United States Environmental Protection Agency
Office of Water
Washington, DC
EPA 841-B-l 7-001
National Rivers and Streams Assessment
2018/19
Quality Assurance
Project Plan
Version 1.2
May 2019
*9 the
U.S. Environmental Protection Agency
Office of Wetlands, Oceans, and Watersheds
1200 Pennsylvania Avenue, NW
4503T
Washington, DC 20460
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Management Approvals: Signature indicates approval for the National Rivers and Streams
Assessment (NRSA) 2018-2019 Quality Assurance Project Plan (QAPP), related Field Operations
Manuals and Laboratory Operations Manual.
S/t/fr
Richard Mitchell
National Rivers and Streams Assessment Project Leader
U.S. EPA Office of Water
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Sarah Lehmann
National Rivers and Streams Assessment Project Quality Assurance Coordinator
U.S. EPA Office of 'Water
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Susan Holdsworth
Chief, Monitoring Branch
U.S. EPA Office of Water
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Cynthia N. Johnson
Office of Wetlands, Oceans, and Watersheds Quality Assurance Officer
U.S. EPA Office of Water
Date
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A.ssi?r:vs^:':n Proust liav: i^aaaw lirarrfc^Ei^r ArkrxHvied?;ei^crt
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QAPP Version
Date Approved
Changes Made
1.0
8/28/2017
Not Applicable
1.1
6/11/2018
Approval Page, Distribution List,
Section 1.9.1, and throughout
QAPP: Updated contact names and
contact information;
Minor editorial changes
throughout QAPP;
Minor corrections to acronym
names;
Section 1.6: Replaced document
number placeholders;
Section 1.10.2: Replaced lab name
placeholders;
Section 2.2.1: Clarification added
for reporting of MDLs and RLs;
Section 3.2: Clarified description of
Hand-Picked Site Selection;
Section 5.5.6.2: Clarified
photovoucher file names;
Section 5.8, 5.9 and throughout
QAPP: Clarified fish tissue analysis
procedures;
References: Corrected reference
citations;
Figure 1.1: Clarified Project
Organization for fish tissue fillet
analysis;
Table 4.1: Added reference to OST
QAPP for Analytical Lab
Responsibilities for fish tissue fillet
analysis;
Table 5.1: Clarification of sampling
locations;
Table 5.2: Clarified lab method
reporting requirements for pH and
ANC;
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Table 5.10: Clarified microcystin
and cylindrospermopsin
requirements
Table 5.6: Added Ammonia-N and
Nitrate-N conversion units
Changes made to NRSA 2018/19
FOM and LOM; see Appendix A for
a summary of those changes
1.2
5/8/2019
Page ii: Removed QAC from
signature page, because no QAC
currently assigned in the Division to
review NARS documents
Page Xiii: Changed Indicator to
Index under MMI
Page Xiv: Changed New England to
National Exposure under NERL
Section 1: Minor editorial change
Changes made to NRSA 2018/19
FOM and LOM; see Appendix A for
a summary of those changes
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The complete documentation of overall NRSA project management, design, methods, and standards is
contained in five companion documents, including:
National Rivers and Streams Assessment 2018/19: Quality Assurance Project Plan EPA-841-B-17-001
National Rivers and Streams Assessment 2018/19: Site Evaluation Guidelines EPA-841-B-17-002
National Rivers and Streams Assessment 2018/19: Non-Wadeable Field Operations Manual EPA-841-B-
17-003a
National Rivers and Streams Assessment 2018/19: Wadeable Field Operations Manual EPA-841-B-17-
003b
National Rivers and Streams Assessment 2018/19: Laboratory Operations Manual EPA-841-B-17-004
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 NRSA,
and is based on the guidelines developed and followed in the Western Environmental Monitoring and
Assessment Program (EMAP) (Peck et al. 2003). Methods described in this document are to be used
specifically in work relating to the NRSA. All Project Cooperators must 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. Reference
to "FOM" means both Field Operations Manuals —Wadeable, and Non-wadeable - if the associated text
applies to both.
The suggested citation for this document is:
USEPA. 2017. National Rivers and Streams Assessment 2018/19: Quality Assurance Project Plan. EPA-
841-B-17-001. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
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NTS
APPROVAL PAGE II
QUALITY ASSURANCE PROJECT PLAN REVIEW & DISTRIBUTION ACKNOWLEDGEMENT & COMMITMENT TO
IMPLEMENT THE NATIONAL RIVERS AND STREAMS ASSESSMENT 2018/19 Ill
VERSION HISTORY IV
NOTICE VI
TABLE OF CONTENTS VIII
LIST OF FIGURES XI
LIST OF TABLES XI
ACRONYMS/ABBREVIATIONS XIII
DISTRIBUTION LIST XVII
1 EXECUTIVE SUMMARY 18
1.1 Background 18
1.2 Project Organization 18
1.3 Quality Assurance Project Plan 18
1.4 Survey Design 18
1.5 Information Management 18
1.6 Field Operations 19
1.7 Laboratory Operations 19
1.8 Peer Review 19
1.9 Project Overview and Management 20
1.9.1 Project Organization 21
1.9.2 Project Schedule 25
1.9.3 Objectives 25
1.9.4 Target population 26
1.9.5 Sample Frame 26
1.9.6 Expected sample size 26
1.9.7 Oversample 26
1.9.8 Field Protocol Development 27
1.9.9 Information Management 27
1.9.10 Assessment 28
1.10 Scope of QA Project Plan 28
1.10.1 Overview of Field Operations 28
1.10.2 Overview of Laboratory Operations 35
1.10.3 Data Analysis and Reporting 38
1.10.4 Peer Review 38
2 DATA QUALITY OBJECTIVES 40
2.1 Data Quality Objectives for the NRSA 40
2.2 Measurement Quality Objectives 40
2.2.1 Method Detection Limits 40
2.2.2 Sampling Precision, Bias, and Accuracy 41
2.2.3 Taxonomic Precision and Accuracy 43
2.2.4 Completeness 44
2.2.5 Comparability 45
2.2.6 Representativeness 45
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SURVEY DESIGN 46
3.1 Probability-Based Sampling Design and Site Selection 46
3.1.1 Target Population 46
3.1.2 Sample Frame 46
3.1.3 Revisit and Resample Sites 47
3.1.4 Evaluation of Sites 47
3.2 Hand-picked (Potential Reference) Site Selection 47
INFORMATION MANAGEMENT 48
4.1 Roles and Responsibilities 48
4.1.1 State/ Tribe-Based Data Management 50
4.2 Overview of System Structure 51
4.2.1 Data Flow 52
4.2.2 Simplified Description of Data Flow 52
4.2.3 Core Information Management Standards 53
4.2.4 Data Formats 54
4.2.5 Public Accessibility 54
4.3 Data Transfer Protocols 55
4.4 Data Quality and Results Validation 56
4.4.1 Design and Site Status Data Files 56
4.4.2 Sample Collection and Field Data 57
4.4.3 Laboratory Analyses and Data Recording 58
4.4.4 Data Review, Verification, and Validation Activities 59
4.5 DataTransfer 61
4.5.1 Database Changes 62
4.6 Metadata 62
4.6.1 Parameter Formats 62
4.6.2 Standard Coding Systems 62
4.7 Information Management Operations 63
4.7.1 Computing Infrastructure 63
4.7.2 Data Security and Accessibility 63
4.7.3 Life Cycle 63
4.7.4 Data Recovery and Emergency Backup Procedures 63
4.7.5 Long-Term Data Accessibility and Archive 63
4.8 Records Management 64
INDICATORS 65
5.1 Water Chemistry and In-situ Measurements (Including chlorophyll-a-) 66
5.1.1 Introduction 66
5.1.2 Pertinent QA/QC Procedures 66
5.1.3 Quality Control Procedures: Field Operations 74
5.2 AlgalToxins: MicrocystinandCylindrospermopsin 77
5.2.1 Sample Design and Methods 77
5.2.2 Pertinent QA/QC Procedures 77
5.3 Periphyton 79
5.3.1 Introduction 79 ^
5.3.2 Sampling Design and Methods 79 ^
5.3.3 Quality Assurance Objectives 80 0
5.3.4 Pertinent QA/QC Procedures for ID Periphyton Sample 80 ^
5.3.5 Quality Control Procedures: Field Operations 81 lu
5.4 Benthic Macroinvertebrates 82 co
5.4.1 Introduction 82 h
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5.4.2 Sampling Design and Methods 82
