United States Environmental Protection Agency
Office of Water
Office of Environmental Information
Washington, DC
EPA 841-B-04-005
Wadeable Streams Assessment
Quality Assurance
Project Plan
August 2004
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WADEABLE STREAMS ASSESSMENT:
QUALITY ASSURANCE PROJECT PLAN
Project Leader: Susan Holdsworth
U.S. Environmental Protection Agency
Office of Wetlands, Oceans, and Watersheds
1200 Pennsylvania Avenue, NW
4503T
Washington, DC 20460
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QA Project Plan
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Page iii of xiv
Wadeable Streams Assessment (WSA)
Quality Assurance (QA) Project Plan
signature
date
signature
date
Management Approvals:
Signature indicates that this QAPP is approved and will be implemented in conducting
this project.
Steve Paulson, Ph.D
Technical Advisor
Western Ecology Division, NHEERL
U.S. EPA Office of Research and Development
Corvallis, Oregon
Robert Ozretich, Ph.D.
Quality Assurance Officer
Western Ecology Division, NHEERL
U.S. EPA Office of Research and Development
Corvallis, Oregon
Jennifer Orme-Zavalejra
Acting Division Director
Western Ecology Division, NHEERL
U.S. EPA Office of Research and Developj
Corvallis, Oregon
Otto Gutenson
WSA Project QA Officer
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Assessment and Watershed Protection Division
Washington, DC
Susan Holdsworth ^x^L^toe-^-^v^rd^wan^ &
WSA Project Leader "^ Signature
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Assessment and Watershed Protection Division
Washington, DC
signature
date
?nt
signature
7date
ilk
'
date'
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Wadeable Streams Assessment. June 2004
QA Project Plan /^l , (\ Page iv
Peter Grevatt, Chief
Monitoring Branch signature date
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Assessment and Watershed Protection Division
Washington, DC
Margarete Heber
OWOW QA Officer
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Washington, DC
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Wadeable Streams Assessment June 2004
QA Project Plan page v
QUALITY ASSURANCE PROJECT PLAN
REVIEW & DISTRIBUTION ACKNOWLEDGMENT AND
COMMITMENT TO IMPLEMENT
for
Wadeable Streams Assessment
We have read the QAPP and the methods manuals for the Wadeable Streams Assessment listed
below. Our agency/organization, , agrees to abide by its
requirements for work performed under our cooperative agreement for Demonstration of
Randomized Design for Assessment of Wadeable Streams (under CWA 104(b)(3)).
Quality Assurance Project Plan D
Site Evaluation Guidelines D
Field Operations Manual D
Benthic Laboratory Methods D
Water Chemistry Laboratory Manual D
Print Name
Title
(Principle Investigator)
Signature.
Date
Address:
Phone:
Fax:
E-mail:
Please return the signed original to the EPA QA officer for this cooperative agreement:
Otto Gutenson
U.S. EPA (4503T)
1200 Pennsylvania Ave, NW
Washington, DC 20460
202-566-1183 (phone)
202-566-1331 (fax)
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Wadeable Streams Assessment June 2004
QA Project Plan Page vi
Retain a copy for your files.
NOTICE
The complete documentation of overall WSA project management, design, methods,
and standards is contained in five companion documents, including:
Wadeable Streams Assessment: Quality Assurance Project PlanEPA 841-B-04-
005
Wadeable Streams Assessment: Site Evaluation Guidelines EPA 841-B-04-008
Wadeable Streams Assessment: Field Operations Manual EPA 841-B-04-004
Wadeable Streams Assessment: Benthic Laboratory MethodsEPA 841-B-04-007
Wadeable Streams Assessment: Water Chemistry Laboratory Manual EPA 841-
B-04-008
This document (Quality Assurance Project Plan) contains elements of the overall
project management, data quality objectives, measurement and data acquisition, and
information management for the WSA, and is based on the guidelines developed and
followed in the Western Environmental Monitoring and Assessment Program (Pecket
al. 2003). Methods described in this document are to be used specifically in work
relating to WSA. All Project Cooperators should follow these guidelines. Mention of
trade names or commercial products in this document does not constitute endorsement
or recommendation for use. More details on specific methods for site evaluation, field
sampling, and laboratory processing can be found in the appropriate companion
document(s) listed above.
The suggested citation for this document is:
USEPA. 2004 (draft). Wadeable Stream Assessment: Integrated Quality
Assurance Project Plan. EPA/841/B-04/005. U.S. Environmental Protection
Agency, Office of Water and Office of Research and Development, Washington,
DC.
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Wadeable Streams Assessment
QA Project Plan
June 2004
Page vii
DISTRIBUTION LIST
This QA Project Plan and associated manuals or guidelines will be distributed to the
following EPA, Tetra Tech, Inc. (Tt), and Great Lakes Environmental Center (GLEC)
senior staff participating in the WSA and to State Water Quality Agencies or
cooperators who will perform the field sampling operations. The Tt and GLEC QA
Officers will distribute the QA Project Plan and associated documents to participating
project staff at their respective facilities and to the project contacts at participating
laboratories, as they are determined.
terafli Coordinators
Name, Region
Darvene Adams,
Regions 1 & 2
Larry Merrill,
Region 3
Susan Holdsworth,
Region 4
Sarah Lehmann,
Region 5
Charlie Howell,
Region 6
Susan Holdsworth,
Region 7
Janet Hashimoto
Region 9
Gretchen Hayslip,
Region 10
Contact Information
(732) 321-6700
Adams.Darvene@epa.gov
(215)814-5452
Merrill.Larry@epa.gov
(202) 566-1187
Holdsworth.Susan@epa.gov
(312) 353-5784
Lehmann.Sarah@epa.gov
(214) 665-8354
Howell.Charlie@epa.gov
(202)566-1187
Holdsworth.Susan@epa.gov
(415)972-3452
Hashimoto.Janet@epa.gov
(206)553-1685
Hayslip.Gretchen@epa.gov
Organization Address
USEPA- Region II
2890 Woodbridge Avenue, (MS220)
Edison, NJ 08837-3679
U.S. EPA- Region III
1650 Arch Street
Philadelphia, PA 19103-2029
USEPA
1200 Pennsylvania Avenue, NW
(4503T)
Washington, DC 20460
U.S. EPA- Region V
77 West Jackson Boulevard
Chicago, IL 60604-3507
U.S. EPA - Region VI , (6WQ-EW)
1445 Ross Avenue - Suite 1200
Dallas, TX 75202-2733
USEPA
1200 Pennsylvania Avenue, NW
(4503 T)
Washington, DC 20460
75 Hawthorne Street
San Francisco, CA 94105
U.S. EPA - Region X, ES-098
1200 Sixth Avenue
Seattle, WA 98101
fieglorral M oftltorteg Coordinators
Hilary Snook,
Region 1
617-918-8670
Snook.Hilary@epa.gov
U.S. EPA - Region 1
11 Technology Drive
North Chelmsford, MA 01863-2431
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Wadeable Streams Assessment
QA Project Plan
June 2004
Page viii
Darvene Adams,
Region 2
Larry Merrill,
Region 3
David Melgaard,
Region 4
Sarah Lehmann,
Region 5
Charlie Howell,
Region 6
Lyle Cowles,
Region 7
Janet Hashimoto
Region 9
Gretchen Hayslip,
Region 10
Becky Weidman,
NEIWPCC
Chris Yoder,
CABB
Mike Beiser
MS DEQ
Seva Joseph
NM ED
Ellen Dickey
DE DNREC
Richard Shertzer
PA DEP
see above
see above
(404) 562-9265
Melgaard.David@epa.gov
see above
see above
(913) 551-7081
Cowles.Lyle@epa.gov
see above
see above
&64>jter$tor$
(978) 323-7929 ext. 229
Rweidman@neiwpcc.org
1740)517-2274
yoder@ilgard.ohiou.edu
(610) 664-3959
(505) 827-0573
sevajoseph@nmenv.state.nm.us
(302)-739-4771
Ellen.Dickev(S),state.de.us
(717)787-9637
rshertzer(2),state.pa.us
see above
see above
U.S. EPA -Region IV
61 Forsyth Street
Atlanta, GA 30303
see above
see above
U.S. EPA -Region VII
726 Minnesota Avenue
Kansas City, KS 66101
see above
see above
NEIWPCC
Boott Mills South
100 Foot of John Street
Lowell, Massachusetts 01852
CABB
P.O. Box 21561
Columbus, Ohio 43221-0561
MS DEQ
P.O. Box 20305
Jackson, Hinds, MS 39289-1305
NM ED
Surface Water Quality Bureau
1190 Saint Francis Dr.
Santa Fe, NM 87502
DNREC
89 Kings Hwy
Dover, DE 19901
PA DEP
PO Box 8467
Hamsburg, PA 17105
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Wadeable Streams Assessment
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Page ix
Mike Miller,
Wl DNR
Paul Kazyak,
MD DNR
Larry Willis,
VADEQ
John Wirts,
WV DEP
Eric Fleek,
NC DEM
Jim Glover,
SC DHEC
Kristen Sanford,
GA DNR
Debbie Arnwine,
TN DEC
Matt Combes,
MO DHE
Steve Haslouer,
KS DNR
Tom Wilton,
IADNR
Don Huggins,
CPC
Marty Matlock,
U. of Arkansas
608-267-2753
millema@dnr.state.wi.us
410-260-8607
pkazyak@dnr. state. md. us
540-562-6825
ldwillis@deq.state.va.us
(304) 558-2837
jwirts@mail.dep.state.wv.us
919-733-6946
eric.fleek@ncmail.net
(803) 898-4081
gloverjb@columb32. dhec.state.se. us
(404)-675-1659
Kristen_Sanford@mail.dnr.state.ga.us
615-532-0703
debbie.arnwine@state.tn.us
1573) 882-9909 ext 3317
Matt.Combes@mdc.mo.gov
(785) 296-0079
shasloue@kdhe. state, ks. us
515-281-8867
torn. wilton@dnr. state. ia. us
(785)864-1548
dhuggins@ukans.edu
(479) 575-2849
mmatlock@uark.edu
Wl DNR
101 S.Webster St.
PO Box 7921
Madison, Wl 53707
MD DNR
580 Taylor Ave., C-2
Annapolis, MD 21401
VA DEQ
P.O. Box 10009
Richmond, VA 23240
WV DEP
Watershed Assessment Section
1201 Greenbrier Street
Charleston, WV 25311
NC DEM
1621 Mail Service Center
Raleigh, NC 27699
SC DHEC
2600 Bull St.
Columbia, SC 29205
GA DNR
4220 International Parkway, Suite
101
Atlanta, Georgia 30354
TN DEC
401 Church Street
Nashville, TN 37243
P.O. Box 180
2709 West Truman Blvd.
Jefferson City, MO 65102
KS DHE
1000 SW Jackson St., Suite 430
Topeka, KS 6661 2-1 367
IADNR
Wallace Bldg.
Des Moines, IA 50319
University of Kansas
2101 Constant Avenue
Lawrence, KS 66047
233 Engineering Hall
University of Arkansas
Fayetteville, AR 72701
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Wadeable Streams Assessment
QA Project Plan
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Page x
Bill Harrison,
TX CEQ
Ron Klein
AKDEC
Dan Butler,
OK CC
Michael Barbour,
Tetra Tech
Marlys Cappaert
CSC
Esther Peters, Tetra
Tech
Dennis McCauley,
GLEC
(512)239-4602
bharriso@tnrcc. state, tx.us
(907) 269-7595
ron kleinfiJ) dec. state. ak. us
405-979-2206
danb@okcc.state.ok.us
Contractor Support
(410) 356-8993
Michael.Barbour@tetratech.com
(541)754-4467
Cappaert.Marlys@epa.gov
(703) 385-6000
Esther.Peters@tetratech-ffx.com
(231)941-2230
dmccauley@glec-tc.com
TCEQ
MC-165
P.O. Box 13087
Austin, TX 78753
Alaska Department of
Environmental Conservation
Division of Air and Water Quality
555 Cordova Street
Anchorage, Alaska 99501
OK CC
4000 Hammer Drive
Norman, OK 73026
10045 Red Run Blvd., Ste. 110
Owings Mills, MD 21 117
200 S.W. 35th St.
Corvallis, OR 97330
10306 Eaton PI., Ste. 340
Fairfax, VA 22030
739 Hastings Street
Traverse City, Ml 49686
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QA Project Plan Page xi
TABLE OF CONTENTS
DISTRIBUTION LIST vii
LIST OF FIGURES xiii
LIST OF TABLES xiv
1.0 PROJECT PLANNING AND MANAGEMENT : 1
1.1 Introduction 1
1.2 WSA Project Organization 2
1.2.1 Project Schedule 6
1.3 Scope of QA Project Plan 7
1.3.1 Overview of Field Operations 7
1.3.2 Overview of Laboratory Operations 10
1.3.3 Data Analysis and Reporting 13
2.0 DATA QUALITY OBJECTIVES 13
2.1 Data Quality Objectives for WSA 13
2.2 Measurement Quality Objectives 13
2.2.1 Method Detection Limits 14
2.2.2 Sampling Precision, Bias, and Accuracy 14
2.2.3 Taxonomic Precision and Accuracy 16
2.2.4 Completeness 18
2.2.5 Comparability 18
2.2.6 Representativeness 18
3.0 SURVEY DESIGN 19
3.1 Probability-Based Sampling Design and Site Selection 22
4.0 INFORMATION MANAGEMENT 23
4.1 Data Policy 23
4.2 Overview of System
Structure 24
4.2.1 Design and Logistic Data
Bases 24
4.2.2 Sample Collection and Field Data Recording 26.
4.2.3 Laboratory Analyses and Data Recording 27.
4.2.4 Data Review, Verification and Validation Activities 29.
4.3 Data Transfer 30
4.4 Core Information Management Standards 31
4.4.1 Metadata 3
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Wadeable Streams Assessment June 2004
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4.4.2 Data Directory 31
4.4.3 Data Catalog 31
4.4.4 Data Formats 31
4.4.5 Parameter Formats 34
4.4.6 Standard Coding Systems 32
4.5 Hardware and Software Control 32
4.6 Data Security 33
5.0 INDICATOR 33
5.1 Benthic Macroinvertebrates 33
5.1.1 Introduction 33
5.1.2 Sampling Design 35
5.1.3 Sampling and Analytical Methodologies 35
5.1.4 Quality Assurance Objectives 36
5.1.5 Quality Control Procedures: Field Operations 36
5.1.6 Quality Control Procedures: Laboratory Operations 36
5.1.7 Data Management, Review and Validation 36
5.1.8 Data Analysis Plan 38
5.2 Physical Habitat Quality Indicator 39
5.2.1 Introduction 39
5.2.2 Sampling Design 41
5.2.3 Sampling Methodologies 41
5.2.4 Quality Assurance Objectives 41
5.2.5 Quality Control Procedures: Field Operations 43
5.2.6 Quality Control Procedures: Laboratory Operations 43
5.2.7 Data Management, Review, and Validation 44
5.3 Water Chemistry Indicator 44
5.3.1 Introduction 44
5.3.2 Sampling Design 45
5.3.3 Sampling and Analytical Methodologies 45
5.3.4 Quality Assurance Objectives 46
5.3.5 Quality Control Procedures: Field Operations 50
5.3.6 Quality Control Procedures: Laboratory Operations 50
5.3.7 Data Reporting, Review, and Management 51
6.0 BIOLOGICAL FIELD AND LABORATORY QUALITY EVALUATION AND
ASSISTANCE VISITS 59
6.1 Field Quality Evaluation and Assistance Visit Plan 61
6.2 Laboratory Quality Evaluation and Assistance Visit Plan 64
7.0 REFERENCES 65
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Wadeable Streams Assessment June 2004
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LIST OF FIGURES
Figure 1. WSA Project Organization 3
Figure 2. Timeline of WSA project activities 6
Figure 3. Site verification activities for stream field surveys 9
Figure 4. Summary of field activities stream and river sampling 11
Figure 5. Organization of information management system modeled after
EMAP-W for the WSA 25
Figure 6. Sampling design for the benthic indicator 34
Figure 7. Laboratory processing activities for the benthic indicator 40
Figure 8. Stream index sampling design for the water chemistry indicator 46
Figure 9. Sample processing activities for water chemistry samples 52
Figure 10. Analysis activities for water chemistry samples 56
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Page xiv
LIST OF TABLES
Table 1. Critical logistics elements 7
Table 2. General guidelines for analytical support laboratories 12
Table 3. Sample and field data quality control activities 27
Table 4. Laboratory data quality control activities 28
Table 5. Biological sample quality control activities 28
Table 6. Data review, verification and validation quality control activities 29
Table 7. Field and laboratory methods: benthic indicator 35
Table 8. Measurement data quality objectives: benthic indicator 36
Table 9. Laboratory quality control: benthic macroinvertebrate sample
processing 37
Table 10. Laboratory quality control: benthic macroinvertebrate taxonomic
identification 37
Table 11. Data validation quality control: benthic indicator 38
Table 12. Research issues: benthic indicator 38
Table 13. Field measurement methods: physical habitat indicator 42
Table 14. Measurement data quality objectives: physical habitat indicator 43
Table 15. Field quality control: physical habitat indicator 44
Table 16. Data validation quality control: physical habitat indicator 44
Table 17. Research questions and hypotheses: water chemistry indicator 45
Table 18. Analytical methodologies: water chemistry indicator 47
Table 19. Measurement data quality objectives: water chemistry indicator 49
Table 20. Sample processing quality control: water chemistry indicator 51
Table 21. Laboratory quality control samples: water chemistry indicator 53
Table 22. Data validation quality control: water chemistry indicator 57
Table 23. Data reporting criteria: water chemistry indicator 58
Table 24. Constants for converting major ion concentrations 59
Table 25. Factors to calculate equivalent conductivities 59
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Wadeable Streams Assessment August 2004
QA Prnjort Plan Pano 1 nf 71
1.0 PROJECT PLANNING AND MANAGEMENT
1.1 Introduction
Several recent reports have identified the need for improved water quality
monitoring and analysis at multiple scales. In 2000, the General Accounting Office
(USGAO.2000) reported that EPA and states cannot make statistically valid inferences
about water quality (via 305[b] reporting) and lack data to support key management
decisions. In 2001, the National Research Council (NRC.2000) recommended EPA
and states promote a uniform, consistent approach to ambient monitoring and data
collection to support core water quality programs. In 2002, the H. John Heinz III Center
for Science, Economics, and the Environment (Heinz Center,2002) found there are
inadequate data for national reporting on fresh water, coastal and ocean water quality
indicators. The National Association of Public Administrators (NAPA.2002) stated that
improved water quality monitoring is necessary to help states make more effective use
of limited resources. EPA's Report on the Environment 2003 (USEPA, 2003) says that
there is insufficient information to provide a national answer, with confidence and
scientific credibility, to the question, "What is the condition of U.S. waters and
watersheds?"
