates; electrofishers, seines, and Fyke nets for fish; and
knives for scraping, eyedroppers, and containers for dislodging epiphytic algae from
macroalgae for algae collections. As is customary for biological programs, pilot
studies (or initial year of data) are recommended to investigate sources or error,
variability, and representativeness of the monitoring program.
Who is responsible for having QAPPs? USEPA QA policy (Order 5360.1) stipulates
that specific monitoring projects or continuing operations undertaken with all or partial
USEPA funding be covered by a QAPP. A continuing operation is one in which the
procedures are not modified significantly from year to year. For this type of
environmental program, a single QAPP that describes these routine activities would be
prepared. The QAPP serves as the blueprint for implementing the data collecting
activity and ensures that the technical and quality goals of the operation are met. It
also provides the necessary link between the required data quality constraints and the
sampling and analysis activities to be conducted.
Programs that have ongoing, repetitive, or small scale sampling events that follow
specific Standard Operating Procedures (SOPs) should develop a QAPP for the
overall program; this alleviates the need for specific QA plans for each sampling
event. The QAPP is then cited in the workplan. State programs developing QAPPs
should query other state agencies (e.g., Department of Fish and Wildlife, Department
of Health, Department of the Environment, Department of Natural Resources) to
determine if a base QAPP currently exists for their type of project. Agencies can draw
from this base plan by outlining the rationale for any changes made in adapting it to
their project; or if it is suitable for a program, the base QAPP can be cited as the
program QAPP. If no base plan exists in the state for community-level, organism-
Community - a group of interacting
assemblages in a given geographic
location. Consists of all living
components: fish, amphibians, benthic
macroinvertebrates, algae, macrophytes,
microbes, etc.
Assemblage - a group of interacting
populations of organisms in a given
geographic location (for example: a fish
assemblage or a benthic
macroinvertebrate assemblage).
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based, biological assessments, the program should use this guidance as the template
for developing their QAPP. In the case of single biological assessment events (i.e., a
non-routine assessment or special study), abbreviated QAPPs can be developed
(Appendix B). Such a plan will not need to include extensive language, rationale, or
justification for all elements and can take the format of an outline. If individual
elements of the QAPP guidance are not related to any aspect of the project, it should
be noted in those sections as "not applicable". This short form also provides an
overview of the QAPP.
What is the process for implementing the QAPP? For internal USEPA projects, the
QAPP is reviewed and approved by the Quality Assurance Officer (QAO). QAPPs are
distributed to the personnel performing the assigned work, and implemented as written
unless modified as described above. The process of preparing and implementing
QAPPs is shown in Figure 1.
Review and control mechanisms are established for each project in the QAPP and will
vary in complexity and scope depending on the particular project. Large-scale,
national projects will form QA-task groups to provide the lead in preparing Data
Quality Objectives (DQOs) (Section 6) and QAPPs. These same groups will review
the QA data on an ongoing basis, conduct audits, and recommend remedial action.
If changes to work in progress are needed, the QAPP should be revised, reviewed,
and approved by the Project Officer and the QAO and then distributed to personnel
performing the work. For continuing operations, the QAPP is reviewed annually and
revised whenever significant changes are made in procedures or organizational
responsibilities. A QAPP must be approved by the QAO prior to the initiation of data
collection activities.
How is this guidance document organized?
Sections 1 and 2 of this document give examples of an appropriate QAPP title
page and table of contents. In addition, all QAPPs must be prepared using a
document control header placed in the upper corner opposite the binding of
each document page. At a minimum, the header should include the information
indicated in Section 1.3.
Possible techniques for presentation of project organization and lines of
responsibility are outlined in Section 4.
Section 5 provides suggestions for producing a project description that
illustrates the background and rationale of the project.
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EPA Office of Water Quality Management Plan (QMP)
Establishes policy on applicability of QAPPs
Establishes Program-level DQOs, policies for preparation of QAPPs
Establishes policies, procedures, and schedules for oversight/auditing of QAPPs
No QAPP Prepared
Project staff prepares draft QAPP
¦ Oevelop Project Specific DQOs (with process for modifying
onsite decisions)
Design in accordance with QMP
> Comply with "Draft-Final EPA Requirements for Quality
Assurance Plans" (EPA QA/R-5)
QAO receives approved and signed QAPP and provides implementation guidance
±
Project staff implements QAPP
I
Audit or other QAPP Implementation review
I
Results integrated into project report
including implications of meeting or
deviating from QAPP
KEY
QMP: Quality Management Plan
QAO. Quality Assurance Officer
QAPP; Quality Assurance Project Plan
FIGURE 1 Generalized flow diagram for the preparation, approval, and
implementation process of QAPPs.
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Production of DQOs for the project are discussed in Section 6. Calculation and
information presentation procedures for data quality requirements (precision,
completeness, representativeness, comparability) are provided in Sections 6,
19, and 23.
Procedural and QA guidance for biomonitoring field and laboratory activities are
presented in Sections 10 through 12,
Section 13 outlines specific QC activities.
Section 19 relates to required activities for rectifying project or procedural
problems in reducing error sources.
Section 20 presents guidance for presenting endpoints in individual QA
procedures or sets thereof within formal QA reports.
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SECTION 1
TITLE AND APPROVAL SHEET
1.1 Each QAPP should include a title page noting the title of the plan, name of the
organization(s) implementing the project, as well as names and titles for;
Organization's Project Manager
Organization's Quality Assurance Manager
USEPA Project Manager (Required)
USEPA Quality Assurance Manager (Required)
Others, as needed (e.g., State, other Federal agency)
1.2 If the project is to be conducted by personnel from more than one institution,
appropriate individuals from each institution should sign the title page. Figure 1-1
presents an example of the title page.
3 All QAPPs must be prepared using a document control header placed in the
upper Qorner opposite the binding of each document page (Figure 1-2), The
following information must be included in the header:
a) Section Number which identifies the section or chapter.
b) Revision Number which identifies the most recent revision.
c) Date is the date of the most recent revision.
d) Page of which identifies the specific page and the total number
of pages in the section.
Section No.
Revision No.
Date
Page of
FIGURE 1-2 Example of a document control header.
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Section 1
Revision No. 5
February 16, 1995
Page 1 of 1
Quality Assurance Project Plan
for
fProiect Name*
Prepared by:
(Name)
(Address)
(Phone Number)
Prepared for:
(Name)
(Address)
(Phone Number)
(Date)
Approvals:
Project Manager, Title/Date
Agency
Primary Qh Manager, Title/Date
Agency
USEPA Project Manager, Title/Date
Agency
USEPA QA Officer, Title/Date
Agency
FIGURE 1-1 Example of title page format for QAPPs.
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SECTION 2
CONTENTS
2.1 List the sections, figures, tables, references, and appendices included in the
document. Corresponding page numbers should be provided for sections/chapters
and the literature cited section.
2.2 In some cases, particularly where abbreviated form QAPPs are produced, the
content section is optional.
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SECTION 3
DISTRIBUTION LIST
3.1 A list of the individuals and their organizations who will receive copies of the
approved QAPP should be included; subsequent revisions should be compiled and
included in the QAPP, All managers who are responsible for implementing any
portion of the plan, as well as the QA managers and representatives of all groups
involved, should be included.
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SECTION 4
PROJECT/TASK ORGANIZATION
4.1 The organizational aspects of a project provide a framework for conducting
tasks within the project. The organizational structure and function can also
facilitate project performance and adherence to QC procedures and QA
requirements. Key individuals, including the QAO, responsible for ensuring the
collection of valid data and the routine assessment of the data analysis for
precision and accuracy must be included in the project organization description.
Also identify the data users and the person(s) responsible for approving and
accepting final products and deliverables. An example of a project organizational
diagram is presented in Figure 4-1. The relationships and lines of communication
among all project participants and data users need to be included in the
organizational chart. Where direct contact between managers and data users does
not occur, such as between a project consultant for a Potentially Responsible Party
and the USEPA risk assessment staff, the chart should illustrate the route by which
information is exchanged. The chart should be realistic and practical, and should
reflect only actual lines of authority and communication for the project.
4.2 Effective QA/QC procedures and a clear delineation of QA/QC responsibilities
are essential to ensure the utility of environmental monitoring results. All aspects
of the project (field operations, laboratory activities, and data handling and
analysis) must be addressed for the organization process to be complete. In order
for a monitoring or assessment study to proceed smoothly and yield valid and
usable data, it is essential that all Individuals are clearly informed of and
understand their responsibilities. Key positions and general duties often included in
the project organization and responsibility section of the QAPP are listed in Table
4-1, It is recognized that some agencies have small staffs, therefore, WITH THE
EXCEPTION OF THE PRIMARY QA OFFICER ROLE, TWO OR MORE OF THE
DUTIES LISTED IN TABLE 4-1 MAY BE THE RESPONSIBILITY OF THE SAME
INDIVIDUAL. These individuals must be identified by title, level of expertise, and a
brief description outlining their responsibilities.
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ADVISORY |
PANEL .
I
Sampling
Design
I
Sampling
Design
Coordinator
Design
Field
QC
QC
Project Manager/Principal Investigator
OA Officer]
ECOLOGICAL PROJECT ACTIVITY CLASSES
Field
Activities
I
Field
Leader
Statistician
Senior
Personnel
User
Contacts
I
Laboratory
Activities
I
Laboratory
Manager/
Leader
Biota
]
-{ Water |
Laboratory
QC
I
Taxonomy
]
Sample Processing |
Habitat | | Sample Handling |
~~r~
Data
Analysis
I
Data
Processing
Leader
Data
QC
Reporting
I
Document
Production
Coordinator
Reporting
QC
| Data Presentation | | Data Interpretation!
| Data Entry | | Technical Editor |
FIGURE 4-1 Organizational chart illustrating project organization and lines of communication.
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TABLE 4-1 Key positions or areas of responsibility often included in a project organization framework (a
sole staff member is NOT required for each of these positions; an individual may be called upon to
perform one, two, or several of these sets of responsibilities}.
TITLE
DESCRIPTION OF DUTIES/RESPONSIBILITIES
Advisory Panel
(if necessary)
The Advisory Panel holds intermittent meetings for review process
of the overall program in order to confirm or refute whether the
objectives are being met. The group may make suggestions for
changing specific procedures or overall organization in the event
that the program design fails to meet the stated goals.
Project Manager/
Principal Investigator
The Project Manager supervises the assigned project personnel
(scientists, technicians, and support staff) in providing for their
efficient utilization by directing their efforts either directly or
indirectly on projects. Other specific responsibilities include:
coordinate project assignments in establishing priorities and
scheduling, ensure the completion of high-quality projects within
established budgets and time schedules, provide guidance and
technical advice to those assigned to projects by evaluating
performance, implement corrective actions and provide professional
development to staff, and prepare and/or review preparation of
project deliverables, interact with clients, technical reviewers, and
agencies to assure technical quality requirements are met in
accordance with contract or grant specifications.
Project QA Officer
The QA Officer reports to the Project Manager and is independent of
the field, laboratory, data, and reporting staff. Major responsibilities
include monitoring QC activities to determine conformance,
distributing quality related information, training personnel on QC
requirements and procedures, reviewing QA/QC plans for
completeness and noting inconsistencies, and signing-off on the QA
plan and reports.
Sampling Design
Coordinator
The Sampling Design Coordinator is responsible for completion of
the sampling design by coordinating resources from the statistician,
senior contributing personnel and the needs of the user or contacts
that are relative to the sample design.
Sampling Design
GC Officer
The Sampling Design QC Officer is responsible for performing QC
evaluations to ensure that quality control is maintained throughout
the sampling design process.
Field/Sampling Leader(s)
The Field or Sampling Leader(s) is responsible for on-schedule
completion of assigned field work with strict adherence to SOPs and
complete documentation. The Field Leader(s) will supervise all field
activities, including implementation of the QA/QC program.
Sampling QC Officer
The Sampling or Field Operations QC Officer is responsible for
performing QC evaluations to ensure that quality control is
maintained throughout the entire field sampling procedure.
Laboratory Manager/
Leader
The Laboratory Manager is responsible for on-schedule completion
of assigned laboratory analyses with strict adherence to laboratory
SOPs. The Lab Manager will supervise all lab activities, including
implementation of the QA/QC program.
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TABLE 4-1 Continued.
TITLE
DESCRIPTION OF DUTIES/RESPONSIBILITIES
Laboratory QC Officer
The Laboratory QC Officer is responsible for performing QC
evaluations to ensure that quality control is maintained throughout
the entire sample processing procedures that occur within the
laboratory.
Data Processing Leader
The Data Processing Leader is responsible for on-schedule
completion of assigned data processing work and complete
documentation. The data processing leader/manager will supervise
all data processing activities, including implementation of the
QA/QC program.
Data QC Officer
The Data Processing QC Officer is responsible for performing QC
evaluations to ensure that quality control is maintained throughout
the data analysis process.
Document Production
Coordinator
Document Production Coordinator is responsible for on-schedule
completion of assigned writing, editing and data interpretation work.
The Document Production Coordinator will direct all reporting
activities, including in-house and outside review, editing, printing,
copying, and distributing or journal submission.