5.4.3 Quality Assurance Objectives 82
5.4.4 Pertinent QA/QC Procedures 83
5.4.5 TaxonomicQC 84
5.4.6 Taxonomic QC Review & Reconciliation 85
5.4.7 Quality Control Procedures: Field Operations 87
5.5 Fish Assemblage 87
5.5.1 Introduction 87
5.5.2 Sampling Design and Methods 88
5.5.3 Quality Assurance Objectives 88
5.5.4 Pertinent QA/QC Procedures 88
5.5.5 Taxonomic QC Review & Reconciliation 89
5.5.6 Quality Control Procedures: Field Operations 89
5.5.7 Quality Control Procedures: Laboratory Operations (Voucher Specimens) 92
5.6 Physical Habitat Quality 93
5.6.1 Introduction 93
5.6.2 Sampling Design and Methods 93
5.6.3 Quality Assurance Objectives 95
5.6.4 Quality Control Procedures: Field Operations 95
5.7 Fecal Indicator: Enterococci 96
5.7.1 Introduction 96
5.7.2 Sampling Design and Methods 96
5.7.3 Pertinent QA/QC Procedures 96
5.7.4 Data Management, Review, and Validation 98
5.8 Whole Fish Tissue Samples for Fillet Analysis 98
5.8.1 Introduction 98
5.8.2 Sampling Design and Methods 99
5.8.3 Pertinent QA/QC Procedures 100
5.8.4 Data Management, Review, and Validation 101
5.9 Fish Tissue Plugs 101
5.9.1 Introduction 101
5.9.2 Sampling Design and Methods 102
5.9.3 Pertinent QA/QC Procedures 103
5.9.4 Data Management, Review, and Validation 104
5.9.5 Quality Control Procedures: Laboratory Operations 104
FIELD AND BIOLOGICAL LABORATORY QUALITY EVALUATION AND ASSISTANCE VISITS 106
6.1 National Rivers and Streams Assessment Field Quality Evaluation and Assistance Visit Plan 106
6.1.1 Preparation Activities 106
6.1.2 Field Day Activities 107
6.1.3 Post Field Day Activities 107
6.1.4 Summary 108
6.2 National Rivers and Streams Assessement Laboratory Quality Evalution and Assistance Visit Plan 108
6.2.1 Remote Evaluation/Technical Assessment 109
6.2.2 Water Chemistry Laboratories 110 ^
6.2.3 Inter-laboratory Comparison 110 ^
6.2.4 Assistan ce Visits 110 £!
6.2.5 NRSA 2018/19 Document Request Form Chemistry Laboratories Ill q
6.2.6 NRSA 2018/19 Document Request Form Biology Labs 112 ^
DATA ANALYSIS PLAN 113 2
I
CD
7.1 Data Interpretation Background 113 <
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7.1.1 Scale of assessment 113
7.1.2 Selecting the best indicators 113
7.1.3 Defining least impacted reference condition 113
7.1.4 Determining thresholds for judging condition 114
7.2 Geospatial Data 114
7.3 Datasets Used for the Report 114
7.3.1 Ecological Integrity 114
7.3.2 Stressor Status/Extent 114
7.3.3 Recreational value 115
1A Indicator Data Analysis 115
7.4.1 Water Chemistry and Chlorophyll a 115
7.4.2 Algal Toxins 115
7.4.3 Benthic Macroinvertebrate, Periphyton and Fish assemblages 115
7.4.4 Physical Habitat 116
7.4.5 Enterococci 117
7.4.6 Fish Tissue Indicator (fillets and plugs) 118
8 REFERENCES 119
9 APPENDIX A: FOM AND LOM REVISION HISTORY 124
LIST
Figure 1.1 Project organization 24
Figure 1.2 Schedule 25
Figure 1.3 NRSA 2018/2019 Base sites 27
Figure 1.4 River and stream field surveys: site verification activities 32
Figure 1.5 Boatable river and stream sampling: summary of field activities 33
Figure 1.6 Wadeable stream sampling: summary of field activities 34
Figure 4.1 Conceptual model of data flow into and out of the master SQL 53
Figure 5.1 Field measurement process: water chemistry samples 75
Figure 5.2 Analysis activities: water chemistry samples 76
LIST
Table 1.1 Critical logistics elements (from Baker and Merritt, 1990) 28
Table 1.2 Proposed schedule 39
Table 4.1 Summary of IM responsibilities 49
Table 4.2 Summary of software 55
Table 4.3 Summary sample and field data quality control activities: sample tracking 57
Table 4.4 Summary laboratory data quality control activities 58
Table 4.5 Data review, verification, and validation quality control activities 61
Table 5.1 Indicators andcollection location 65
Table 5.2 Laboratory method performance requirements: water chemistry 67
Table 5.3 Laboratory quality control samples: water chemistry 69
Table 5.4 Data validation quality control: water chemistry 71
Table 5.5 Data reporting criteria: water chemistry 72
Table 5.6 Constants for converting major ion concentration from mg/Lto heq/L 73
Table 5.7 Factors to calculate equivalent conductivities of major ions 73
l/l
Table 5.8 Field quality control: water chemistry 74 u-j
Table 5.9 Measurement data quality objectives: microcystin and cylindrospermopsin 77 ^
Table 5.10 Sample analysis quality control activities: microcystin and cylindrospermopsin 77
Table 5.11 Measurement data quality objectives: diatom periphyton 80 O
Table 5.12 Quality control: all activities 80 ti
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Table 5.13 Data validation: diatom 81
Table 5.14 Sample collection and field processing quality control: periphyton 81
Table 5.15 Measurement data quality objectives: benthic macroinvertebrates 83
Table 5.16 Laboratory quality control: benthic macroinvertebrates 85
Table 5.17 Sample collection and field processing quality control: benthic macroinvertebrates 87
Table 5.18 Measurement data quality objectives: fish community 88
Table 5.19 Sample collection and field processing quality control: fish community 89
Table 5.20 Sample receipt and processing quality control: fish community 92
Table 5.21 Laboratory quality control: fish voucher taxonomic identification 92
Table 5.22 Data validation: fish voucher taxonomic identification 93
Table 5.23 Field measurement methods: physical habitat 94
Table 5.24 Measurement data quality objectives: physical habitat 95
Table 5.25 Field quality control: physical habitat 96
Table 5.26 Measurement data quality objectives: pathogen-indicator DNA sequences 96
Table 5.27 Sample collection and field processing quality control: fecal indicator 97
Table 5.28 Laboratory quality control: pathogen-indicator DNA sequences 97
Table 5.29 Data validation quality control: fecal indicator 98
Table 5.30 Recommended target species: whole fish tissue collection 99
Table 5.31 Field datatypes: whole fish tissue samples for fillet analysis 100
Table 5.32 Field quality control: whole fish tissue samples for fillet analysis 100
Table 5.33 Data validation quality control: whole fish tissue samples for fillet analysis 101
Table 5.34 Recommended target and alternate species: fish tissue plug collection 102
Table 5.35 Field datatypes: fish tissue plug 103
Table 5.36 Field quality control: fish tissue plug 103
Table 5.37 Data validation quality control: fish tissue plug 104
Table 5.38 Measurement data quality objectives: fish tissue plug 104
Table 5.39 Lab quality control: fish tissue plug 104
Table 6.1 Equipment and supplies: field evaluation and assistance visits 107
Table 6.2 Summary: field evaluation and assistance visits 108
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A Absorbance
AFDM Ash-Free Dry mass
Ascii American Standard Code for Information Interchange
ASTM American Society of Testing and Materials
Ca Calcium
CAS Chemical Abstract Service
CI Chloride
Ct Threshold Cycle
Cp Crossing Point
CSDGM Content Standards for Digital Geospatial Metadata
CSV Comma separated values
CV Coefficient of Variation
DO Dissolved Oxygen
DOC Dissolved Organic Carbon
DQO Data Quality Objective
EMAP Environmental Monitoring and Assessment Program
ENT Enterococcus
EPA Environmental Protection Agency
FGDC Federal Geographic Data Committee
FOIA Freedom of Information Act
FOM Field Operations Manual
FR Federal Registry
FTP File Transfer Protocol
GIS Geographic Information System
GPS Global Positioning Device
HQ Head Quarters
IBD Ionic Balance Difference
IQG Information Quality Guideline
IM Information Management
ITIS Integrated Taxonomic Information System
K Potassium
LDL Lower Detection Limit
LIMS Laboratory Information management System
LOM Lab Operations Manual
LRL Lower Reporting Limit
LT Long Term
ISO International Organization for Standardization
Mdb a file-extension used in certain versions of Microsoft Access databases
MDL Method Detection Levels (limit)
Mg Magnesium
MMI Multimetric Index
MQO Measurement Quality Objective
MRLC Multi-Resolution Land Characteristics
Na Sodium
NABS North American Benthological Society
NACEC North American Commission for Environmental Cooperation
NAD North American Datum
NAPA National Association of Public Administration
NARS National Aquatic Resource Surveys
NAWQA National Water-Quality Assessment Program
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NCCA National Coastal Condition Assessment
NELAC National Environmental Laboratory Accreditation Conference
NELAP National Environmental Laboratory Accreditation Program
NERL National Exposure Regional Laboratory
ND Not Detected
NHD National Hydrology Database
NH3 Ammonia
NH4 Ammonium
NIST National Institute of Standards
NLA National Lakes Assessment
NLCD National Land Cover Dataset
NO2 Nitrite
NO3 Nitrate
NRC National Research Council
NRSA National Rivers and Streams Assessment
NTU Nephelometric Turbidity Units
NWCA National Wetland Condition Assessment
O/E "Observed" over "Expected"
OMB Office of Management and Budget
ORD Office of Research and Development
OW Office of Water
PBT Persistent Bioaccumulative Toxic Chemical
PC Personal Computer
PctDiff Percent Difference
PD Percent Difference
PDE Percent Difference in Enumeration
PE Performance Evaluation
PFCs Perfluorinated chemicals
PPT Parts per thousand
PRISM Parameter-elevation Regressions on Independent Slopes Model
PTD Percent Taxonomic Disagreement
QA Quality Assurance
QAPP Quality Assurance Project Plan
QA/QC Quality Assurance/Quality Control
QC Quality Control
QPCR Quantitative Polymerase Chain Reaction
QCCS Quality Control Sample Check
QRG Quick Reference Guide
R Statististical software and graphics package
RBS Relative Bed Stability
RL Reporting Limit
RSD Relative Standard Deviation
RTE
Rare, Threatened and Endangered
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SAS
Statistical Analysis System
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Spatial Data Transfer Standard
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SEG
Site Evaluation Guideline
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Silica
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Standard Operating Procedures
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Standard Query Language
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STORET
Storage and Retrieval Data Warehouse
TOC
Total Organic Carbon
TP
Total Phosphorus
TSS
Total Suspended Solids
USGAO
United States General Accounting Office
USGS
United States Geological Survey
WED
Western Ecology Division
WSA
Wadeable Streams Assessment
WQX
Water Quality Exchange
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DISTRIBUTION LIST
This Quality Assurance Protection Plan (QAPP) and associated manuals will be distributed to the following EPA senior
staff participating in the NRSA and to State Water Quality Agencies or cooperators who will perform the field
sampling operations. The Quality Assurance (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.
National Monitoring Coordinators
Richard Mitchell
NRSA Project Leader
mitchell.richard(SeDa.gov
202-564-0644
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Washington, DC
Sarah Lehman
NRSA Project QA
Coordinator
lehmann.sarah (Sepa.gov
202-566-1379
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Washington, DC
Cynthia N. Johnson
OWOW QA Officer
Johnson.CvnthiaN(Sepa.gov
202-566-1679
U.S. EPA Office of Water
Office of Wetlands, Oceans, and Watersheds
Washington, DC
Bernice L. Smith
OWOW OA Coordinator
smith.bernicel(Sepa.gov
202-566-1244
U.S. EPA Office of Water
1200 Pennsylvania Ave., NW
Washington, DC 20460
Steven G. Paulsen
EPA ORD Technical Advisor
Daulsen.steve(Seoa.gov
541-754-4428
Freshwater Ecology Branch Western Ecology Division,
NHEERL, ORD, EPA
200 S.W. 35th St. Corvallis, OR 97330
Marlys Cappaert, SRA
NARS Information
Management Coordinator
cappaert.marlvs Pepa.gov
541-754-4467
541-754-4799 (fax)
Computer Science Corporation
200 S.W. 35th Street
Corvallis, OR 97330
Chris Turner
Contract Logistics
Coordinator
cturner(Sglec.com
715-829-3737
Great Lakes Environmental Center
739 Hastings St.
Traverse City, Ml 49686
Leanne Stahl
OST Fish Tissue
Coordinator
stahl.leannePepa.gov
202-566-0404
U.S. EPA Office of Water
Office of Science and Technology
Washington, DC
Regional Monitoring Coordinators
Tom Faber, Region 1
Faber.TomPepa.gov
617-918-8672
U.S. EPA - Region 1
11 Technology Drive North Chelmsford, MA 01863-
2431
Emily Nering, Region 2
nering.emilvfaJeoa.gov
732-321-6764
U.S. EPA - Region 2
2890 Woodbridge Ave Edison, NJ 08837-3679
Bill Richardson, Region 3
richardson.william (Seoa.gov
215-814-5675
U.S. EPA - Region 3
1650 Arch Street, Philadelphia, PA 19103-2029
Elizabeth Belk, Region 4
belk.elizabethPepa.gov
404-562-9377
U.S. EPA - Region 4
61 Forsyth Street, S.W. Atlanta, GA 30303-8960
Mari Nord, Region 5
nord. mari Pepa.gov
312-353-3017
U.S. EPA - Region 5
77 West Jackson Blvd Chicago, IL 60604-3507
Rob Cook, Region 6
cook, robe rt Pepa.gov
214-665-7141
U.S. EPA - Region 6
1445 Ross Ave -Ste 1200 Dallas, TX 75202-2733
Gary Welker, Region 7
welker.garv (Sepa.gov
913-551-7177
U.S. EPA - Region 7
300 Minnesota Ave, Kansas City, KS 66101
Tom Johnson, Region 8
iohnson.tom(Sepa.gov
303-312-6226
U.S. EPA - Region 8
1595 Wynkoop St .Denver, CO 80202-1129
Matt Bolt, Region 9
bolt. matthew(Sepa.gov
415-972-3578
U.S. EPA - Region 9
75 Hawthorne Street San Francisco, CA 94105
Lil Herger, Region 10
herger.lillian (Sepa.gov
206-553-1074
U.S. EPA-Region 10
1200 Sixth Avenue Seattle, WA 98101
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1 EXECUTIVE SUMMARY
1.1 Background
The National Rivers and Streams Assessment (NRSA) 2018/19 effort will provide important information
to states and the public about the condition of the nation's river and stream resources and key stressors
on a national and regional scale. The United States Environmental Protection Agency (EPA) developed
this Quality Assurance Project Plan (QAPP) to support project participants and to ensure that the final
assessment is based on data of high quality information. The QAPP contains elements of the overall
project management, data quality objectives, measurement and data acquisition, and information
management for NRSA 2018/19. This QAPP is supported by several other NRSA 2018/19 documents
listed in the Notice section of this document. They describe in detail the methods for sampling and
analysis for all core indicators that are part of the NRSA, and detailed quality control measures are
described throughout the QAPP.
1.2 Project Organization
Overall project coordination is conducted by EPA's Office of Water (OW) in Washington, DC, with
technical support from the ORD's Western Ecology Division (WED) in Corvallis, Oregon. Each of the EPA
Regional Offices has identified regional coordinators to assist in implementing the survey and coordinate
with the state/tribal crews who collect the water and tissue samples following NRSA 2018/19 protocols.
EPA began planning the NRSA 2018/19 with state, tribal, and other federal partners in 2016 and is
continuing this partnership effort. EPA expects to report the results in December 2021 in compliance
with the Data Quality Act.
1.3 Quality Assurance Project Plan
The purpose of this QAPP is to document the project data quality objectives and quality
assurance/quality control measures that will be implemented in order to ensure that the data collected
meets those needs. The plan contains elements of the overall project management, data quality
objectives, measurement and data acquisition, and information management for the NRSA 2018/19 and
identifies where these elements are described in detail. This QAPP and its associated documents; the
Field Operations Manual, Laboratory Operations Manual and Site Evaluation Guidelines, are
interdependent, integrated and collectively make up the full QAPP for the NRSA 2018/19.
1.4 Survey Design
Sample collection for NRSA 2018/19 is designed to be completed during the index period of June
through the end of September of 2018 and 2019. EPA used an unequal probability design to select
approximately 1800 streams and rivers (both wadeable and non-wadeable) from across the 48
conterminous United States. To improve our ability to assess changes, the design includes 983 resample
sites that were sampled during the NRSA 2008/09 and/or NRSA 2013/14. In addition, approximately 200
hand-picked reference sites will be sampled using the same techniques as the probabilistic sites.
1.5 Information Management
Environmental monitoring efforts that amass large quantities of information from various sources
present unique and challenging data management opportunities. To meet these challenges, the NRSA
2018/19 employs a variety of well-tested information management (IM) strategies to aid in the
functional organization and ensured integrity of stored electronic data. IM is integral to all aspects of the
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NRSA 2018/19 from initial selection of sampling sites through the dissemination and reporting of final,
validated data.
A technical workgroup convened by the Environmental Protection Agency (EPA) Project Leader is
responsible for the development of a data analysis plan that includes a verification and validation
strategy. These processes are summarized in the data analysis plan section of this QAPP. Validated data
are transferred to the central database managed by NARS information management support staff
located at the Western Ecology Division facilities in Corvallis. This database is known as the National
Aquatic Resource Surveys Information Management System (NARS IM). All validated measurement and
indicator data from the NRSA 2018/19 are eventually transferred to EPA's Water Quality Exchange
(WQX) for archival in EPA's Storage and Retrieval Data Warehouse (STORET) warehouse for public
accessibility. NRSA 2018/19 IM staff provides support and guidance to all program operations in addition
to maintaining NARS IM.
1.6 Field Operations
Field data acquisition activities are implemented in a consistent manner across the entire country. Each
site is assigned a unique ID which identifies it throughout the pre-field, field, laboratory, analysis, and
data management phases of the project. Specific procedures for evaluating each sampling location and
for replacing non-sampleable sites are documented in NRSA 2018/19 Site Evaluation Guidelines (SEG,
E PA -841 -B-l 7-002).
NRSA 2018/19 indicators include: in-situ, water chemistry and chlorophyll a, algal toxins (microcystins
and cylindrospermopsin), periphyton (ID/enumeration and meta-genomics), benthic
macroinvertebrates, fish assemblage, physical habitat, fecal indicators (Enterococci), fish tissue plugs,
and fish tissue fillet. Field measurements and sampling methods are outlined in the NRSA 2018/19 FOMs
(EPA-841-B-17-003a and EPA-841-B-17-003b). Field crews are trained on these methods at a required
EPA-sponsored training session. Field sampling assistance visits will be completed for each field crew for
quality assurance purposes.
1.7 Laboratory Operations
NRSA 2018/19 laboratory analyses are conducted either by state/tribal-selected laboratories or
"National Laboratories" set up by EPA to conduct analyses for any state/tribe which so elects. The
designated National Laboratories and state/tribal laboratories must comply with the Quality
Assurance/Quality Control (QA/QC) requirements described in this document and in the NRSA 2018/19:
Laboratory Operations Manual (LOM, EPA-841-B-17-004). Any laboratory selected to conduct analyses
with NRSA 2018/19 samples must demonstrate that it can meet the quality standards presented in this
NRSA 2018/19 QAPP and LOM.
1.8 Peer Review
The NARS program, including the NRSA, utilizes a three-tiered approach for peer review of the Survey.
¦ internal and external review by USEPA, states, other cooperators and partners;
¦ external scientific peer review (when applicable); and
¦ public review (when applicable).