In response to this need, the U.S. Environmental Protection Agency (EPA) Office
of Water (OW), in concert with EPA's Office of Research and Development (ORD) and
the 10 EPA Regions, conceived of the Wadeable Streams Assessment (WSA) - a
national assessment of the condition of wadeable streams and rivers in the
conterminous U.S. This assessment was to be the first assessment on flowing waters
to be based on data collected using the same field and laboratory protocols and based
on a statistical survey design that would allow inferences about all waters based on a
sample of the streams across the country. The desire was to implement this effort in
cooperation with the States and other eligible entities. Therefore, OW issued a
Request for Pre-Proposals under Clean Water Act Section 104(b)(3) to invite eligible
applicants to participate in a demonstration project to apply a consistent set of
monitoring protocols and monitoring design to characterize the wadeable streams and
rivers across the United States. The WSA builds upon the Environmental Monitoring
and Assessment Program's (EMAP) Western Study implemented by ORD, the EPA
Regions, States and Tribal nations in 12 western states. The WSA will provide
important information to the public about the status of wadeable streams and rivers,
information that does not currently exist for large parts of the country.
The WSA Quality Assurance Project Plan (QAPP) is designed to support the
participants in this project and to ensure that the final assessment is based on high
quality data and information. The QAPP contains elements of the overall project
management, data quality objectives, measurement and data acquisition, and
information management for the WSA. The participants in the WSA have agreed to
follow this QAPP and the protocols and design laid out in this document.
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Wadeable Streams Assessment August 2004
f)A Prnj*>rtt Plap Pgno 9 nf 71
The WSA was designed to work in concert with ORD's Environmental Monitoring
and Assessment Program (EMAP). Between 2000 and 2004, EMAP West has
successfully sampled nearly 1000 streams from 12 states in the Western United States.
WSA will sample the remaining 36 states with 500 sites selected using a probability-
based design and the same field and laboratory protocols to complete sampling in the
48 contiguous states. WSA has two objectives:
Estimate the current status in selected indicators of the condition of the
Nation's streams and tributaries on a regional basis with known statistical
confidence.
Seek associations between selected indicators of natural and
anthropogenic stresses and indicators of condition of wadeable streams
and rivers .
Monitoring and assessment tools developed for EMAP will contribute to
improving the ecological assessments in WSA. These assessments will provide
estimates (with quantifiable uncertainty) of the biological integrity of the
macroinvertebrate communities in wadeable streams and rivers. Chemical and physical
habitat information, along with watershed characteristics will also be collected to assist
in explaining the patterns found in macroinvertebrate communities across the country.
1.2 WSA Project Organization
The major areas of activity and responsibilities are described here and illustrated
in Figure 1. The overall coordination of the project will be provided by EPA's Office of
Water (OW) in Washington, DC, with technical support from the Western Ecology
Division (WED) of the Office of Research and Development (ORD) in Corvallis, Oregon
and the ten EPA Regional Offices. Because this project builds upon the EMAP western
study and integrates ecological data from that study, ORD will have a primary role in
training field crews and managing the complex data derived from the surveys and the
laboratory processing of the samples. This comprehensive quality assurance (QA)
program has been established to ensure data integrity and provide support for the
reliable interpretation of the findings from this project.
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OA Prnjort Plan
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Proj ect M anagement
Project Lead - Susan Holdsvrorth, OW
ProjectQA -Otto Gutenson, OW
Technical Advisor - Steve Paulsen, ORD
Project Coordinator - Mike Harbour, Tetra Tech
OWOW QA
Oversight and Review
Margarete Heber, OW
Study Design
TonyOlsen, ORD
Field Protocols
JohnStoddard, Dave Peck,
Phil Kaufmann - ORD
Field Logistics
Implementation Coordinator - Mike Baibour, Tetra Tech
Training
Lead - ORD-WED, EPA Regions, Tetra Tech
Field Implementation
Centeral Plains Center AK DEC NC DEM
Center for Applied DE DNREC OKCC
BioassessmentandBiocriteria OADNR SCDHEC
Univeisity of Arkansas IA DNR TNDEC
Mississippi State University KS DNR TX CEQ
LEGS MDDNR VADEQ
MO DHE WV DEP
NEIWPCC WI DNR
Tetra Tech
GLEC
Sample Flow
Chemistry
WED-Dynamac
Benthic Invertebrates
8 States/Coo perators -
NC, TN, MD, WI, CPC, CABB,GLEC, Tt
Information Management
WED-CSC - Marlys Cappaert
Final Data
STORET-OW EMAP-ORD-AED
STATES
Assessment
OW-Lead
ORD, Regional Monitoring
Coordinators, States, Tribes,
Cooperates, and other partners
Figure 1. WSA Project Organization
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Wadeable Streams Assessment August 2004
QA PrnJAcL^lan Pane 4 nf 71
Program level QA will be the responsibility of the OWOW QA Officer and the Project
QA Officer. A QA records system will be used to maintain indefinitely a permanent
hardcopy file of all WSA documentation from site selection to data analysis. This will
be housed in OW Headquarters Office.
The primary responsibilities of the principals and cooperators are as follows:
Project Management:
EPA Project Leader- provides overall coordination of the project and makes decisions
regarding the proper functioning of all aspects of the project. Makes assignments and
delegates authority, as needed to other parts of the project organization.
EPA Project QA Lead - provides leadership, development and oversight of project level
quality assurance for WSA in Office of Water
EPA ORD Technical Advisor - advises the Project Leader on the relevant experiences
and technology developed within ORD's EMAP that are to be used in this project.
Serves as primary point-of-contactfor project coordination in the absence or
unavailability of Project Leader.
Project Coordination - contractor providing day-to-day coordination of field
implementation as well as technical development of analysis of data.
Study Design:
The assessment will utilize data from two separate field studies: EMAP-West and a new
field effort in the eastern 36 States. The same sample survey design was used for
EMAP-West as well as the eastern 36 States. The design for both was developed by
ORD's Western Ecology Division to ensure comparability. Site selection is part of the
study design.
Field Protocol Development:
The field sampling protocols were developed by ORD for use in EMAP and were
developed with the purpose of providing consistent and representative information
across the country. The EMAP protocols used in the western US will be used the
eastern States contributing to WSA.
Field Logistics:
Implementation Coordinator - a contractor who functions on behalf of the Project
Leader to support all phases of the field implementation of the project. Primary
responsibility is to ensure all aspects of the project, i.e., technical, logistical,
organizational, are operating as smoothly as possible. Serves as point-of-contact for
questions from field crews and cooperators for all activities.
Training- eight training sessions will be conducted in various locations throughout the
eastern US. The EMAP team from ORD's Western Ecology Division will conduct the
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OA Prnjort Plan Pano 5 nf 71
initial 2 training sessions and a third session that focuses on training the trainers. A
Tetra Tech team will conduct the remaining 4 training sessions with an observer from
ORD there to assist if needed. A monitoring specialist from each EPA Regional Office
will also participate in each of the trainings. Each field crew must have a crew leader
who has received 3 days of lecture and field training to prepare them for this study. At
the end of the training period, each team will conduct a day long sampling on their own
under the watch of the trainers. This field readiness review will be the final QA check of
the training sessions. All field crews will be audited early in their sampling schedule to
be certain any corrections will be made at the onset of sampling.
Field Implementation -Fifteen States, six cooperators and two contract teams staffed by
Tetra Tech and GLEC will conduct the field implementation to collect samples using the
WSA protocols.
Field Quality Evaluation and Assistance Reviews - Each field team will be visited by a
trained team from either an EPA Region, GLEC or Tetra Tech. The purpose of this
field evaluation and assistance review is to observe the crews implementing the
protocols as trained and provide any assistance or corrections necessary. This is
intended to catch deviations from the protocols before they become widespread.
Sample Flow:
Field samples will be shipped by the crews to one of several locations. All water
samples will be sent to the Western Ecology Division laboratory staffed by Dynamac.
The macroinvertebrate samples will be shipped to one of 6 States or Cooperator
laboratories or GLEC for identification. The field data sheets will be shipped to the
Western Ecology Division information management team staffed by CSC for scanning
and entry into the database. Each of the organizations processing samples will
electronically transfer the results to CSC using the naming conventions and standards
provided by CSC.
Information Management:
The first stage of data processing will be to take the input from each of the responsible
laboratories and enter them into a common database for final verification and validation.
Once the final data sets are made available for the assessment, copies of the data will
be transferred to EPA's STORET and EPA's EMAP dataset for long-term storage and
access. Working copies of the final data sets will be distributed to the States and
Cooperators and maintained at WED for analysis leading to the assessment.
Assessment:
The final assessment will be developed by a team, led by OW, that will include Office of
Water, Office of Environmental Information, several ORD research facilities, EPA
Regional Monitoring Coordinators, interested States and Cooperators. All States will be
invited to participate in collaborative process to interpret results and shape data
assessment and report. The final assessment will include an appendix describing the
quality of the data used in the assessment. The final assessment will be delivered to
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the Assistant Administrator for Office of Water in December of 2005.
1.2.1 Project Schedule
The U.S. EPA has responded to a State and OW goal to report on the quality of the
Nation's streams by no later than December, 2005. Figure 2 gives an overview of the
2004
Site e \alaitoiAwon
Field team, hainixg
Reid sarrpHngA limping
Field e \aloaiioK
Saitple processing
Lab evaluations
Data nura^irEtrt
(QA/QCyirisgiaiiai
Data, analysis
Kfipcrt ^yapaaaitijofli
Rejxrtieviav
Final Report
nouUctajGuv
I I
1 I * i » ! I I I 1 I ! I i 1 1 * I 1 1 1 1
major tasks leading up to the final report. These activities are described throughout the
QAPP.
Figure 2. Timeline of WSA project activities
1.3 Scope of QA Project Plan
This QA Project Plan addresses all aspects of the data acquisition efforts of
WSA, which focuses on the 2004 sampling of streams in the eastern United States.
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The QA plan also deals with the data integration necessary between WSA and EMAP
Western Pilot Study (2001-2004) to create one complete report on the ecological status
of the Nation's streams.
Data from approximately 500 stream sites in the eastern two-thirds of the United
States will be integrated with data from approximately 1000 stream sites in the western
United States for a comprehensive assessment. Relevant Companion documents to
this QAPP are: WSA: Site Evaluation Guidelines, WSA: Field Operations Manual, WSA:
Benthic Laboratory Methods, and WSA: Water Chemistry Laboratory Manual.
1.3.1 Overview of Field Operations
Field data acquisition activities are implemented for WSA (Table 1), based on
guidance developed for earlier EMAP studies (Baker and Merritt 1990). Survey
preparation is initiated with selection of the sampling locations by the EMAP Design
group (WED in Corvallis). The list of sampling locations is distributed to the EPA
Regional Monitoring Coordinators and cooperators. With the sampling location list,
Cooperator's field crews can begin site reconnaissance on the primary sites and
alternate replacement sites and begin work on obtaining access permission to each
site. Specific procedures for evaluating each sampling location and for replacing non-
target sites are documented in the WSA: Site Evaluation Guidelines. Scientific
collecting permits from State and Federal agencies will be procured, as needed by the
respective State or cooperating organization. The field teams will use standard field
equipment and supplies which are being provided by EPA and GLEC. Field logistic
coordinators (GLEC and Tetra Tech) will work with Regional Monitoring Coordinators,
Cooperators, States and Contractors to make certain the field crews have the
equipment and supplies they require in a timely fashion. Detailed lists of equipment
required for each field protocol, as well as guidance on equipment inspection and
maintenance, are contained in the Field Operations Manual.
Table 1. Critical logistics elements (from Baker and Merritt, 1990)
Logistics Plan Component I Required Elements
Project Management Overview of Logistic Activities
Staffing and Personnel Requirements
Communications
Access and Scheduling Sampling Schedule
Site Access
Reconnaissance
Safety Safety Plan
Waste Disposal Plan
Procurement and Inventory Control Equipment, Supplies, and Services Requirements
Procurement Methods and Scheduling
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Training and Data Collection Training Program
Field Operations Scenario
Laboratory Operations Scenarios
Quality Assurance
Information Management
Assessment of Operations Field Crew Debriefings
Logistics Review and Recommendations
Field measurements and samples are collected by trained teams. All members
of each field team will attend one EPA-sponsored training session before the field
season in their state. Field quality evaluation and assistance review visits will be
completed for each team. Each team is comprised of 3 members; the number of teams
will be determined by the site evaluation/reconnaissance teams depending on number
of reaches sampled. The number and size of teams depends on the duration of the
sampling window, geographic distribution of sampling locations, number and complexity
of samples and field measurements, and other factors. The training program stresses
hands-on practice of methods, comparability among crews, collection of high quality
data and samples, and safety. Training will be provided in seven central locations for
cooperators and contractors. Project organizations responsible for training oversight
are identified in Figure 1. Training documentation will be maintained by the Tetra Tech
Training Support Team.
For each sampling location, a dossier is prepared and contains the following
applicable information: road maps, copies of written access permissions, scientific
collection permits, coordinates of index sites, information brochures on the program for
interested land owners, a topographic map with the index site location marked, and
local area emergency numbers. Team leaders will contact landowners approximately 2
days before the planned sampling date. As the design requires repeat visits to selected
sampling locations, it is important for the field teams to do everything possible to
maintain good relationships with landowners. This includes prior contacts, respect of
special requests, closing gates, minimal site disturbance, and removal of all materials
including flagging and trash.
A variety of methods may be used to access a site, including vehicles and boats.
Some sampling locations require teams to hike in, transporting all equipment in
backpacks. For this reason, ruggedness and weight are important considerations in the
selection of equipment and instrumentation. Teams may need to camp out at the
sampling location and so are equipped with the necessary camping equipment.