Reporting QC Officer
The Reporting QC Officer is responsible for performing QC
evaluations to ensure that quality control is maintained throughout
the entire reporting and document production process.
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SECTION 5
PROBLEM DEFINITION/BACKGROUND; PROJECT DESCRIPTION
5.1 The specific problem to be solved or decision to be made is stated in the
problem definition. Sufficient background information should be included to
provide a historical perspective articulating the regulatory or alleged toxic exposure
situation that led to the need for the project. The past history and problem
situation should include any previous work or data as well as any regulatory or
legal elements that will allow a technically-trained reader to understand the project
objectives.
5.2 The purpose of the project description is to define the specific objectives of
the bioassessment project and describe how the project will be designed to obtain
the information needed to accomplish the project goals and uses. As this section
supplies information needed by the intended users of the data, the project
description should include a general overview, study/monitoring design features
and rationale (methods for selecting sampling station locations, sampling period,
etc.), and project timetable.
5.2.1 The general overview contained in the project description should include, at
a minimum, the following information:
statement of problem, decision, or specific questions to be resolved;
description of the study site, facility, process, or operating activities
to be evaluated;
applicable technical, regulatory, or program-specific quality standards,
criteria, or objectives;
requirements for any special personnel or equipment;
the assessment tools needed for the project {i.e., program technical
reviews, peer reviews, and technical audits as needed and/or specified
by the Quality Assurance Plan (QAP);
anticipated uses of data to answer questions and make decisions;
the consequences of Type I or Type II errors based on these results;
historical conditions, existing datasets,
5.2.2 Evaluation of Historical Datasets
Previous projects that provide information on habitat, biota, or methods should be
evaluated. Such evaluation can give invaluable guidance in study design, including
sampling gear and study site selection. Use of comparable design, gear, site
location, and index period can considerably strengthen the temporal component of
an ecological study. Aspects of historical datasets evaluated include (but are not
limited to):
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sample types (assemblage, physical habitat assessment,
accompanying water/sediment chemistry)
dates of sample collection
location of sampling sites
type of sampling gear
intensity of laboratory processing (taxonomy, sorting).
5.3 The study/monitoring design features should be described and should include
the following:
A list of all measurements or variables to be taken (e.g., assemblage
metrics, physical and biological habitat parameters, or water chemistry
variables), including a designation of which measurements are critical
versus non-critical to the accomplishment of project goals. It should
be noted that many biological metrics and parameters will undergo
revision and fine tuning after evaluations of pilot studies and/or
revaluation of their effectiveness for the program; these revisions can
be appended to the overall program QAPP until the next annual review
and revision.
A statement of how measurements will be evaluated; e.g., by
comparison to reference data, literature, models, internal statistical
properties, or other historical information. If statistics are used to
analyze data, the rationale used to select the statistic should be
stated.
Explicit delineation of ecosystems to which decisions will be applied,
and a summary table listing the following for each sampling station:
types of samples (benthos, fish, periphyton, plankton, physical
and biological habitat assessment, or water quality)
numbers of samples of each type (designate primary and quality
control)
sampling gear.
5.4 A project timetable is included with beginning and ending dates for the
general project and for specific activities within the project. Any constraints, such
as seasonal variations in biota or stream flow, sampling logistics or site access,
should be identified in the timetable. The timetable needs to be detailed yet
flexible to account for unanticipated problems such as bad weather; guidance
should be included for handling such problems.
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SECTION 6
QUALITY OBJECTIVES FOR MEASUREMENT DATA
6.1 The QAPP must include a statement of the project's Data Quality Objectives
(DQOs). DQOs are qualitative and quantitative statements developed by data
users, at the programmatic, project and measurement levels, to specify the quality
of data needed to support specific decisions. Data users include representatives
from public or private sectors, stakeholders, and managers.
The logic process related to DQO development is made up of several of the
components of the project description outlined previously in Section 5. DQOs
encompass all aspects of data collection, analysis, validation, and evaluation. The
DQO process provides a means of ensuring the required confidence level in the
data needed by decisionmakers.
Evaluation of candidate measurement parameters (indicators) relative to stated
selection criteria (Table 6-1) ensures linkage of the decision process to DQOs. The
process involves establishing the allowable uncertainty of a data set which may
lead to Type I or Type II errors: false positives fa problem is found to exist when in
fact it does not) and false negatives (a problem is not found when in fact it does
exist). The acceptance probabilities of those errors as established by
decisionmakers are the DQOs. The DQO process entails establishing action-
triggering values and selecting rates of false positive and false negatives that are
acceptable to the decisionmaker.
The quality of a particular data set is a measure of the types and amount of error
associated with the data. Data quality is described by qualitative and quantitative
parameters, including precision, accuracy, representativeness, completeness,
comparability, and sensitivityall included in the QAPP. The seven steps of the
DQO process are in Figure 6-1.
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TABLE 6-1 Summary of soma measurement (indicator) selection criteria.
CRITERIA/QUALITY
DEFINITIONS)
Scientific Validity (Technical Considerations)
Measurable/ Quantitative
Feature of environment measurable over time; has defined numerical
scale and can be quantified simply.
Sensitivity
Responds to broad range of conditions or perturbations within an
appropriate timeframe and geographic scale; sensitive to potential
impacts being evaluated.
Resolution/
Discriminatory Power
Ability to discriminate meaningful differences in environmental condition
with a high degree of resolution (high signal ; noise ratio).
Integrates Effects/
Exposure
Integrates effects or exposure over time and space.
Validity/Accuracy
Parameter is true measure of some environmental condition within
constraints of existing science.
Related or linked unambiguously to an endpoint in an assessment
process.
Reproducible
Reproducible within defined and acceptable limits for data collection over
time and space.
Sampling produces minimal environmental impact.
Representative
Changes in parameter/species indicates trends in other parameters they
are selected to represent.
Scope/Applicability
Responds to environmental changes on a geographic and temporal scale
appropriate to the goal or issue.
Reference Value
Has reference condition or benchmark against which to measure
progress.
Data Comparability
Can be compared to existing datasets/past conditions.
Anticipatory
Provides an early warning of changes,
Practical Considerations
Cost/Cost Effective
Information is available or can be obtained with reasonable cost/effort.
High information return per cost.
Level of Difficulty
Ability to obtain expertise to monitor.
Ability to find, identify, and interpret chemical parameters, biological
species, or habitat parameter.
Easily detected.
Generally-accepted method available
Programmatic Considerations
Relevance
Relevant to desired goal, issue, or agency mission (e.g., fish fillets for
consumption advisories; species of recreational or commercial value).
Program Coverage
Program uses suite of indicators that encompass major components of
the ecosystem over the range of environmental conditions that can be
expected.
Understandable
Indicator is or can be transformed into a format that target audience can
understand (e.g., nontechnical for public).
17
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FIGURE 6-1 The seven step DQO process.
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6.2 The scope of the project should be described (e.g., the geographic locale,
environmental medium, time period, etc.), and any constraints such as time (index
period} or resources.
6.3 Sources of error or uncertainty associated with variables and indicators should
be evaluated in pilot studies and include;
measurement error: the difference between sample values and in situ
true values;
analytical error: error associated with the measurement process;
sampling error: a function of natural spatial and temporal variability
and sampling design (non-measurement error);
sources such as collection, handling, storage, and preservation.
QA activities and QA documentation procedures described in this guide are
intended to assist in reducing the magnitude of these sources and their frequency
of occurrence.
6.4 The uncertainty component of a multimetric index can be represented by the
range of values from aggregated reference sites. The values are either individual
metric values or total bioassessment scores calculated from X number of reference
sites within a stream class. Reference sites are selected based on the concept of
minimal anthropogenic disturbance, their being typical for the ecoregion,
subecoregion, and waterbody type, or their potential for consideration as a natural
landscape (Hughes 1995; Hughes et al. 1994). Repeat sampling within the same
index period from year to year will address natural interannual variability which can
be useful for evaluating uncertainty.
Expression of uncertainty is difficult when there is insufficient statistically-valid
measurements. Costanza et al. (1992) have developed a data-quality grading
system intended to allow statements of uncertainty on data ranging from
quantitative measurements to informed guesses. The approach is a notational
system that attaches a five-part description of data quality to datasets, including
numeric, unit, spread, assessment, and pedigree; it is introduced in this document
to help fill a gap in the ability to report confidence in environmental measurements.
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Measurement Quality Objectives
6.5 For each major measurement or value, the QA objectives for precision,
representativeness, comparability, accuracy, and completeness (measurement
quality objectives or MQOs) should be presented. These features are defined in
Smith et al. {1988) and USEPA (1989).
6.6 Precision is a measure of mutual agreement among individual measurements
or enumerated values of the same property of a sample, usually under
demonstrated similar conditions.
Assuring consistency of sampling and sample processing, and striving for
repeatability of measurements (Platts et al. 1983) will increase the precision of
data. For example, take replicate samples at adjacent sites where different
assessment results are not expected (due to the apparent absence of additional
stressors) and measure the precision of the procedure. Precision,
representativeness, and comparability can also be compared using raw data, metric
index values, and, possibly, final bioassessment scores. Appropriate methodology
and adequate training and instruction of personnel in methods application is the
most certain way to ensure consistency, repeatability, and precision.
6.7 If precision is to be calculated from two replicate samples, use Relative
Percent Difference (RPD) calculated as
(c,-cyxtoo
RPO=
(c1+cy*2
where C, = the larger of the two values and C2 = the smaller of the two values.
And, if it is to be calculated from three or more replicate samples, use Relative
Standard Deviation (RSD) calculated as
RSD==x 100
x
where s = standard deviation and x = mean of replicate samples. The standard
deviation or the standard error of a sample mean (s) is calculated as
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\
f (X,-X)2
M
n-1
where = measured value of the replicate, x = mean of replicate sample
measurements, n = number of replicates. Precision can also be expressed in
terms of the range of measurement values,
6.8 Data representativeness is the degree to which data accurately and precisely
represent a characteristic of a population or community, natural variability at a
sampling point, or an environmental condition. Representativeness of a sample
depends largely on randomized sampling of the target assemblage (Green 1979;
Smith et al, 1988; Freedman et al, 1991} and therefore is highly dependent on the
sampling program design. Generally, the sampling program should be designed to
ensure representative sample collection of the habitat or population being sampled
and adequate sample replication. The original Rapid Bioassessment Protocols
(RBPs) (Plafkin et al. 1989) were developed primarily for higher gradient streams
with a predominance of riffles, which are considered to be the most biologically-
productive habitat in such streams. However, in streams that are in a lower-
gradient topography (as in many coastal plains and deltaic zones), there is often a
lack of riffles. The Mid-Atlantic Coastal Plains Streams Workgroup has developed
a multihabitat sampling procedure for that area (MACS 1993, draft). For example,
for collection of benthic macroinvertebrates in low gradient (primarily non-riffle)
streams using the 20-jab dipnet collection method, the sampling would focus on
representative sampling reach characteristics. That is, if the suitable sampling
habitat within the sampling reach consisted of 70 percent snags, 20 percent
banks/ shorezone vegetation, and 10 percent submerged macrophytes, the
collection effort would comprise 14 snags, four banks, and two submerged
macrophytes. Representativeness is, in part, addressed by the description of the
sampling techniques and the rationale used to select the sampling locations.
Sampling techniques should be verified and validated in separate studies (Section
10).
6.9 Comparability is a measure of the confidence with which one data set can be
compared to another. It is often described in non-quantitative terms, but must be
considered in designing the sampling plans, index period, critical habitat
characteristics, topographic, geological, and hydrogeologic information, analytical
methodology, quality control, and data reporting. The use of standardized
sampling techniques and USEPA-approved analytical methods enhances the
comparability of parameters being measured with data similarly generated from
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other sources. Reporting of data in units used by other organizations improves
comparability. For biological assessments, comparability of data would need to be
determined by classifications such as ecoregion (or smaller geographic unit), index
period, and sampling gear. For example, samples collected within the same
ecoregion using the same gear but collected during different seasons (index
periods) may not be comparable.
6.10 Completeness is defined as the percentage of measurements made that are
judged to be valid according to specific validation criteria and entered into the data
management system. To achieve this objective, every effort is made to avoid
sample and/or data loss through accidents or inadvertence. Accidents during
sample transport or lab activities that cause the loss of the original sample will
result in irreparable loss of data. Collection of sufficient samples allows reanalysis
in the event of an accident involving a sample. The assignment of a set of
continuous (serial) laboratory numbers to a batch of samples which have
undergone chain-of-custody inspection makes it more difficult for the technician or
taxonomist to overlook samples when preparing them for processing and
identifications. The laboratory serial numbers also make it easy during the data
compilation stage to recognize if some samples have not been analyzed.
6.11 Percent completeness (% C) for all measurements can be defined as follows
(USEPA 1989);
Where v = the number of measurements judged valid and T = the total number of
measurements.
6.12 Table 6-2 provides hypothetical examples of MQOs for precision and
completeness. For example, when comparing two samples to determine precision,
a relative percent difference of 50% of the number of individuals (benthos) may be
an acceptable difference depending on the objectives and MQOs stated for the
project. Data quality requirements should be based on prior knowledge of the
sampling procedure or measurement system by use of replicate analyses, reference
conditions (site-specific or ecoregional), or requirements of the specific project
(USEPA 1989).