¦ Cooperators have been actively involved in the development of the overall project
management, design, indicator selection, and methods. Outside scientific experts from
universities, research centers, and other federal agencies have been instrumental in
indicator development and will continue to play an important role in data analysis
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1.9 Project Overview and Management
Several publications 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, states, and
tribes collectively 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, states, and tribes 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 that there is not adequate 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 and tribes make more effective use of limited resources. EPA's Report on the Environment 2003
(USEPA 2003) states 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?'
In response to this need, the Office of Water (OW), in partnership with states and tribes, has begun a
program to assess the condition of the nation's waters via a statistically valid approach. The current
assessment, the National Rivers and Streams Assessment 2018/19 (referred to as NRSA 2018/19
throughout this document), builds upon the National Rivers and Streams Assessment 2013/14, the
National Rivers and Streams Assessment 2008/09, the Wadeable Streams Assessment (WSA)
implemented by EPA in 2004 to monitor and assess the condition of the nation's wadeable stream
resources, as well as other National Aquatic Resource Surveys (NARS) surveys such as the National
Coastal Condition Assessment (NCCA), the National Lakes Assessment (NLA) and the National Wetland
Condition Assessment (NWCA). The NRSA 2018/19 effort will provide important information to states
and the public about the condition of the nation's river and stream resources and key stressors on a
national and regional scale. It will also provide a change analysis from the NRSA 2013/14, the NRSA
2008/09 and the WSA 2004.
EPA developed this QAPP to support project participants and to ensure that the final assessment is
based on high quality data.The QAPP contains elements of the overall project management, data quality
objectives, measurement and data acquisition, and information management for NRSA 2018/19. EPA
recognizes that states and tribes may add elements to the survey, such as supplemental indicators, that
are not covered in the scope of this integrated QAPP. EPA requires that any supplemental elements are
addressed by the states, tribes, or their designees, in a separate approved QAPP. This document covers
all core NRSA 2018/19 QA activities. The NRSA 2018/19 participants have agreed to follow this QAPP
and the protocols and design laid out in this document, and its associated documents - the NRSA
2018/19 Field Operations Manuals (FOMs), Lab Operations Manual (LOM), and Site Evaluation
Guidelines (SEG).
This cooperative effort between states, tribes, and federal agencies makes it possible to produce a
broad-scale assessment of the condition of the Nation's rivers and streams with both a known
confidence and scientific credibility. Through this survey, states and tribes have the opportunity to
collect data that can be used to supplement their existing monitoring programs or to begin development
of new programs.
The NRSA 2018/19 has three main objectives:
¦ Estimate the current status, trends, and changes in selected trophic, ecological, and
recreational indicators of the condition of the nation's rivers and streams with known
statistical confidence;
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¦ Seek associations between selected indicators of natural and anthropogenic stresses and
indicators of ecological condition; and
¦ Assess changes from the earlier Wadeable Streams Assessment, NRSA 2008/09 and NRSA
2013/14.
1.9.1 Project Organization
The responsibilities and accountability of the various principals and cooperators are described here and
illustrated in (Figure 1.1). Overall, the project will be coordinated by the Office of Water (OW) in
Washington, DC, with support from EPA Western Ecology Division (WED) in Corvallis, Oregon. Each EPA
Regional Office has identified a Regional EPA Coordinator who is part of the EPA team providing a critical
link with state and tribal partners. Cooperators will work with their Regional EPA Coordinator to address
any technical issues. A 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.
Contractor support is provided for all aspects of this project. Contractors will provide support ranging
from implementing the survey, sampling and laboratory processing, data management, data analysis,
and report writing. Cooperators will interact with their Regional EPA Coordinator and the EPA Project
Leader regarding contractual services.
The primary responsibilities of the principals and cooperators are as follows:
EPA NRSA Project Leader: Richard Mitchell, EPA Office of Water
¦ 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.
¦ Leads the NRSA Steering Committee and establishes needed technical workgroups.
¦ Interacts with EPA Project Team on technical, logistical, and organizational issues on a
regular basis.
EPA NRSA Field Logistics Coordinator: Brian Hasty, EPA Office of Water
¦ EPA employee who functions to support implementation of the project based on technical
guidance established by the EPA Project Leader and serves as point-of-contact for questions
from field crews and cooperators for all activities.
¦ Tracks progress of field sampling activities.
EPA NRSA Project OA Coordinator: Sarah Lehmann, EPA Office of Water
¦ Provides leadership, development, and oversight of project-level quality assurance for
NARS.
¦ Assembles and provides leadership for a NRSA 2018/19 Quality Team.
¦ Maintains official, approved QAPP.
¦ Maintains all training materials and documentation.
¦ Maintains all laboratory accreditation files.
EPA Technical Advisor: Steven Paulsen, EPA ORD Western Ecology Division
¦ Advises the Project Leader on the relevant experiences and technology developed within
the Office of Research and Development (ORD) that may be used in this project.
¦ Facilitates consultations between NRSA personnel and ORD scientists.
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EPA Laboratory Oversight Coordinator: Kendra Forde, EPA Office of Water
¦ Ensures participating laboratories complete sample analysis following LOM.
¦ Ensures participating laboratories follow QA activities.
¦ Ensures data submitted within the specified timelines.
¦ Coordinates activities of individual lab Task Order Project Officers to ensure
methods are followed and QA activities take place.
Information Management Coordinator: Marlys Cappaert, SRA International, Inc.
¦ A contractor who functions to support implementation of the project based on technical
guidance established by the EPA Project Leader and Alternate EPA Project Leader.
¦ Oversees all sample shipments and receives data forms from the Cooperators.
¦ Oversees all aspects of data entry and data management for the project.
EPA OA Coordinator (OAC): Bernice L Smith, EPA Office of Water
¦ Oversees the quality management activities of the Monitoring Branch
¦ Serves as the contact person for the technical staff and branch chief on quality management
activities including reviewing and approving quality assurance review forms, quality
management plans, and quality assurance project plans.
EPA OA Officer (QAO): Cynthia N. Johnson, EPA Office of Water
¦ Functions as an independent officer overseeing all quality assurance (QA) and quality
control (QC) activities.
¦ Responsible for ensuring that the QA program is implemented thoroughly and adequately to
document the performance of all activities.
EPA OST Fish Tissue Coordinator: Leanne Stahl, EPA Office of Water
¦ Coordinates with the Project Leader to integrate the fish fillet indicator into the project
¦ Provides materials and contractor personnel for fish tissue training
¦ Manages all aspects of the fish fillet indicator and advises the Project Leader on fish plug
indicator technical issues.
Regional EPA Coordinators
¦ Assists EPA Project Leader with regional coordination activities.
¦ Serves on the Technical Experts Workgroup and interacts with Project Facilitator on
technical, logistical, and organizational issues on a regular basis.
¦ Serves as primary point-of-contact for the Cooperators.
Steering Committee (Technical Experts Workgroup): States, EPA, academics, other federal
agencies
¦ Provides expert consultation on key technical issues as identified by the EPA Coordination
crew and works with Project Facilitator to resolve approaches and strategies to enable data
analysis and interpretation to be scientifically valid.
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Cooperator(s): States, Tribes, United States Geological Survey (USGS), others
¦ Under the scope of their assistance agreements, plans and executes their individual studies
as part of the cross jurisdictional NRSA 2018/19 and adheres to all QA requirements and
standard operating procedures (SOPs).
¦ Interacts with the Grant Coordinator, Project Facilitator and EPA Project Leader regarding
technical, logistical, organizational issues.
Field Sampling Crew Leaders
¦ Functions as the senior member of each Cooperator's field sampling crew and the point of
contact for the Field Logistics Coordinator.
¦ Responsible for overseeing all activities of the field sampling crew and ensuring that the
Project field method protocols are followed during all sampling activities.
National Laboratory Task Order Managers
¦ Responsible for managing activities of the national contract laboratories.
¦ Provides direction to national and State labs on methods, timelines and QA activities to
ensure all actions are followed.
¦ Provides updates to EPA Lab Coordinator on the sample processing status of labs and any
questions or concerns raised by participating labs in regards to timelines and deliverables.
Field Logistics Coordinator (FLC): Chris Turner, Great Lakes Environmental Center
¦ A contractor who functions to support implementation of the project based on technical
guidance established by the EPA Field Logistics Coordinator and the Project Leader.
¦ Serves as point-of-contact for questions from field crews and cooperators for all activities.
¦ Tracks progress of field sampling activities.
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M
Project Management
Project Lead - Richard Mitchell, EPA OW
Project QA-Sarah Lehmann, EPA OW
Technical Advisor - Steve Paulsen, EPA ORD
Study Design
Tony Olsen, EPA ORD
Field Logistics
Implementation Coordinator
Training
EPA HQ, EPA ORD, EPA Regions, Contractors
Field Implementation
EPA HQ EPA Regions, States, Tribes,
Contractors
Indicator Team
M
Quality Assurance
Cynthia N.
Johnson, EPA OW
Field Protocols
NRSA Steering
Committee
^
Sample Collection
In Situ measurements
Water Chemistry /Chlorophyll a
Jl
Algal Toxins
Benthic Macroinvertebrates
Fish Assemblage
Physical Habitat
Fecal Indicators (Enterococci)
Fish Tissue Plugs
Metagenomic
Periphyton
Fish Tissue Fillets
T
Laboratory Processing Oversight
EPA Lab Task Order Managers
EPA HQ - Richard Mitchell
Information Management
EPA WED - Marlys Cappaert
Final Data
Web. STORET/WQX-OW
I
Assessment
OW - Lead
EPA ORD, EPA Regions, States, Tribes, Federal Partners,
Cooperators
Laboratory
Processing
Oversight &
Information
Management
EPA OST Fish Tissue
Coordonator-
Leanne Stahl
I
Assessment
EPA ORD
Figure 1.1 Project organization
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1.9.2 Project Schedule
Training and field sampling will be conducted in 2018 and 2019. Sample processing and data analysis will
be completed by 2020 to support a published report in 2021. Figure 1.2 gives an overview of the major
tasks leading up to the final report.
2016
2017
2018/2019
2018-2020
2021
research
design
field
lab / data
report
survey planning
-
-
-
pilot studies
-
select indicators
-
-
design frame
-
select sites
-
implementation
-
-
manuals
-
-
field training
-
sampling season
-
sample processing
-
-
data analysis
-
draft report
-
peer review
-
final report
-
Figure 1.2 Schedule
1.9.3 Objectives
The objectives, or design requirements, for the NRSA are to produce:
¦ Estimates of the 2018/2019 status of flowing waters nationally and regionally (9 aggregated
Omernik ecoregions);
¦ Estimates of the 2018/2019 status of wadeable streams and non-wadeable rivers nationally
and regionally (9 aggregated Omernik ecoregions); and
¦ Estimates of the change in status in wadeable streams between 2018/2019, 2013-2014,
2008-2009 and 2004, nationally and regionally (9 aggregated Omernik ecoregions) and
estimates of the changes in status of all rivers/streams between 2018/2019 and 2013/14,
2008-2009, nationally and regionally (9 aggregated Omernik ecoregions).
Omernik Ecoregions: Ecoregions are areas with generally similar ecosystems and with similar types,
qualities, and quantities of environmental resources. The Omernik ecoregion system is hierarchical and
considers the spatial patterns of both the living and non-living components of the region. It is broken
into 4 levels currently. Ecoregion boundaries were determined by examining patterns of vegetation,
animal life, geology, soils, water quality, climate, and human land use, as well as other living and non-
living ecosystem components.
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1.9.4 Target population
The target populations consists of all streams and rivers within the 48 contiguous states that have
flowing water during the study index period excluding portions of tidal rivers up to head of salt defined
as 0.5 parts per thousand (ppt) measured in the field. The study index period extends from the
beginning of June to the end of September and is characterized by low flow or base flow conditions.
State crews that request an early start due to the condition of streams in their area can be granted
permission to begin in May with direction from the EPA Project Lead. The target population includes the
Great Rivers (i.e. main stem of the Mississippi River). Run-of-the-river ponds and pools are included
while reservoirs are excluded (those that have greater than 7 day retention period).
1.9.5 Sample Frame
The sample frame was derived from the National Hydrography Dataset (NHD), in particular NHD-Plus.
Attributes from NHD-Plus and additional attributes added to the sample frame that are used in the
survey design include: (1) state, (2) EPA Region, (3) USGS National Water Quality Assessment (NAWQA)
Mega Region, (4) Omernik Ecoregion Level 3 (North American Commission for Environmental
Cooperation (NACEC) version), (4) WSA aggregated ecoregions (nine and three regions), (5) Strahler
order, (6) Strahler order categories (1st, 2nd, ..., 7th and 8th +), (6) FCode, (7) Urban, and (8) Frame07.
1.9.6 Expected sample size
Expected sample size is 1808 flowing water sites: 983 resample sites and 825 new sites.. The study is
designed to sample 1808 probabilistic (Figure 1.3), 10% of which will be repeat sampled, and 200 hand-
picked (potential reference) (approximately 2200 total) river and stream sites across the country.
1.9.7 Oversample
For the NRSA 2018/19 design, the over sample list of sites is nine times the expected sample size within
each state. The large over sample size was done to accommodate those states who may want to
increase the number of sites sampled within their state for a state-level design and to provide an
adequate number of replacement sites.
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Design Sites for the
2018-2019 National Rivers & Streams Assessment
Legend
NRSA 2018-19 Base Sites
I Kilometers
1,000
1,500
2,000
Figure 1.3 NRSA 2018/2019 Base sites
1.9.8 Field Protocol Development
The field sampling protocols for ecological indicators are based on protocols used in the WSA (USEPA
2006b) and the NRSA 2008/09 (USEPA 2016). These protocols were developed by ORD for use in the
EMAP1 program and were developed with the purpose of providing consistent and representative
information across the country. During the initial design phase of the project, collaborators and partners
worked to refine those protocols for use in NRSA 2008/09, 2013/14 and 2018/19. This involved
modifications to the original protocols used in the EMAP program for use in the Great Rivers, tidal
systems, and sites that were in between a wadeable and a boatable system. Field protocols for
collection of fish for fish tissue are based on protocols from EPA's Office of Science and Technology
(USEPA 2000).
1.9.9 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
1 Environmental Monitoring and Assessment Program (EMAP.) http://www.epa.gov/emap/.
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made available for the assessment, copies of the data will be transferred to EPA's Water Quality
Exchange /Storage and Retrieval Data Warehouse (WQX/ STORET) and EPA's NARS Information
Management (NARS IM) 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.