The site verification process is shown in Figure 3. Upon arrival at a site, the
location is verified by a Global Positioning System (GPS) receiver, landmark
references, and/or local residents. Samples and measurements for various indicators
are collected in a specified order (Figure 4). This order has been set up to minimize the
impact of sampling for one indicator upon subsequent indicators; for example, water
chemistry samples from streams are collected before collecting benthic invertebrates as
the benthic invertebrate method calls for kicking up sediments. All methods are fully
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SITE VERIFICATION ACTIVITIES
PRE-VISIT PREPARATION
Contact landowner to inform of visit and confirm access
Review site dossier and maps for directions and access requirements
SITE VERIFICATION DATA
Record directions to site
Confirm identity of stream
Site description
Determine location with GPS
Determine sampling status
LOCATE SAMPLING & MEASUREMENT SITES
STREAMS
Locate index site and determine location with GPS
Locate upper and lower ends of samp ling reach (40
channel widths)
Establish habitat transects across channel (11 per reach)
Figure 3. Site verification activities for stream field surveys.
documented in step-by-step procedures in the WSA: Field Operations Manual (USEPA
2004). The manual also contains detailed instructions for completing documentation,
labeling samples, any field processing requirements, and sample storage and shipping.
Any revision of methods must be approved in advance by the EPA Project Leader.
Field communications will be available through Field Coordinators, regularly scheduled
conference calls, a Communications Center, or an electronic mail/bulletin board.
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Standardized field data forms are provided to the field crews as the primary means
of data recording. On completion, the data forms are reviewed by a field crew member
other than the person who initially entered the information. Prior to departure from the
field site, the field team leader reviews all forms and labels for completeness and legibility
and ensures that all samples are properly labeled and packed. Each site has a unique
identifier provided by the design. All jars from a site have the same number. If additional
jars are needed, extra labels are provided.
On return from a field sampling site (either to the field team's home office or to a
motel), completed data forms are sent to the information management staff at WED for
entry into a computerized data base. At WED, electronic data files are reviewed
independently to verify that values are consistent with those recorded on the field data
form or original field data file.
Samples are stored or packaged for shipment in accordance with instructions
contained in the field manual. Water samples that exceed time limitations will not be
used. Samples which must be shipped are delivered to a commercial carrier. The
recipient is notified to expect delivery; thus, tracing procedures can be initiated quickly in
the event samples are not received. Bills of lading and chain-of-custody forms are
completed for all transfers of samples maintained by the labs, with copies also
maintained by the field team. The implementation coordinator maintains a centralized
tracking system of all shipments.
The field operations phase is completed with collection of all samples or expiration
of the sampling window. Following completion of all sampling, a debriefing session will
be scheduled (see Table 1). These debriefings cover all aspects of the field program and
solicit suggestions for improvements.
1.3.2 Overview of Laboratory Operations
Holding times for samples vary with the sample types and analytes. Thus, some
analytical analyses begin as soon as sampling (e.g., water chemistry) begins while others
are not even initiated until sampling has been completed (e.g., benthic
macroinvertebrates). Analytical methods are summarized in the specific SOPs (manuals)
that are companion documents to this QAPP. In most cases, standard methods are used
and are referenced. Where experimental methods are used or standard methods are
modified, these methods are documented in the laboratory methods manual or in internal
documentation, and may be described in SOPs developed by the analytical laboratory
and benthic macro invertebrate laboratory.
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SUMMARY OF SAMPLING AND MEASUREMENT ACTIVITIES: STREAMS
FIELD CREW S
SITE LOCATION AND VERIFICATION
Mark index site and hab itat transects
V erify stream and reach locations
PHYSICAL HABITAT QUALITY
(Intensiv e)
Thalweg profile measurements
Channel cross-section characterization
Substrate characterization
Woody debris characterization
R ip arian c o ve r c ha ra c te riz atio n
C anopy cover characterization
Bank characterization
Fish cover characterization
WATER CHEMISTRY
Collect samples
Measure discharge
STREAM BENTHOS
C ollect kick net samples
Composite samples
PHYSICAL HABIT AT QUALITY
(Rapid)
Conduct RBP habitat characterization
Complete visual stream assessment
NEXT DAY
FINAL ACTIVITIES
S hip water chemistry samples and
data forms
Travel to next stream
Figure 4. Summary of field activities stream and river sampling.
Chemical samples will be analyzed by the contract laboratory maintained
by ORD Western Ecology Division. The physical habitat measurements are made in the
field and recorded on the field data sheets and then scanned into a database at the
information management center at ORD Western Ecology Division. Benthic
macroinvertebrate samples will be processed by State, other Cooperator, and contractor
facilities. Laboratories providing analytical support must have the appropriate facilities to
properly store and prepare samples, and appropriate instrumentation and staff to provide
data of the required quality within the time period dictated by the project. Laboratories
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are expected to conduct operations using good laboratory practices (Table 2).
All laboratories providing analytical support to WSA (benthic and water
chemistry) must adhere to the provisions of this integrated QAPP. Laboratories will
provide information documenting their ability to conduct the analyses with the required
level of data quality. Such information will include results from interlaboratory
comparison studies, analysis of performance evaluation samples, control charts and
results of internal QC sample or internal reference sample analyses to document
achieved precision, bias, accuracy, and method detection limits. Contracted laboratories
will be required to provide copies of their SOPs and audit reports. Water chemistry
laboratories may also be required to successfully analyze at least one performance
evaluation sample for target analytes before routine samples can be analyzed.
Laboratory operations will be evaluated by technical systems audits, performance
evaluation studies, and by participation in interlaboratory sample exchange.
Table 2. Guidelines for analytical support laboratories
A program of scheduled maintenance of analytical balances, water purification systems,
microscopes, laboratory equipment, and instrumentation.
Checking and recording the composition of fresh calibration standards against the previous
lot. Acceptable comparisons are ±2 percent of the theoretical value.
Recording all analytical data in bound logbooks in ink, or on standardized recording forms.
Monitoring and recording (in a logbook or on a recording form) temperatures and
performance of cold storage areas and freezer units. During periods of sample collection
operations, monitoring must be done on a daily basis.
Verifying the efficiency of fume hoods.
If needed, having a source of reagent water meeting American Society of Testing and
Materials (ASTM) Type I specifications for conductivity (< 1 uS/cm at 25 'C; ASTM 1984)
available in sufficient quantity to support analytical operations.
Appropriate microscopes or other magnification for biological sample sorting and organism
identification.
Labeling all containers used in the laboratory with date prepared, contents, and initials of the
individual who prepared the contents.
Dating and storing all chemicals safely upon receipt. Chemicals are disposed of properly
when the expiration date has expired.
Using a laboratory information management system to track the location and status of any
sample received for analysis.
Reporting results using standard formats and units compatible with the information
management system.
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1.3.3. Data Analysis and Reporting
A technical workgroup convened by and under the leadership of the EPA Project
Leader is responsible for outlining the final assessment report. Data analysis to support
this report will be conducted by the EMAP team at the Western Ecology Division
Information management activities in support of this effort are discussed further in
Section 4. Data in the database are available to Cooperators for their own use upon
completion of the final verification and validation. The final data from the WSA will be
transferred to the OW STORET system and the EMAP information management system
administered at the Atlantic Ecology Division.
2.0 DATA QUALITY OBJECTIVES
It is a policy of the U.S. EPA and its laboratories that Data Quality Objectives
(DQOs) be developed for all environmental data collection activities. Data quality
objectives are statements that describe the level of uncertainty that can be associated
with environmental data for their intended use. Data quality objectives thus provide the
criteria to design a sampling program within cost and resource constraints or technology
limitations imposed upon a project or study.
2.1 Data Quality Objectives for WSA
Target DQOs established for WSA relate to the goal of describing the current
status in the condition of selected indicators of the condition of wadeable streams in the
conterminous U.S. and subregions of interest. The formal statement of the DQO for
national estimates is as follows:
Estimate the proportion of stream length (± 5%) in the conterminous U.S. that falls
below the designated threshold for good conditions for selected
macroinvertebrates with 95% confidence.
For the subregions of interest (Omernik Level II Ecoregions) the DQO is:
Estimate the proportion of stream length (± 15%) in a specific Level II Ecoregion
that falls below the designated threshold for good conditions for selected
macroinvertebrate measures with 95% confidence.
2.2 Measurement Quality Objectives
For each indicator, performance objectives (associated primarily with
measurement error) are established for several different attributes of data quality
(following Smith et al, 1988). Specific objectives for each indicator are presented in the
indicator section of this QAPP. The following sections define the data quality attributes
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and present approaches for evaluating them against acceptance criteria established for
the program.
2.2.1 Method Detection Limits
For chemical measurements, requirements for the method detection limit (MDL)
are established. The MDL is defined as the lowest level of analyte that can be
distinguished from zero with 99 percent confidence based on a single measurement
MDL = '[.-O.Ol.v-n-l]** (1)
(Glaser et al., 1981). The MDL for an individual analyte is calculated as:
where f is a Students' f value at a significance level (a) of 0.01 and n-1 degrees of
freedom (v), and s is the standard deviation of a set of n measurements of a standard
solution. The standard contains analyte concentrations between two and three times the
MDL objective, and is subjected to the entire analytical method (including any preparation
or processing stages). At least seven non-consecutive replicate measurements are
required to calculate a valid estimate of the MDL. Replicate analyses of the standard
should be conducted over a period of several days (or several different calibration
curves) to obtain a long-term (among-batch) estimate of the MDL.
Laboratories should periodically monitor MDLs on a per batch basis. Suggested
procedures for monitoring MDLs are: (1) to analyze a set of serial dilutions of a low level
standard, determining the lowest dilution that produces a detectable response; and (2)
repeated analysis (at least seven measurements) of a low-level standard within a single
batch.
Estimates of MDLs (and how they are determined) are required to be submitted
with analytical results. Analytical results associated with MDLs that exceed the detection
limit objectives are flagged as being associated with an unacceptable MDL. Analytical
data that are below the estimated MDL are reported, but are flagged as being below the
MDL.
2.2.2 Sampling Precision, Bias, and Accuracy
Precision and bias are estimates of random and systematic error in a
measurement process (Kirchmer, 1983; Hunt and Wilson, 1986). Collectively, precision
and bias provide an estimate of the total error or uncertainty associated with an individual
measurement or set of measurements. Systematic errors are minimized by using
validated methodologies and standardized procedures. Precision is estimated from
repeated measurements of samples. Net bias is determined from repeated
measurements of solutions of known composition, or from the analysis of samples that
have been fortified by the addition of a known quantity of analyte. For analytes with large
ranges of expected concentrations, objectives for precision and bias are established in
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both absolute and relative terms, following the approach outlined in Hunt and Wilson,
1986. At lower concentrations, objectives are specified in absolute terms. At higher
concentrations, objectives are stated in relative terms. The point of transition between an
absolute and relative objective is calculated as the quotient of the absolute objective
divided by the relative objective (expressed as a proportion, e.g., 0.10 rather than as a
percentage, e.g., 10%). Final estimates will be calculated by the analysis staff at WED.
Precision in absolute terms is estimated as the sample standard deviation when
the number of measurements is greater than two:
SD =
n-l
where xt is the value of the replicate, X is the mean of repeated sample measurements,
and n is the number of replicates. Relative precision for such measurements is
estimated as the relative standard deviation (RSD, or coefficient of variation, [CV]):
RSD = i x 100 (3)
X
where s is the sample standard deviation of the set of measurements, and X equals the
mean value for the set of measurements.
Precision based on duplicate measurements is estimated based on the range of
measured values (which equals the difference for two measurements). The relative
percent difference (RPD) is calculated as:
\
(4)
RPD=| L IxlOO
where A is the first measured value, B is the second measured value. Precision
objectives based on the range of duplicate measurements can be calculated as:
Critical Range = s x -J2 (5)
where s represents the precision objective in terms of a standard deviation. Range-
based objectives are calculated in relative terms as:
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Critical RPD = RSD x ^2 (6)
where RSD represents the precision objectives in terms of a relative standard deviation.
For repeated measurements of samples of known composition, net bias (B) is
estimated in absolute terms as:
B=X-T (7)
where X equals the mean value for the set of measurements, and 7 equals the
theoretical or target value of a performance evaluation sample. Bias in relative terms
(B/%/) is calculated as:
where X equals the mean value for the set of measurements, and 7 equals the
theoretical or target value of a performance evaluation sample.
Accuracy is estimated for some analytesfrom fortified or spiked samples as the
percent recovery. Percent recovery is calculated as:
c c
% re cov ery = * '- x 100
where Cti is the measured concentration of the spiked sample, Cf is the concentration' '
of the unspiked sample, and Cg is the concentration of the spike.
2.2.3 Taxonomic Precision and Accuracy
For WSA, taxonomic precision will be quantified by comparing whole-sample
identifications completed by independent taxonomists or laboratories. Accuracy of
taxonomy will be qualitatively evaluated through specification of target hierarchical levels
(e.g., family, genus, or species); and the specification of appropriate technical taxonomic
literature or other references (e.g., identification keys, voucher specimens). To calculate
taxonomic precision, 10 percent of the benthic macroinvertebrate samples will be
randomly-selected by Tetra Tech, the QA lab, sent by the WSA labs for re-identification
by Mike Winnell, Freshwater Benthic Services (FBS), 3250 Krause Rd. Petoskey, Ml
49770 (Dr. Winnell's extensive reference book bibliography is in the QA file at OWOW
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Hq and is available upon request.). Comparison of the results of whole sample re-
identifications will provide a Percent Taxonomic Disagreement (PTD) calculated as:
PTD-\\-\ --SL\ I x 100
where comppos is the number of agreements, and N is the total number of individuals in
the larger of the two counts. The lower the PTD, the more similar are taxonomic results
and the overall taxonomic precision is better. A measurement quality objective (MQO) of
15% is recommended for taxonomic difference or disagreement (overall mean <;15% is
acceptable based on similar projects). Individual samples exceeding 15% are examined
for taxonomic areas of substantial disagreement, and the reasons for disagreement
investigated.
Sample enumeration is another component of taxonomic precision. Sample
enumeration agreement will be checked with the same 10% of samples used to check
taxonomic precision. Final specimen counts for samples are dependent on the
taxonomist, not the rough counts obtained during the sorting activity. Comparison of
counts is quantified by calculation of percent difference in enumeration (PDE), calculated
as:
Labi }
An MQO of 5% is recommended (overall mean of <;5% is acceptable). Individual
samples exceeding 5% are examined to determine reasons for the exceedance.
Corrective actions for samples exceeding these MQOs can include defining the
taxa for which re-identification may be necessary (potentially even by third party), for
which samples (even outside of the 10% lot of QC samples) it is necessary, and where
there may be issues of nomenclatural or enumeration problems. Taxa lists will be
changed when disagreements are resolved by a third party.
Taxonomic accuracy is evaluated by having individual specimens representative of
selected taxa identified by recognized experts, usually contract or university affiliated
persons who have peer-reviewed publications for the taxonomic group they are
reviewing. Samples will be identified using the most appropriate technical literature that
is accepted by the taxonomic discipline and reflects the accepted nomenclature. The
Integrated Taxonomic Information System (ITIS, http://www.itis.usda.gov/) will be used to
verify nomenclatural validity and reporting. A reference collection will be compiled by
each lab as the samples are identified. Specialists in several taxonomic groups will verify
selected individuals of different taxa, as determined by the WSA workgroup.
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2.2.4 Completeness
Proiort Plan Pgno 1 H nf 71
Completeness requirements are established and evaluated from two perspectives.
First, valid data for individual indicators must be acquired from a minimum number of
sampling locations in order to make subpopulation estimates with a specified level of
confidence or sampling precision. The objective of this study is to complete sampling at
95% or more of the 500 initial sampling sites and the 100 reference sites. Percent
completeness is calculated as:
%C =V 77x100
where V = number of measurements/samples judged valid, and T = total number
of planned measurements/samples. Within each indicator, completeness objectives are
also established for individual samples or individual measurement variables or analytes.
These objectives are estimated as the percentage of valid data obtained versus the
amount of data expected based on the number of samples collected or number of
measurements conducted. Where necessary, supplementary objectives for
completeness are presented in the indicator-specific sections of this QAPP.
2.2.5 Comparability
Comparability is defined as the confidence with which one data set can be
compared to another (Stanley and Verner, 1985; Smith et al., 1988). For all indicators,
comparability is addressed by the use of standardized sampling procedures, sampling
equipment and analytical methodologies by all sampling crews and laboratories. These
are also the same used to collect data in EMAP West studies. Comparability of data
within and among indicators is also facilitated by the implementation of standardized
quality assurance and quality control techniques and standardized performance and
acceptance criteria. For all measurements, reporting units and format are specified,
incorporated into standardized data recording forms, and documented in the information
management system. Comparability is also addressed by providing results of QA sample
data, such as estimates of precision and bias, conducting methods comparison studies
when requested by the grantees and conducting interlaboratory performance evaluation
studies among state, university, and WSA contract laboratories. If some incompatibility
between sampling crews comes to light, the data will be rejected.