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TABLE 6-2 Example summary table of some hypothetical data quality requirements.
MEASUREMENT PARAMETER
REFERENCE
METHODS
PRECISION
(e.g., RPDJ
COMPLETENESS
(%»
Benthos
No. individuals
Plafkin et al. 1989
50
95
No. taxa
15
95
Fish
No. individuals
Karr et al. 1986
25
95
No. species
15
95
DQOs that cannot be expressed in terms of precision, accuracy, or comparability
should be reported by describing the specified method that will satisfy all
regulatory requirements specified; all other QAPP requirements would still need to
be fulfilled.
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SECTION 7
PROJECT NARRATIVE
7.1 Discuss in narrative form the following issues as they pertain to the project;
anticipated use(s) of the data
how the success and/or failure of the project will be determined
survey design requirements and description
sample type and sampling location requirements
sample handling and custody requirements
selection of analytical methods
calibration and replicate samples
SOPs for field sampling activities
plans for peer reviews prior to data collection, and
any ongoing assessments during actual operations (oversight).
The narrative should allow technical or QA readers to relate the project to the
DQOs and to the problem definition. Since this element addresses many other
QAPP elements in narrative form, it is not necessary to repeat information for
those categories that are covered in more detail elsewhere. For example, SOPs for
field sampling are discussed in detail in section 10 of a QAPP and therefore would
not have to be repeated in great detail in this section.
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SECTION 8
SPECIAL TRAINING REQUIREMENTS/CERTIFICATION
8.1 Specialized training or certification requirements needed for personnel to
successfully complete the project should be identified and described. Describe
how the training will be provided and how the required skills will be assured and
documented.
8.2 STAFF TRAINING
8.2.1 All personnel conducting assessments must be trained in a consistent
manner, preferably by the same person(s), to maximize the likelihood of
standardization and properly conducted assessments. If possible, sampling crews
should be trained by the most experienced individual; this individual would be
considered "certified" to train and may verify others to train upon sufficient
demonstration of knowledge and consistency of techniques. The certified trainer is
considered part of the QA program and should conduct revisits of sampling sites to
determine bias. Crews could revisit some of their own assessment sites to
determine among-crew precision. Precision of multiple sampling teams can be
determined after adequate evaluation of among-crew precision.
8.2.2 The designated QAO is responsible for confirming consistency among
investigators. At regularly scheduled intervals, a different field group should visit
selected overlap sites and perform assessment techniques to use as a replicate of a
previous assessment. Results from two separate assessments conducted by two
different teams can determine if reproducible results are being attained. This is an
ongoing process that should, over time, allow for consistent investigations of all
sampling teams. Quality control of picking, sorting, and taxonomic identifications
can also be evaluated in this way.
8.2.3 Training is not only for the inexperienced, but also is used to maintain
consistency among ail crews. Training should be conducted at regularly scheduled
intervals and should occur in all aspects of a program. This can be accomplished
through workshops, seminars, or field demonstrations. Management should
periodically assess the training needs of all personnel engaged in fieldwork and
recommend and support their participation in appropriate and relevant seminars,
training courses, and professional meetings. Biologists and technicians should be
expected to participate regularly in evaluation and/or certification programs where
appropriate. These programs should be included as current resumes which are on
file for each person responsible for the sampling, analysis, evaluation, and reporting
of biological data.
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SECTION 9
DOCUMENTATION AND RECORDS
9.1 State what type of information and records that must be included in a report
package for the project or task, and specify the reporting format if applicable.
Documentation can include raw data or QC checks. Specify required laboratory
turnaround time and whether a field sampling and/or laboratory analysis "case
narrative" is required to provide a complete description of any difficulties
encountered during sampling or analysis.
9.2 Specify any requirements for the final disposition of records and documents
from the project, including location and length of retention period. This section
can also identify the length of time that a voucher collection is to be maintained for
a particular project. Since for most institutions space is a premium, long-term or
indefinite maintenance may be accomplished by storing voucher collections at
universities and museums.
9.3 Be specific regarding the preparation, maintenance, and location (address) of
voucher collection(s), and the primary person responsible for it (them). Also
identify the maximum time period for which voucher materials will be maintained.
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SECTION 10
SAMPLING PROCESS DESIGN (EXPERIMENTAL DESIGN)/
SAMPLING METHODS REQUIREMENTS
10.1 SAMPLING PROCESS DESIGN
The experimental design and anticipated project activities, including the types of
samples required, sampling frequencies, measurement parameters, and the
rationale for the design, should all be outlined. Specific techniques or guidelines
for selecting sampling sites or use of sampling equipment should be described. All
specified measurements should be classified as critical (required to achieve project
objectives) or non-critical (informational purposes only).
Complete documentation and validation of the sampling and analytical
methodologies must be included. If non-standard methods or unusual gear will be
used, the rationale and appropriate methods validation information need to be
included. In the event that validation studies were not previously performed, they
must be conducted during the project study, and the results included as part of the
project results. Methods citations that allow for various options to be selected
should include the exact option chosen for the project.
10.2 SAMPLING METHODS REQUIREMENTS
Collecting representative samples is crucial to subsequent decision making.
Obtaining good results on non-representative samples are inappropriate because
such results could lead to incorrect decisions. Specific implementation
requirements for the selected method and gear should be outlined and any
procedures for onsite modification of sampling methods or corrective actions for
disruptions in the sampling methodology should be included. For example, if a
sampling method usually produced one liter of sample, but due to habitat and
seasonal effects during a sampling event the same sampling method produced 10
liters of sample (due to large amounts of detritus), the adjusted sampling
methodology or subsampling measures needed for sample processing should be
described. For all sampling events, the sampling crew needs to have access to the
Field/Sampling Leader or the Principle Investigator to discuss any onsite
adjustments that may not be outlined in the SOPs or QAPP. The sample containers
used and preservation methods appropriate for the selected assemblage should
also be described.
For example, the collection of periphyton may follow Procedure 6.2.2 in the Field
Procedures Manual of the Montana Water Quality Bureau (DHES 1989), using a
pocket knife for scraping, and a large-bore eyedropper for lifting microalgae.
Specific requirements for sample collection include the scraping of the entire
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surface of several rocks selected at random; lifting microalgae from mud or silt
substrates with an eyedropper; hand picking macroalgae in proportion to their
abundance; and removing epiphitic algae from macroalgae by shaking in a separate
container (Bahls 1992). The goal of this SOP is to obtain a single composite
sample that is a miniature replica of the algae present at the site. The algae is then
placed in a water tight, non-breakable sample container (125ml capacity) and
preserved with enough ambient water to cover the sample and iodine potassium
iodide (Lugol's solution) to a reddish-brown tint. Any onsite modifications should
be made by the crew leader or the approval (phone) of the Principle Investigator
with documentation of the rationale used for the modification along with the
appropriate methods validation information. The sample should be kept dark and
cold until processing (refrigeration is not needed for transport). Samples stored at
room temperature in daylight should have the Lugol's solution replenished every
few weeks. Any further specific requirements for assemblage collections would
also be included in this section.
10.3 STANDARD OPERATING PROCEDURES (SOPs)
10.3.1 SOPs are written procedures that detail the precise methods to be used
during each step of the sample collection, handling, transportation, and holding
time process. When existing written procedures are applicable and available, they
may be incorporated into the QAPP by reference. The SOPs should provide an
outline of the steps to be taken to assure the quality of the samples and sample
data. A complete set of SOPs should be approved by the Project Manager and
bound together in a looseleaf notebook that is easily accessible to personnel for
referral (ASTM 1991). Any deviations from the SOPs should be documented with
the reason for the deviation and any possible effect the deviation might have on
the resulting data. Modifications made to SOPs due to addition of new information
or correction of errors need approval by the Project Manager. SOPs should contain
document headers (as described in section 1) in order to track the latest revisions;
old versions should be discarded.
10.3.2 QAPPs should provide for the review of all activities which could directly
or indirectly influence sample quality and the determination of those operations
which must be covered by SOPs. The SOPs should describe the following:
method summary/rationale
selection of target assemblage(s)
sampling methodology, including decontamination procedures
physical and biological habitat assessment methodology
equipment/materials
reagents
details of preservation, holding times, and transport
use and calibration of instruments
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replication and QC requirements
safety
sampling site selection (including reference sites)
sample labeling
sample subsampling
data reduction formulas.
10,4 GUIDELINES USED TO SELECT SAMPLING SITES
10.4.1 Proper selection of sampling sites should be directed toward maximizing
accuracy, minimizing uncertainty or, at least, providing a means by which
variability may be reduced. This is related to the concept of only comparing
community-level data (between reference and test sites) if similar habitat exists.
Ideally, site selection criteria would be concise so that if two researchers were to
follow them, each would choose similar locations. The criteria should minimize the
amount of subjectivity that enters into the site selection process. For example, the
two primary criteria used in selecting reference sites for stream bioassessment are
minimal impairment and representativeness (Gibson 1994). The conditions at
reference sites should represent the best range of minimally-impaired conditions
that can be achieved by similar streams within a particular ecological region
(Hughes et al. 1995) (Section 6.4).
10.4.2 A probabilistic site selection process is one whereby sampling sites are
selected at random to ensure representativeness. Random selection provides a
statistically-valid estimate of the condition of a waterbody class or other habitat
class (e.g., lakes, large rivers, streams). These gross-level classes can be further
stratified into finer divisions based on geographic or other ecological, physical, and
chemical factors, and are used to group sites that share them. Probabilistic site
selection is the foundation of the regional monitoring approach of
USEPA/Environmenta! Monitoring and Assessment Program (EMAP).
For point source assessments, the sampling site selection should include stations
upstream and downstream of the source, as well as at least three regional
reference stations. For instance, the selection of sampling sites should be
conducted in such a way as to reduce variability and uncertainty by ensuring that
the physical characteristics between sampling sites are similar. If surveys are
conducted to determine use designations, sampling locations should be
representative of the stream reach. Reference conditions should include minimally
impaired sites in the same ecoregion, size class, and stream type (width, depth,
gradient). The objectives of the study will determine the selection of specific
sampling habitat and the gear best suited for the physical habitat sampled within a
sampling reach.
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10.5 HABITAT CHARACTERIZATION
10.5.1 An evaluation of physical and biological habitat quality is critical to any
assessment of ecological integrity (Karr et al. 1986; Barbour and Stribling 1991;
Plafkin et al. 1989; Kaufmann 1993). Habitat assessment supports understanding
of the relationship between physical habitat quality and biological conditions and
should consist of parameters appropriate for the assemblage being sampled since
habitat features important for one assemblage may be different than those for
another. Such assessment identifies obvious constraints on the attainable
potential of the site, assists in the selection of appropriate sampling stations, and
provides basic information for interpreting biosurvey results. Different habitats or
habitat types often become strata in the design.
10.5.2 QA methods for physical and biological habitat assessment should be
documented. Standardized field data sheets should be employed as should
multiple observers at some percentage of the study sites which range from
physically impaired to unimpaired.
10.5.3 Each investigator involved in the physical and biological habitat
characterization must be appropriately trained to minimize variability in the final
conclusions.
10.6 SAMPLING PERIOD
10.6.1 Sample timing should be consistent to reduce variability within or among
datasets. A critical value to assess when considering data variability is the most
appropriate period for sample collection, which can vary with the target
assemblage; both season and time of day should be considered. Pilot studies
should be conducted to determine an index period with the least sampling
variability. The sampling period, like the sampling area, defines the domain of
study and should be documented. By considering the following issues, some QA
concerns can be addressed:
Seasonal Influence - Time of year should be considered to determine
its influence on the objectives of the project. For example, food
availability, flow, and temperature are important seasonal factors that
influence condition of the biota. This determination can be
accomplished through literature searches on similar ecosystem
monitoring studies as well as through reconnaissance and pilot
studies.
* Community Succession and Life Stages - A familiarity of life cycles
may be critical in monitoring some community assemblages.
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Habitat/substrate disturbance (e.g., elapsed time since last storm
event, etc.).
As general guidance, sampling periods should be selected in order to:
maximize the efficiency of the chosen sampling gear;
maximize the accessibility of the targeted assemblage or species;
minimize natural variability;
maximize the availability of technical personnel.
Sampling periods should also be selected so that extremes in climatic conditions
are avoided {e.g., water temperature and flow [drought, rainy season, snowmelt]}
unless the object of the study is to investigate the limiting affects of seasonal
variations on the biota, or system complexity and recovery following storm events
(which could be hazardous to the sampling crew). Sampling conducted during
periods of stress should be well documented since natural stressors (i.e., high and
low flow, temperature, etc.) can mask or accentuate impacts.
10.7 DOCUMENTATION
10.7.1 The field data sheets should be filled out completely and accurately to
provide a record in support of the survey and analysis conclusions. Abbreviations
commonly used in documentation (e.g., scientific names) should be standardized
and defined to decrease data manipulation errors. Portable data recorders (PDR)
may be used to increase the completeness and accuracy of field data and
computer entry time.