1.9.10 Assessment
The final assessment will be developed by a team, led by OW, which will include several ORD research
facilities, EPA Regional Monitoring Coordinators, interested States/Tribes, and Cooperators. All
States/Tribes will be invited to participate in a collaborative process to interpret results and shape the
data assessment and report. The final assessment will include an appendix describing the quality of the
data used in the assessment.
1.10 Scope of QA Project Plan
This QAPP addresses all aspects of the data acquisition efforts of the NRSA, which focuses on the 2018
and 2019 sampling of approximately 2200 river and stream sampling events in the contiguous United
States. This QA plan also addresses the data integration necessary to create one complete report on the
ecological and human health status of the Nation's rivers and streams based on selected indicators.
Relevant Companion documents to this QAPP are: NRSA 2018/19: Site Evaluation Guidelines, NRSA
2018/19: Field Operations Manuals, and NRSA 2018/19: Laboratory Operations Manual (See
introductory pages for citation information for each document).
1.10.1 Overview of Field Operations
Field data acquisition activities are implemented for the NRSA (Table 1.1), based on guidance developed
for earlier Environmental Monitoring and Assessment Program (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 all 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 SEG. Scientific collecting permits from State and Federal agencies will
be procured, as needed by the respective State or cooperating organization.
Table 1.1 Critical logistics elements (from Baker and Merritt, 1990)
Logistics Plan Component 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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3
LT)
3
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28
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Logistics Plan Component Required Elements
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
1.10.1.1 Equipment and Supplies
The field crews will use standard field equipment and supplies which are being provided by EPA and
EPA's Field Logistics Coordinator (FLC). The FLC 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 FOMs.
1.10.1.2 RequestForm
Field Crews will submit requests for field forms, labels and site kits via an electronic form (Appendix B).
This form will be submitted to the NARS IM Coordinator who will ensure that the request reaches the
appropriate entity. Crews must submit sampling schedules at or before the time of submitting request
forms. Crews should submit the form at least 2 weeks prior to their desired sampling date.
1.10.1.3 Base Kit
The Base Kit is comprised of the subset of durable equipment and supplies needed for NRSA 2018/19
sampling that is provided by USEPA through the FLC. Typically, one Base Kit is provided to each Field
Crew and contains some of the equipment that is used throughout the field season. See FOMs for a list
of the items provided by USEPA in the Base Kit. We anticipate that this equipment will be available for
use in future NRSA efforts.
1.10.1.4 Site Kit
A Site Kit contains the subset of consumable supplies (i.e., items used up during sampling or requiring
replacement after use) provided by USEPA through the FLC. The site kit for core indicators will contain
all the sample bottles and labels necessary for sampling a single site. A new Site Kit is provided for each
site sampled. A separate site kit will be prepared and distributed for collection of whole fish tissue
samples for fillet analysis at 478 designated sampling sites. See FOMs for the consumable items that will
be provided by USEPA.
1.10.1.5 Field Crew Supplied Items
The field crew will also supply particular items for the field sampling day. These are typical field
equipment (like a Global Positing Device (GPS)), or boat equipment and might also include supplies from
the previous surveys. See FOMs for the items that the field crew will need to provide.
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Field crews will receive a NRSA 2018/19 Quick Reference Guide (QRG) containing tables and figures
summarizing field activities and protocols from the NRSA 2018/19 FOMS. The QRG is meant to be used
in the field to give NRSA 2018/19 Field Crews a list of the required sampling protocols at each site. While
comprehensive, the steps contained in this QRG are not as detailed as the descriptions found within the
NRSA 2018/19 FOM. The user is assumed to have attended Field Training and completely read and
understood the FOM before using this QRG at a field site. This waterproof handbook will be a field
reference used by field crews after completing a required field training session. The field crews are also
required to keep the QRG and FOM available in the field for reference and for possible protocol
clarification.
1.10.1.7 Site Evaluation Guidelines
The NRSA 2018/19 Site Evaluation Guidelines (SEG) outlines the process to compile the final list of
candidate sites for sampling. The process includes locating a candidate river/stream, evaluating the site
to determine if it meets the criteria for inclusion in the target population and is accessible for sampling,
and if not, replacing it with an alternate candidate river/stream.
1.10.1.8 Lab Operations Manual
The methods used for the laboratory sample analysis are available in the NRSA 2018/19 Laboratory
Operations Manual (LOM).
1.10.1.9 Field Training
Field measurements and samples are collected by trained crews. Each Field Crew Leader and a minimum
of one other field crew member, preferably the fish taxonomist, must be trained at an EPA-sponsored
training session prior to the start of the field season, along with as many crew members as possible. EPA
will provide the four-day training sessions in a number of locations around the country for cooperators
and contractors. It is required that field crews attend all four days of training in their entirety. The
training program stresses hands-on practice of methods, comparability among crews, collection of high
quality data and samples, and safety. All field crews providing field operational support to NRSA 2018/19
must adhere to the provisions of this integrated QAPP, FOM, and SEG. Trainers will maintain a list of all
personnel trained and provide the information to the NRSA Project Lead and the QA Project Lead.
Training documentation will be maintained by the NARS QA Lead in NRSA 2018/19 QA files. Field crews
may not operate without a trained field crew leader and another trained field crew member present-
Personnel conducting Assistance Visits of field crews must also attend the complete training.
1.10.1.10 Health and Safety
Collection and analysis of samples can involve significant risks to personal health and safety. All field
crews should develop a safety plan according to the requirements of their organization. Additional
information on health and safety can be found in the FOM. It is the responsibility of the group safety
officer or project leader from participating organizations, however, to ensure that the necessary safety
courses are taken by all field personnel and that all safety policies and procedures are followed.
1.10.1.11 Field Quality Evaluation and Assistance Reviews (auditing)
Each crew will be visited by a trained person from an EPA Region, Headquarters, or contractor.
Evaluation and assistance visits will be conducted with each Field Crew early in the sampling and data
collection process, and corrective actions will be conducted in real time. These visits provide EPA with a
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basis for the uniform evaluation of the data collection techniques, and an opportunity to conduct
procedural reviews to minimize data loss due to improper technique or interpretation of program
guidance. The field visit evaluations will be based on the uniform training, plans, and checklists.
1.10.1.12 Field Activities
Typically, each field crew is comprised of four to five members. The number and size of crews depends
on the duration of the sampling window, geographic distribution of sampling locations, number and
complexity of samples and field measurements, and other factors. A variety of methods may be used to
access a site. Some sampling locations require crews to hike in, transporting all equipment in backpacks.
For this reason, ruggedness and weight are important considerations in the selection of equipment and
instrumentation. Crews may need to camp out at the sampling location and may need to provide
themselves with the necessary camping equipment.
For each sampling location, a dossier will be prepared by the field crew 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. Crew leaders
will contact landowners at least two days before the planned sampling date. As the design requires
repeat visits to select sampling locations, it is important for the field crews 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.
The site verification process is shown in Figure 1.4. Upon arrival at a site, the location is verified by a GPS
receiver, landmark references, and/or local contacts.
Samples and measurements for various indicators are collected in a specified order (see example work
flows in Figure 1.5 and Figure 1.6). This order has been set up to minimize the impact of sampling for
one indicator upon subsequent indicators; for example, water chemistry samples from rivers and
streams are collected before collecting benthic invertebrates as the benthic invertebrate method calls
for kicking up sediments which would likely impact the quality of the water sample. Crews may choose
to allocate resources as they see fit, but should always be careful not to compromise samples. All
methods are fully documented in step-by-step procedures in the NRSA FOMs.
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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 or river
• 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 sampling reach (40
channel widths)
• Establish habitat transects across channel (11 per reach)
/
Figure 1.4 River and stream field surveys: site verification activities
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Whole Crew
Group B Activities:
Group A Activities:
RETURN TO STAGING AREA
Collect benthic samples
Collect fecal indicator
sample at last transect
Filter fecal indicator sample;
prepare for transport
Filter chlorophy ll-a sample;
prepare for transport
Prepare periphyton samples
for transport
Collect periphyton samples
Preserve benthic sample&
prepare for transport
SHIP SAMPLES
Collect water chemistry,
chlorophy ll-a and algal toxin
samples
Conduct physical habitat
Characterizations
Measure in situ temperature
pH, DO, & conductivity
Report sampling event through Site and Sam-
ple Status Form
Rev iew data forms for completeness
Inventory supplies for next sampling event.
Request additional supplies if needed
Inspect and clean boat, mo-
tor, & trailer to prevent trans-
fer of nuisance species and
contaminants
Prepare forms, equipment & supplies
RETURN TO STAGING AREA
LOCATE & TRAVEL TO PHYSICAL H ABITAT STATIONS
LOCATE & TRAVEL TO TRANSECT A
Calibrate multi-parameter probe
Load equipment and supplies onto boat(s) (if boatable)
Clean and organize equipment for loading
Collect fish tissue samples
Conduct fish assessment
Prepare fish tissue samplesfor
transport
Locate Xsite
Verify site as target
Determine launch site &set upstaging area
Figure 1.5 Boatable river and stream sampling: summary of field activities >
I—
3
U
LU
33
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Whole Crew
Group B Activities
Group A Activities
Collect benthic samples
Collect periphyton samples
Conduct fish assessment
Review data forms
Collect fish tissue samples
Prepare fish tissue samples for transport
Travel to Transect A
Inspect and
decontaminate boat
Ship samples
Return to staging area
Clean & organize equipment
Return to staging area
Conduct physical habitat
characterizations
Collect fecal indicator
sample at last transect
Calibrate multi-parameter probe
Prepare forms, equipment & supplies
Lay out sampling reach (from X-site to Transect K)
;in sampling activities at Transect A
Lay out sampling reach (from X-site to Transect A)
Measure in situ temperature,
pH, DO, & conductivity
Return to transect F (X-site)
Preserve benthic
samples & prepare for
transport
Filter enterococci
sample; prepare for
transport
Filter chlorophyll a
samples; prepare for
transport
Report sampling event through
Site and Sample Status form
Inventory supplies; request more
supplies if needed
Collect water chemistry,
chlorophyll-a, & algal toxin
samples
Locate X-site
Verify site as target
Set upstaging area
Figure 1.6 Wadeable stream sampling: summary of field activities
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The FOMs also contain detailed instructions for completing documentation, labeling samples, any field
processing requirements, and sample storage and shipping. Field communications will be through Field
Crew Leaders, and will involve regularly scheduled conference calls or contacts with the NRSA 2018/19
Communications Center.
Standardized field data forms are the primary means of data recording. For NRSA 2018/19, crews will
have the option to use paper or electronic forms. On completion, the data forms are reviewed by a
person other than the person who initially entered the information. Prior to departure from the field
site, the field crew leader reviews all forms and labels for completeness and legibility and ensures that
all samples are properly labeled and packed. This review process will be done for either form of data
collection (paper or electronic).
Upon return from field sampling to the office, field crews using paper forms send completed data forms
to the information management staff at WED in Corvallis, Oregon for entry into a computerized
database. Field crews using electronic forms send completed forms via email as soon as they have
internet access. At WED, the IM team review electronic data files independently to verify that values are
consistent with those recorded on the field data form or original field data file (Section 4.4.2).
Field crews store or package samples for shipment in accordance with instructions contained in the
FOMs, including taking precautions to ensure holding times are not exceeded. Samples which must be
shipped are delivered by field crews to a commercial carrier; copies of bills of lading or other
documentation are maintained by the crew. Using the pertinent tracking form, crews notify the NARS IM
Center about sample shipment; thus, tracking procedures can be initiated quickly in the event samples
are not received. Chain-of-custody forms are completed by the crews for all transfers of samples, with
copies maintained by the field crew. The FLC or NARS IM team will follow up with field crews about any
missing samples and/ or incomplete files.
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 (Table 1.1). These
debriefings cover all aspects of the field program and solicit suggestions for improvements.
1.10.2 Overview of Laboratory Operations
Holding times for surface water samples vary with the sample types and analyte. Field crews begin some
analytical measurements during sampling (e.g., in situ measurements) while other analytical
measurements are not initiated until sampling has been completed (e.g., water chemistry, algal toxins,
fecal indicators (Enterococci)). Analytical methods are summarized in the LOM. When available,
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 the laboratory coordinator will work with appropriate experts to describe them in
Standard Operating Procedures (SOPs) developed by the analytical laboratories.
Contractor and/or cooperator laboratories will perform chemical, physical, and biological analyses.
National contract labs will process most samples. Where those labs are currently in place, EPA
has identified them here. Willamette Research Station (WRS), a lab managed by the Phil Manaco, will
analyze water chemistry and chlorophyll-a samples. A national contract lab, GLEC, will analyze algal toxin
samples. EPA anticipates that a few pre-approved state labs may opt to analyze samples for algal toxins.
A national contract lab, Ecoanalysts, will conduct benthic macroinvertebrate identifications as will a few
pre-approved state labs. A national contract lab, GLEC, will conduct periphyton identifications as will a
few pre-approved State labs. EPA's National Exposure Research Laboratory_ (NERL) will analyze samples
for enterococci and the periphyton meta-genomics indicators. A national contract lab, Physis
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Environmental Laboratories, Inc. will analyze fish tissue plugs, and fish tissue filet samples will be stored
at the national contract lab,Microbac Lab, until analytical labs are identified. A national contractor, ESS
Group, Inc., will conduct fish identification vouchers for quality control purposes as will a few pre-
approved state labs/fish taxonomists. Field crews record the physical habitat measurements in the field
on the field data sheets. Field crews send data from the forms either electronically, if using the NRSA
electronic form application, or by mail, if using the hard copy forms to the NARS IM team. The NARS IM
team uploads data provided electronically or scans field forms into the NARS IM database.
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 are expected to conduct operations using
good laboratory practices. The following are general guidelines for analytical support laboratories:
¦ A program of scheduled maintenance of analytical balances, water purification systems,
microscopes, laboratory equipment, and instrumentation.
¦ Verification of the calibration of analytical balances using class "S" weights which are
certified by the National Institute of Standards and Technology (NIST)
(http://www.nist.gov/).
¦ Verification of the calibration of top-loading balances using NIST-certified class "P" weights.