2.2.6 Representativeness
Representativeness is defined as "the degree to which the data accurately and
precisely represent a characteristic of a population parameter, variation of a property, a
process characteristic, or an operational condition" (Stanley and Verner, 1985, Smith et
al., 1988). Atone level, representativeness is affected by problems in any or all of the
other attributes of data quality.
At another level, representativeness is affected by the selection of the target
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surface water bodies, the location of sampling sites within that body, the time period
when samples are collected, and the time period when samples are analyzed. The
probability-based sampling design should provide estimates of condition of surface water
resource populations that are representative of the region. The individual sampling
programs defined for each indicator attempt to address representativeness within the
constraints of the sampling design and index sampling period. Holding time requirements
for analyses ensure analytical results are representative of conditions at the time of
sampling. Use of QC samples which are similar in composition to samples being
measured provides estimates of precision and bias that are applicable to sample
measurements.
3.0 SURVEY DESIGN
Many of the questions which USEPA's Office of Water, States and Tribes are
attempting to address fundamentally require information about large numbers of systems
rather than individual systems. ORD has studied the role of monitoring surveys, their
evolution and the nature of existing federal monitoring programs, and can provide
information and assistance to the States and Tribes in this area.
The survey design for WSA is the same as used for EMAP-West. The design is a
sample survey design (a.k.a. probability design) that ensures a representative set of
sample sites from which inferences can be made about the target population. For the
WSA, the target population is all wadeable streams and rivers in the conterminous US.
There is a large body of statistical literature dealing with sample survey designs
which addresses the problem of making statements about many by sampling the few
(e.g., Cochran 1977, Kish 1965, Kish 1987, Sarndal et al. 1992). Sample surveys have
been used in a variety of fields (e.g., election polls, monthly labor estimates, forest
inventory analysis, national wetlands inventory) to determine the status of populations
(large groups of sites) of interest, especially if the population is too numerous to census
or if it is unnecessary to census the population to reach the desired level of precision for
describing the population's status. A key point in favor of probability based designs is
that they allow lower cost sampling programs because a smaller number of sites are able
to support conclusions with known accuracy and precision about status and trends of a
region.
Probability sampling surveys have been consistently used in some natural
resource fields. The National Agricultural Statistics Survey (MASS) conducted by the
U.S. Department of Agriculture and the Forest Inventory Analysis (FIA) conducted by the
U.S. Forest Service (Bickford etal. 1963, Hazard and Law 1989) have both used
probability based sampling concepts to monitor and estimate the condition and
productivity of agricultural and forest resources from a commodity perspective. National
Resources Inventory (NRI) was instituted initially because of concerns about the impact
of soil erosion on crop production. More recently, the National Wetland Inventory (NWI)
developed by the U.S. Fish and Wildlife Service (Wilen 1990) to estimate the extent of
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wetland acreage in the United States has used a probability based sampling design. But
no thorough review of all national programs has occurred until recently.
The survey designs used in EMAP to date have been documented in published
reports for each resource group and in the peer reviewed literature. Below a brief
description of the design concepts and the specific application for riverine systems is
provided. Much of this is extracted from various publications and from Stevens (1994)
which provides an excellent overview of the design concepts, issues and applications for
the entire program.
The EMAP sampling design strategy is based on the fundamental requirement for
a probability sample of an explicitly defined regional resource population, where the
sample is constrained to reflect the spatial dispersion of the population.
A key property of a probability sample is that every element in the population has
some chance of being included in the sample. If this were not the case, then some parts
of the population might as well not exist, since no matter what, their condition could have
no influence on estimates of population characteristics. This property has a side benefit,
in that it forces an explicit and complete definition of the population being described. This
may seem trivial; however, in practice, it is almost never easy to tightly delimit a real,
physical population. For example, "lake" is a concept that has meaning for most people,
and the notion of "all lakes in the continental United States" would seem to define a'
population. Nevertheless, an operational definition of membership is missing. The
operational definition must be complete enough to establish any body of water, from a
rain puddle up to Lake Superior, as either in or out of the population. Thus, the definition
must address such aspects as size limits (at least lower limits on area and depth), natural
lake versus constructed reservoir, temporal fluctuation (If a "lake" dries up during a
drought, is it still a lake? Was it a lake before the drought?), and amount of open water.
Without such an operational definition, any statement about "all lakes in the United
States" has an unquantifiable vagueness.
The stream resource does not fall neatly into either the discrete or extensive
category. The National Stream Survey (Messer et al., 1986; Overton, 1985) split streams
into reaches defined as the length of stream between confluences, or from the
headwaters down to the first confluence. Thus, streams were treated as a finite discrete
population. A grid was used to sample stream reaches by randomly placing a grid over a
topographic map of the area of interest, and then proceeding downhill along the fall line
until a stream reach was intersected. The approach that was taken avoids the necessity
of delimiting the resource areal units. The approach of EMAP-West is somewhat
different. The program focuses on the population of stream miles rather than stream
reaches. We wish to characterize the population in terms of the condition of length of
streams rather than numbers of stream reaches. Therefore, we want a sampling method
that samples a stream in proportion to its length; this is accomplished by viewing streams
as an extensive resource with length. The method described here is currently being used
in a pilot study, which among other goals, will examine the suitability of the method for a
larger study.
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Stream traces are identified on 1:100,000-scale Digital Line Graphs, and a
Geographical Information System is used to intersect these with the sampling templates.
Each stream segment within a template is identified and its length determined. The
endpoints of a segment are defined as confluences, headwaters ends, or intersections
with a template edge. Sets of connected segments of the same order are always kept
together in the sample selection process. The appropriate Strahler stream order is also
determined for each segment.
Some differential weighting by size is necessary because of the predominance of
lower-order streams. The sample selection proceeds with inclusion probability for a
segment proportional to its length times the weight for its order. The total inclusion
probability for each template is calculated as the weighted sum of stream lengths in the
template, the templates are partitioned into groups using the partitioning algorithm
described for lakes, and the samples are selected in an analogous manner: the
partitions are randomized, the templates are randomized within the partitions, and the
sets of connected segments are randomized within the templates. The same systematic
selection protocol is used; however, in this case, the selection not only identifies the
stream segment to be sampled, but also identifies the point on that segment where the
sample is to be located. This is accomplished by recording the relative distance from the
beginning of the segment to the selected point on the segment.
The types of questions which have been posed from various State and Tribal
agencies suggest that they would like to make statements about all streams and rivers.
Clearly, sampling every mile of stream in the country is not economically feasible nor is it
necessary. Probability designs have been used in wide range of disciplines to address
this need (Converse 1987).
The primary objectives of this study are to estimate the condition of mapped
perennial wadeable streams and rivers, and the extent (total length) of mapped channels,
in conterminous states of the U.S. The objectives specify an interest in the target
population of wadeable perennial streams and rivers.
One estimate of extent is provided by River Reach File Version 3 (RF3) which is
based on digitized blue lines from 1:100,000 scale maps. Based on prior information, it is
known that RF3 incorrectly codes some stream segments. Incorrect code information
occurs for (1) designating Strahler stream order; (2) delineating perennial and
intermittent, (3) defining natural versus constructed channels, including newly modified
channels, and (4) distinguishing irrigation return flow from irrigation delivery channels. In
some cases, RF3 includes stream channels that are not actually present, due to (1) no
definable channel present, (2) location is wetland/marsh with no defined channel, or (3)
channel may be an impoundment. RF3 may also exclude some stream channels due to
(1) mapping inconsistencies in construction of 1:100,000 maps, (2) digitization of map
blue lines, or (3) inadequacy of photo information used to develop maps, e.g. heavily
forested areas with low order streams. This study assumes that RF3 includes all stream
channels specified by the definition of the target population. That is, if stream channels
exist that are not included in RF3, they will not be addressed by this study.
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A secondary outcome of estimating the extent of the stream channel resource will
be estimates on the amount of miscoding present in RF3. Those stream segments
actually selected in the survey sample that are found to be miscoded will be submitted to
RF3 staff for correction.
3.1 Probability-Based Sampling Design and Site Selection
Target Population: Within the conterminous U.S, all wadeable stream and river channels
(natural and constructed) mapped at 1:100,000 scale
Sample Frame: RF3 stream and river channel segments coded as R, S, T, N, W, (412,
413, 999) and U (414, 415).
This frame is subdivided into two major parts: (1) all RF3 stream, river and canal
segments coded as perennial, and (2) all RF3 stream, river and canal segments coded
as non-perennial, ie, all other stream, river and canal segments. The purpose of
subdividing the frame is to allow a sampling focus on systems that have an exceedingly
high probability of being flowing waters during the index sampling period.
Sites were selected for the WSA project using a hierarchical randomization design
process described by Stevens and Olsen (1999, 2003, 2004). The national hydrography
database (NHD) served as the frame representing streams and rivers in the US. Data
from approximately 500 stream sites in the eastern two-thirds of the United States will be
integrated with data from approximately 1000 stream sites in the western United States
for a comprehensive assessment. This total sample size will allow national reporting as
well as regional reporting at the scale of Omernik Level II ecoregions, the ten EPA
Regions and 10-15 major drainage basins.
Key features of the approach are (1) utilizing survey theory for continuous
populations within a bounded area, (2) explicit control of the spatial dispersion of the
sample through hierarchical randomization, (3) unequal probability of selection by
Strahler order, and (4) nested subsampling to incorporate intensified sampling in special
study regions.
Revisit Sites: Of the sites visited in the field and found to be target sites, a total of 10%
will be revisited. The 10% will be the first 10% of the sites visited. The primary purpose
of this revisit set of sites is to allow variance estimates that would provide information on
the extent to which the population estimates might vary.
Site Evaluation Sites: The number of sites that must be evaluated to achieve the
expected number of field sites that can be sampled can only be estimated based on
assumptions concerning expected error rates in RF3, percent of landowner refusals, and
percent of physically inaccessible sites. Based on the estimates gained in previous
studies, a list of alternate sites was selected at the same time as the base sites. These
alternate sites will be using in order until the desired sample size of 50 per region has
been achieved.
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4.0 INFORMATION MANAGEMENT
Like QA, information management (IM) is integral to all aspects of the WSA from
initial selection of sampling sites through dissemination and reporting of final, validated
data. QA and QC measures implemented for the IM system are aimed at preventing
corruption of data at the time of their initial incorporation into the system and maintaining
the integrity of data and information after incorporation into the system. The general
organization of, and QA/QC measures associated with, the IM system are described in
this section.
Long-term data from WSA, which includes data from the EMAP-West survey
activities and the survey activities conducted directly under WSA in the eastern 36
States, will be maintained in STORET and the EMAP data system at ORD's Atlantic
Ecology Division. Project data management activities will be handled at EPA's Western
Ecology Division and will be compliant with all relevant EPA and Federal data standards.
Data will be shipped from sample processing laboratories to WED no later than March
2005.
4.1 Data Policy
The WSA requires a continuing commitment to the establishment, maintenance,
description, accessibility, and long-term availability of high-quality data and information.
All data used in the WSA will be maintained, following final verification and validation of
dataset, in EPA's STORET and EPA's EMAP data system.
Full and open sharing of the full suite of data and published information produced
by the study is a fundamental objective. Data and information will be available without
restriction for no more than the cost of reproduction and distribution. Where possible, the
access to the data will be via the World Wide Web through STORET and EMAP to keep
the cost of delivery to a minimum and to allow distribution to be as wide as possible. All
data collected by this study will be publicly available following verification and validation
of the dataset.
Organizations and individuals participating in the Study will ship all samples in a
timeline consistent with the field operations manual. Field data sheets will be sent
directly to WED for data entry. All laboratories processing samples will send final
electronic dataset to WED by March 2005. Data and metadata will be available for
assessment preparation by June 2005. Final dataset with metadata will be available via
STORET and EMAP at the time of delivery of the final report, December 2005.
All data sets and published information used in the study will be identified with a
citation; for data sets an indication of how the data may be accessed will be provided.
Data from this study will be maintained indefinitely. All EPA data policies will be followed
including EPA data standards, GIS, etc., as discussed in section 4.3.
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4.2 Overview of System Structure
At each point where data and information are generated, compiled, or stored, the
information must be managed. Thus, the IM system includes all of the data-generating
activities, all of the means of recording and storing information, and all of the processes
which use data. The IM system includes both hardcopy and electronic means of
generating, storing, and archiving data. All participants in the WSA have certain
responsibilities and obligations which make them a part of the IM system. In its entirety,
the IM system includes site selection and logistics information, sample labels and field
data forms, tracking records, map and analytical data, data validation and analysis
processes, reports, and archives. IM staff supporting the WSA at WED provide support
and guidance to all program operations in addition to maintaining a central data base
management system for the WSA data.
The central repository for data and associated information collected for use by the
WSA is a DEC Alpha server system located at WED-Corvallis. The general organization
of the information management system is presented in Figure 5. Data are stored and
managed on this system using the Statistical Analysis System (SAS) software package.
This centrally managed IM system is the primary data management center for the WSA
research conducted at WED and elsewhere. The IM staff receives, enters, and maintains
data and information generated by the site selection process (see Section 3), field
sample and data collection, map-based measurements, laboratory analyses, and
verification and validation activities completed by the states, cooperators and contractors.
In addition to this inflow, the IM system provides outflow in provision of data files to WSA
staff and other users. The IM staff at WED is responsible for maintaining the security
integrity of both the data and the system.
The following sections describe the major inputs to the central data base and the
associated QA/QC processes used to record, enter, and validate measurement and
analytical data collected for EMAP surface waters research projects. Activities to
maintain the integrity and assure the quality of the contents of the IM system are also
described.
4.2.1 Design and Logistics Data Bases
The site selection process described in Section 3 produces a list of candidate
sampling locations, inclusion probabilities, and associated site classification data (e.g.,
target status, ecoregion, stream order, etc.). This "design" data base is provided to the
IM staff, implementation coordinators, and field coordinators. Field coordinators
determine ownership and contacts for acquiring permission to access each site, and
conduct reconnaissance activities. Ownership and reconnaissance information for each
site are compiled into a "logistics" data base. Generally, standardized forms are used
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during reconnaissance activities. Information from these forms may be entered into a
SAS compatible data management system. Whether in electronic or hardcopy format, a
copy of the logistics data base is provided to the IM for archiving storage.
ORGANIZATION OF EMAP-WEST INFORMATION MANAGEMENT
SYSTEM
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Figure 5. Organization of information management system modeled after EMAP-WEST for WSA.
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4.2.2 Sample Collection and Field Data Recording
Prior to initiation of field activities, the IM staff works develops standardized field
data forms and sample labels. Preprinted adhesive labels having a standard recording
format are completed and affixed to each sample container. Precautions are taken to
ensure that label information remains legible and the label remains attached to the
sample. Examples of sample labels are presented in the field operations manual.
Field sample collection and data forms are designed in conjunction with IM staff to
ensure the format facilitates field recording and subsequent data entry tasks. All forms
which may be used onsite are printed on water-resistant paper. Copies of the field data
forms and instructions for completing each form are documented in the field operations
manuals. Recorded data are reviewed upon completion of data collection and recording
activities by a person other than the one who completed the form. Field crews check
completed data forms and sample labels before leaving a sampling site to ensure
information and data were recorded legibly and completely. Errors are corrected if
possible, and data considered as suspect are qualified using a flag variable. The field
crew enters explanations for all flagged data in a comments section. Completed field
data forms are transmitted to the IM staff at WED for entry into the central data base
management system.
All samples are tracked from the point of collection. Hardcopy tracking and
custody forms are completed by the field crews. Copies of the shipping and custody
record accompany all sample transfers; other copies are transmitted to the IMC and
applicable indicator lead. Samples are tracked to ensure that they are delivered to the
appropriate laboratory, that lost shipments can be quickly identified and traced, and that
any problems with samples observed when received at the laboratory are reported
promptly so that corrective action can be taken if necessary. Detailed procedures on
shipping and sample tracking can be found in Section 8.2 of the Field Operations Manual
Procedures for completion of sample labels and field data forms, and use of PCs
are covered extensively in training sessions. General QC checks and procedures
associated with sample collection and transfer, field measurements, and field data form
completion for most indicators are listed in Table 3. Additional QA/QC checks or
procedures specific to individual indicators are described in the indicator sections in
Section 5 of this QAPP.