10.7.2 Each sample collected should also be documented by assigning a unique
identification number, log number, and internal and external labels. An example of
the sample labelling information can be found in Figure 10-1; sample numbering
examples are presented in Figure 10-2. Data should be documented to allow
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Agency/Client
Project No. Sample No.
Location/Waterbody Station
Assemblage Habitat
Method (Gear! Preservative
Collected by QC Sample
Date Time Log #
FIGURE 10-1 Example of sample label information.
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1) A13 / 91 / 006
corresponds to:
Alex Branch, Station 13 / Year / Serial log number of
project
2) 6874-01 / A13 /006
corresponds to:
Project number / Alex Branch, Station 13 / Serial log
number of project
FIGURE 10-2 Alternative examples of sample identification numbering.
Example 1 relates the sample to the year; example 2 relates the sample to a
contract or project number.
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complete reconstruction from initial field record through data storage system
retrieval.
10.7.3 The Field/Sampling Leader and Laboratory Leader personnel should keep
complete and permanent records of all conditions and activities that apply to each
individually-numbered sample. All field and laboratory data sheets should be dated
and signed by the individual doing the sampling and the analyst, respectively;
taxonomic reference documents should be approved and noted. Notebooks, data
sheets, and all other records that may be needed to document the integrity of the
data should be archived at study completion and kept permanently filed in a safe
and fireproof location. Records and voucher samples should be maintained at least
3 years or other specified time as stated in the workplan and/or contract. Archived
records are the responsibility of the study sponsor, unless the responsibility is
delegated.
10.8 REPLICATION
10.8.1 Field sampling validation involves two procedures:
1) collection of replicate samples at a randomly selected 10 percent of
sampling stations to document the precision of the collection effort;
and
2) for visual-based qualitative habitat structure assessments, two or
more observers should independently complete field sheets for at least
10 percent of stations. The precision of this procedure can then be
evaluated by relative percent difference (RPD) (Section 6.6).
The 10 percent figure should be viewed as rule-of-thumb guidance for replication.
For large projects, 10 percent replication would probably be too many; similarly, for
small projects, 10 percent would likely not be enough. General recommendations
for different levels of replication are presented in Table 10-1.
10.9 METHOD AND GEAR SELECTION
10.9.1 Selection of sampling gear should be appropriate for the target
assemblage, habitat, and analytical methods employed and depends on the DQOs
of the decisionmakers, the expertise of the biologist, and the unresolvable
components of variation (USEPA 1990a). Where appropriate, methods should
include decontamination SOPs. For example when using a net for collection of
small organisms, the nets must be thoroughly cleaned between samples so that
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TABLE 10-1 Rute-of-thumb for number of raplicata QC samples based on numbers of sites.
No. of sampling sites in project (n)
No. of replicates
n 150
15
30 n ^ 150
10%
n < 30
3
n = 2
3
n = 1
3
organisms captured at one station do not get carried over and included in the next
station. In cases where it is suspected that an organism has been carried over
from one station to the next, the field notes should be consulted to determine if
the (outlier) organism could indeed be left over from a previous sample. If it
becomes obvious that an organism was left on the gear from a previous sample, a
statement should be included on the data sheet that the carry-over organism will
not be included in the metric calculations. This situation is rare but when it
occurs, it will most often occur with gear used in sampling small organisms.
10.9.2 The type of gear used in the sampling process depends on the assemblage
and the specific habitat sampled within a site. For instance, macroinvertebrate
sampling gear may include kicknets, Hess sampler, surber samplers, or other
stream-net samplers (USEPA 1990a) used in riffles; dipnets are used in low
gradient streams in shorezones, banks, snags, and submerged vegetation.
Electrofishers, seines, and Fyke nets may be used for fish. Knives used for
scraping algae from rock, spoons or large-bore eyedroppers for lifting microalgae
from silt substrates, hand picking macroalgae, and dislodging epiphytic algae from
macroalgae by shaking in a container are some gear types or methods used in algal
collections.
10.10 LEVEL OF EFFORT (LOE)
10.10.1 The level of effort required for the completion of a specific task should be
outlined before the task is undertaken. For example, for field sampling, the
appropriate number of people for unbiased and consistent operation of field gear
should be available (e.g., two people for collections with a net or hand collections,
three to four people for electrofishers), and the appropriate amount of time per site
needs to be allowed for consistent and proper application of methodologies. The
amount of time necessary for effective gear operation will vary per site with the
target plant or animal assemblages and the gear used. For planning purposes,
Plafkin et al. (1989) provided approximate estimates for the amount of time
needed for sampling protocols for use of the square meter kicknet in sampling
35
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benthic macroinvertebrates in riffles. Approximately 1.5 person-hours are required
per site (two people, approximately 40 minutes each}. The 40 minutes of actual
time includes writing labels, preparing sample containers, taking a double
composite square meter sample, field preservation, and collection of supplemental
Course Particulate Organic Material (CPOM) samples. An additional 15 to 20
minutes is required to complete the habitat assessment forms. More time is added
to total station LOE for additional observers completing duplicate habitat
assessments. Additionally, time must be allocated for traveling to the site.
36
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SECTION 11
SAMPLE HANDLING AND CUSTODY REQUIREMENTS
11.1 SAMPLE HANDLING
11.1.1 Sample handling requirements vary with the assemblage being studied in
the survey. For most biological assessments, the minimum sample size needed to
fulfill the data quality objective for representativeness should be incorporated into
the sampling design so that sampling produces minimal environmental impact. For
those samples that will be analyzed in the laboratory, the organisms are sacrificed
and field preservation, labelling and transport protocols must be followed. For
many fish surveys, experienced fish biologists are proficient in field identifications
and thus most specimens are returned to the water following identification and
enumeration. Exceptions are juveniles, hybrids, and difficult-to-identify species.
Also, if temporary crews are used, samples of all collections should be verified.
Voucher specimens are appropriate for all species and should be stored in fish
museums or universities whenever possible. For a review of fish methods, readers
should refer to USEPA (1993a), Meador et al. (1993), or Ohio EPA {1989). Most
of the following information is appropriate for those types of samples that are
returned to laboratories for processing and identification (benthic invertebrates,
phytoplankton, and periphyton).
11.1.2 All activities categorized under sample handling should be documented (as
SOPs) and followed closely to prevent or minimize the introduction of error.
Consistency should be the rule in all of the following activities:
field preservation
labelling
storing or transportation.
11.1.3 The following information associated with each sample should be
identified:
exact location and ambient conditions associated with sample
collection should be maintained in field notebooks, field collection
sheets, or PDRs; possession and analysis logs should be maintained in
the laboratory;
chain-of-custody forms, sample preservation, if any, and dates and
times of sample transfer and analysis;
procedures for transferring and maintaining custody of samples.
37
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11.1.4 Certain sampling protocols (e.g., the Tier 2 protocols of Plafkin et al.
[1989] and most fish sampling) involve sorting, identification, and enumeration of
specimens in the field. When benthic macroinvertebrate and fish samples are field-
identified, the field data sheets become the item of custody as do any preserved
specimens used for taxonomic verification. All header information should be
completely filled out and copies of all sheets distributed. As specimens are
laboratory-identified, they can be preserved, archived as vouchers, and placed in a
repository. Location of the repository and a record of the specimen preservation is
entered into a log book.
11.2 SAMPLE CUSTODY
11.2.1 The primary objective of the chain-of-custody procedure is to create a
written record that can be used to trace the possession of the sample from the
moment of collection through the entire data analysis. Field crews as well as
laboratory personnel should follow written chain-of-custody procedures for
collecting, transferring, storing, analyzing, and disposing samples. Sample custody
procedures are important to ensure the integrity of the samples whether for legal or
other purposes. Explicit procedures must be followed to maintain the
documentation. An example chain-of-custody form is presented in Figure 11-1;
separate forms should be filled out for each sample if the samples are likely to
become separated. Notations should be entered in the logbook regarding the
condition of the samples.
All sample labels, as well as the chain-of-custody form, should contain the
following information at a minimum;
ID or log number (can be same as sample no.)
Location - state, county, approximate distance from nearest town,
name of waterbody being sampled
Date - date of sample collection
Time - time of sample collection
Sampled By - initials of personnel collecting the sample
Type of Sample - e.g., benthos, periphyton
Preservative - e.g., formalin, 95 percent ethanol, Lugol's solution
Station - numbers or letters to designate station location
Sampling Gear - e.g., kicknet, seine, eyedropper.
11.2.2 Samples from which courtbound data are to be derived are kept in sample
storage areas of the laboratory where access is limited to laboratory personnel and
controlled by locked doors: treating all samples as if they were courtbound
decreases the likelihood of mishandling actual courtbound samples. The samples
are routinely retained at the laboratory as required by the project after the data
have been forwarded to the appropriate person(s) so that any analytical problems
38
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Project Manager or Contact:
Address/Phone:
#
s
a
m
P
1
e
s
#
j
a
r
s
Sample Type
CHAIN-OF-CUSTODY J
Agency Name:
Address:
Project Number Project Name:
Sample Location:
Page
of
Gear
Method
Log Number
Date
Time
Sample Station
Sampled by:
(signature)
Date/Time:
Relinquished by:
(signature)
Date/Time:
Received by:
(signature)
Date/Time:
Received by:
(signature)
Date/Time:
Received by:
(signature)
Date/Time:
Received by:
(signature)
Date/Time:
FIGURE 11-1. Chain-of-custody record.
39
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can be addressed. The samples are discarded at the end of a specified time period
{see Table 12-1); 1 to 5 years may be appropriate, depending on project
requirements. Long term preservation methods of biological samples can be found
in Klemm et al. (1990). A sample evidence file should be maintained which
includes copies or original laboratory bench sheets, field notes, chain-of-custody
forms, logbooks, sample location and project information, and final report. The
location and responsible agency of the evidence file should be named in the project
plan.
11.2.3 Specific tasks/conditions for sample storage may include the following:
Samples will be stored in a secure area.
The secure area will be designed to comply with the storage
method(s) defined in the contract (i.e., fireproof, ventilated, etc.).
The samples will be removed from the shipping container and stored
in their original containers unless damaged.
"Damaged and unusable samples" (for example, a sample container
that broke and part or all of the sample was not recoverable) will be
disposed of in an appropriate manner and disposal will be
documented.
"Damaged and usable samples" (for example, a sample container that
broke in such a way as to salvage all organisms) will be documented
and transferred to a new container, if possible and necessary. The
field leader and the Project Manager will be notified immediately of
any damaged or disposed samples.
The storage area will be kept secure at all times. The sample
custodian will control access to the storage area. Duplicate keys for
locked storage areas should be maintained only by the appropriate
personnel.
Whenever samples are removed from storage, this removal will be
documented; all transfers of samples can be documented on internal
chain-of-custody records.
Samples, reference, and voucher specimens (section 12.7) will be
stored after completion of analysis in accordance with the contract or
until instructed otherwise by the Project Manager.
40
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The location of stored reference or voucher specimens will be
recorded.
Reference or voucher specimens will not be stored with samples.
The sample storage area will be described.
41
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SECTION 12
ANALYTICAL METHODS REQUIREMENTS
12.1 Methods of sample arid data analyses should be well-documented, These
methods must be appropriate for all parameters and should be USEPA-approved or
otherwise validated/published standard methods.
12.2 For USEPA-approved or standard methods, pertinent literature should be
referenced. Pertinent literature would include appropriate validation data for the
methods to be used.
12.3 For non-standard, state developed, or modified methods (Caton 1991),
detailed SOPs should be provided which include methods for all sample
preparation, picking and sorting, and identification procedures.
12.4 BIOLOGICAL SAMPLE LABORATORY PROCESSING
12.4.1 Biological sample laboratory processing generally falls into two broad
divisions. The initial or primary sample processing may include sorting,
subsampling, and re-sorting checks. Secondary or final phase processing may
include taxonomic identification and verification procedures, tabulation,
enumeration, and measurements. The secondary phase might also include
calculation of metrics or indices. An example of a macroinvertebrate data sheet is
presented in Figure 12-1.
12.5 PRIMARY PHASE SAMPLE PROCESSING
12.5.1 Subsampling - In biomonitoring programs where resource limitations
restrict expendable sampling and analytical effort, subsampling is recommended as
a cost-effective and valid procedure for (a) selecting a representative estimate of
the total sample collected and (b) standardizing the level of effort expended on
each sample. Subsampling methods vary according to the assemblage. For
example, methods may include procedures for cleaning diatom strewn mounts, and
establishing counting transects on coverslip (Bahls 1992). Caton (1991) has
developed a gridded screen technique for increased objectivity in field or laboratory
subsampling of benthic macroinvertebrates. As subsampling methods are
developed, every attempt should be made to reduce bias. SOPs should, therefore,
be developed to standardize the unit of effort and to eliminate subsampler
subjectivity and errors in sorting and picking. Subsampling error should be
quantified depending on the type and volume of the subsample.
12.5.2 Sorting macroinvertebrates includes rough segregation of individuals
within a sample or subsample by some predetermined taxonomic grouping into pre-
42
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labelled containers. Sorters should be trained so that they can identify the
organisms from the surrounding debris; QC checks on new sorters should be
frequent until it is clear that the sorter knows what he or she is looking for.