¦ Checking and recording the composition of fresh calibration standards against the previous
lot of calibration standards. Participating laboratories will keep a percentage of the previous
lot of calibration standard to check against the next batch of samples processed. This will
ensure that a comparison between lots can occur. Acceptable comparisons are less than or
equal to two percent of the theoretical value. (This acceptance is tighter than the method
calibration criteria.)
¦ Recording all analytical data in bound logbooks in ink, or on standardized recording forms.
¦ Verification of the calibration of uniquely identified daily use thermometers using NIST-
certified thermometers.
¦ Monitoring and recording (in a logbook or on a recording form) temperatures and
performance of cold storage areas and freezer units (where samples, reagents, and
standards may be stored). During periods of sample collection operations, monitoring must
be done on a daily basis.
¦ An overall program of laboratory health and safety including periodic inspection and
verification of presence and adequacy of first aid and spill kits; verification of presence and
performance of safety showers, eyewash stations, and fume hoods; sufficiently exhausted
reagent storage units, where applicable; available chemical and hazardous materials
inventory; and accessible material safety data sheets for all required materials.
¦ An overall program of hazardous waste management and minimization, and evidence of
proper waste handling and disposal procedures (90-day storage, manifested waste streams,
etc.).
¦ If needed, having a source of reagent water meeting American Society of Testing and
Materials (ASTM) Type I specifications for conductivity (< 1 piS/cm at 25 °C; ASTM 2011)
available in sufficient quantity to support analytical operations.
¦ Appropriate microscopes or other magnification for biological sample sorting and organism
identification.
¦ Approved biological identification and taxonomic keys/guides for use in biological
identification (diatoms, benthic macroinvertebrates) as appropriate.
¦ Labeling all containers used in the laboratory with date prepared contents, and initials of the
individual who prepared the contents.
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¦ 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 electronically using standard formats and units compatible with NARS IM
(see LOM for data templates). These files will be labeled properly by referencing the
indicator and/or analyte and date.
All laboratories providing analytical support to NRSA 2018/19 must adhere to the provisions of this
integrated QAPP and LOM. Laboratories will provide information documenting their ability to conduct
the analyses with the required level of data quality prior to data analysis. Different requirements will be
provided based on the type of analysis being completed by the laboratory (i.e. chemistry vs. biological
analyses).
Laboratories will send the documentation to the Quality Assurance Lead at EPA Headquarters (or other
such designated parties) in NRSA 2018/19 QA files. Such information may include the following,
depending on the evaluation by the Project Quality Assurance Officer.
¦ Signed Quality Assurance Project Plan by the laboratory performing analysis;
¦ Signed Laboratory Form;
¦ Valid Accreditation or Certification;
¦ Laboratory's Quality Manual and/or Data Management Plan;
¦ Method Detection Limits (MDL);
¦ Demonstration of Capability (DOC);
¦ Results from inter-laboratory comparison studies;
¦ Analysis of performance evaluation samples; and
¦ Control charts and results of internal QC sample or internal reference sample analyses to
Document achieved precision, bias, accuracy.
Other requirements may include:
¦ Participation in calls regarding laboratory procedures and processes with participating
laboratories;
¦ Participation in a laboratory technical assessment or audit;
¦ Participation in performance evaluation studies; and
¦ Participation in inter-laboratory sample exchange.
See Section 5 of this QAPP for additional information related to laboratory certification. All qualified
laboratories shall work with the NARS IM Center to track samples as specified by the NARS IM Lead.
1.10.2.1 Water Chemistry and Chlorophyll A Lab Quality Evaluation
Participating laboratories will send requested documentation to the NRSA 2018/19 QA Team for
evaluation of qualifications. The NRSA 2018/19 QA Team will maintain these records in the project QA
file.
1.10.2.2 Biological Laboratory Quality Evaluation
The NRSA 2018/19 Quality Team requested and, whenever possible, reviewed the past performance of
biological laboratories. The biological laboratories shall adhere to the quality assurance objectives and
requirements as specified for the pertinent indicators in the LOM.
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1.10.3 Data Analysis and Reporting
A technical data analysis and reporting workgroup convened by the EPA Project Leader is responsible for
development of a data analysis plan that includes a verification and validation strategy. These processes
are summarized in the data analysis sections of this QAPP.
Validated data are transferred to the central database,the National Aquatic Resource Surveys
Information Management database, NARS IM, managed by IM support staff located at WED in Corvallis.
IM activities are discussed further in Section 4. Data in the NARS IM database are available to
cooperators for use in development of indicator metrics. All validated measurement and indicator data
from NRSA 2018/19 are eventually transferred to EPA's Water Quality Exchange (WQX) and then the
National STORET warehouse. The periphyton meta-genomics data, as research data, will not be
incorporated into NARS IM.
1.10.4 Peer Review
If deemed necessary, the NRSA 2018/19 report will undergo a thorough peer review process, where the
scientific community and the public will be given the opportunity to provide comments. Cooperators
have been actively involved in the development of the overall project management, design, methods,
and standards including the drafting of five key project documents:
¦ Quality Assurance Project Plan
¦ Site Evaluation Guidelines
¦ Field Operations Manuals (Wadeable and Non-wadeable)
¦ Laboratory Operations Manual
The USEPA NARS program, including the NRSA 2018/19, utilizes a three-tiered approach for peer review
of the Survey: (1) internal and external review by EPA, states, other cooperators and partners, (2)
external scientific peer review, when applicable, and (3) public review, when applicable.
Once data analysis has been completed, cooperators examine the results. The NRSA team reviews
comments and feedback from the cooperators and incorporate such feedback into the draft report,
when appropriate. The NRSA Project Team follows Agency and OMB requirements for public and peer
review. External scientific peer review and public review is initiated for new analyses or approaches as
appropriate. Additionally, following applicable guidance other aspects of the NRSA may undergo public
and scientific peer review.
¦ Follow the Agency's Information Quality Guidelines (IQG) and complete the IQG checklist.
¦ Develop and maintain a public website with links to standard operating procedures, quality
assurance documents, fact sheets, scientific peer review feedback, and final report.
¦ Conduct technical workgroup meetings composed of scientific experts, cooperators, and
EPA to evaluate and recommend data analysis options and indicators.
¦ Complete data validation on all chemical, physical and biological data.
¦ Conduct final data analysis with workgroup to generate assessment results.
¦ Engage peer review contractor to identify external peer review panel (if applicable).
¦ Develop draft report presenting assessment results.
¦ Develop final draft report incorporating input from cooperators and results from data
analysis group to be distributed for peer a review.
¦ Issue Federal Register (FR) Notice announcing document availability and hold public
comment (30-45 days) (if applicable).
¦ Consider public comments and produce a final report (if applicable).
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The proposed peer review schedule is provided below in Table 1.2 and is contingent upon timeliness of
data validation and schedule availability for regional meetings and experts for data analysis workshop.
Table 1.2 Proposed schedule
Proposed Schedule
Activity
May 2018- November 2019
Data validation
December 2019-June 2021
Internal data analysis and review meetings (e.g., web conferences)
June 2021
Draft released for external peer review (if applicable)
December 2021
Draft released for public review (if applicable)
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2 DATA QUALITY OBJECTIVES
It is a policy of the U.S. EPA that Data Quality Objectives (DQOs) be developed for all environmental data
collection activities following the prescribed DQO Process. DQOs are qualitative and quantitative
statements that clarify study objectives, define the appropriate types of data, and specify the tolerable
levels of potential decision errors that will be used as the basis for establishing the quality and quantity
of data needed to support decisions (USEPA 2006). 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. DQOs are typically expressed in terms of acceptable uncertainty (e.g., width of an
uncertainty band or interval) associated with a point estimate at a desired level of statistical confidence
(USEPA 2006). The DQO Process is used to establish performance or acceptance criteria, which serve as
the basis for designing a plan for collecting data of sufficient quality and quantity to support the goals of
a study (USEPA 2006). As a general rule, performance criteria represent the full set of specifications that
are needed to design a data or information collection effort such that, when implemented, it will
generate newly-collected data that are of sufficient quality and quantity to address the project's goals
(USEPA 2006). Acceptance criteria are specifications intended to evaluate the adequacy of one or more
existing sources of information or data as being acceptable to support the project's intended use (USEPA
2006).
2.1 Data Quality Objectives for the NRSA
Target DQOs established for the NRSA 2018/19 relate to the goal of describing the current status of
selected indicators of the condition of rivers and streams in the conterminous U.S. and ecoregions of
interest.
The formal statement of the DQO for national estimates is as follows:
¦ Estimate the proportion of rivers/streams (± 5%) in the conterminous U.S. that fall below
the designated threshold for good conditions for selected measures with 95% confidence.
For the ecoregions of interest the DQO is:
¦ Estimate the proportion of rivers/streams (± 15%) in a specific ecoregion that fall below the
designated threshold for good conditions for selected measures with 95% confidence.
For estimates of change, the DQOs are:
¦ Estimate the proportion of rivers/ streams (± 7%) in the conterminous U.S. that have
changed condition classes for selected 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 (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 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% confidence
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based on a single measurement (1) (Glase et al., 1981). The MDLfor an individual analyte is calculated
as:
where t is a Students' t value at a significance level (a) of 0.01 and n-1 degrees of freedom (<), and s is
the standard deviation of a set of n measurements of a standard solution. Standard solutions should
contain analyte concentrations between two and three times the MDL objective, and should be
subjected to the entire analytical method (including any preparation or processing stages). At least
seven non-consecutive replicate measurements of a standard solution 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 shall 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.
Laboratories must submit estimates of Reporting Limits (RLs) (and how they are determined) with
analytical results. Laboratories must flag analytical results associated with RLs that exceed the objectives
as being associated with unacceptable RLs. Laboratories must report analytical data that are below the
estimated RLs, but above the laboratory's MDL, but laboratories also flag these as "estimated" values
(detected but not quantified). Laboratories should report (if possible), values below the MDL, but the
laboratory must flag the value as being below the MDL. If a laboratory has to report values below the
MDL as being equal to the MDL, this must be clearly stated in the metadata submitted with any
analytical results to avoid the misuse of these results in assessment analyses. For fish fillet tissue
samples, all values below the MDL will be reported as "< 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 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:
Equation 2.1
MDL — t^a=o oi,v=«-i] x s
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Equation 2.2 SD =
i = ^ (xz - x)2
n — 1
Where:
I 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]):
Equation 2.3 RSD = -^ x 100
x
Where:
5 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:
Equation 2.4 RPD =
f
\A~B\
(.A + B)/2
: 100
Where:
A is the first measured value, and
B is the second measured value.
Precision objectives based on the range of duplicate measurements can be calculated as:
Equation 2.5 Critical Range s x
Where:
s represents the precision objective in terms of a standard deviation. Range-based objectives are
calculated in relative terms as:
Equation 2.6 Critical RPD = RSD / -Jl
Where:
i/i
LU
RSD represents the precision objectives in terms of a relative standard deviation. >
t>
For repeated measurements of samples of known composition, net bias (6) is estimated in absolute terms
as:
Equation 2.7 B — X — T
Where:
X equals the mean value for the set of measurements and
CQ
O
<
3
a
<
!<
Q
42
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T equals the theoretical or target value of a performance evaluation sample.
Bias in relative terms [B(%)\ is calculated as:
Equation 2.8 B(%) = —-—xlOO
Where:
X equals the mean value for the set of measurements, and
T equals the theoretical or target value of a performance evaluation sample.
Accuracy is estimated for some analytes from fortified or spiked samples as the percent recovery. Percent
recovery is calculated as:
Ci s - C,
Equation 2.9 % recovery = —~ ! Lxl00
Cs
Where:
Cu is the measured concentration of the spiked sample,
Ci is the concentration of the unspiked sample, and
Cs is the concentration of the spike.
2.2.3 Taxonomic Precision and Accuracy
For the NRSA, 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% of the biological samples from each
participating laboratory will be randomly-selected by EPA HQ, and sent to an independent taxonomist
for re-identification. Comparison of the results of whole sample re-identifications will provide a Percent
Taxonomic Disagreement (PTD) calculated as:
Equation 2.10 PTD
Where:
1-
C comppos^
N
x 100
comppos is the number of agreements, and
N\s 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) for benthic
macroinvertebrates and fish. Individual samples exceeding 15% are examined for taxonomic areas of
substantial disagreement, and the reasons for disagreement investigated. Periphyton and algal samples
have a higher PTD due to the variance amongst species (perhaps as much as 50%).
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Sample enumeration is another component of taxonomic precision with macroinvertebrates. 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:
Equation 2.11 PDE =
^'Labi - Lab2\ ^
Labi + Lab2
xlOO
An MQO of 5% is recommended (overall mean of < 5% is acceptable) for several biological samples,
while others will have higher PDE's. This is based on the laboratory approaches used and the nature of
the indicator.
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.gov/).
Encyclopedia of Life (EOL) or the Catalogue of Life (COL) 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 NRSA
workgroup.
2.2.4 Completeness
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 1800 initial sampling sites and the 200 reference
sites. Percent completeness is calculated as:
Equation 2.12 %C = V/T x 100
Where:
i/i
LU
>
b
LU
3
o
£
I
<
3
a
<
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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 training, 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 and WSA
studies. Comparability of data within and among indicators is also facilitated by the implementation of
standardized quality assurance and quality control techniques aand 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. If some incomparability between sampling crews comes to light, the data collected by those
crews will be evaluated and possibly 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, 1986, Smith et al., 1988). At one 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 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.
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3 SURVEY DESIGN
The survey design for the NRSA 2018/19 is the same as used for the previous NRSA 2008/09 and EMAP-
West plus the Great Rivers and the tidal system surveys. 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 NRSA, the target population represents perennial rivers and
streams in the conterminous US, excluding sites below the head of salt and reservoirs.
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, and 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.
The survey designs used in EMAP to date have been documented in published reports for each resource
group and in the peer reviewed literature. A brief description of the design concepts and the specific
application for riverine systems is provided below. 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.
3.1 Probability-Based Sampling Design and Site Selection
3.1.1 Target Population
The target population for NRSA 2018/19 includes perennial stream and river channels (natural and
constructed) mapped at 1:100,000 scale within the conterminous U.S, excluding sites below the head of
salt and reservoirs.