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4.2.3 Laboratory Analyses and Data Recording
Upon receipt of a sample shipment, analytical laboratory receiving personnel
check the condition and identification of each sample against the sample tracking record.
Each sample is identified by information written on the sample label and by a barcode
label. Any discrepancies, damaged samples, or missing samples are reported to the IM
staff and indicator lead by telephone.
Table 3. Sample and field data quality control activities
Quality Control
Activity
Contamination
Prevention
Sample
Identification
Data Recording
Data Qualifiers
Sample Custody
Sample Tracking
Data Entry
Data Submission
Data Archival
Description and/or Requirements
All containers for individual site sealed in plastic bags until use; specific
contamination avoidance measures covered in training
Pre-printed labels with unique ID number on each sample
Data recorded on pre-printed forms of water-resistant paper; field crew
reviews data forms- for accuracy, completeness, and legibility
Defined qualifier codes used on data form; qualifiers explained in comments
section on data form
Unique sample ID and tracking form information entered in LIMS; sample
shipment and receipt confirmed
Sample condition inspected upon receipt and noted on tracking form with
copies sent to Indicator Lead, Communications Center, and/or IM
Data entered using customized entry screens that resemble the data forms;
entries reviewed manually or by automated comparison of double entry
Standard format defined for each measurement including units, significant
figures, and decimal places, accepted code values, and required field width
All data archived in an organized manner for a period of seven years or until
written authorization for disposition has been received from the Surface
Waters Technical Director.
Most of the laboratory analyses for the WSA indicators, particularly chemical and
physical analyses, follow or are based on standard methods. Standard methods
generally include requirements for QC checks and procedures. General laboratory
QA/QC procedures applicable to most WSA indicators are described in Table 4.
Additional QA/QC samples and procedures specific to individual indicator analyses are
described in the indicator sections in Part II of this QAPP. Biological sample analyses
are generally based on current acceptable practices within the particular biological
discipline. Some QC checks and procedures applicable to most WSA biological samples
are described in Table 5. Additional QA/QC procedures specific to individual biological
indicators are described in the indicator sections in Part II of this QAPP.
A laboratory's IM system may consist of only hardcopy records such as bench
sheets and logbooks, an electronic laboratory information management system (LIMS),
or some combination of hardcopy and electronic records. Laboratory data records are
reviewed at the end of each analysis day by the designated laboratory onsite QA
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coordinator or by supervisory personnel. Errors are corrected if possible, and data
considered as suspect by laboratory analysts are qualified with a flag variable. All
flagged data are explained in a comments section. Private contract laboratories
generally have a laboratory quality assurance plan and established procedures for
recording, reviewing, and validating analysis data.
Once analytical data have passed all of the laboratory's internal review
procedures, a submission package is prepared and transferred to the IM staff. The
contents of the submission package are largely dictated by the type of analysis (physical,
chemical, or biological), but generally includes at least the elements listed in Tables 4 or
5. All samples and raw data files (including logbooks, bench sheets, and instrument
tracings) are to be retained for a period of seven years or until authorized for disposal, in
writing, by the WSA Project Leader.
Table 4. Laboratory data quality control activities
Quality Control
Activity
Instrument
Maintenance
Calibration
QC Data
Data Recording
Data Qualifiers
Data Entry
Submission
Package
Description and/or Requirements
Follow manufacturer's recommendations and specific guidelines in methods;
maintain logbook of maintenance/repair activities
Calibrate according to manufacturer's recommendations and guidelines given
in Section 6; recalibrate or replace before analyzing any samples
Maintain control charts, determine MDLs and achieved data attributes; include
QC data summary in submission package
Use software compatible with EMAP-SW IM system; check all data entered
against the original bench sheet to identify and correct entry errors.
Review other QA data (e.g. condition upon receipt, etc.) for possible problems
with sample or specimens.
Use defined qualifier codes; explain all qualifiers
Automated comparison of double entry or 100% manual check against original
data form
Includes: Letter by the laboratory manager; data, data qualifiers and
explanations; electronic format compatible with EMAP-SW IM system,
documentation of file and data base structures, variable descriptions and
formats' summary report of any problems and corrective actions implempntpH
Table 5. Biological sample quality control activities
Quality Control
Activity
Sorting/Enumeration
Taxonomic Nomenclature
Taxonomic Identifications
Independent Identifications
Duplicate Identifications
Taxonomic
Reasonableness Checks
Description and/or Requirements
Re-sort 10% of samples and check counts of organisms
Use accepted common and scientific nomenclature and unique entry
codes
Use standard taxonomic references and keys; maintain bibliography of
all references used
Uncertain identifications to be confirmed by expert in particular taxa
At least 5% of all samples completed per taxonomist reidentified by
different analyst; less than 10% assigned different ID
Species or genera known to occur in given conditions or geographic
area
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4.2.4 Data Review, Verification, Validation Activities
Raw data files are created from entry of field and analytical data, including data for
QA/QC samples and any data qualifiers noted on the field forms or analytical data
package. After initial entry, data are reviewed for entry errors by either a manual
comparison of a printout of the entered data against the original data form or by
automated comparison of data entered twice into separate files. Entry errors are
corrected and reentered. For biological samples, species identifications are corrected for
entry errors associated with incorrect or misspelled codes. Errors associated with
misidentification of specimens are corrected after voucher specimens have been
confirmed and the results are available. Files corrected for entry errors are considered to
be raw data files. Copies of all raw data files are maintained in the centralized IM
system.
Table 6. Data review, verification, and validation quality control activities
Quality Control Activity
Review any qualifiers associated with
variable
Summarize and review replicate sample
data
Determine if data quality objectives have
been achieved
Exploratory data analyses (univariate,
bivariate, multivariate) utilizing all data
Confirm assumptions regarding specific
types of statistical techniques being
utilized in development of metrics and
indicators
Description and/or Requirements
Determine if value is suspect or invalid; assign validation
qualifiers as appropriate
Identify replicate samples with large variance; determine
if analytical error or visit-specific phenomenon is
responsible
Determine potential impact on achieving research and/or
program objectives
Identify outlier values and determine if analytical error or
site-specific phenomenon is responsible
Determine potential impact on achieving research and/or
program objectives
Some of the typical checks made in the processes of verification and validation are
described in Table 6. Automated review procedures may be used. The primary purpose
of the initial checks is to confirm that a data value present in an electronic data file is
accurate with respect to the value that was initially recorded on a data form or obtained
from an analytical instrument. In general, these activities focus on individual variables in
the raw data file and may include range checks for numeric variables, frequency
tabulations of coded or alphanumeric variables to identify erroneous codes or misspelled
entries, and summations of variables reported in terms of percent or percentiles. In
addition, associated QA information (e.g., sample holding time) and QC sample data are
reviewed to determine if they meet acceptance criteria. Suspect values are assigned a
data qualifier until they can be corrected or confirmed as unacceptable and replaced with
a new acceptable value from sample reanalysis.
A second review is conducted after all analyses have been completed and the raw
data file is created. The internal consistency among different analyses or measurements
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conducted on a sample is evaluated. Examples of internal consistency checks include
calculation of chemical ion balances or the summation of the relative abundances of taxa.
Samples identified as suspect based on internal consistency checks are qualified with a
flag variable and targeted for more intensive review. Data remain qualified until they can
be corrected, are confirmed as acceptable in spite of the apparent inconsistency, or until
new acceptable values are obtained from sample reanalysis. Upon completion of these
activities, copies of the resultant data files are transmitted for archival storage.
In the final stage of data verification and validation, exploratory data analysis
techniques may be used to identify extreme data points or statistical outliers in the data
set. Examples of univariate analysis techniques include the generation and examination
of box-and-whisker plots and subsequent statistical tests of any outlying data points.
Bivariate techniques include calculation of Spearman correlation coefficients for all pairs
of variables in the data set with subsequent examination of bivariate plots of variables
having high correlation coefficients. Recently, multivariate techniques have been used in
detecting extreme or outlying values in environmental data sets (Meglen, 1985; Garner et
al., 1991; Stapanian et al., 1993). A software package, SCOUT, developed by EPA and
based on the approach of Garner et al. (1991) may be used for validation of multivariate
data sets.
Suspect data are reviewed to determine the source of error, if possible. If the error
is correctable, the data set is edited to incorporate the correct data. If the source of the
error cannot be determined, data are qualified as questionable or invalid. Data qualified
as questionable may be acceptable for certain types of data analyses and interpretation
activities. The decision to use questionable data must be made by the individual data
users. Data qualified as invalid are considered to be unacceptable for use in any
analysis or interpretation activities and will generally be removed from the data file and
replaced with a missing value code and explanatory comment or flag code. After
completion of verification and validation activities, a final data file is created, with copies
transmitted for archival and for uploading to the centralized IM system.
Once verified and validated, data files are made available for use in various types
of interpretation activities, each of which may require additional restructuring of the data
files. These restructuring activities are collectively referred to as "data enhancement." In
order to develop indicator metrics from one or more variables, data files may be
restructured so as to provide a single record per stream site. To calculate site population
estimates based on individual measurements or indicators, missing values and suspect
data points may need to be replaced with alternate data (such as a value from a replicate
measurement) or values calculated from predictive relationships based on other
variables.
4.3 Data Transfer
Field crews may transmit data electronically via modem or floppy disc; hardcopies
of completed data and sample tracking forms may be transmitted to the IM staff at WED
via portable facsimile (FAX) machine or via express courier service. Copies of raw,
verified, and validated data files are transferred from states, cooperators, and contractors
to the IM staff for inclusion in the central IM system. All transfers of data are conducted
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using a means of transfer, file structure, and file format that has been approved by the IM
staff. Data files that do not meet the required specifications will not be incorporated into
the centralized data access and management system.
4.4 Core Information Management Standards
Participants will adhere to the "Core Information Management Standards for the
EMAP Western Study." National and international standards will be used to the greatest
extent possible. This section details a list of standards pertaining to information
management that all participants in the WSA agree to follow. The goal of these core
standards is to maximize the ability to exchange data with other studies conducted under
the monitoring framework of the Committee on the Environment and Natural Resources
(CENR 1997). The main standards are those of the Federal Geographic Data Committee
(FGDC 1999), the National Spatial Data Infrastructure (NSDI 1999), and the National
Biological Information Infrastructure (NBII 1999).
4.4.1 Metadata
Federal Geographic Data Committee Content standard for digital geospatial
metadata, version 2.0. FGDC-STD-001-1998 (FGDC 1998), including the Biological Data
Profile and the Biological Names and Taxonomy Data Standards developed by the
National Biological Information Infrastructure (NBII 1999).
For tabular data, metadata that meet the FGDC content standard are contained by
a combination of the EMAP Data Directory and the EMAP Data Catalog. For ARC/INFO
coverages, the metadata are in the .DOC file embedded in the coverage. This file stays
with the coverage. When the coverage is moved to the EMAP public web sites, it will be
duplicated to an ASCII text file.
4.4.2 Data Directory
The EMAP Data Directory is maintained as an Oracle database. The guidelines
are given in Frithsen and Strebel (1995), Frithsen (1996a, b) and USEPA (1996b).
Other data: Environmental Information Management system (EIMS 1999). EMAP
Directory entries are periodically uploaded to the EIMS. The EIMS will become EPA's
node for the National Spatial Data Infrastructure and will make directory information
available to other federal agencies through the Z39.50 protocol in accordance with the
US Global Change Research Program (USGCRP 1998)
4.4.3 Data Catalog
Data catalog standards are given in Frithsen and Strebel (1995), Frithsen (1996a),
and USEPA (1996c).
4.4.4 Data Formats
4.4.4.1 Attribute data
ASCII files: comma-separated values, or space-delimited, or fixed column
SAS export files
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Oracle
4.4.4.2 CIS data
ARC/INFO export files; compressed .tar file of ARC/INFO workspace
Spatial Data Transfer Standard (SDTS) (FGDC 1999) format available on request
4.4.5 Parameter Formats
Sampling Site (EPA Locational Data Policy (USEPA 1991)
Latitude and Longitude in decimal degrees (+/- 7.4)
Negative longitude values (west of the prime meridian)
NAD83
Date: YYYYMMDD (year, month, day)
Hour: HHMMSS (hour, minute, second)
Greenwich mean time
Local time
Data loaded to STORET will take on the STORET formats upon loading.
4.4.6 Standard Coding Systems
Chemical Compounds: Chemical Abstracts Service (CAS 1999)
Species Names: Integrated Taxonomic Information system (ITIS 1999)
Land cover/land use codes: Multi-Resolution Land Characteristics (MRLC 1999)
4.5 Hardware and Software Control
All automated data processing (ADP) equipment and software purchased for or
used in WSA surface waters research is subject to the requirements of the federal
government, the particular Agency, and the individual facility making the purchase or
maintaining the equipment and software. All hardware purchased by EPA is identified
with an EPA barcode tag label; an inventory is maintained by the responsible ADP
personnel at the facility. Inventories are also maintained of all software licenses; periodic
checks are made of all software assigned to a particular PC.
The development and organization of the IM system is compliant with guidelines
and standards established by the EMAP Information Management Technical
Coordination Group, the EPA Office of Environmental Information (OEI), and the EPA
office of Administrative Resources Management (OARM). Areas addressed by these
policies and guidelines include, but are not limited to, the following:
Taxonomic Nomenclature and Coding
Locational data
Sampling unit identification and reference
Hardware and software
Data catalog documentation
The WSA is committed to compliance with all applicable regulations and guidance
concerning hardware and software procurement, maintenance, configuration control, and
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QA/QC. As new guidance and requirements are issued, the WSA information
management staff will assess the impact upon the IM system and develop plans for
ensuring timely compliance.
4.6 Data Security
All data files in the IM system are protected from corruption by computer viruses,
unauthorized access, and hardware and software failures. Guidance and policy
documents of EPA and management policies established by the IM Technical
Coordination Group for data access and data confidentiality are followed. Raw and
verified data files are accessible only to the WSA collaborators. Validated data files are
accessible only to users specifically authorized by the EPA Project Leader. Data files in
the central repository used for access and dissemination are marked as read-only to
prevent corruption by inadvertent editing, additions, or deletions.
Data generated, processed, and incorporated into the IM system are routinely
stored as well as archived on redundant systems. This ensures that if one system is
destroyed or incapacitated, IM staff will be able to reconstruct the data bases.
Procedures developed to archive the data, monitor the process, and recover the data are
described in IM documentation.
Several backup copies of all data files and of the programs used for processing
the data are maintained. Backups of the entire system are maintained off-site. System
backup procedures are utilized. The central data base is backed up and archived
according to procedures already established for WED. All laboratories generating data
and developing data files must have established procedures for backing up and archiving
computerized data.
5.0 INDICATORS
5.1 Benthic Macroinvertebrates
5.1.1 Introduction
The benthic invertebrate assemblage found in sediments and on substrates of
streams reflect an important aspect of the biological condition of the stream. The
response of benthic communities to various stressors can often be used to determine the
type of stressor and to monitor trends (Klemm et al., 1990). The overall objectives of the
benthic invertebrate indicators are to detect stresses on community structure in wadeable
streams and to assess and monitor the relative severity of those stresses. The benthic
invertebrate indicator procedures are based on various recent bioassessment literature
(Barbour et al. 1999, Hawkins et al. 2000, Peck et al. 2003).
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5.1.2 Sampling Design
Benthic invertebrates are collected at randomly selected sampling locations on the
cross-sectional transects established along the stream reach. A composite of
invertebrates is collected from a multi-habitat approach and consists of sampling pools,
riffles, runs, and glides. The index sampling design is illustrated in Figure 6.
CRO8I SECTION IRAN SECTS (A to K)
TRANSECT SAMP LES(1p«rtani»ct)
Sampling point of » ic M tan 18 ct(1«, Ifl.SMJitlwtod lyitemiUcslly after randan ifert
Mod fed h ft let em (in met 0
1 ff q ial st sari pled tot 3D sec
ComtUnt all hlchn»t iimp«i
rt ffle i and runi and torn pod i
SOD |im me si
li(pertaid Rmoue asm id
debtfc aid me sedttsitas
poesbt
SODfliLorl-Lalqiot
FII10moe uaisminIulttsample
. Pretemeiunt 9S%e«aioltotlial
Figure 6. Sampling design for the benthic indicator.
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5.1.3 Sampling and Analytical Methodologies
Sample Collection: Benthic invertebrates are collected from an approximately 930
cm2 (0.09 m2) area randomly selected at each of 11 cross-sectional transects. Samples
collected using a modified D-frame kick-net (500 urn mesh) procedure are composited
together to produce a 1.02 m2 areal sample. Samples are field-processed to remove
large detritus(rinsed and inspected for organisms) and preserved in ethanol. Detailed
sampling and processing procedures are described in section of the field operations
manual. A condensed description of key elements of the field activities is provided for
easy reference onsite.