Subsequent sorting results in containers of finer taxonomic groupings. For algae
samples, this step may include estimates of relative abundance of non-diatom
(soft-bodied) algal taxa determined by cells per field of view of a composite wet
mount (Bahls 1992).
12.5.3 Sorting Checks (postprimary sorting") - A portion of sample residues
must be re-checked by intralaboratory QC personnel for missed specimens (under-
recovery). Re-sorting checks can be used to measure repeatability. A portion of
sample residues may also be re-checked by separate laboratories for interlaboratory
QC.
12.6 SECONDARY PHASE SAMPLE PROCESSING
12.6.1 Taxonomic Identifications, Verification Procedures - Training, experience,
and possession of proper laboratory equipment and taxonomic literature are crucial
factors affecting the quality of identification activities. Abbreviations commonly
used in documentation {e.g., for scientific names) should be standardized and
defined in the data pack to decrease data manipulation errors. A general guide is
that specimens should be identified to the lowest possible taxonomic level using
the most current literature available. Some parameters or other analytical
techniques, however, may only require identification to the ordinal, familial, or
generic level (Plafkin et al. 1989; Ohio EPA 1987). An argument against such an
approach is that sensitivity and tolerance information is more accurate at the
species level of identification.
All questionable taxonomic identifications should have a post-determined level of
uncertainty identified. For instance, define a scale of uncertainty (e.g., 1-5 where
1 is most certain and 5 is least certain) for each identification and specify reasons
for any uncertain identification (e.g., missing gills, headless specimen, etc.).
Define the criteria for assigning tolerance values to uncertain identifications. For
example, if the generic level of identification is questionable, determine an average
tolerance value for the family levet. For those taxa that are in good condition and
easily identified by the taxonomist, the rating can either be noted as 1 (certain) or
left blank.
12.6.2 Verification should be done in one of two ways; Comparison with a pre-
established reference or research specimen collection can yield rapid and
43
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Benthic Macroinvertebrates Laboratory Bench Sheet
Site/Project#
Location
Samolina Station
Type of Sample {Gear!
Subsamole: Total 100 200 300 Other
Taxonomist
Date Sampled
Sorter
truer hamiiy ana/or oenus and Si
jocies name on Dianic line. a = aouit i = immature
Oroanisms
No
A
i
TCR
Oroanisms
Imo
A
I
TCR
Diotera
Heteroutera
Chironomidae
Coleootera
Other
Neurootera and Meoalootera
Trichootera
Crustacea
Oliaochaeta
Plecootera
Eohemerootera
Hirudinea
Bivalvia
Gastropoda
Odonata
Other
Taxonomic certainty rating (TCR) 1-5: 1 =most certain, 5 = least certain. If rating is 3-5, give reason
(e.g., missing gills).
Total No, Organisms Total No. Taxa
FIGURE 12-1 Macroinvertebrate laboratory bench sheet.
44
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accurate results. A reference collection is defined as a set of biological specimens,
each representing some taxonomic level and not necessarily limited to specific
projects or activities. Reference collections should have expert confirmation of
each taxon.
Reference collections are used for verifying identifications of subsequent samples.
One potential problem with this approach may be the previous misidentification of
the reference specimens. An approach most likely entailing the least uncertainty is
to send samples to taxonomic experts familiar with the group in question for
confirmation (Borror et al. 1989). Detailed documentation of independent
taxonomic verification by recognized experts should be provided along with
address and telephone number. Potential problems might result by establishing a
set of contacts among recognized experts in various groups of organisms. The
taxonomist should always be contacted by telephone or correspondence prior to
sending specimens. Just as important is the receipt of advice on proper methods
for preserving, packing, and shipping samples to them. Damaged specimens are
often useless and impossible to identify; thus, careful preservation and packing is
essential.
12.7 VOUCHER COLLECTION
12.7.1 The true data of a project are the actual specimens collected in a survey
for that project. Following identification and enumeration, these specimens should
be maintained in a voucher collection. Voucher collections can be maintained any
specified length of time for the project. For instance, if space is a critical issue,
the voucher collection can be disposed of after the data have been reviewed and
the report finalized.
12.7.2 Voucher collections may sometimes serve as reference collections but
usually not vice-versa. This is primarily because reference collections are
arranged/curated based on taxonomic and/or phylogenetic order and are not usually
associated with particular projects or specific waterbodies (although that
information will be included with label data). If there are ever questions regarding
the accuracy of taxonomic identification that have been used in parameter
calculation and reporting, referral to the voucher collection will be an initial step
taken in resolution. Also, a complete list of taxonomic references used should be
compiled for each project such as is found in USEPA (1990a). A comparison of
various attributes of reference and voucher collections is presented in Table 12-1.
45
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TABLE 12-1 Comparison of reference and voucher collections.
CONSIDERATIONS
REFERENCE
VOUCHER
Usual Curatorial
Arrangement
Taxonomic and/or phylogenetic
By project or sample lot
Taxonomic
Verification
By expert
By expert, or comparison to reference
collection
Number of individual
organisms by
designated taxon
At least one, but several may be
included {or added over time) to
illustrate sexual dimorphisms or
other morphological variability
{including deformities) as well as
to document geographic
distribution
If full number of individuals enumerated
from sample and used in data
calculation is archived, it serves as the
sample voucher; if a selected number of
individuals from a sample {e. g., 10-20
out of 100 total) that represent an
identifier's concept of a particular
taxon, the specimens serve as the
taxonomic voucher
Slide-Mounted
Specimens
Permanent
Permanent or temporary
Required Storage
Space
Large, but would tend to
increase only as fast as
additional taxa are incorporated;
will be somewhat dependent on
geographic area of responsibility
Could be very large, and could continue
to grow at rapid pace if there are
vouchers retained from all samples from
all projects; particularly a problem with
archiving of fish samples, progressively
less so with benthos and periphyton.
Fish vouchers should be deposited in a
state or regional fish museum or
university, where they are also useful
for systematic and biogeographical
research by others.
Can serve as
reference collection?
Sometimes, if curated in taxonomic
arrangement within each project or
sample lot; if project-oriented samples
are segregated by taxon and distributed
in collection among appropriate
taxonomic groupings
Can serve as
voucher collection?
Usually not, reference collections
don't contain the totality of a
sample; if they do, it could (but,
not necessarily} take
considerable effort to reassemble
the sample; also, will not
normally contain representative
specimens from all project
samples thus limiting its utility
as a voucher collection
46
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TABLE 12-1 Continued.
CONSIDERATIONS
REFERENCE
VOUCHER
Duration of
Maintenance
Permanent, ongoing
As required by contract or project
specifications [e. g., project terms may
specify maintenance of vouchers for a
period of 5 years following final report
approval after which they may be
discarded}; may be permanent; after
this, and if not already done, voucher
specimens may be incorporated into the
reference collection
Major function and
use
Taxonomic verification of future
identifications
Data verification for specific projects
47
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SECTION 13
QUALITY CONTROL REQUIREMENTS
13.1 The QC checks that are needed are determined by the project QA objectives
and the anticipated uses of the results. QC checks apply to field activities,
laboratory activities, and the data analysis.
13.2 Field activity QC checks should include:
collection of replicate samples at various stations (usually 10 percent
of total number of samples collected Section 10.9.2 and Table 10-1)
to assess the consistency of the collection effort;
repeat {and/or parallel) field collections and analyses performed by
separate field crews to provide support for the bioassessment (Table
10-1);
occasional alternating and mixing of field personnel to maintain
objectivity (minimize individual bias) in the bioassessment; and
in visual-based physical habitat assessment, final conclusions are
potentially subject to variability among investigators. This limitation
can be minimized, however, by ensuring that each investigator is
appropriately trained in the evaluation technique and periodic cross-
checks are conducted among investigators to promote consistency.
Consistency among parallel and independent physical habitat
assessments can be evaluated by rank order comparisons of the
evaluated sites. Thus, comparing the score for each parameter is not
as important as comparing the total score for each habitat assessment
which gives the rank order of sites (their placement in the assessment
from good to bad).
13.3 Laboratory activity QC checks should include:
Periodic sorting checks of samples to uphold a minimum established,
at least 90 percent, percent recovery error to maintain sample
processing and sorting efficiency. When the established percent
recovery error is not met, then an appropriate number of samples
should be re-checked until the percent recovery error is within
accepted limits.
A record of all samples sorted along with a list of QC checks should
be maintained to document the QC process for the samples.
48
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* Taxonomists, who are identifying organisms, should have adequate
taxonomic references to perform the level of identification required.
These references should be on file at the laboratory so that periodic
checks can be made to facilitate obtaining new references or updating
existing references needed for the identification of specimens to the
lowest taxonomic level possible.
* Representative specimens of all taxa identified should be checked and
verified by a specialist in that particular taxonomic group. These
specimens should be properly labelled as reference or voucher
specimens {including the name of the verifying authority), permanently
preserved, and stored in the laboratory for future reference.
13.4 Data management QC checks should include:
* hard copies of all computer-entered data should be reviewed by the
data entry personnel by direct (side-by-side) comparison with the field
or laboratory handwritten data sheets.
13.5 Data analysis QC checks should include the following:
* Periodic checks by trained staff or peer review throughout the data
analysis process. Data validation and verification QC checks include
examination of outliers, total numbers, odd numbers, and unusual
species. Errors can occur if inappropriate statistics are used to
analyze the data.
* Transcription error and a poor presentation can occur if care is not
taken to provide adequate training and appropriate review, QC
checking of data reports by peer review, the use of a technical editor,
and following a standard format will help to ensure complete and
relevant data analyses and reporting.
49
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SECTION 14
INSTRUMENT/EQUIPMENT TESTING, INSPECTION,
AND MAINTENANCE REQUIREMENTS
14.1 To help ensure collection of consistently high-quality data, a plan of routine
inspection and preventive maintenance should be developed for all field and
laboratory equipment and facilities. The following types of preventive maintenance
items should be considered and addressed in the QAPP:
a schedule of important preventive maintenance tasks that must be
carried out to minimize downtime in the field and laboratory should be
kept;
a list of any critical spare parts that must be on hand to minimize
downtime in the field and laboratory {Section 15.3) should be
included;
personnel whose duties include operation of specific pieces of
sampling gear or detection instruments should have primary
responsibility for inspection of such equipment;
personnel should be assigned responsibility for locating and gathering
of all necessary field equipment at least 24 hours in advance of
departure to sampling stations.
14.2 Example equipment and supply lists for benthic macroinvertebrates and fish
are presented (Tables 14-1, 14-2).
50
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TABLE 14-1 Example of equipment and supply list for banthic macrolnvertebrate sampling (Plafkin et al.
1989J.
1.
Square meter kicknet, standard no. 30 mesh (595 //m openings) {including pole attachments)
2.
Additional kicknet, as backup
3.
Sample containers, two-three 1-liter, plastic, opaque, straight-sided, w/screw tops (per station}
4.
Maps of site location and access routes
5.
Two internal labels (per station)
6.
12 Pencils, no. 2 soft lead
7.
Grease pencils, two-three (per trip)
8.
Scissors, one pair
9.
Forceps, three or four pair
10.
Gridded screen subsampling equipment ICaton 1991]
11.
Wash bottle, 1-liter capacity
12.
Sieve bucket, standard no. 30 mesh (595 fjm openings) (Wildco cat. no. 90)
13.
Two 1-gallon buckets (plastic)
14.
One clipboard
15.
95 percent ethanol, or 10 percent formalin; 0.5 gallon per station [container should be appropriate
for pouring into sample containers w/minimum spillage)
16.
Funnel
17.
Hip waders, one per crew member
18.
Log book (boundi/Field notebook
19.
Data sheets (may be rite-in-rain) or PDR
20,
Box or cooler for sample transport
21.
Dice for random numbers determination
22.
First aid kit
23.
Rubber gloves, heavy gloves
24.
Rain gear for each person
25.
Waterproof tape
26.
Compass
27.
Kim wipes in ziplock bags
28.
Watch with timer or stop watch
29.
Camera
30.
Patch kit for waders
51
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TABLE 14-2 Example of equipment list for fish sampling in wadabls streams.
1.
Backpack electrofisher
2.
Spare batteries (or gasoline) and spare electrofisher if distant from base
3.
Insulated rubber gloves, one pair per person
4.
Waders, hip or chest, one pair per person
5.
Long-handled nets, two
6,
Plastic buckets, three or four 1-5 gallon capacity
7.
Block nets, two 20 meters in length
8.
Measuring tape, 100 meter
9.
Fish measuring board (length!
10.
Weight scales
11.
Clipboard
12.
Data sheets (may be rite-in-rain] or PDR
13.
12 Pencils, no. 2 soft lead
14.
Ear plugs (if gasoline generator)
15.
Patch kit for waders
16.
Fish field guide
17.
Anesthesia (MS-222)
18.
Plastic collection jars with tight fitting lids (multiple sizes)
19.
Electricians tape
20.
Labels
21.
Preservative (formaldehyde or isopropyl alcohol)
22.
Probe
23.
Calipers
24.
Stopwatch with timer
25.
Small dip net
26.
White enamel pan
27.
Conductivity pen
28.