3.1.2 Sample Frame
The NRSA 2018/19 sample frame was derived from the National Hydrography Dataset-Plus (NHD), in
particular NHDPIus V2. 2The National Hydrography Dataset (NHD) is the surface water component of The
National Map. The NHD is a digital vector dataset used by geographic information systems (GIS). It
contains features such as lakes, ponds, streams, rivers, canals, dams and streamgages. These data are
designed to be used in general mapping and in the analysis of surface-water systems. NHDPIus is an
integrated suite of application-ready geospatial data sets that incorporate many of the best features of
the National Hydrography Dataset (NHD), the National Elevation Dataset (NED), and the Watershed
Boundary Dataset (WBD). Further information about the codes used within the NHD-Plus can be found
on the NHD webpage (http://www.horizon-svstems.com/NHDPIus/index.php).
2 These refer to the old digital line graph file codes used in the NHD. These codes are: rapid, stream, braided
stream, aqueduct, and canal.
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This frame is subdivided into two major parts: (1) all National Hydrography Database (NHD)-Plus stream,
river and canal segments coded as perennial, and (2) all NHD-Plus stream, river and canal segments
coded as non-perennial, i.e., 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 NRSA project using a hierarchical randomization design process described by
Stevens and Olsen (1999, 2003, 2004). The NHD served as the frame representing streams and rivers in
the US. Data from approximately 1800 river and stream sites in the United States will be used in the
assessment and sampled over a two year index period. This total sample size will allow national
reporting as well as regional reporting at the scale of 9 aggregated Omernik Level III ecoregions, the ten
EPA Regions and 10-15 major drainage basins. Several States have added additional sites to be able to
report on the condition of streams and/or rivers within their boundaries.
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.
3.1.3 Revisit and Resample Sites
Of the sites visited in the field and found to be target sites, a total of 10% will be revisited. The 10% are
designated by the EPA for each State - two wadeable and two non-wadeable per State. 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 over the sampling season.
In addition, 983 sites from the NRSA 2013/14 and the NRSA 2008/09 will be resampled during the 2018
and 2019 sampling season to evaluate change from the previous NRSA and the WSA.
3.1.4 Evaluation of 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 Reach File
version 3.0, 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 used in order until the desired sample size designated for the state
has been achieved.
3.2 Hand-picked (Potential Reference) Site Selection
EPA selected a set of potential reference sites to sample in NRSA 2018/19. This hand-picked set of
candidate sites comes from various sources. States submitted potential reference sites for selection as
well as EPA Regional offices.. Previously sampled reference sites were also evaluated for re-sampling..
Final targeted sites were selected based on geographic distribution to ensure spatial coverage,
distribution across the resource type, and landowner permission.
Although crews will sample these potential reference sites during this field season, the final set of
reference rivers/streams, (i.e., those that EPA will use in the assessment), will be determined after the
complete set of data is returned. At that point, EPA will run a set of screening criteria similar to that
used in NRSA 2008/09. This screening approach can be found in the NRSA 2008-2009 report,
https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-
2009-results.
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4 INFORMATION MANAGEMENT
Environmental monitoring efforts that amass large quantities of information from various sources
present unique and challenging data management opportunities. To meet these challenges, the NRSA
2018/19 employs a variety of well-tested information management (IM) strategies to aid in the
functional organization and ensured integrity of stored electronic data. IM is integral to all aspects of the
NRSA 2018/19 from initial selection of sampling sites through the dissemination and reporting of final,
validated data. And, by extension, all participants in the NRSA 2018/19 have certain responsibilities and
obligations which also make them a part of the IM system. This "inclusive" approach to managing
information helps to:
¦ Strengthen relationships among NRSA 2018/19 cooperators;
¦ Increase the quality and relevance of accumulated data; and
¦ Ensure the flexibility and sustainability of the NRSA 2018/19 IM structure.
This IM strategy provides a congruent and scientifically meaningful approach for maintaining
environmental monitoring data that will satisfy both the scientific and technological requirements of the
NRSA 2018/19.
4.1 Roles and Responsibilities
At each point where data and information are generated, compiled, or stored, the NRSA 2018/19 IM
team must manage the information (Table 4.1). 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 that use data.
The IM system also includes both hardcopy and electronic means of generating, storing, organizing and
archiving data, and the effort to achieve a functional IM process is all encompassing. To that end, all
participants in the NRSA 2018/19 play an integral part within the IM system. The following table
provides a summary of the IM responsibilities identified by NRSA 2018/19 group. Specific information on
the field crew responsibilities for tracking and sending information is found in the FOMs.
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Table 4.1 Summary of IM responsibilities.
NRSA 2018/19 Contact
Primary Role
Responsibility
Group
Field Crews
State/tribal
partners and
contractor or
other field
crews (regional
EPA, etc.)
Acquire in-situ
measurements and
prescribed list of
biotic/abiotic
samples at each site
targeted for the
survey
Complete and review field data forms and sample tracking forms
for accuracy, completeness, and legibility.
Email/Ship/fax field and sample tracking forms to NARS IM Center
so information can be integrated into the central database
Work with the NARS IM Center staff to develop acceptable file
structures and electronic data transfer protocols should there be
a need to transfer and integrate data into the central database
Provide all data as specified in FOM, SEG or as negotiated with
the NRSA Project Leader.
Maintain open communications with NARS IM Center regarding
any data issues
Analytical
Laboratories
State/tribal
partners and
contractors
Analyze samples
received from field
crews in the
manner appropriate
to acquire
biotic/abiotic
indicators/measure
ments requested.
Review all electronic data transmittal files for completeness and
accuracy (as identified in the QAPP).
Work with the NARS IM Center staff to develop file structures and
electronic data transfer protocols for electronically-based data.
Submit completed sample tracking forms to NRSA 2018/19 IM
Center so information can be updated in the central database
Provide all data and metadata as specified in the laboratory
transmittal guidance section of the LOM, with specific templates
for each indicator or as negotiated with the NRSA Project Leader.
Maintain open communications with NRSA 2018/19 IM Center
regarding any data issues.
Whole fish tissue fillet responsibilities are specified in a separate
QAPP developed by U.S EPA Office of Science and Technology
IM Center staff
USEPAORD
NHEERL
Western
Ecology
Division-
Co rvallis,
Contractors
Provides support
and guidance for all
IM operations
related to
maintaining a
central data
management
system for NRSA
2018/19
Develop/update field data forms (electronic and paper versions).
Plan and implement electronic data flow and management
processes.
Manage the centralized database and implement related
administration duties.
Receive, scan, and conduct error checking of field data forms.
Monitor and track samples from field collection, through
shipment to appropriate laboratory.
Receive data submission packages (analytical results and
metadata) from each laboratory.
Run automated error checking, e.g., formatting differences, field
edits, range checks, logic checks, etc.
Receive verified, validated, and final indicator data files (including
record changes and reason for change) from QA reviewers.
Maintain history of all changes to data records from inception
through delivery to WQX.
Organize data in preparation for data verification and validation
analysis and public dissemination.
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NRSA 2018/19 Contact
Primary Role
Responsibility
Group
Implement backup and recovery support for central database.
Implement data version control as appropriate.
Project Quality
Assurance
Coordinator
USEPA Office
Of Water
Review and
evaluate the
relevancy and
quality of
information/data
collected and
generated through
the NRSA 2018/19
surveys.
Monitor quality control information.
Evaluate results stemming from field and laboratory audits.
Investigate and take corrective action, as necessary, to mitigate
any data quality issues.
Issue guidance to NRSA 2018/19 Project Leader and IM Center
staff for qualifying data when quality standards are not met or
when protocols deviate from plan.
Steering
Committee
NRSA Project
Lead and other
team
members, EPA
Regional and
ORD staff,
States, tribes,
other federal
agencies
Provide technical
recommendations
related to data
analysis, reporting
and overall
implementation
Provide feedback and recommendations related to QA, data
management, analysis, reporting and data distribution issues
Review and comment on QA and information management
documentation (QAPP, data templates, etc.).
Data Analysis
and Reporting
Team
USEPA Office
of Water, ORD
WED, Partners
Provide the data
analysis and
technical support
for NRSA 2018/19
reporting
requirements
Provide data integration, aggregation and transformation support
as needed for data analysis.
Provide supporting information necessary to create metadata.
Investigate and follow-up on data anomalies using identified data
analysis activities.
Produce estimates of extent and ecological condition of the target
population of the resource.
Provide written background information and data analysis
interpretation for report(s).
Document in-depth data analysis procedures used.
Provide mapping/graphical support.
Document formatting and version control.
Develops QA report for management.
Data
Finalization
Team
TBD
Provides data
librarian support
Prepare NRSA 2018/19 data for transfer to USEPA public web-
servers).
Generate data inventory catalog record (Science Inventory
Record).
Ensure all metadata is consistent, complete, and compliant with
USEPA standards.
4.1.1 State/ Tribe-Based Data Management
Some state and tribal partners will be managing activities for both field sampling and laboratory
analyses and may prefer to handle data management activities in-house. While the NARS program
encourages states and tribes to use these in-house capabilities, it is imperative that NRSA 2018/19
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partners understand their particular role and responsibilities for executing these functions within the
context of the national program. If a state or tribe chooses to do IM in-house, the state or tribe must
perform all of the functions associated with the following roles:
¦ Field Crew—including shipping/faxing of field data forms to the IM Coordinator (NRSA
2018/19 paper or electronic field forms must be used and the original field forms must be
sent to the NARS IM Center as outlined in the NRSA 2018/19 FOM).
¦ Quality Control Team for laboratory data.
¦ NRSA QA Project Coordinator for ensuring that laboratory results meet specified QA
requirements.
¦ All data will flow from the state or tribe to the NARS IM Center. Typically, the state or tribe
will provide a single point of contact for all things related to NRSA 2018/19 data. However, it
may be advantageous for the NARS IM Center staff to have direct communication with the
state or tribe participating laboratories to facilitate the transfer of data, a point that may be
negotiated between the primary state or tribal contact, the regional coordinator and the
NRSA 2018/19 Project Leader (with input from the NARS IM Center staff).
¦ Data transfers to the NARS IM Center must be timely. States and tribes must submit all
initial laboratory results (i.e., those that have been verified by the laboratory and have
passed all internal laboratory QA/QC criteria) in the appropriate format to NARS IM Center
by May 2019 (for 2018 data) and May 2020 (for 2019 data), in order to meet NRSA 2018/19
product deadlines.
¦ Data transfers must be complete. For example, laboratory analysis results submitted by a
state or tribe must be accompanied by related quality control and quality assurance data,
qualifiers code definitions, contaminant/parameter code cross-references/descriptions, test
methods, instrumentation information and any other relevant laboratory-based
assessments or documentation related to specific analytical batch runs.
¦ The state or Tribe will ensure that data meet minimum quality standards and that data
transfer files meet negotiated content and file structure standards.
The NARS IM Center will provide the necessary guidance for IM requirements. Each group that will
perform in-house IM functions will incorporate these guidelines as is practicable or as previously
negotiated.
4.2 Overview of System Structure
In its entirety, the NARS IM system includes site selection and logistics information, sample labels and
field data forms, tracking records, mapping and analytical data, data validation and analysis processes,
reports, and archives. NARS IM staff provides support and guidance to all program operations in
addition to maintaining a central database management system for the NRSA data.
I—
The central repository for data and associated information collected for use by NRSA 2018/19 is a [S
secure, access-controlled server located at WED-Corvallis. This database is known as the NARS IM. Data ^
are stored and managed on this system using the Structured Query Language (SQL). Data review (e.g., ^
verification and validation) and data analysis (e.g., estimates of status and extent) are accomplished ^
primarily using programs developed in either Statistical Analysis System (SAS) or 'R' language software ^
packages. O
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4.2.1 Data Flow
The NRSA 2018/19 will accumulate large quantities of observational and laboratory analysis data. To
manage this information appropriately, it is essential to have a well-defined data flow model and
documented approach for acquiring, storing, and summarizing the data. This conceptual model (Figure
4.1) helps focus efforts on maintaining organizational and custodial integrity, ensuring that data
available for analyses are of the highest possible quality.
4.2.2 Simplified Description of Data Flow
There are several components associated with the flow of information, these are:
¦ Communication between the NARS IM Center and the various data contributors (e.g., field
crews, laboratories and the data analysis and reporting team) is vital for maintaining an
organized, timely, and successful flow of information and data.
¦ Data are captured or acquired from four basic sources; field data transcription, laboratory
analysis reporting, automated data capture, and submission of external data files (e.g.,
Geographic Information Systems (GIS) data) encompassing an array of data types (site
characterization, biotic assessment, sediment and tissue contaminants, and water quality
analysis). Data capture generally relies on the transference of electronic data, e.g., optical
character readers and email, to a central data repository. However, some data must be
transcribed by hand in order to complete a record.
¦ Data repository or storage provides the computing platform where raw data are archived,
partially processed data are staged, and the "final" data, assimilated into a final, user-ready
data file structure, are stored. The raw data archive is maintained in a manner consistent
with providing an audit trail of all incoming records. The staging area provides the IM Center
staff with a platform for running the data through all of its QA/QC paces as well as providing
data analysts a first look at the incoming data. This area of the data system evolves as new
data are gathered and user-requirements are updated. The final data format becomes the
primary source for all statistical analysis and data distribution.
¦ Metadata—a descriptive document that contains information compliant with the Content
Standards for Digital Geospatial Metadata (CSDGM) developed by the Federal Geographic
Data Committee (FGDC).
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ECOLOGICAL INDICATOR FIELD AND LABORATORY DATA FLOW
LABORATORY
SAMPLE
COLLECTION
FIELD DATA COLLECTION
SAMPLE
Portable
SAMPLE
ANALYSIS
Labeled ^^5
Samples
Forms
RECEIPT
Recorders
LABORATORY
INFORMATION
MANAGEMENT
SYSTEM
i Notebook PC
(&J
Sample
Tracking
Form(s)
QAQC
REVIEW
OTHER
~ATA FILES
OFFICE
REVIEW
e.g., Survey
=jy RAW DATA
SUBMISSION PACKA
attribute data)
RAW DATA
SUBMISSION PACKAGE
INFORMATION
MANAGEMENTCENTE
(WED-Corvallis)
DATA ENTRY
RAW DATA FILES
(NARS IM Spec)
NARS IM
SQL SERVER
QA review
Create flat
files for use
with SAS or R
Relational
1 record per datum
VERIFIED DATA FILES
Update
records in
SQL tables
Data i Data
Table I Table
1 I 2
QA review
VAL DATED DATA F LES
FINAL INDICATOR
DATA FILES
FINAL DATA
RECORDS
(EPA WATER
QUALITY
EXCHANGE
[WQX])
Permanent
Archival
FINAL DATA
RECORDS
(Flat files)
Posted to
Webpage or
FTP site
TA
ANALYSIS
ASSESSMENT DATA FILES
(Extent and status estimates)
Figure 4.1 Conceptual model of data flow into and out of the master SQL
The following sections describe core information management standards, data transfer protocols, and
data quality and results validation. Additionally, Section 4.4 describes the major data inputs to the
central database and the associated QA/QC processes used to record, enter, and validate measurement
and analytical data collected.