Analysis: Preserved composite samples are sorted, enumerated, and
invertebrates identified to the genus level (see Attachment 4 of the Benthic Laboratory
Manual) using specified standard keys and references. Processing and archival methods
are based on standard limnological practices. Detailed procedures are contained in the
laboratory operations manual and cited references. There is no maximum holding time
associated with preserved benthic invertebrate samples. Five hundred benthic organism
count is the target number to match the EMAP West protocal. A 10% external check is
standard QA for EMAP West. For operational purposes of the WSA, laboratory sample
processing should be completed by March 2005. Table 7 summarizes field and analytical
methods for the benthic invertebrates indicator.
Table 7. Field and laboratory methods: benthic indicator
Variable or
Measurement
Sample
Collection
Sorting and
Enumeration
Identification
QA
Class
C
C
C
Expected
Range
and/or Units
NA
0 to 500
organisms
genus
Summary of Method
One-man D-frame kick net (500
u mesh) used to collect
organisms, which are
composited from 11 transects
Random systematic selection of
grids with target of 500
organisms from sample
Specified keys and references
References
Barbour etal. 1999,
Peck et al. 2003,
WSA Field Operation
Manual 2004
WSA Benthic
Laboratory Methods
2004
C = critical, N = non-critical quality assurance classification.
5.1.4 Quality Assurance Objectives
Measurement quality objectives (MQOs) are given in Table 8. General
requirements for comparability and representativeness are addressed in Section 2. The
MQOs given in Table 8 represents the maximum allowable criteria for statistical control
purposes. Precision is calculated as percent efficiency, estimated from examination of
randomly selected sample residuals by a second analyst and independent identifications
of organisms in randomly selected samples. The MQO for picking accuracy is estimated
from examinations (repicks) of randomly selected residues by experienced taxonomists.
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Table 8. Measurement data quality objectives: benthic indicator
Variable or Measurement
Sort and Pick
Identification
Precision
95%
85%
Accuracy
90%
90%"
Completeness
99%
99%
NA = not applicable
Taxonomic accuracy, as calculated using Equation 10 in Section 2.
The completeness objectives are established for each measurement per site type
(e.g., probability sites, revisit sites, etc.). Failure to achieve the minimum requirements
for a particular site type results in regional population estimates having wider confidence
intervals. Failure to achieve requirements for repeat and annual revisit samples reduces
the precision of estimates of index period and annual variance components, and may
impact the representativeness of these estimates because of possible bias in the set of
measurements obtained. '
5.1.5 Quality Control Procedures: Field Operations
Specific quality control measures are listed in Table 9 for field operations.
5.1.6 Quality Control Procedures: Laboratory Operations
Specific quality control measures are listed in Table 10 for laboratory operations.
Figure 6 presents the general process for collecting and analyzing benthic invertebrate
samples.
5.1.7 Data Management, Review, and Validation
Checks made of the data in the process of review, verification, and validation are
summarized in Table 11. The Project Facilitation Team is ultimately responsible for
ensuring the validity of the data, although performance of the specific checks may be
delegated to other staff members. Once data have passed all acceptance requirements,
computerized data files are prepared in a format specified for the WSA project by EMAP
and copied onto a floppy diskette. The diskettes are transferred to the WSA IM
Coordinator (Marlys Cappaert) for entry into a centralized data base. A hard copy output
of all files accompanies each diskette.
A reference specimen collection is prepared as new taxa are encountered in
samples. This collection consists of preserved specimens in vials and mounted on slides
and is provided to the responsible EPA laboratory as part of the analytical laboratory
contract requirements. The reference collection is archived at the responsible EPA
laboratory.
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Table 9.Laboratory Quality Control: benthic macroinvertebrate sample processing
Check or
Sample
Description
Frequency
Acceptance Criteria
Corrective Action
SAMPLE PROCESSING (PICK AND SORT)
Sample
residuals
examined by
different
analyst
within lab
Sorted
samples
sent to
indepen-
dent lab
10% of all
samples
completed
per analyst
10% of all
samples
Efficiency of picking a90%
Accuracy of contractor laboratory
picking and identification ^90%
If <90%, examine all
residuals of samples by
that analyst and retrain
analyst
If picking accuracy <90%,
all samples in batch will be
reanalyzed by contractor
Table 10: Laboratory Quality Control: benthic macroinvertebrate taxonomic identification
Check or
Sample
Description
Duplicate
identification
by different
taxonomist
within lab
Independent
identification
by outside
taxonomist
Use
widely /com
monly
excepted
taxonomic
references
Prepare
reference
collection
Frequency
10% of all
samples
completed
per
laboratory
All
uncertain
taxa
For all
identificatio
ns
Each new
taxon per
laboratory
Acceptance Criteria
Efficiency s85%
Uncertain identifications to be
confirmed by expert in particular
taxa
All keys and references used
must be on bibliography
prepared by another laboratory
Complete reference collection to
be maintained by each individual
laboratory
Corrective Action
If £85%, reidentify all
samples completed by that
taxonomist
Record both tentative and
independent IDs
If other references desired,
obtain permission to use
from Project QA Officer
Benthic Lab Manager
periodically reviews data
and reference collection to
ensure reference collection
is complete and
identifications are accurate
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Sample residuals, vials, and slides are archived by each laboratory until the WSA
Project Leader has authorized, in writing, the disposition of samples. All raw data
(including field data forms and bench data recording sheets) are retained in an organized
fashion indefinitely or until written authorization for disposition has been received from the
WSA Project Leader.
Table 11: Data validation quality control: benthic indicator
Check Description
Taxonomic "reasonableness"
checks
Frequency
All data
sheets
Acceptance Criteria
Genera known to occur in
given stream conditions or
geographic area
Corrective Action
Second or third
identification by
expert in that taxon
5.1.8 Data Analysis Plan
Specific research issues to be addressed from this year's activities and the
ecological attributes or metrics associated with the benthic indicator are summarized in
Table 12.
Table 12. Research issues: benthic indicator
Research Issues
Variance Estimates
Indicator Development
and Evaluation
Methods Comparability
Threshold Development
for Assessment
Biological Condition
Design Strategy
Obtain estimates of variance components from duplicate samples and revisits to
sites.
Identify best set of ecological attributes or metrics that are broadly applicable to
assessing biological condition and are informative as to detection and
characterization of impairment. Candidate attributes are selected measures of
richness, O/E, representatives of sensitive taxa. These are based on EPA's
biological condition graient attributes as part of the aquatic life use initiative.
Use standardized guidelines ( from the NWQMC Methods and Data Comparability
Board) for methods comparability studies (to measure precision and sensitivity
along environmental and disturbance gradients), and select ecological attributes
best suited to compare performance of methods (e.g., compositional metrics, or
richness adjusted for reference).
Develop general expectations for each attribute (for each ecoregion) from
collection of reference sites sampled with WSA methods. Supplement with
information from states and existing data where methods differences are not an
issue. Combining data for an integrated assessment is based on minimizing
sampling bias. Explore the use of thresholds based on % difference, e.g., 20%
deviation from reference as a consistent means of evaluating biological condition
across ecoregions.
Develop an ordinal scale related to a biological condition gradient to reflect
varying degrees of quality.
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5.2 Physical Habitat Quality Indicator
5.2.1 Introduction
Naturally occurring differences in physical habitat structure and associated
hydraulic characteristics among surface waters contributes to much of the observed
variation in species composition and abundance within a zoogeographic province.
Structural complexity of aquatic habitats provides the variety of physical and chemical
conditions to support diverse biotic assemblages and maintain long-term stability.
Anthropogenic alterations of riparian physical habitat, such as channel alterations,
wetland drainage, grazing, agricultural practices, weed control, and streambank
modifications such as revetments or development, generally act to reduce the complexity
of aquatic habitat and result in a loss of species and ecosystem degradation. References
are in Table 13.
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Subsampling should
result inc
1 jar of organisms
1 jar of sort residue
_1 jar of unseated
remains
3 (at least) total jars,
labeled accordingly
Sample
received
Sample logged
MX and respread
3 original grids
in fray
Sample cleaned & spread in
gridded screen
Choose 3 new grid
squares (A-F; 1-6)
Randomly select 3 grid
squares (A-F; 1-6)
H ace in white
"picking" tray
Sort enough
grids to achieve
target number,
UptolQx
magnification
used
Select and sort
enough grids to
achieve target
number, up to lOx
used
Choose last grid,
respread and sort
until target
number is
achieved
Potential!
over 600
orgs. with
final grid?
Verify organisms
under microscope
Botde inserted
sample remains
and sort residue;
Labd properly
Figure 7: Laboratory Processing Activities for the benthic indicator
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For WSA, indicators derived from data collected on physical habitat quality will be
used to help explain or characterize stream condition relative to biological response and
trophic state indicators. Specific groups of physical habitat attributes important in stream
ecology include: channel dimensions, gradient, substrate; habitat complexity and cover;
riparian vegetation cover and structure; anthropogenic alterations; and channel-riparian
interaction (Kaufmann, 1993). Overall objectives for this indicator are to develop
quantitative and reproducible indices, using both multivariate and multimetric
approaches, to classify streams and to monitor biologically relevant changes in habitat
quality and intensity of disturbance.
5.2.2 Sampling Design
As the physical habitat indicator is based on field measurements and
observations, there is no sample collection associated with this indicator. Field crews are
provided with 1:24,000 maps with the midpoint (index site) of the stream reach marked.
At WSA sites, eleven cross-sectional measurement transects are spaced at equal
intervals proportional to baseflow channel width, thereby scaling the sampling reach
length and resolution in proportion to stream size. A systematic spatial sampling design
is used to minimize bias in the selection of the measurement sites. Additional
measurements are made at equally spaced intervals between the cross-sectional sites.
A "rapid" assessment of habitat quality of the entire sampling reach is conducted based
on the Rapid Bioassessment Protocol (RBP; Barbour et al., 1999).
5.2.3 Sampling Methodologies
Field Measurements: Field measurements, observations, and associated
methodology for the protocol are summarized in Table 13; methodology for the RBP is
described in Barbour et al. (1999) and in the WSA Field Operations Manual (2004).
Detailed procedures for completing both protocols are provided in the field operations
manual; equipment and supplies required are also listed. All measurements and
observations are recorded on standardized forms which are later entered in to the central
EMAP surface waters information management system at WED-Corvallis.
There are no sample collection or laboratory analyses associated with the physical
habitat measurements.
5.2.4 Quality Assurance Objectives
Measurement data quality objectives (measurement DQOs or MQOs) are given in
Table 14. General requirements for comparability and representativeness are addressed
in Section 2. The MQOs given in Table 14 represent the maximum allowable criteria for
statistical control purposes. Precision is determined from results of revisits by a different
crew (field measurements) and by duplicate measurements by the same crew on a
different day.
The completeness objectives are established for each measurement per site type (e.g.,
WSA sites, revisit sites, state comparability sites). Failure to achieve the minimum
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requirements for a particular site type results in regional population estimates having
wider confidence intervals. Failure to achieve requirements for repeat and annual revisit
samples reduces the precision of estimates of index period and annual variance
components, and may impact the representativeness of these estimates because of
possible bias in the set of measurements obtained.
Table 13. Field measurement methods: physical habitat indicator.
Variable or
Measurement
Units
QA
Class
Summary of Method
THALWEG PROFILE
Thalweg depth
Wetted width
Habitat class
cm
0.1m
none
C
C
N
Measure maximum depth at 100-150
points along reach with surveyor's
rod and meter stick
Measure wetted width with meter
stick or measuring tape on
perpendicular line to mid-channel
line
Visually estimate channel habitat
using defined class descriptions
WOODY DEBRIS TALLY
Large woody
debris
number
of
pieces
N
Visually estimate amount of woody
debris in baseflow channel using
defined class descriptions
CHANNEL AND RIPARIAN CROSS-SECTIONS
Slope and
bearing
Substrate size
Bank angle
Bank incision
Bank undercut
Bankful width
Bankful height
percent/
degrees
mm
degrees
0.1m
cm
0.1m
0.1m
C
C
N
N
N
N
N
Backsight between cross-section
stations using clinometer,
rangefinder compass, and tripod
At 5 points on cross section,
estimate size of one selected particle
using defined class descriptions
Use clinometer and surveyors rod to
measure angle
Visually estimate height from water
surface to first terrace offloodplain
Measure horizontal distance of
undercut
Measure width at top of bankful
height
Measure height from water surface
to estimated water surface during
bankful flow
References
Frissell etal, 1986
Robison and
Beschta, 1990
Stack, 1989;
Robison and
Kaufmann, in
prep.
Wollman, 1954;
Bain etal, 1985;
Plafkin etal, 1989
Platts etal, 1983
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Table 13. Field measurement methods: physical habitat indicator.
Variable or
Measurement
Canopy cover
Riparian
vegetation
structure
Fish cover,
algae,
macrophytes
Human
influence
Units
points
of inter-
section
percent
percent
none
QA
Class
C
N
C
C
Summary of Method
Count points of intersection on
densiometerat specific points and
directions on cross-section
Observations of ground cover,
understory, and canopy types and
coverage of area 5 m on either side
of cross section and 10m back from
bank
Visually estimate in-channel features
5 m on either side of cross section
Estimate presence/absence of
defined types of anthropogenic
features
STREAM DISCHARGE
Discharge
m/s or
L/min.
N
Velocity-Area method, Portable Weir
method, timed bucket discharge
method
References
Lemmon, 1957;
Mulveyetal, 1992
Linsley et al, 1982
Table 14. Measurement data quality objectives: physical habitat indicator
Variable or Measurement
Field Measurements and Observations
Map-Based Measurements
Precision
±10%*
±10%
Accuracy
NA
NA
Completeness
90%
100%
NA = not applicable *Not for RBP measures
5.2.5 Quality Control Procedures: Field Operations
Specific quality control measures are listed in Table 15 for field measurements and
observations.
5.2.6 Quality Control Procedures: Laboratory Operations
There are no laboratory operations associated with this indicator.
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Table 15. Field quality control: physical habitat indicator
Check Description
Check totals for cover
class categories
(vegetation type, fish
cover)
Check completeness
of thalweg depth
measurements
Check calibration of
multiprobe
Frequency
Each transect
Each site
Prior to each sampling
day
Acceptance Criteria
Sum must be
reasonable
(best professional
judgement)
Depth measurements
for all sampling points
Specific to instrument
Corrective
Actions
Repeat
observations
Obtain best
estimate of depth
where actual
measurement not
possible
Adjust and
recalibrate,
redeploy gear
5.2.7 Data Management, Review, and Validation
Checks made of the data in the process of review, verification, and validation are
summarized in Table 16. The Indicator Lead is ultimately responsible for ensuring the
validity of the data, although performance of the specific checks may be delegated to
other staff members. All raw data (including all standardized forms and logbooks) are
retained in an organized fashion for seven years or until written authorization for
disposition has been received from the WSA Project Coordinator.
Table 16. Data validation quality control: physical habitat indicator
Check Description
Estimate precision of
measurements based on
repeat visits by different
crews
Frequency
At least 2 teams visit
stream 1 time each at
10% of streams (may
be same team or
different teams)
Acceptance Criteria
Measurements
should be within 10
percent
Corrective Action
Review data for
reasonableness;
Determine if
acceptance criteria
need to be modified
5.3 Water Chemistry Indicator
5.3.1 Introduction
Ecological indicators based on lake and stream water chemistry information
attempt to evaluate stream condition with respect to stressors such as acidic deposition
and other types of physical or chemical contamination. Data are collected for a variety of
physical and chemical constituents to provide information on the acid-base status of each
stream, water clarity, primary productivity, nutrient status, mass balance budgets of
constituents, color, temperature regime, and presence and extent of anaerobic
conditions.
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At each stream site, crews fill one 4-L Cubitainer and two 60-mL syringes with
stream water. These samples are stored in a cooler packed resealable plastic bags filled
with ice and shipped to the analytical laboratory within 24 hours of collection. The
primary function of the water chemistry information is to determine:
Acid-base status
Trophic state (nutrient enrichment)
Chemical stressors
Classification of water chemistry type
Specific research questions and hypotheses to be addressed from this year's activities
are listed in Table 17.
Table 17. Research questions and hypotheses: water chemistry indicator
WSA Design Evaluation: Obtain estimates of regional variation.