Camera
52
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SECTION 15
INSTRUMENT CALIBRATION AND FREQUENCY
15.1 The purpose of this section is to document detailed description or reference
of the appropriate SOPs for assuring that field and laboratory equipment are
functioning optimally. Instruments used for measuring water quality, current
velocity, or any other measurable parameters should be calibrated with certified
equipment and/or standards (with known, valid relationships to nationally
recognized performance standards) prior to gathering data at each sample location.
In the absence of nationally recognized standards, documentation for the basis of
the calibration is needed. Permanent records with dates and details of these
calibrations and checks must be maintained. Documentation is necessary to
identify each specific measuring device, where and when it is used, what
maintenance was performed, and the dates and steps used in instrument
calibration. This information should be traceable to each instrument. Definition
should be given for the acceptance criteria for all calibration measurements. All
field measurements should be accompanied by documentation of the type of
instrument and the identification number of the instrument used.
15.2 For biological field equipment, there should be routine procedures to ensure
that equipment is appropriate for the needed sample and is in proper working
order. For example, for benthic macroinvertebrates and algal collections using
artificial or introduced substrata should have confirmation of surface area.
Multiplate samplers (e.g., Hester-Dendy), should have the number, area, and
thickness of plates and spacing dividers confirmed and documented prior to
departure from storage. Rock baskets (introduced substrate) should have the
surface area of the rocks confirmed and documented prior to departure from
storage. For benthic macroinvertebrate net collections, there should be
measurement of the device dimensions and knowledge of size of mesh net
openings. There should also be effort toward repairing holes or replacing nets.
Zooplankton and phytoplankton pumps, traps, and nets should be checked for
proper working order and size of mesh net openings; hand collection gear, bottles,
knives, and droppers should be clean and in good working order.
For fish (electrofishers) there should be consistent checks of voltage, amperage,
wattage, and field pattern in the context of conductivity. Calibration of electrical
instruments should occur at each sampling site. Confirmation and notation of
condition for proper biological sampling gear should be at least 24 hours prior to
scheduled departure for fieldwork.
15.3 For biological field gear, there are several components that need to be
checked initially and then prior to fieldwork. When gear is constructed or received
53
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from the manufacturer, initial documentation of equipment specifications should be
recorded. For example,
gear dimensions
gear specifications
gear condition
net dimensions
mesh size
appropriateness of gear for study objectives.
Prior to each field effort, gear should be checked. For example,
gear condition
working order
spare parts
repair kits
extra units.
15.4 In essence, taxonomic identification performance is partly accomplished by
ensuring use of the most current technical taxonomic literature, by development
and use of an appropriate reference collection, and by use of an expert taxonomist
(Sections 12.6, 12.7).
54
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SECTION 16
INSPECTION/ACCEPTANCE REQUIREMENTS FOR
SUPPLIES AND CONSUMABLES
16.1 Discuss how and by whom supplies and consumables, such as sample
bottles, reagents, nets, etc., will be inspected and accepted for use in the project.
Identify the acceptance criteria for such supplies in order to satisfy the technical
and quality objectives of the project.
55
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SECTION 17
DATA ACQUISITION REQUIREMENTS (NON-DIRECT MEASUREMENTS)
17.1 Identify the types of data that will likely be acquired from non-measurement
sources such as computer databases, spreadsheets, and literature files; for
example, metric calculations performed on a personally designed spreadsheet
routine, identification of literature review for tolerance values, information from
topographical maps, historical reviews, and raw data received electronically in
addition to bench sheets from laboratories. Define acceptance criteria for the use
of the data in the project. Discuss any limitations on the use of the data based on
uncertainty in the quality of the data and explain the nature of that uncertainty.
For instance, if raw data are entered electronically from laboratory bench sheets,
each entry should then be confirmed.
56
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SECTION 18
DATA MANAGEMENT
18.1 Outline the project data management scheme by tracing the path of the data
through receipt from the field or laboratory to the use or storage of the final
reported form. Describe the standard record keeping procedures, document control
system, and the approach used for data storage and retrieval on electronic media.
Explain the control mechanism for detecting and correcting paperwork errors and
for preventing loss of data during data reduction, data reporting, and data entry to
forms, reports and databases. Provide examples of any forms or checklists to be
used.
18.2 Identify and describe all data handling equipment and procedures that will be
used to process, compile, and analyze the data. This includes procedures for
addressing data generated as part of the project as well as data from other
sources. The specifications should include any required computer hardware and
software and should address any specific performance requirements for the
hardware/software configuration used. Describe the procedures that will be
followed to demonstrate acceptability of the hardware/software configuration.
57
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SECTION 19
ASSESSMENT AND RESPONSE ACTIONS
19.1 Identify the number, frequency, and type of activities needed to assess and
evaluate the project. Assessments include system audits and performance audits
which are part of every quality control program. Each QC plan must describe the
internal and external performance and system audits required to monitor the
capability and performance of the project.
19.2 A systems audit consists of a review of the total data production process
which includes onsite reviews of the field and laboratory operational systems and
facilities for sampling and processing of samples.
19.3 A performance audit is a type of audit in which the quantitative data
generated (e.g., species enumeration and identification) is independently
enumerated and identified. This type of audit can test accuracy.
19.4 To the extent possible, these audits should be conducted by individuals who
are not directly involved in the measurement process. Audits serve three
purposes:
1) to determine if a particular personnel or organizational group has the
capability to conduct the monitoring before the project is initiated;
2) to verify that the QAPF and associated SOPs are being implemented;
and
3) to detect and define problems so that immediate corrective action can
begin.
19.5 The QAPP should specify who will conduct the audit, their relationship
within the project organization or their independent affiliation, what the acceptance
criteria will be, if or what type of audit will be used, and to whom the audit reports
will go. A list should be prepared of the approximate schedule of activities and
outline the information expected from the audit. The QAPP should also explicitly
define under what conditions the assessor has the ability to order a work
suspension.
19.6 The QAPP should explain how and by whom response actions to non-
conforming conditions will be addressed, and identify the person(s) responsible for
implementing the corrective action. The plan should also describe how corrective
actions will be verified, validated, and documented. A corrective action program
must have the capability to plan and implement measures to correct identified
58
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problems, maintain documentation of the results of the corrective process, and
continue the process until each problem is eliminated. The corrective action is the
process to remediate defects.
19.7 Corrective actions may be initiated as a result of the following QA activities:
1) performance audits
2) systems audits
3) internal quality control checks.
19.8 When sampling or data analyses are shown to be unsatisfactory as a result
of audits or QC sample analysis, a corrective action should be implemented. In
addition, corrective actions should be taken during the course of sample and data
analysis by field and laboratory crew when the routine QC check criteria are not
met. The Project Manager, Laboratory Manager, Quality Assurance Manager, and
support technicians may be involved in the corrective action. If data are affected
by the situation requiring correction or if the corrective action will impact the
project budget or schedule, the action should directly involve the Project Manager.
19.9 Corrective actions are of two basic kinds:
1) Immediate - the need for such an action will most frequently be
identified by the field or laboratory technician as a result of calibration
checks and QC sample analyses.
2) Long-Term - the need for such actions may be identified by audits.
Examples of this type of action include:
staff training in technical skills or in implementing the QA/QC
program
rescheduling of field, laboratory, data handling activities to
ensure analysis within allowed holding times
reassessment of field and laboratory operation procedures and
personnel.
19.10 For either immediate or long-term corrective actions, the following steps
should be taken:
Specify what type of conditions require corrective action.
* Define the specific problem.
* Assign responsibility for investigating the problem.
* Establish who initiates, approves, implements, evaluates, and reports
corrective action.
Investigate and determine the cause of the problem.
Determine a corrective action to eliminate the problem.
59
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Assign and accept responsibility for implementing the corrective
action.
Establish effectiveness of the corrective action and implement the
correction.
Verify that the corrective action has eliminated the problem.
19.11 Internal auditing of field, laboratory, and data handling activities may result
in the discovery of non-conforming procedures that, left uncorrected, could
jeopardize the quality and integrity of project data and results. When such auditing
is part of a project and a non-conformance is found, corrective action is initiated by
documenting the finding and recommendations of the audit. The corrective action
undertaken by the designated responsible party is documented with an
implementation schedule and management approval. The implementation is
verified by the auditor, which is then made part of the project audit report record.
60
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SECTION 20
REPORTS TO MANAGEMENT
20.1 A formal QA report should be issued to inform appropriate management on
the performance and progress of the project workplan. The purpose of the report
will be to identify the individuals responsible for reporting QC results, and to
present the QC data so that management can monitor the data quality effectively.
Assume that all readers of the report will potentially use the document for
establishing additional biomonitoring or biosurvey programs for validation of models
or for validation of a project. Availability of complete QA/QC program descriptions
and data quality requirement calculations is essential (Smith et al. 1988).
20.2 The following items should be described in the QA report:
Individuals Preparing and Receiving Reports
Type of Report
Written or oral, frequency
Interim or final
Contents
Status of the project
Results of performance evaluation audits
Significant QA/QC problems, recommended solutions, and
results of corrective actions
Changes in the QAPP
Summary of QA/QC program, training, and accomplishments
Uncertainty estimates
Data quality assessment in terms of precision, accuracy,
representativeness, completeness, and comparability
Reporting of whether the QA objectives were met, and the
resulting impact on decision making.
20.3 The majority of points within the above list are either previously detailed in
this document or are largely self-explanatory. The following elaboration for
reporting uncertainty and data quality requirements is taken primarily from Smith et
al. (1988). See also Section 6 of this document, Green (1979), and Freedman et
al. (1991).
Uncertainty estimates may be either qualitative or quantitative. Estimates may
focus on the probabilities for false positives or false negatives in hypothesis-testing
designs or they may be in the form of confidence intervals around parameter
values.
61
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20.4 PRECISION
20.4.1 Precision-reporting can be presented as an index either as standard
deviation, relative standard deviation, or as relative percent difference (Smith et al.
1988; USEPA 1989). These numbers can be presented as a function of the
measured value within a range, on a graph illustrating actual measurement values,
or as data points with a best-fit curve, including confidence intervals.
20.4.2 Numbers should be presented in tabular form with the data quality
assessment values, standard deviations, and, if appropriate, regression equation
coefficients.
20.4.3 Additional appropriate information should be included that indicates
interlaboratory precision versus intralaboratory precision, procedures for arrival at
the estimates and assignment of outlier status, and description of the temporal
acceptability of the interlaboratory estimates.
20.5 REPRESENTATIVENESS
20.5.1 Representativeness cannot be quantified (Smith et al. 1988). In lieu of
quantification, a description of program/project design and implementation
activities, along with photographs and drainage area of sampling site distribution
(to reflect degree of ecological stratification), and an assessment of resulting
representativeness should be presented.
20.6 COMPLETENESS
20.6.1 Missing data should be identified and practical reasons presented that
caused their deletion from the dataset. This information will aid in identification of
specific procedural problems and in rectification prior to subsequent sampling
events.
20.7 COMPARABILITY
20.7.1 Rationale for the validity of comparing one dataset to another should be
given. Selected reasons might include:
9
0
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62
time/date of sampling
comparison of site selection criteria
measured parameters, recorded observations
field and laboratory methods
comparison of QA/QC programs
comparison of data quality requirement estimates.
-------
20.8 ACCURACY
20.8.1 Accuracy is the degree of agreement between an observed value and an
accepted reference value. Accuracy of data should be checked for transcription
errors through the entire sample processing and analyzing phases. Each data entry
should be checked to the original field sheet and random quality control checks
should be made on subsequent data that have been manipulated.
63
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SECTION 21
DATA REVIEW, VALIDATION, AND VERIFICATION REQUIREMENTS
21.1 The purpose of this section is to ensure good data by maintaining quality
throughout data reduction, transfer, storage, retrieval, and reporting. The project
management scheme should outline the path of the data from the field or
laboratory; topics to be addressed include details of data storage, data reduction,
data validation, and data reporting. All data handling equipment required
hardware and software, and procedures to be used should be identified and
described in the plan.
21.2 For each step in the data handling, state the criteria used to review and
validate data (accept, reject, or qualify) in an objective and consistent manner. List
any calculations that are necessary to prove or disprove the project objectives.
64
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SECTION 22
VALIDATION AND VERIFICATION METHODS
22.1 Outline the process used for validating and verifying data including the chain-
of-custody for data throughout the life cycle of the project. Describe how issues
shall be resolved and what authorities will handle such issues. Describe how
results are conveyed to data users. The review can include checks of field and
laboratory QC data, instrument calibration, technical system audits, and statistical
data treatments.
22.2 RAW DATA
22.2.1 Data such as species names and number of individuals should be legibly
recorded by hand whether on standardized field or laboratory bench sheets, or in
notebooks. These sheets should be checked by intralaboratory QC personnel.
Raw data (non-manipulated) should be stored in hard copy in one or more separate
location(s) and in an electronic database medium with ample backup (if possible).
For data validation, compare every computer entry to field sheets to ensure correct
data entry.
22.3 DATA REDUCTION
22.3.1 Data reduction is the process of transforming raw data by arithmetic or
statistical calculations and collation into a more useful form (such as the Index of
Biotic Integrity [IBI] or total taxa). Errors are commonly found in the calculations,
reductions, and transfer of data to various forms and reports and into data storage
systems. Therefore these data should be quality checked to ensure accuracy.