4.2.3 Core Information Management Standards
The development and organization of the NARS IM system is compliant with current EPA guidelines and
standards. 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; and
Data catalog documentation.
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NRSA 2018/19 is committed to compliance with all applicable regulations and guidance concerning
hardware and software procurement, maintenance, configuration control, and QA/QC. To that end, the
NRSA 2018/19 team has adopted several IM standards that help maximize the ability to exchange data
within the study and with other aquatic resource surveys or similar large-scale monitoring and
assessment studies (e.g. NARS, past EMAP and R-EMAP studies). Specific information follows.
4.2.4 Data Formats
4.2.4.1 Attribute Data
¦ SQL Tables;
¦ SAS Data Sets;
¦ R Data Sets3; and
¦ American Standard Code for Information Interchange (Ascii) Files: Comma-Separated values,
or space-delimited, or fixed column.
4.2.4.2 GIS Data
¦ ARC/INFO native and export files; compressed .tar file of ARC/INFO workspace; and
¦ Spatial Data Transfer Standard (SDTS; FGDC 1999) (format available upon request).
4.2.4.3 Standard Coding Systems
¦ Sampling Site: (EPA Locational Data Policy; EPA 1991);
¦ Coordinates: Latitude and Longitude in decimal degrees (±0.002);
¦ Datum: NAD83;
¦ Chemical Compounds: Chemical Abstracts Service (CAS 1999) (http://www.cas.org/) ;
¦ Species Codes: Integrated Taxonomic Information System when possible; and
¦ Land cover/land use codes: Multi-Resolution Land Characteristics; National Hydrography
Dataset Plus Version 2.0.
4.2.5 Public Accessibility
While any data created using public funds are subject to the Freedom of Information Act (FOIA), some
basic rules apply for general public accessibility and use. Briefly, those rules are:
¦ Program must comply with Data Quality Act before making any data available to the public
and person generating data must fill out and have a signed Information Quality Guidelines
package before any posting to the Web or distribution of any kind.
¦ Data and metadata files are made available to the contributor or participating group for
review or other project-related use from NARS IM or in flat files before moving to an EPA-
approved public website.
¦ Data to be placed on a public website will undergo QA/QC review according to the approved
QAPP.
¦ Only "final" data (those used to prepare the final project report) are readily available
through an EPA-approved public website.
3 R is a free software programming language and a software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical software and data
analysis.
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As new guidance and requirements are issued, the NARS IM staff will assess the impact upon the IM
system and develop plans for ensuring timely compliance.
4.3 Data Transfer Protocols
Field crews are expected to send in hard copies of field forms or use the provided electronic field forms
containing in situ measurement and event information to the NARS IM Center defined in the FOM for
submission. Laboratories will submit electronic data files. Field crews and laboratories must submit all
sample tracking and analytical results data to the NARS IM Center in electronic form using a standard
software package to export and format data. Data submission templates for laboratories are included in
the LOM. Examples of software and the associated formats are:
Table 4.2 Summary of software
Software
Export Options (file extensions)
Microsoft Excel"
xls, xlsx, csv, formatted txt delimited
Microsoft Access"
mdb, csv, formatted txt delimited
SAS"
csv, formatted txt delimited
R
csv, formatted txt delimited
All electronic files must be accompanied by appropriate documentation (e.g., metadata, laboratory
reports, QA/QC data and review results). This documentation must contain sufficient information to
identify field contents, field formats, qualifier codes, etc. It is very important to keep EPA informed of
the completeness of the analyses. Labs may send files periodically, before all samples are analyzed, but
EPA must be informed that more data are pending if a partial file is submitted. All data files sent by the
labs must be accompanied by text documentation describing the status of the analyses, any QA/QC
problems encountered during processing, and any other information pertaining to the quality of the
data. Following is a list of general transmittal requirements each laboratory, state, or tribal based IM
group should consider when packaging data for electronic transfer to the IM Center:
¦ Provide data in row/column data file/table structure - see Appendix E in LOM for templates.
All cooperators and contractors should further consider the following:
a. Include NRSA site and sample ID provided on the sample container label in a field
for each record (row) to ensure that each data file/table record can be related to a
site visit.
b. Use a consistent set of column labels.
c. Use file structures consistently.
d. Use a consistent set of data qualifiers.
e. Use a consistent set of units.
f. Include method detection limit (MDL) as part of each result record.
g. Include reporting limit (RL) as part of each result record for water chemistry.
h. Provide a description of each result/QC/QA qualifier.
i. Provide results/measurements/MDL/RL in numeric form.
j. Maintain result qualifiers (e.g., <, Not Detected (ND)) in a separate column.
k. Use a separate column to identify record-type. For example, if QA or QC data are
included in a data file, there should be a column that allows the IM staff to readily
identify the different result types.
I. Include laboratory sample identifier.
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m. Include batch numbers/information so results can be paired with appropriate
QA/QC information,
n. Include "true value" concentrations, if appropriate, in QA/QC records,
o. Include a short description of preparation and analytical methods used to analyze
samples (where appropriate) either as part of the record or as a separate
description for the test(s) performed on the sample. For example, EPAxxxx.x,
ASTMxxx.x, etc. Provide a broader description (e.g., citation) if a non-standard
method is used.
p. Include a short description of instrumentation used to acquire the test result (where
appropriate). This may be reported either as part of the record or as a separate
description for each test performed on the sample. For example, GC/MS-ECD, ICP-
MS, etc.
q. Ensure that data ready for transfer to NARS IM are verified and validated, and
results are qualified to the extent possible (final verification and validation are
conducted by EPA).
r. Data results must meet the specified requirements for each indicator found in the
LOM as specified by contract or agreement,
s. Identify and qualify missing data (why are the data missing?),
t. Submit any other associated quality assurance assessments and relevant data
related to laboratory results (i.e., chemistry, nutrients). Examples include summaries
of QC sample analyses (blanks, duplicates, check standards, matrix spikes) standard
or certified reference materials, etc.), results for external performance evaluation or
proficiency testing samples, and any internal consistency checks conducted by the
laboratory. For requirements, please see specific indicator sections of this QAPP and
LOM.
Laboratories will work with the NARS IM Coordinator to establish a data load process into NARS IM.
4.4 Data Quality and Results Validation
Data quality is integrated throughout the life cycle of the data. This includes development of appropriate
forms, labels etc. for capturing data as well as verifying data entry, results, and other assessments.
Indicator workgroup experts, the data analysis and reporting team submit any recommended changes to
the Project QA Coordinator who recommends and submits any changes (deletions, additions,
corrections) to the NARS IM data center for inclusion in the validated data repository. All explanation for
data changes is included in the record history.
4.4.1 Design and Site Status Data Files
The site selection process described in Section 3 produces a list of candidate sampling locations, h
inclusion probabilities, and associated site classification data (e.g., target status, ecoregion, etc.). The ^
Design Team provides this file to the NRSA 2018/19 Project Leader, who in turn distributes to the IM g
staff, and field coordinators. Field coordinators determine ownership and contacts for acquiring <
permission to access each site, and conduct site evaluation and reconnaissance activities. Field Crews <
document information from site evaluation and reconnaissance activities following the SEG and the z
FOM. The site evaluation spreadsheets are submitted to the Project Lead by the field crews. The NARS Q
IM Center compiles all information such as ownership, site evaluation, and reconnaissance information <
for each site into a "site status" data file. Any missing information from the site status data file is ^
O
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identified and a request is made by the NARS IM Center to the field crew (or site evaluator) to complete
the record.
4.4.2 Sample Collection and Field Data
Field crews record sampling event observational data in a standard and consistent manner using field
data collection forms (Appendix B of the NRSA 2018/19 FOM). Prior to initiation of field activities, the
NARS IM staff works with the indicator leads and analytical support laboratories to develop standardized
field data forms and sample labels. Adhesive labels, completed by the field crews, have a standard
recording format and are affixed to each sample container. Field protocols include precautions to ensure
that label information remains legible and the label remains attached to the sample.
NRSA 2018/19 provides two options for completing field forms: electronic data entry using pre-
developed e-forms or "traditional" paper. Paper forms are printed for field crews on water resistant
paper. Copies of the field data forms and instructions for completing each form are documented in the
NRSA 2018/19 FOM. Recorded data whether through e-forms or paper are reviewed upon completion of
data collection and recording activities by the Field Crew Leader. 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 by field crews if possible, and data considered as suspect
are qualified using a flag variable. The field sampling crew enters explanations for all flagged data in a
comments section. Field crews transmit e-forms to the NARS IM Staff by selecting the "submit" button
as described in the FOM. Alternately, field crews, ship completed paper field data forms to the NARS IM
staff for entry into the central database management system.
All samples are tracked from the point of collection. Tracking of samples refers to the documentation of
the specified location of each sample in the centralized NARS IM Center database. This is done by
requiring that field crews ensure that copies of the shipping and custody record accompany all sample
transfers; other copies are transmitted to the IM Center. Each sample has a custody record that
laboratory manager is required to enter into NARS IM Center upon receipt of sample. The IM Center
tracks samples 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 the FOMs.
Procedures for completion of sample labels and field data forms and use of personal computers (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 4.3. Additional QA/QC checks or procedures specific to individual indicators are described
in the LOM.
Table 4.3 Summary sample and field data quality control activities: sample tracking
Quality Control Activity Description and/or Requirements
Contamination
Prevention
All containers for individual site sealed in plastic bags until use; specific contamination
avoidance measures covered in training
Sample Identification
Pre-printed labels with unique ID number on each sample
Data Recording
Data recorded on pre-printed forms of water-resistant paper; field sampling crew
reviews data forms for accuracy, completeness, and legibility
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Quality Control Activity Description and/or Requirements
Data Qualifiers
Defined qualifier codes used on data form; qualifiers explained in comments section on
data form
Sample Custody
Records
Unique sample ID and tracking form information entered in LIMS; sample shipment
and receipt confirmed
Sample Tracking
Sample condition inspected upon receipt and noted on tracking form with copies sent
to NRSA Field Logistics Coordinator and/or IM
Data Entry
Data entered using customized entry screens that resemble the data forms; entries
reviewed manually or by automated comparison of double entry
Data Submission
Standard format defined for each measurement including units, significant figures, and
decimal places, accepted code values, and required field width
Data Archival
All data records, including raw data, archived in an organized manner. For example,
following verification/validation of the last submission into the NARS database, it is
copied to a terabit external hard drive and sent to the Project Leader for inclusion in
his project file, scheduled as 501, permanent records.
Processed samples and reference collections of taxonomic specimens submitted for
cataloging and curing at an appropriate museum facility
4.4.3 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. The lab reports any discrepancies, damaged samples, or
missing samples to the NARS IM staff and NRSA 2018/19 Project Lead electronically.
Most of the laboratory analyses for the NRSA 2018/19 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 NRSA 2018/19
indicators are described in Section 5. Additional QA/QC procedures specific to individual indicator and
parameter analyses are described in the LOM and the QAPP. Biological sample analyses are generally
based on current acceptable practices within the particular biological discipline. Some QC checks and
procedures applicable to most NRSA 2018/19 biological samples are described in the LOM and the
QAPP. Table 4.4 provides a summary of the lab data QC activities for NRSA 2018/19.
Table 4.4 Summary laboratory data quality control activities
Quality Control Activity
Description and/or Requirements
Instrument Maintenance
Follow manufacturer's recommendations and specific guidelines in methods;
maintain logbook of maintenance/repair activities
Calibration
Calibrate instruments according to manufacturer's recommendations for each
specific indicator; recalibrate or replace before analyzing any samples
QC Data
Maintain control charts, determine LT-MDLs and achieved data attributes; include
QC data summary (narrative and compatible electronic format) in submission
package
<
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Quality Control Activity Description and/or Requirements
Data Recording
Use software compatible with NARS 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 specimen.
Data Qualifiers
Use defined qualifier codes; explain all qualifiers
Data Entry
Automated comparison of double entry or 100% manual check against original
data form
Submission Package
Includes:
¦ Letter by laboratory manager
¦ Data
¦ Data qualifiers and explanations
¦ Electronic format compatible with NARS IM
¦ Documentation of file and database structures
¦ Metadata: variable descriptions and formats
¦ Summary report of any problems and corrective actions implemented
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 coordinator or by supervisory personnel. Errors are corrected by
laboratory personnel 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 Project Plan and established procedures for recording,
reviewing, and validating analysis data.
Once analytical data have passed all of the laboratory's internal review procedures, the lab prepares and
transfers a submission package using the prescribed templates in the LOM. The contents of the
submission package are largely dictated by the type of analysis (physical, chemical, or biological).
Remaining sample material and voucher specimens may be transferred to EPA's designated laboratory
or facilities as directed by the NRSA 2018/19 Project Lead. All samples and raw data files (including
logbooks, bench sheets, and instrument tracings) are to be retained by the laboratory for 3 years or until
authorized for disposal, in writing, by the EPA Project Leader. Deliverables from contractors and
cooperators, including raw data, are permanent as per EPA Record Schedule 258
(http://www.epa.gov/records/policy/schedule/sched/258.htm). EPA's project records are scheduled 501
(http://www.epa.gov/records/policy/schedule/sched/501.htm) and are also permanent.
4.4.4 Data Review, Verification, and 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. ^
4.4.4.1 Paper Forms
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corrected immediately at the time of scanning. Suspected errors that cannot be confirmed at the time of
scanning are qualified for later review by someone with the appropriate background and experience
(e.g., a chemist or aquatic ecologist). The process continues until the transcribed data are 100% verified
or no corrections are required.