Indicator Development and Evaluation: Development of chemical classes based on different types of
chemical stressors (e.g., acid min drainage), examine relationship of chemical condition/stress to
watershofl landiifiB anri rievBlnn inrliratr>rfs\ nf rhamiral rnnHitinn fa n trnnhir stata [Carlson 19771V
5.3.2 Sampling Design
The plot design for stream sampling is shown in Figure 7. The plot design for
water chemistry sampling is based on that used for the Wadeable Streams Assessment
(Kaufmann et al., 1988). At each stream, a single index site is located at the midpoint of
the designated stream reach. At each index site, a single water sample is collected. .
5.3.3 Sampling and Analytical Methodologies
Sample Collection: At the stream index site, a water sample is prepared from a
series of eight 500-mL grab samples collected from the upper portion of the water
column. In lotic systems the flowing water gives a composite from different parcels.
These grab samples are composited into a single 4-L bulk water sample. Two syringe
samples for closed system measurements are collected by immersing each 60 ml syringe
into the stream at the index site and drawing water from under the surface into the
syringe without exposure to the atmosphere. Detailed procedures for sample collection
and handling are described in the field operations manual.
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INDEX SAMPLE COLLECTION: WATER CHEMISTRY INDICATOR
Figure 8. Stream index sampling design for the water chemistry indicator.
Analysis: Table 18 summarizes analytical methodologies for the chemical
parameters. Analytical methods are based on EPA-validated methods, modified for use
with aqueous samples of low ionic strength. Modified methods are thoroughly
documented in the laboratory methods handbook prepared for the Aquatic Effects
Research Program ( U.S. EPA, 1987).
5.3.4 Quality Assurance Objectives
Measurement data quality objectives (measurement DQOs or MQOs) are given in
Table 19. General requirements for comparability and representativeness are addressed
in Section 2. The MQOs given in Table 19 represent the maximum allowable criteria for
statistical control purposes. Method detection limits are monitored over time by repeated
measurements of low level standards and calculated using Equation 2-1. For major
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cations and anions, the required MDLs are approximately equivalent to 1.0 ueq/L (0.5
ueq/L for nitrate). The analytical laboratory may report results in mg/L; these results are
converted to ueq/L for interpretation. For total suspended solids determinations, the
"detection limit" is defined based on the required sensitivity of the analytical balance.
For precision, the objectives presented in Table 19 represent the 99 percent
confidence intervals about a single measurement and are thus based on the standard
deviation of a set of repeated measurements (n > 1). Precision objectives at lower
concentrations are equivalent to the corresponding MDL. At higher concentrations, the
precision objective is expressed in relative terms, with the 99 percent confidence interval
based on the relative standard deviation (Section 2). Objectives for accuracy are equal
to the corresponding precision objective, and are based on the mean value of repeated
measurements. Accuracy is generally estimated as net bias or relative net bias (Section
2). For total phosphorus and total nitrogen measurements, accuracy is also determined
from analyses of matrix spike samples (also sometimes called fortified samples) as
percent recovery (Section 2). Precision and bias are monitored at the point of
measurement (field or analytical laboratory) by several types of QC samples described in
the Section 5.3.6, and from performance evaluation (PE) samples
Table 18. Analytical methodologies: water chemistry indicator
Analyte
pH, closed
system
pH, equilibrated
Acid
Neutralizing
Capacity (ANC)
Carbon,
dissolved9
inorganic (DIG),
closed system
Carbon,
dissolved
organic (DOC)
Conductivity
QA
Class
C
N
C
N
C
C
Expected Range
3 to 9 pH units
3 to 9 pH units
-100 to 5,OOOueq/L
0.1 to 50 mg C/L
0.1 to 30 mg C/L
1 to 500 uS/cm
Summary of Method
Sample collected and
analyzed without exposure
to atmosphere;
electrometric determination
(pH meter and glass
combination electrode)
Equilibration with 300 ppm
CO2for 1 hr prior to
analysis; Electrometric
determination (pH meter
and glass combination
electrode)
Acidimetric titration to pH s
3.5, with modified Gran plot
analysis
Sample collected and
analyzed without exposure
to atmosphere; acid-
promoted oxidation to CO2,
with detection by infrared
spectrophotometry
UV-promoted persulfate
oxidation, detection by
infrared spectrophotometry.
Electrolytic (conductance
cell and meter)
References
EPA 150.6 (modified);
U.S. EPA (1987)
EPA 150.6 (modified);
U.S. EPA (1987)
EPA 310.1 (modified);
U.S. EPA (1987)
U.S. EPA (1987)
EPA 415.2, U.S. EPA
(1987)
EPA 120.6, U.S. EPA
(1987)
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Analyte
Aluminum, total
dissolved
Aluminum,
monomeric and
organic
monomeric
QA
Class
C
N
Expected Range
10 to 1,OOOug/L
0 to 500 ug/L
Summary of Method
Atomic absorption spec-
troscopy (graphite furnace)
Collection and analysis
without exposure to at-
mosphere. Portion of
sample passed through a
cation exchange column
before analysis to obtain
estimate of organic-bound
fraction. Colorimetric
analysis (automated
pyrocatechol violet).
References
EPA 202.2; U.S. EPA
(1987)
APHA 3000-AI E.; APHA
(1989), U.S. EPA (1987)
Major Cations (dissolved)
Calcium
Magnesium
Sodium
Potassium
Ammonium
C
C
C
C
N
0.02 to 76 mg/L
(1 to 3,800Meq/L)
0.01 to 25 mg/L
(1 to 2,000 ueq/L)
0.01 to 75 mg/L
(0.4 to 3.3 peq/L)
0.01 to 10 mg/L
(0.3 to 250Meq/L)
0.01 to 5 mg/L
(0.5 to 300 Meq/L)
Atomic absorption spec-
troscopy (flame)
Colorimetric (automated
phenate)
EPA 200.6, U.S. EPA
(1987)
EPA 350.7; U.S. EPA
(1987)
Major Anions, dissolved
Chloride
Nitrate
Sulfate
Silica, dissolved
Phosphorus,
total
C
C
C
N
C
0.03 to 100 mg/L
(1 to 2,800 Meq/L)
0.06 to 20 mg/L
(0.5 to 350 ueq/L)
0.05 to 25 mg/L
(1 to 500 ueq/L)
0.05 to 15 mg/L
0 to 1000 ug/L
Ion chromatography
Automated Colorimetric
(molybdate blue)
Acid-persulfate digestion
with automated Colorimetric
determination (molybdate
blue)
EPA 300.6; U.S. EPA
(1987)
EPA 370.1 (modified),
U.S. EPA (1987)
USGS I-4600-78;
Skougstadetal. (1979),
U.S. EPA (1987)
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Analyte
Nitrogen, total
True Color
Turbidity
Total
Suspended
Solids (TSS)
QA
Class
N
N
N
N
Expected Range
0 to 25,000 ug/L
0 to 300 Platinum
Cobalt Units (PCU)
1 to lOONephelo-
metric Turbidity
Units (NTU)
1 to 200 mg/L
Summary of Method
Alkaline persulfate diges-
tion with determination of
nitrate by cadmium reduc-
tion and determination of
nitrite by automated
colorimetry (EDTA/sulfanil-
imide).
Visual comparison to
calibrated glass color disks
Nephelometric
Gravimetric
References
EPA 353.2 (modified);
U.S. EPA (1987)
EPA 100.2 (modified),
APHA204 A.; U.S. EPA
(1987)
APHA214 A., EPA
180.1; U.S. EPA (1987)
EPA 160.3; APHA
(1989)
Table 19. Measurement data quality objectives: water chemistry indicator
Variable or Measurement
Oxygen, dissolved
Temperature
pH, closed system and
equilibrated
Acid Neutralizing Capacity
Carbon, dissolved
inorganic, closed system
Carbon, dissolved organic
Conductivity
Aluminum, total dissolved,
total monomeric, and
organic monomeric
Major Cations:
Calcium
Magnesium
Sodium
Potassium
Ammonium
Method
Detection
Limit
NA
NA
NA
NA
0.10 mg/L
0.1 mg/L
NA
10 ug/L
0.02 mg/L
0.01 mg/L
0.02 mg/L
0.04 mg/L
0.02 ma/L
Precision and Accuracy
±0.5 mg/L
±1 ±C
±0.075 or ±0.15 pH units
±5 peq/L or ±5%
0.10 mg/L or 10%
±0.1 mg/L or ±10%
±1 pS/cm or ±2%
±10 ug/L or ±10%
±0.02 mg/L or ±5%
±0.01 mg/L or ±5%
±0.02 mg/L or ±5%
±0.04 mg/L or ±5%
±0.02 mq/L or ±5%
Transition
Value8
NA
NA
pH5.75
100 ueq/L
1 mg/L
1 mg/L
50 pS/cm
100 pg/L
0.4 mg/L
0.2 mg/L
0.4 mg/L
0.8 mg/L
0.4 ma/L
Completeness
95%
95%
95%
95%
95%
95%
95%
95%
95%
95%
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Variable or Measurement
Major Anions:
Chloride
Nitrate
Sulfate
Silica
Phosphorus, total
Nitrogen, total
True Color
Turbidity
Total Suspended Solids
Method
Detection
Limit
0.03 mg/L
0.03 mg/L
0.05 mg/L
0.05 mg/L
1 ug/L
1 ug/L
NA
NA
0.1 mg
Precision and Accuracy
±0.03 mg/L or ±5%
±0.03 mg/L or ±5%
±0.05 mg/L or ±5%
±0.05 mg/L or ±5%
±1 ug/L or ±5%
±1 ug/L or ±5%
±5 PCD or ±10%
±2 NTUor±10%
±1 mg/L or ±10%
Transition
Value3
0.6 mg/L
0.6 mg/L
1 mg/L
1 mg/L
20 ug/L
20 ug/L
50 PCD
20NTU
10 mg/L
Completeness
95%
95%
95%
95%
95%
95%
95%
NA = not applicable
a Represents the value above which precision and bias are expressed in relative terms.
.5.3.5 Quality Control Procedures: Field Operations
Water chemistry field measurements are optional for this project.
5.3.6 Quality Control Procedures: Laboratory Operations
5.3.6.1 Sample Receipt and Processing
QC activities associated with sample receipt and processing are presented in
Table 20. The communications center and information management staff are notified of
sample receipt and any associated problems as soon as possible after samples are
received. The general schemes for processing stream water chemistry samples for
analysis is presented in Figure 9. In addition to the four syringes prepared in the field,
several additional aliquots are prepared from bulk water samples. Ideally, all analyses
are completed within a few days after processing to allow for review of the results and
possible reanalysis of suspect samples within seven days. Critical holding times for the
various analyses are the maximum allowable holding times, based on current EPA and
American Public Health Association (APHA) requirements (American Public Health
Association, 1989). Analyses of samples after the critical holding time is exceeded will
likely not provide representative data.
5.3.6.2 Analysis of Samples
QC protocols are an integral part of all analytical procedures to ensure that the
results are reliable and the analytical stage of the measurement system is maintained in
a state of statistical control. Most of the QC procedures described here are detailed in
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the references for specific methods. However, modifications to the procedures and
acceptance criteria described in this QAPP supersede those presented in the methods
references. Information regarding QC sample requirements and corrective actions are
summarized in Table 21. Figure 10 illustrates the general scheme for analysis of a batch
of water chemistry samples, including associated QC samples.
5.3.7 Data Reporting, Review, and Management
Checks made of the data in the process of review, verification, and validation are
summarized in Table 22. Data reporting units and significant figures are given in Table
23. The Indicator Lead is ultimately responsible for ensuring the validity of the data,
although performance of the specific checks may be delegated to other staff members.
Table 20. Sample processing quality control: water chemistry indicator
Quality
Control
Activity
Description and Requirements
Corrective Action
Sample
Storage
Store samples in darkness at 4'C
Monitor temperature daily
Qualify sample as suspect for
all analyses
Holding time
Complete processing bulk samples within 48 hours of
collection
Qualify samples
Aliquot
Containers
and
Preparation
Rinse collection bottles 2 times with stream water to be
sampled
Filtration
0.4 um polycarbonate filters required for all dissolved
analytes except DIG (0.45 um)
Rinse filters and filter chamber twice with 50-ml portions
of deionized water, followed by a 20-mL portion of
sample. Repeat for each filter used on a single sample.
Rinse aliquot bottles with two 25 to 50 mL portions of
filtered sample before use.
Preservation
Use ultrapure acids for preservation.
Add sufficient acid to adjust to pH < 2. Check pH with
indicator paper.
Record volume of preservative on container label.
Store preserved aliquots in darkness at 4'C until
analysis.
Holding
Times for
preserved
aliquots
Closed system determinations from syringe samples
must be completed within 72 hours of collection.
Holding times for other analyses holding times range
from 3 days to 6 months, based upon current APHA
criteria.
Sample results are qualified as
being in violation of holding
time requirements.
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PROCEB B KO WAT ER CH BUI IT RT G AU n Ea
BXWP1ERBCBPI
Figure 9. Sample processing activities for water chemistry
samples.
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Table 21. Laboratory quality control samples: water chemistry indicator
QC Sample Type
(Analytes), and
Description
Laboratory Blank: (all
analyses except pH and
total suspended
solids[TSS])
Reagent Blank: (DOC, Al
[total, monomeric, and
organic monomeric], ANC,
NH4+, SiO2)
Filtration Blank: (All
dissolved analytes,
excluding syringe samples)
ASTM Type II reagent water
processed through filtration
unit.
Detection Limit Quality
Control Check Sample
(QCCS): (All analyses
except true color, turbidity,
andTSS)
Prepared so concentration
is approximately four to six
times the required MDL.
Calibration QCCS:
For turbidity, a QCCS is
prepared at one level for
routine analyses (U.S. EPA,
1987). Additional QCCSs
are prepared as needed for
samples having estimated
turbidities greater than 20
NTU.
For total suspended solids
determinations, QCCS is a
standard weight having
mass representative of
samples.
Frequency
Once per
batch prior
to sample
analysis
Prepare
once per
week and.
archive
Once per
batch
Before and
after
sample
analyses
Acceptance
Criteria
Control limits < ±MDL
Measured concentrations <
MDL
Control limits < ±MDL
Control limits < precision
objective: Mean value < bias
objective
Corrective Action
Prepare and analyze new
blank. Determine and
correct problem (e.g.,
reagent contamination,
instrument calibration, or
contamination introduced
during filtration) before
proceeding with any
sample analyses.
Reestablish statistical
control by analyzing three
blank samples.
Measure archived
samples if review of other
laboratory blank
information suggest
source of contamination is
sample processing.
Confirm achieved MDL by
repeated analysis of
appropriate standard
solution. Evaluate
affected samples for
possible re-analysis.
Repeat QCCS analysis.
Recalibrate and analyze
QCCS.
Reanalyze all routine
samples (including PE
and field replicate
samples) analyzed since
the last acceptable QCCS
measurement.
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QC Sample Type
(Analytes), and
Description
Internal Reference Sample:
(Suggested when available
for a particular analyte)
Laboratory Replicate
Sample: (All analyses)
For closed system analyses,
a replicate sample
represents a second
injection of sample from the
sealed syringe.
Frequency
One
analysis in
a minimum
of five
separate
batches
One per
batch
Acceptance
Criteria
Control limits < precision
objective.
Mean value < bias objective
Control limits < precision
objective
Corrective Action
Analyze standard in next
batch to confirm
suspected imprecision or
bias.
Evaluate calibration and
QCCS solutions and
standards for
contamination and
preparation error. Correct
before any further
analyses of routine
samples are conducted.
Reestablish control by
three successive
reference standard
measurements which are
acceptable.
Qualify all sample batches
analyzed since the last
acceptable reference
standard measurement
for possible reanalysis.
If results are below MDL:
Prepare and analyze split
from different sample
(volume permitting).
Review precision of
QCCS measurements for
batch.
Check preparation of split
sample.
Qualify all samples in
batch for possible
reanalysis.
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QC Sample Type
(Analytes), and
Description
Matrix spike samples: (Only
prepared when samples
with potential for matrix
interferences are
encountered)
Frequency
One per
batch
Acceptance
Criteria
Control limits for recovery
cannot exceed 100±20%
Corrective Action
Select two additional
samples and prepare
fortified subsamples.
Reanalyze all suspected
samples in batch by the
method of standard
additions. Prepare three
subsamples (unfortified,
fortified with solution
approximately equal to the
endogenous
concentration, and
fortified with solution
approximately twice the
endogenous
concentration.