22.3.2 This subsection should highlight at least the following information:
names of individuals responsible (Table 4-1)
examples of data sheets
summary of statistical approach for reducing data
summary of data reduction procedures
control mechanisms for detecting and correcting errors.
22.4 DATA VALIDATION
22.4.1 Data validation is the process of substantiating specified performance
criteria. Each program must establish technically-sound and documented data
validation criteria which will serve to accept/reject data in a uniform and consistent
manner. Pilot studies may be used to determine metrics with the least variability
65
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and to evaluate metrics for their biological relevance; the rationale for their use
should be documented.
22.4.2 Information for substantiating data validation should include:
names of individuals responsible (Table 4-1)
procedures for determining outliers
identification of critical control points.
22.5 DATA REPORTING
22.5.1 Data are collected from the summary sheets, bound notebooks, or
computerized databases by the data management group and transferred to a draft
report table and/or graphical representation. The assembled data and the raw data
are then examined for nonsensical, computational, and transcriptional errors. For
example, field data sheets should be thoroughly and routinely compared to
computer printout data. After reviewing the data, the laboratory leader and
laboratory QC officer sign-off on the data report, and the report is forwarded to the
Project Manager.
22.5.2 Important information that should be included in this subsection are:
key individuals who will handle the data reporting (Table 4-1);
flowchart of the data handling process covering all data collection,
transfer, storage, recovery, and processing steps, and including QC
data for both field and laboratory; and
identification of critical control points (i.e., What are the criteria for
data points to be considered outliers and when are individual data
points rejected from a database?).
66
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SECTION 23
RECONCILIATION WITH DATA QUALITY OBJECTIVES
23.1 The purpose of this section is to describe how the results obtained from the
project will be reconciled with the DQOs and how any issues will be resolved. Any
limitations on the use of the data must be discussed and reported to
decisionmakers. Detailed plans for data assessment procedures for precision,
accuracy, and completeness must be identified. Routine assessment procedures
including statistics, equations, reporting units, and assessment frequency should
be summarized {USEPA 1989). Section 6 details formulae for calculating precision
(relative percent difference [RPD] and relative standard deviation [RSD]). Accuracy
is usually calculated as "percent recovery". Percent recovery normally applies to
chemical analytical laboratory procedures; however, in the case of biological
laboratories, percent recovery can be applied in the form of sample sorting checks.
Usual procedures for calculating accuracy in this sense then are related to the
laboratory. Precision should be calculated based on replicated samples taken from
adjacent reaches.
23.2 RPDs or RSDs and completeness should be calculated as soon as possible
after each sampling event in order to implement corrective actions (Section 19)
prior to subsequent data-gathering efforts. Further statistical approaches which
could be calculated and reported (USEPA 19801 are:
Central tendency and distribution
Arithmetic mean
Range
Standard deviation
Pooled standard deviation
Geometric mean
Data distribution by percentiles
Measures of variability (USEPA 1989)
Accuracy
Bias
Precision
Coefficient of variability (C.V.)
Confidence limits (Platts et al. 1983)
Testing for outliers
23.3 Additional statistical guidance can be obtained from Sokal and Rohlf (1969);
the statistics associated with the multimetric approach are described by Fore et al.
(1994).
67
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LITERATURE CITED
ASTM. 1991. Standard Guide for Documenting the Standard Operating
Procedures Used for the Analysis of Water, ASTM Designation: D5172-91.
Annual Book of ASTM Standards 11.0:56-57.
Bahls, L. 1992. Periphyton bioassessment methods for Montana streams.
Montana Water Quality Bureau, Helena, MT.
Barbour M.T. and J.B. Stribling. 1991. Use of habitat assessment in evaluating
the biological integrity of stream communities, pp. 25-38. In Biological
* > I I I | .j m 41 wmm mm* MM * W 4k mm* « | mm* mm* Jk
Criteria: Research and Regulation, 1991. EPA-440/5-91-005. U.S. EPA,
Office of Water, Washington, DC.
Borror, D.J., C.A. Triplehorn, and N.F. Johnson. 1989. An Introduction to the
Study of Insects. 6th edition. Saunders College Publ., Philadelphia, PA.
Caton, L.W. 1991. Improved subsampling methods for the EPA "rapid
bioassessment" benthic protocols. Bulletin of the North American
Benthological Society 8(3):317-319.
Costanza, R., S.O. Funtowicz, and J.R. Ravetz. 1992. Assessing and
Communicating Data Quality in Policy-Related Research. Environmental
Management 16(1);121-131.
DHES, 1989. Field procedures manual: collection, analysis and reporting of water
quality samples. Department of Health and Environmental Science, Water
Quality Bureau, Helena MT.
Freedman, D., R. Pisani, R. Purves, and A. Adhikari. 1991. Statistics. W.W.
Norton and Company. New York. 617p.
Fore, L.S., J.R. Karr, and L.L. Conquest. 1994. Statistical properties of an Index
of Biotic Integrity. Canadian J. Fish Aquat. Sci. 51:1077-1087.
Green, R.H. 1979. Sampling design and statistical methods for environmental
biologists. John Wiley and Sons. New York. 257p.
Gibson, G.R. (editor). 1994. Biological Criteria: Technical guidance for streams
and small rivers. EPA-822-B-94-001. U.S. Environmental Protection
Agency, Office of Science and Technology, Washington, DC. 162. pp.
Hughes, R.M. 1995. Defining acceptable biological status by comparing with
reference conditions. Pp. 31-47, Chapter 4 In W.S. Davis and T.P. Simon,
68
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eds., Biological Assessment and Criteria. Tools for Water Resource Planning
and Decision Making. Lewis Press, Boca Raton, FL.
Hughes, R.M., S.A. Heiskary, W.J. Matthews, and C.O. Yoder. 1994. Use of
ecoregions in biological monitoring, Chapter 8, pp. 125-151. In S.L. Loeb
and A. Spacie, eds., Biological Monitoring in Aquatic Systems. Lewis
Publishers, Ann Arbor, Ml.
ITFM. 1994. Water quality monitoring in the United States; 1993 report of the
Intergovernmental Task Force on Monitoring Water Quality, Technical
Appendixes. Intergovernmental Task Force on Monitoring Water Quality,
January, 1994.
Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant, and I.J. Schlosser. 1986.
Assessing biological integrity in running waters: A method and its rationale.
Illinois Natural History Survey, Special Publication 5. 28 pp.
Kaufman, P.R. 1993. Physical habitat, pp. 59-69. In R.M. Hughes, eds., Stream
Indicator and Design Workshop. EPA/600/R-93/138. U.S. EPA, Corvallis,
OR.
MACS. 1993 (draft). Standard operating procedures and technical basis.
Macroinvertebrate collection and habitat assessment for low gradient, non-
tidal streams. Prepared by: The Mid-Atlantic Coastal Streams Workgroup.
August 24, 1993. (For further information, contact John Maxted, Delaware
Department of Natural Resources, 302-739-4590.)
Meador, M.R., T.F. Cuffney, and M.E. Gurtz. 1993. Methods for sampling fish
communities as part of the national water-quality assessment program.
Open-file Report 93-104. US Geological Survey, Raleigh, NC. 40p.
Ohio Environmental Protection Agency. 1987. Biological Criteria for the
Protection of Aquatic Life: Volumes l-lll. Ohio EPA, Division of Water
Quality Monitoring and Assessment, Surface Water Section, Columbus, OH,
Ohio Environmental Protection Agency. 1989. Standardized biological field
sampling and laboratory methods for assessing fish and macroinvertebrate
communities. Subsection 4. Fisheries, Ohio EPA. Columbus. 43p.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross, and R.M. Hughes. 1989.
Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic
Macroinvertebrates and Fish. EPA/444/4-89-001. U.S. EPA, Office of
Water, Washington, DC.
69
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Platts, W.S., W.F. Megahan, arid G.W. Minshalf. 1983. Methods for Evaluating
Streams, Riparian, and Biotic Conditions. General Tech. Rep. INT-138. U.S.
Dep. Agrie., U.S. Forest Serv., Ogden, UT.
Smith, F., S. Kulkarni, L.E. Myers, and M.J. Messner. 1988. Evaluating and
presenting quality assurance data, Chapter 10, pp. 157-168. Jn L.H. Keith, ed.,
Principles of Environmental Sampling. ACS Professional Reference Book,
American Chemical Society, Washington, DC.
Sokal, R.R, and F.J. Rohlf. 1969. Biometry. The Principles and Practice of
Statistics in Biological Research. W.H. Freeman and Co., San Francisco, CA.
776pp.
U.S. Environmental Protection Agency. 1980. Interim Guidelines and Specifications
for Preparing Quality Assurance Project Plans. GAMS-005/80, USEPA, Office
of Research and Development, Quality Assurance Management Staff,
Washington, DC.
U.S. Environmental Protection Agency. 1984. Guidance for Preparation of Combined
Work/Quality Assurance Project Plans for Environmental Monitoring. OWRS
QA-1. U.S. EPA, Office of Water Regulations and Standards, Washington, DC.
U.S. Environmental Protection Agency. 1989. Preparing Perfect Project Plans. A
Pocket Guide for the Preparation of Quality Assurance Project Plans. October.
EPA/600/9-89/087. USEPA, Office of Research and Development, Risk
Reduction Engineering Laboratory, Cincinnati, OH.
U.S. Environmental Protection Agency. 1990a. Macroinvertebrate Field and
Laboratory Methods for Evaluating the Biological Integrity of Surface Waters.
Klemm, D.J., P.A. Lewis, F. Fulk, and J.M. Lazorchak, EPA/600/4-90/030.
USEPA, Office of Research and Development, Environmental Monitoring
Systems Laboratory, Cincinnati, OH.
U.S. Environmental Protection Agency. 1990b. Quality Assurance Glossary and
Acronyms. USEPA, Quality Assurance Management Staff, Office of Modeling,
Monitoring Systems and Quality Assurance, Office of Research and
Development, Washington, DC. July, 1990.
U.S. Environmental Protection Agency. 1994 (Draft Interim Final). EPA
Requirements for Quality Assurance Project Plans for Environmental Data
Operations. EPA QA/R-5. USEPA, Quality Assurance Management Staff,
Washington, D.C. July.
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U.S. Environmental Protection Agency. 1993a. Fish Field and Laboratory Methods
for Evaluating the Biological Integrity of Surface Waters. Klemm, D.J., Q.J.
Stober, and J.M. Lazorchak, EPA/600/R-92/111. U.S. EPA, Office of
Research and Development, Environmental Monitoring Systems Laboratory,
Cincinnati, OH.
U.S. Environmental Protection Agency. 1993b. Master Glossary; Environmental
Monitoring and Assessment Program. EPA/620/R-93/013. U.S.
Environmental Protection Agency, Office of Research and Development,
Washington, DC.
U.S. Environmental Protection Agency. 1994. Quality Management Plan. U.S.
EPA, Office of Water, Washington, DC.
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APPENDIX A
ABBREVIATED QAPP FORM
A-l
-------
Appendix A. TABLE A-1 Form for completion of an abbreviated format QAPP.
1. TITLE PAGE
(Project Name)
(Responsible Agency)
(Date)
Project Manager Signature
Name/Data
Project QA Officer Signature
Name/Date
USEPA Project Manager Signature
Name/Date
USEPA QA Officer Signature
Name/Date
2. TABLE OF CONTENTS - List sections with page numbers, figures, tables, references and
appendices (attach pages).
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TABLE A-l Continued.
3. DISTRIBUTION LIST - names and telephone numbers of those receiving copies of this
QAPP. Attach additional page, if necessary.
i.
ii.
iii.
iv.
V.
vi.
vii.
viii.
ix.
x.
xi.
xii.
4. PROJECT/TASK ORGANIZATION - List key project personnel and their corresponding
responsibilities. Please note that an organizational diagram should be presented with this
section.
Name Project Title
Advisory Panel (contact)
Project Manager/Principal Investigator
A-3
-------
TABLE A-1 Continued.
OA Officer
Sample Design Coordinator
Sample Design QC Officer
Field/Sampling Leader
Sampling QC Officer
Laboratory Manager/Leader
Laboratory QC Officer
Data Processing Leader
Data QC Officer
Document Production Coordinator
Reporting QC Officer
5. PROBLEM DEFINITION/BACKGROUND; PROBLEM/TASK DESCRIPTION -
A. Objective and Scope Statement
B.
Intended Usage of Data
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TABLE A-1 Continued.
C. General Overview of Project
D. Sampling Station Network Design/Rationale
E. Project Timetable
Anticipated Date of
Activity Initiation Completion
A-5
-------
TABLE A-1 Continued.
6. MEASUREMENT QUALITY OBJECTIVES
Parameter
Detection
Limit
Estimated
Accuracy
Accuracy
Protocol*
Estimated
Precision
Precision
Protocol**
'Accuracy Protocol Formula - Percent recovery
** Precision Protocol Formulas -
If precision is to be calculated from two replicate samples, use Relative Percent Difference (RPD)
calculated as
RPC W-qj«ioo
(C^+C2)+2
where C, = the larger of the two values and C2 = the smaller of the two values. And, if it is to be
calculated from three or more replicate samples, use Relative Standard Deviation {RSD) calculated
as
where s = standard deviation and X = mean of replicate samples. The standard deviation or the
A-6
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TABLE A-1 Continued.
RSD=4x 100
X
standard error of a sample mean (s) is calculated as
f (*i-*)2
M" ""I
where Xj = measured value of the replicate, 8 = mean of replicate sample measurements, n
number of replicates,
values.
Precision can also be expressed in terms of the range of measurement
B. Data Representativeness
C. Data Comparability
A-7
-------
TABLE A-1 Continued.
D. Data Completeness
Parameter
No. Valid Samples
Anticipated
No. Valid Samples
Collected and
Analyzed
Percent Complete
7. PROJECT NARRATIVE - Paragraph relating project to the Data Quality Objectives and
problem definition.
8. SPECIAL TRAINING REQUIREMENTS AND CERTIFICATION -
Position Title Requirements Date of Training/Certification
A-8
-------
TABLE A-1 Continued.
9. DOCUMENTATION AND RECORDS
10. SAMPLING PROCESS DESIGN/SAMPLING METHODS REQUIREMENTS
Type of
Sample/
Parameter
Sampling
Gear/
Method ISOP
No., if
available)
Number of
Samples
Sampling
Frequency
(Number per
year)
Method of
Analysis
Biological
Physical
Chemical
B. Rationale for Selection of Sampling Sites
A-9
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TABLE A-1 Continued,
11. SAMPLE HANDLING AND CUSTODY PROCEDURES
12. ANALYTICAL METHODS REQUIREMENTS
A. Sample processing procedures
B. Location of voucher collection
13. QUALITY CONTROL REQUIREMENTS
A. Field QC checks
B. Laboratory QC checks
A-10
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TABLE A-1 Continued.
C. Data Analysis QC checks
14. INSTRUMENT/EQUIPMENT TESTING, INSPECTION, AND MAINTENANCE SCHEDULE
Item Serial No. Date of Last Examination
15. INSTRUMENT CALIBRATION AND FREQUENCY
16. INSPECTION/ACCEPTANCE REQUIREMENTS
A-11
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TABLE A-1 Continued.
17. ACQUISITION OF NON-DIRECT MEASUREMENT DATA
18. DATA MANAGEMENT PROGRAM/SYSTEM
19. ASSESSMENT AND RESPONSE ACTIONS
20. REPORTING PLANS
21. DATA REVIEW AND VALIDATION REQUIREMENTS
A-12
-------
TABLE A-1 Continued.
22. VALIDATION AND VERIFICATION
23. RECONCILIATION WITH DQOs
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APPENDIX B
QAPP GLOSSARY OF TERMS
S>
B-1
-------
QAPP Glossary of Terms
Acceptance criteria - criteria specifying the limit above which data quality is
considered satisfactory and below which it is not. [Modified from USEPA (1990b)
"Acceptable quality level"].
Accuracy - the degree of agreement between an observed value and an accepted
reference value. Accuracy includes a combination of random error (precision) and
systematic error (bias) components which are due to sampling and analytical
operations; a data quality indicator. EPA recommends that this term not be used
and that precision and bias be used to convey the information usually associated
with accuracy [USEPA (1993a)].
Assemblage - an association of interacting populations of organisms in a given
waterbody, for example, fish assemblage or a benthic macroinvertebrate
assemblage [Gibson (1994)],
Bias - the systematic or persistent distortion of a measurement process which
deprives the result of representativeness (i.e., the expected sample measurement is
different than the sample's true value.) A data quality indicator [USEPA (1993a)].
Biological Assessment I Bioassessment - an evaluation of the condition of a
waterbody using biological surveys and other direct measurements of the resident
biota in surface waters [Gibson (1994), USEPA (1991)].
Biological criteria I Blocriteria - numerical values or narrative expressions that
describe the reference biological condition of aquatic communities inhabiting
waters of a given designated aquatic life use. Biocriteria are benchmarks for water
resources evaluation and management decision making [Gibson (1994)].
Biological integrity - the condition of an aquatic community inhabiting unimpaired
waterbodies of a specified habitat as measured by an evaluation of multiple
attributes of the aquatic biota. Three critical components of biological integrity are
that the biota is (1) the product of the evolutionary process for that locality, or
site, (2) inclusive of a broad range of biological and ecological characteristics such
as taxonomic richness and compositions, trophic structure, and (3) is found in the
biogeographic region of study [Gibson (1994)].
Biomonitoring - multiple, routine biological assessments over time using consistent
sampling and analysis methods for detection of changes in biological condition.
Calibration - to determine, by measurement or comparison with a standard, the
correct value of each scale reading on a meter or other device, or the correct value
B-2
-------
for each setting of a control knob. The levels of the calibration standards should
bracket the range of planned measurements [USEPA (1990b)].
Community - any group of organisms belonging to a number of different species
that co-occur in the same habitat or area; an association of interacting
assemblages in a given waterbody.
Comparability - the degree to which different methods, data sets and/or decisions
agree or can be represented as similar; a data quality indicator [USEPA (1990,
1993b)].
Completeness - the amount of valid data obtained compared to the planned
amount, and usually expressed as a percentage; a data quality indicator [USEPA
{1990b, 1993a)].
Confidence level - the probability, usually expressed as a percentage, that a
confidence interval will include a specific population parameter; confidence levels
usually range from 90 to 99 percent [USEPA (1990b)].
Confidence interval - an interval that has the stated probability (e.g., 95 percent)
of containing the true value of a fixed (but unknown) parameter [Gibson (1994)].
Corrective action - corrective actions are measures to correct identified problems,
maintain documentation of the results of the corrective process, and continue the
process until each problem is eliminated. The corrective action is the process to
remediate defects.
Damaged and unusable samples - are samples that have been damaged and part or
all of the sample was destroyed or not recoverable.
Damaged and usable samples - samples that have been damaged but the entire
sample was salvageable (i.e., all organisms were saved).
Data quality objectives (DQOs) - qualitative and quantitative statements developed
by data users to specify the quality of data needed to support specific decisions;
statements about the level of uncertainty that a decisionmaker is willing to accept
in data used to support a particular decision. Complete DQOs describe the
decision to be made, what data are required, why they are needed, the calculations
in which they will be used; and time and resource constraints. DQOs are used to
design data collection plans [Gibson (1994)].
Data reduction - the process of transforming raw data by arithmetic or statistical
calculations, standard curves, concentration factors, etc., and collation into a more
useful form [USEPA (1990b)].
B-3
-------
Data validation - see validation.
Data verification - see verifiable.
Ecological integrity - the condition of an unimpaired ecosystem as measured by
combined chemical, physical (including habitat), and biological attributes [Gibson
(1994)].
Ecoregion - geographic regions of ecological similarity defined by similarity of
climate, landform, soil, potential natural vegetation, hydrology, or other
ecologically relevant variables [Gibson (1994)].
Endpoints - a measurable ecological characteristic [USEPA (1993a)].
Environmental monitoring - the periodic collection of data to be used to determine
the condition of ecological resources [USEPA (1993a)]
In situ - used to describe measurements taken in the natural environment.
Index period - the sampling period with selection being based on temporal
behavior of the indicator and the practical considerations for sampling [ITFM
(1994)],
Indicator - characteristics of the environment, both abiotic and bio tic, that can
provide quantitative information on ecological resources [USEPA (1993a)].
Interlaboratory - activities that occur among different laboratories [USEPA
(1990b)].
Intralaboratory - activities that occur within a laboratory [USEPA (1990b)).
Level of effort - the amount of effort (e.g., person-hours, sampling effort per time,
or sampling vigor) needed to complete a task or project.
Measurement parameters - any quantity such as a mean or standard deviation
characterizing a population. Commonly misused for "variable", "characteristic" or
"property" [USEPA (1990b)].
Measurement quality objectives - the QA objectives for precision,
representativeness, comparability and completeness for each measurement [this
document].
Metric - a calculated term or enumeration which represents some aspect of
biological assemblage structure, function or other measurable aspect of a
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characteristic of the biota that changes in some predictable way with increased
human influence [Gibson (1994)].
Monitoring design features - includes listing ail measurements or variables to be
taken; a statement of how measurements will be evaluated; the rationale used to
select the statistic that will be used to analyze data; explicit delineation of
ecosystems to which decisions will be applied, and a summary table listing the
types and numbers of samples and the sampling gear.
Multimetric approach - is an assessment approach that uses a combination of
multiple metrics to provide synthetic assessments of the status of water resources
[Gibson (1994)].
Percent recovery - Accuracy is usually calculated as "percent recovery" and is
applied in the form of sample sorting checks [this document].
Performance audit - 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 [USEPA
(1990b)].
Pilot studies - studies implemented based on questions that require field work to
evaluate indicators, sampling strategy, methods and logistics [USEPA (1993a)].
Potentially Responsible Party - individual or group of individuals that may be liable
for degradation of a natural resource.
Precision - the degree of variation among individual measurements of the same
property, usually obtained under similar conditions; a data quality indicator.
Precision is usually expressed as standard deviation, variance or range, in either
absolute or relative terms [USEPA (1990b)].
Preventive maintenance - an orderly program of activities designed to ensure
against equipment failure (USEPA (1990b)].
Primary sample processing - the first phase of sample processing for those samples
that require more than field processing, identification and counting, and, for
example, laboratory subsampling of macroinvertebrate samples.
Probabilistic site - sampling sites are selected at random to ensure
representativeness. Random site selection and sampling can provide a statistically-
valid estimate of the condition of a waterbody class or other habitat class (e.g.,
lakes, large rivers, streams).
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Qualitative - non-quantitative or subjective.
Quality Assurance (OA) - an integrated system of activities involving quality
planning, quality control, quality assessment, quality reporting and quality
improvement to ensure that a product or service meets defined standards of quality
with a stated level of confidence [USEPA (1990b)].
Quality objectives - the upper and lower limiting values of the data quality
indicators as defined by the data user's acceptable error bounds [USEPA (1990b)].
Quality Control (QC) - the overall system of technical activities whose purpose is
to measure and control the quality of a product or service so that it meets the
needs of users. The aim is to provide quality data or results that are satisfactory,
adequate, dependable, and economical [USEPA (1990b)].
Quality Assurance Project Plan (QAPP) - a formal document describing the detailed
quality control procedures by which the data quality requirements defined for the
data and decisions in a specific project are to be achieved [USEPA (1990b)],
Quantitative - non-subjective.
Rank order comparisons - comparing the position of a site in its assessment
relative to other sites.
Rapid bioassessment protocols - a framework for assessing biological condition of
streams and wadable rivers using scientifically-valid and cost-effective procedures
[Plafkin et al. (1989)].
Raw data - data that have not been manipulated; the actual measurements taken.
Reference site - a specific locality on a waterbody which is minimally impaired and
is representative of the expected ecological integrity of other localities on the same
waterbody or nearby waterbodies [Gibson (1994)].
Reference condition - the set of selected measurements or conditions of minimally
impaired waterbodies characteristic of a waterbody type in a region [Gibson
(1994)].
Reference collection - a set of biological specimens, each representing some
taxonomic level and not necessarily limited to specific projects or activities
Representativeness - the degree to which data accurately and precisely represent
the frequency distribution of a specific variable in the population; a data quality
indicator [USEPA (1990b)].
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Risk assessment - Qualitative and quantitative evaluation of the risk posed to
human health and/or the environment by the actual or potential presence and/or
use of specific pollutants [USEPA (1993a)].
Sample evidence file - a file containing anything pertaining to the sample including
copies or original laboratory bench sheets, field notes, chain-of-custody forms,
logbooks, sample location and project information, and final report.
Secondary sample processing - the second phase of sample processing for those
samples that require more than field processing, identification and counting, for
example, taxonomic identification of macroinvertebrate samples.
Selection criteria - a set of statements describing suitable indicators; rationale for
selecting indicators (ITFM (1994)].
Sensitivity - capability of method or instrument to discriminate between
measurement responses of a variable of interest [USEPA (1990b)].
Subsampling - a subset of a sample; subsample may be taken from any laboratory
or field sample [USEPA (1990b)].
System audit - consists of a review of the total data production process which
includes onsite reviews of the field and laboratory operational systems and facilities
for sampling and processing of samples [this document].
Tolerance values - numeric values given for biota to reflect their relative tolerance
to chemical pollution or other environmental degradation. Values may be pollution
specific and may be given at the family, genus and/or species level.
Type II error - (beta error) an incorrect decision resulting from acceptance of a
false hypothesis (a false negative decision) [USEPA (1990b)].
Type I error - (alpha error) an incorrect decision resulting from the rejection of a
true hypothesis (a false positive decision) [USEPA (1990b)].
Uncertainty of data - a measure of the total variability associated with sampling
and measuring, taking into account two major error components: systematic error
(bias) and random error [USEPA (1990b)].
Validation - the process of substantiating specified performance criteria [USEPA
(1993b)].
Verifiable - the ability to be proven or substantiated [USEPA (1993b)].
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Voucher collection - a curated collection consisting of the actual specimens
collected in a survey that is maintained following identification and enumeration.
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