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UMPLE ANALYSIS: '-AATER CHEMBTRV SAMPLE!
Figure 10. Analysis activities for water chemistry samples.
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Table 22. Data validation quality control: water chemistry indicator
Activity or Procedure
Requirements and Corrective Action
Range checks, summary statistics, and/or
exploratory data analysis (e.g., box and
whisker plots)
Correct reporting errors or qualify as suspect or invalid.
Review holding times
Qualify value for additional review
Ion balance: Calculate percent ion balance
difference (%IBD) using data from cations,
anions, pH, and ANC.
If total ionic strength z 100 ueq/L, %IBD z ±25%.
If total ionic strength > 100 ueq/L, %IBD z ±10%.
Determine which analytes, if any, are the largest
contributors to the ion imbalance. Review suspect
analytes for analytical error and reanalyze.
If analytical error is not indicated, qualify sample to attribute
imbalance to unmeasured ions. Reanalysis is not required.
Flag= unacceptable %IBD
Flag= %IBD outside acceptance criteria due to
unmeasured ions
Conductivity check: Compare measured
conductivity of each sample to a calculated
conductivity based on the equivalent
conductances of major ions in solution
(Hillman et al., 1987).
If measured conductivity i 25 uS/cm,
([measured - calculated] * measured) <. ±25%.
If measured conductivity > 25 uS/cm,
([measured - calculated] + measured) $ ±15%.
Determine which analytes, if any, are the largest
contributors to the difference between calculated and
measured conductivity.
Review suspect analytes for analytical error and reanalyze.
If analytical error is not indicated, qualify sample to attribute
conductivity difference to unmeasured ions. Reanalysis is
not required.
Aluminum check: Compare results for
organic monomeric aluminum, total
monomeric aluminum, and total dissolved
aluminum.
[organic monomeric] < [total monomeric] < [total dissolved].
Review suspect measurement(s) to confirm if analytical
error is responsible for inconsistency.
ANC check: Calculate ANC based on pH
and DIG. Compare to measured ANC
Review suspect measurements for samples with results
outside of acceptance criteria. Determine if analytical error
or non-carbonate alkalinity are responsible for lack of
agreement.
Review data from QA samples (laboratory
PE samples, and interlaboratory comparison
samples)
Compare with results from other years to determine
comparability. Determine impact and possible limitations
on overall usability of data
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Table 23. Data reporting criteria: water chemistry indicator
Measurement
Dissolved Oxygen
Temperature
PH
Carbon, dissolved inorganic
Carbon, dissolved organic
Acid neutralizing capacity
Conductivity
Aluminum (total dissolved, total
monomeric, and organic
monomeric)
Calcium, magnesium, sodium,
potassium, ammonium, chloride,
nitrate, and sulfate
Silica
Total phosphorus and total nitrogen
Turbidity
True color
Total suspended solids
Units
mg/L
c
pH units
mg/L
mg/L
ueq/L
MS/cm at 25 'C
ug/L
ueq/L
mg/L
M9/L
NTU
PCU
mg/L
No.
Significant
Figures
2
2
3
3
3
3
3
3
3
3
3
3
2
3
Maximum No.
Decimal Places
1
1
2
2
1
1
1
0
1
2
0
0
0
1
The ion balance for each sample is computed using the results for major cations,
anions, and the measured acid neutralizing capacity. The percent ion difference (%IBD)
for a sample is calculated as:
%IBD =
(E cations - E onions) - ANC
ANC + E anions + E cations + 2[H*]
where ANC is the acid neutralization capacity, cations are the concentrations of calcium,
magnesium, sodium, potassium, and ammonium, converted from mg/L to Meq/L, anions
are chloride, nitrate, and sulfate (converted from mg/L to |jeq/L), and r-T is the hydrogen
ion concentration calculated from the antilog of the sample pH. Factors to convert major
ions from mg/L to ueq/L are presented in Table 24. For the conductivity check,
equivalent conductivities for major ions are presented in Table 25.
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Table 24. Constants for converting major ion concentrations from mg/L to ueq/L
Analyte
Calcium
Magnesium
Potassium
Sodium
Ammonium
Chloride
Nitrate
Sulfate
Conversion from
mg/L to ueq/L"
49.9
82.3
25.6
43.5
55.4
28.2
16.1
20.8
a Measured values are multiplied by the conversion factor.
Table 25. Factors to calculate equivalent conductivities of major ionsa
Ion
Calcium
Magnesium
Potassium
Sodium
Ammonium
Chloride
Equivalent
Conductance per
mg/L (uS/cm at 25 *C)
2.60
3.82
1.84
2.13
4.13
2.14
Ion
Nitrate
Sulfate
Hydrogen
Hydroxide
Bicarbonate
Carbonate
Equivalent
Conductance per
mg/L (uS/cm at 25 *C)
1.15
1.54
3.5 x 10s b
1.92 x 10s "
0.715
2.82
a From Hillman etal. (1987).
b Specific conductance per mole/L, rather than per mg/L.
6.0 FIELD AND BIOLOGICAL LABORATORY QUALITY EVALUATION
AND ASSISTANCE VISITS
No national program of accreditation for benthic macroinvertebrate collections and
sample processing currently exists. However, national standards of performance and
audit guidance for biological laboratories are being considered by the National
Environmental Laboratory Accreditation Conference (NELAC). For this reason, a
rigorous program of field and laboratory evaluation and assistance visits has been
developed to support the Wadeable Streams Assessment Program.
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Procedural review and assistance personnel are trained to the specific
implementation and data collection methods detailed in the WSA field operations manual.
Plans and checklists for field evaluation and assistance visit have been developed to
reinforce the specific techniques and procedures for both field and laboratory
applications. The plans and checklists are included in this section and describe the
specific evaluation and corrective actions procedures.
It is anticipated that evaluation and assistance visits will be conducted with each
Field Team early in the sampling and data collection process, and that corrective actions
will be conducted in real time. These visits provide a basis for the uniform evaluation of
the data collection techniques, and an opportunity to conduct procedural reviews as
required to minimize data loss due to improper technique or interpretation of program
guidance. Through uniform training of field crews and review cycles conducted early in
the data collection process, sampling variability associated with specific implementation
or interpretation of the protocols will be significantly reduced. The field visits evaluations,
while performed by a number of different supporting collaborator agencies and
participants, will be based on the uniform training, plans, and checklists. This review and
assistance task will be conducted for each unique crew collecting and contributing data
under this program; hence no data will be recorded to the project database that were
produced by an 'unaudited' process, or individual.
Similarly, laboratory evaluation and assistance visits will be conducted early in the
project schedule and soon after sample processing begins at each laboratory to ensure
that specific laboratory techniques are implemented consistently across the multiple
laboratories generating data for the program. Laboratory evaluation and checklists have
been developed to ensure uniform interpretation and guidance in the procedural reviews.
These laboratory visits are designed such that full corrective action plans and remedies
can be implemented in the case of unacceptable deviations from the documented
procedures observed in the review process without recollection of samples.
The Field and Laboratory Evaluation and Assistance Visit Plans are described in
sections 6.1 and 6.2.
6.1 NATIONAL WADEABLE STREAMS ASSESSMENT FIELD QUALITY
EVALUATION AND ASSISTANCE VISIT PLAN
Evaluators: One or more designated EPA or Contractor staff members who are
qualified (i.e., have completed training) in the procedures of the WSA field
sampling operations.
To Evaluate: Field Sampling Teams during sampling operations on site.
Purpose: To identify and correct deficiencies during field sampling operations.
1. Tetra Tech project staff will review the Field Evaluation and Assistance Visit Plan and
Check List with each Evaluator during field operations training sessions.
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2. The Tetra Tech QA Officer or authorized designee will send a copy of the final Plan
and 4-part carbonless copy versions of the final Check List pages, envelopes to return
the Check Lists, a clipboard, pens, and WSA QAPP and Field Operations Manual to
each participating Evaluator.
3. Each Evaluator is responsible for providing their own field gear sufficient to
accompany the Field Sampling Teams (e.g., protective clothing, sunscreen, insect
repellent, hat, hip boots or waders, water bottle, food, back pack, cell phone) during a
complete sampling cycle. Schedule of the Field visits will be made by the Evaluator in
consultation with the Tetra Tech QA Officer and respective Field Crew Leader.
Evaluators should be prepared to spend additional time in the field if needed
(see below).
4. Tetra Tech and the Regional Monitoring Coordinators will arrange the schedule of
visitation with each Field Team, and notify the Evaluators concerning site locations,
where and when to meet the team, and how to get there. Ideally, each Field Team will
be evaluated within the first two weeks of beginning sampling operations, so that
procedures can be corrected or additional training provided, if needed. GLEC or EPA
Evaluators will visit Tetra Tech Field Teams and Tetra Tech or EPA Evaluators will
visit GLEC Field Teams. Any EPA or Contractor Evaluator may visit State Field
Teams.
5. A Field Team for the WSA consists of a three-person crew where, at a minimum, the
Field Crew Leader is fully trained.
6. If members of a Field Team changes, and a majority (i.e., two) of the members have
not been evaluated previously, the Field Team must be evaluated again during
sampling operations as soon as possible to ensure that all members of the Field
Team understand and can perform the procedures.
7. The Evaluator will view the performance of a team through one complete set of
sampling activities as detailed on the Field Evaluation and Assistance Check List.
a. Scheduling might necessitate starting the evaluation midway on the list of tasks at
a site, instead of at the beginning. In that case, the Evaluator will follow the team
to the next site to complete the evaluation of the first activities on the list.
b. If the Team misses or incorrectly performs a procedure, the Evaluator will note this
on the checklist and immediately point this out so the mistake can be corrected on
the spot. The role of the Evaluator is to provide additional training and guidance
so that the procedures are being performed consistent with the Field Operations
Manual, all data are recorded correctly, and paperwork is properly completed at
the site.
c. When the sampling operation has been completed, the Evaluator will review the
results of the evaluation with the Field Team before leaving the site (if practicable),
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noting positive practices and problems (i.e., weaknesses [might affect data
quality], deficiencies [would adversely affect data quality]). The Evaluator will
ensure that the Team understands the findings and will be able to perform the
procedures properly in the future.
d. The Evaluator will record responses or concerns, if any, on the Field Evaluation
and Assistance Check List.
e. If the Evaluator's findings indicate that the Field Team is not performing the
procedures correctly, safely, or thoroughly, the Evaluator must continue working
with this Field Team until certain of the Team's ability to conduct the sampling
properly so that data quality is not adversely affected.
f. If the Evaluator finds major deficiencies in the Field Team operations (e.g., less
than three members, equipment or performance problems) the Evaluator must
contact one of the following QA officials:
Dr. Esther Peters, Tetra Tech QA Officer (703-385-6000)
Ms. Robin Silva-Wilkinson, GLEC QA Officer (231-941-2230)
Mr. Otto Gutenson, EPA WSA Project QA Officer (202-566-1183)
The QA official will contact the Project Implementation Coordinator (Michael
Barbour - 410-356-8993) or Project Technical Advisor (Steve Paulsen - 541 -754-
4428) to determine the appropriate course of action.
Data records from sampling sites previously visited by this Field Team will be
checked to determine whether any sampling sites must be redone.
g. Complete the Field Evaluation and Assistance Check List, including a brief
summary of findings, and ensure that all Team members have read this and
signed off before leaving the Team.
8. Retain the back copy of each page of the Field Evaluation and Assistance Check List
(color: ). Fasten the pages of the check list for each Field Team
together with a paper clip.
9. Mail the remaining pages of each completed Field Evaluation and Assistance Check
List to
Dr. Esther Peters
Tetra Tech, Inc.
10306 Eaton Place, Suite 340
Fairfax, VA 22030
10. The Tetra Tech QA Officer or authorized designee will review the returned Field
Evaluation and Assistance Check Lists, note any issues, check off the completion
of the evaluation for each Field Team, and distribute the remaining pages of each
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check list as follows:
Original: Tetra Tech QA Officer file, Fairfax, VA
Color: Tetra Tech Project Manager file, Owings Mills, MD
Color: WSA QA Officer file, Washington, DC
6.2 NATIONAL WADEABLE STREAMS ASSESSMENT LABORATORY
QUALITY EVALUATION AND ASSISTANCE VISIT PLAN
Evaluators: One or more designated Contractor staff members who are qualified
(i.e., have completed training) in the procedures of the WSA biological laboratory
operations.
To Evaluate: Biological laboratories performing subsampling, sorting, and
taxonomic procedures to analyze collected stream samples.
Purpose: To identify and correct deficiencies during laboratory operations.
1. Tetra Tech project staff will review the Laboratory Evaluation and Assistance Visit
Plan and Check List with each Evaluator prior to conducting laboratory evaluations.
2. The Tetra Tech QA Officer or authorized designee will send a copy of the final Plan
and 4-part carbonless copy versions of the final Check List pages, envelopes to return
the Check Lists, a clipboard, pens, and WSA QAPP and Benthic Laboratory Methods
manual to each participating Evaluator.
3. Schedule of lab visits will be made by the Evaluator in consultation with the Tetra
Tech QA Officer and the respective Laboratory Supervisor Staff. Evaluators should
be prepared to spend additional time in the laboratory if needed (see below).
4. Tetra Tech will arrange the schedule of visitation with each participating Laboratory,
and notify the Evaluators concerning site locations, where and when to visit the
laboratory, and how to get there. Ideally, each Laboratory will be evaluated within the
first two weeks following initial receipt of samples, so that procedures can be
corrected or additional training provided, if needed.
5. The Evaluator will view the performance of the laboratory sorting process and QC
Officer through one complete set of sample processing activities as detailed on the
Laboratory Evaluation and Assistance Check List.
a. Scheduling might necessitate starting the evaluation midway on the list of tasks for
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processing a sample, instead of at the beginning. In that case, the Evaluator will
view the activities of the Sorter when a new sample is started to complete the
evaluation of the first activities on the list.
b. If a Sorter or QC Officer misses or incorrectly performs a procedure, the Evaluator
will note this on the checklist and immediately point this out so the mistake can be
corrected on the spot. The role of the Evaluator is to provide additional training
and guidance so that the procedures are being performed consistent with the
Benthic Laboratory Methods manual, all data are recorded correctly, and
paperwork is properly completed at the site.
c. When the sample has been completely processed, the Evaluator will review the
results of the evaluation with the Sorter and QC Officer, noting positive practices
and problems (i.e., weaknesses [might affect data quality], deficiencies [would
adversely affect data quality]). The Evaluator will ensure that the Sorter and QC
Officer understand the findings and will be able to perform the procedures properly
in the future.
d. The Evaluator will record responses or concerns, if any, on the Laboratory
Evaluation and Assistance Check List.
e. If the Evaluator's findings indicate that Laboratory staff are not performing the
procedures correctly, safely, or thoroughly, the Evaluator must continue working
with these staff members until certain of their ability to process the sample
properly so that data quality is not adversely affected.
f. If the Evaluator finds major deficiencies in the Laboratory operations, the Evaluator
must contact one of the following QA officials:
Dr. Esther Peters, Tetra Tech QA Officer (703-385-6000)
Mr. Dennis McCauley, GLEC QA Officer (231-941-2230)
Mr. Otto Gutenson, EPA WSA Project QA Officer (202-566-1183)
The QA official will contact the Project Implementation Coordinator (Michael
Barbour - 410-356-8993) or Project Technical Advisor (Steve Paulsen - 541 -754-
4428) to determine what should be done.
Data records from samples previously processed by this Laboratory will be
checked to determine whether any samples must be redone.
g. Complete the Laboratory Evaluation and Assistance Check List, including a brief
summary of findings, and ensure that the Sorter and QC Officer have read this
and signed off before leaving the Laboratory.
6. Retain the back copy of each page of the Laboratory Evaluation and Assistance
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Check List (color: ). Fasten the pages of the check list for each Sorter
together with a paper clip.
Mail the remaining pages of each completed Laboratory Evaluation and Assistance
Check List to
Dr. Esther Peters
Tetra Tech, Inc.
10306 Eaton Place, Suite 340
Fairfax, VA 22030
7. The Tetra Tech QA Officer or authorized designee will review the returned Laboratory
Evaluation and Assistance Check Lists, note any issues, check off the completion of
the evaluation for each participating Laboratory, and distribute the remaining pages of
each check list as follows:
Original: Tetra Tech QA Officer file, Fairfax, VA
Color: Tetra Tech Project Manager file, Owings Mills, MD
Color: WSA QA Officer file, Washington, DC
7.0 REFERENCES
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