4.4.4.2 Electronic Forms
The NARS IM Center directly uploads information from the electronic field collection forms into their
database. During the upload process, incoming data are subjected to a number of automated error
checking routines. Omissions and errors are automatically noted in an email message to the field crew
lead.
4.4.4.3 Additional Review
Additional validation is accomplished by the NARS IM Center staff using a specific set of guidelines and
executing a series of programs (computer code) to check for: correct file structure and variable naming
and formats, outliers, missing data, typographical errors and illogical or inconsistent data based on
expected relationships to other variables. Data that fail any check routine are identified in an "exception
report" that is reviewed by an appropriate scientist for resolution.
The NARS IM Center brings any remaining questionable data to the attention of the EPA Project QA
Coordinator and individuals responsible for collecting the data for resolution. The EPA Project QA
Coordinator reviews all data to determine completeness and validity. Additionally, the data are run
through a rigorous inspection using SQL queries or other computer programs such as SAS or R to check
for anomalous data values that are especially large or small, or are noteworthy in other ways. Focus is
on rare, extreme values since outliers may affect statistical quantities such as averages and standard
deviations.
The EPA Project QA Coordinator examines all laboratory quality assurance (QA) information to
determine if the laboratory met the predefined data quality objectives - available through the QAPP.
Some of the typical checks made in the processes of verification and validation are described in Table
Automated review procedures may be used. The primary purpose of the initial checks is to confirm that
each 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. They will either be corrected, replaced
with a new acceptable value from sample reanalysis, or confirmed suspect after sample reanalysis. 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 NARS IM
System. Any suspect data will be flagged for data qualification.
The NARS IM staff, with the support of the NRSA 2018/19 Quality Team, correct and qualify all
questionable data. Copies of the raw data files are maintained in NARS IM, generally in active files until
4.5.
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completion of reporting and then in archive files. Redundant copies of all data files are maintained and
all files are periodically backed up to the EPA HQ shared G drive system.
Table 4.5 Data review, verification, and validation quality control activities
Quality Control Activity
Description and/or Requirements
Review any qualifiers associated with variable
Determine if value is suspect or invalid; assign
validation qualifiers as appropriate
Determine if Measurement Quality Objective (MQOs) and
project DQOs have been achieved
Determine potential impact on achieving research
and/or program objectives
Exploratory data analyses (univariate, bivariate,
multivariate) utilizing all data
Identify outlier values and determine if analytical
error or site-specific phenomenon is responsible
Confirm assumptions regarding specific types of statistical
techniques being utilized in development of metrics and
indicators
Determine potential impact on achieving research
and/or program objectives
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. Multivariate techniques have also been used in detecting
extreme or outlying values in environmental data sets (Meglen, 1985; Garner et al., 1991; Stapanian et
al., 1993).
The Quality Team reviews suspect data 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, the Quality Team qualifies the data 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 activity 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 h
one or more variables, data files may be restructured so as to provide a single record per site. ^
LU
4.5 Data Transfer <
Field crews may transmit data electronically; hardcopies of completed data and sample tracking forms ^
are sent via express courier service. Copies of raw, verified, and validated data files are transferred from z
the Project QA Coordinator to the IM staff for inclusion in the central IM system. All transfers of data are p
conducted using a means of transfer, file structure, and file format that has been approved by the EPA ^
IM Project lead. Data files that do not meet the required specifications will not be incorporated into the g
centralized data access and management system. ^
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4.5.1 Database Changes
The NARS IM Center staff complete data corrections at the lowest level to ensure that any subsequent
updates will contain only the most correct data. The NARS IM Center sends back laboratory results
found to be in error to the originator (e.g., analysis laboratory) for correction. After the originator makes
any corrections, the entire batch or file is resubmitted to the NARS IM Center. The NARS IM Center uses
these resubmissions to replace any previous versions of the same data.
The NARS IM Center uses a version control methodology when receiving files. This methodology is
explained in the following sentences. Incoming data are not always immediately transportable into a
format compatible with the desired file structures. When this situation occurs, the IM staff creates a
copy of the original data file, which then becomes the working file in which any formatting changes will
take place. The original raw data will remain unchanged. This practice further ensures the integrity of
the data and provides an additional data recovery avenue, should the need arise.
All significant changes are documented by the NARS IM Center staff. The NARS IM Center includes this
information in the final summary documentation for the database (metadata).
After corrections have been applied to the data, the NARS IM Center will rerun the validation programs
to re-inspect the data.
If requested by the NARS Project QA Coordinator and funds are available, the NARS IM Center will
implement database auditing features to track changes.
4.6 Metadata
All metadata will be kept according to the Federal Geographic Data Committee, Content standard for
digital geospatial metadata, version 2.0. FGDC-STD-001-1998 (FGDC 1998).
4.6.1 Parameter Formats
The following parameter formats will be used:
• Sampling Site (EPA Locational Data Policy (USEPA 1991)
• Latitude and Longitude in decimal degrees (+/- 7.4), Negative longitude values (west of the
prime meridian),
• Datum: 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.
4.6.2 Standard Coding Systems
The following standard coding systems will be used:
• Chemical Compounds: Chemical Abstracts Service (CAS 1999)
• Taxonomic Names: USGS BioData (https://aquatic.biodata.usgs.gov/landing.action)
• Land cover/land use codes: Multi-Resolution Land Characteristics (MRLC 1999)
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4.7 Information Management Operations
4.7.1 Computing Infrastructure
Electronic data are collected and maintained within a central server housed at WED using a Windows
Server (current configuration) or higher computing platform in SQL native tables for the primary data
repository and SAS® native data sets or R datasets for data analysis. Official IM functions are conducted
in a centralized environment.
4.7.2 Data Security and Accessibility
The NARS IM Center ensures that all data files in NARS IM 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 NRSA 2018/19
collaborators. Validated data files are accessible only to users specifically authorized by the NRSA
2018/19 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 by the NARS IM Center. This ensures that if one system is destroyed or
incapacitated, IM staff can reconstruct the databases. Procedures developed to archive the data,
monitor the process, and recover the data are described in IM documentation.
Data security and accessibility standards implemented for NRSA 2018/19 IM meet EPA's standard
security authentication (i.e., username, password) process in accordance to EPA's Information
Management Security Manual (1999; EPA Directive 2195 Al) and EPA Order 2195.1 A4 (2001D). Any
data sharing requiring file transfer protocol (FTP) or internet protocol is provided through an
authenticated site.
4.7.3 Life Cycle
Data may be retrieved electronically by the NRSA 2018/19 team, partners and others throughout the
records retention and disposition lifecycle or as practicable (Section 4.4).
4.7.4 Data Recovery and Emergency Backup Procedures
The NARS IM Center maintains several backup copies of all data files and of the programs used for
processing the data. Backups of the entire system are maintained off-site by the NARS IM Center. The
IM process used by the NARS IM Center for NRSA 2018/19 uses system backup procedures. The NARS IM
Center backs up and archives the central database according to procedures already established for EPA
Western Ecology Division and NARS IM. All laboratories generating data and developing data files are
expected to establish procedures for backing up and archiving computerized data.
4.7.5 Long-Term Data Accessibility and Archive
All data are transferred by OW's Water Quality Exchange (WQX) team working with the NARS IM Team
to U.S. EPA's agency-wide WQX data management system for archival purposes. WQX is a repository for
water quality, biological, and physical data and is used by state environmental agencies, EPA and other
federal agencies, universities, and private citizens. Data from the NRSA 2018/19 project will be run
through an Interface Module in an Excel format and uploaded to WQX by the WQX team. Once
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uploaded, states and tribes and the public will be able to download data (using Oracle software) from
their region. Data will also be provided in flat files on the NARS website.
4.8 Records Management
Removable storage media (i.e., CDs, USB Drives) and paper records are maintained in a centrally located
area at the NARS IM Center. Paper records will be returned to OW once the assessment is complete. The
IM Team identifies and maintains files using standard divisional procedures as established by EPA
Western Ecology Division. Records retention and disposition comply with U.S. EPA directive 2160
Records Management Manual (July, 1984) in accordance with the Federal Records Act of 1950.
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5 INDICATORS
A description of the NRSA indicators is found in Table 5.1.
Table 5.1 Indicators and collection location
Indicator Description Specs/Location in Sampling Reach
In Situ measurements (pH,
DO, temperature,
conductivity)
Measurements for temperature,
pH, dissolved oxygen (DO), and
conductivity taken to detect
extremes in condition that might
indicate impairment.
One set of measurements taken at the
index site (Wadeable) or Transect A
(Boatable); readings are taken at 0.5 m
depth, or at mid-depth if depth is less
than one meter.
Water chemistry (TP, TN, NH3-
N, NO3-NO2, NOs), basic anions
and cations, silica, alkalinity
[ANC], dissolved organic
carbon (DOC), TOC, TSS,
conductivity, pH, turbidity,
true color)
Water chemistry measurements
will be used to determine the
acidic conditions and nutrient
enrichment, as well as
classification of water chemistry
type.
Collected from a depth of 0.5 m at the
index site (Wadeable) or Transect A
(Boatable)
Chlorophyll-a
Chlorophyll-a is used to determine
algal biomass in the water.
Collected as part of water chemistry and
periphyton samples
Algal Toxins (Microcystin and
Cylindrospermopsin)
Measurement used to determine
the harmful algal bloom biomass
in the water.
Collected from a depth of 0.5 m at the
index site (Wadeable) or Transect A
(Boatable)
Periphyton
Periphyton community
information is used to assess the
biological health of rivers and
streams algal community. The
NRSA will measure attributes of
the overall structure and function
of the periphyton community,
diversity and abundance to
evaluate biological integrity.
Collected from 11 locations systematically
placed at each site and combined into a
single composite sample. Sub-sampled for
taxonomy, chlorophyll-a, Ash Free Dry
Mass (AFDM), and metagenomics.
Benthic macroinvertebrate
assemblage (Littoral)
Benthic macroinvertebrate
community information is used to
assess the biological health of
rivers and streams. The NRSA will
measure attributes of the overall
structure and function of the
benthic macroinvertebrate
community, diversity,
abundances, etc to evaluate
biological integrity.
Collected from 11 locations systematically
placed at each site and combined into a
single composite sample
Fish Assemblage
The assessment will measure
specific attributes of the overall
structure and function of the
ichthyofaunal community to
Sampled throughout the sampling reach
at specified locations
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evaluate biological integrity and
water quality.
Physical habitat assessment The physical habitat assessment
of the sampling reach and the
riparian zone (the region lying
along a bank)
Measurements collected throughout the
sampling reach at specified locations
Fecal indicator [Enterococci) Enterococci are bacteria that are
endemic to the guts of warm
blooded creatures. These
bacteria, by themselves, are not
considered harmful to humans
but often occur in the presence of
potential human pathogens (the
definition of an indicator
organism).
Collected at the last transect one meter
off the bank
Fish Tissue Plug Fish Tissue plugs will provide
information on the national
distribution of mercury, a
bioaccumulative and toxic
chemical in fish species.
Target species collected throughout the
sampling reach at every site where
suitable fish species and lengths are
available
Fish Tissue Fillet
Fish Tissue Fillet samples will
provide information on the
national distribution of mercury,
PCBs, and PFCs in U.S. rivers for
human health applications.
Target species collected throughout the
sampling reach at 478 pre-selected river
sites.
5.1 Water Chemistry and In-situ Measurements (Including chlorophyll-a-)
5.1.1 Introduction
Ecological indicators based on field and laboratory collected river 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. Data are collected for chlorophyll-a to provide
information on the algal loading and gross biomass of cyanobacteria and other algae within each stream
and river.
Detailed sample collection and handling procedures are described in the FOMs.
5.1.2 Pertinent QA/QC Procedures
A single central laboratory and some State laboratories will analyze the water chemistry samples. The
specific quality control procedures used by each laboratory are implemented to ensure that:
¦ Objectives established for various data quality indicators being met.
¦ Results are consistent and comparable among all participating laboratories.
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The central laboratory demonstrated in previous studies that it can meet the required Laboratory
Reporting Levels (RLs) (USEPA 2004). All laboratories will follow the QA/QC procedures outlined in the
QAPP and the LOM will be followed to ensure these Laboratory RLs are met. A summary and diagram of
the QA processes related to water chemistry samples for the NRSA 2018/19 is found in Figure 5.2.
5.1.2.1 Laboratory Performance Requirements
Table 5.2 summarizes the pertinent laboratory performance requirements for the water chemistry
indicators.
Table 5.2 Laboratory method performance requirements: water chemistry
Analyte
Units
Potential
Lower Reporting
Transitio
Precision
Bias
Range of
Limit5
n Value6
Objective7
Objective8
Samples4
Conductivity
|j.S/cm at
25°C
1 to 15,000
2.0
20
± 2 or±10%
± 2 or 5%
pH (laboratory)
Standard
(Std) Units
3.5 to 10
N/A
5.75, 8.25
>5.75 or
<8.25= ±0.15
<5.75 or
>8.25=±0.07
>5.75 and
<8.25 = ±0.05
<5.75 or >8.25
= ±0.15
Turbidity
Nephelome
trie
Turbidity
Units (NTU)
0 to 44,000
2.0
20
± 2 or±10%
± 2 or±10%
Acid Neutralizing
Capacity (ANC)
Heq/L
(1 mg/Las
CaCOs=20
M-eq/L
-300 to
+75,000
(-16 to 3,750
mg as
CaC03)
N/A
±50
± 5 or ±10%
± 5 or ±10%
Dissolved
Organic Carbon
(DOC)
mg C/L
0.1 to 109
0.20
< 1
> 1
±0.10 or
±10%
±0.10 or
±10%
1. Estimated from samples analyzed at the WED-Corvallis laboratory between 1999 and 2005 for TIME, EMAP-West, and WSA streams
from across the U.S.
2. The lower reporting limit is the lowest value that needs to be quantified (as opposed to just detected), and represents the value of the
lowest nonzero calibration standard used. It is set to 2 times the long-term method detection limit, following USGS Open File Report
99-193 New Reporting Procedures Based on Long-Term Method Detection Levels and Some Considerations for Interpretations of Water-
Quality Data Provided by the U.S. Geological Survey National Water Quality Laboratory.
3. Value at which performance objectives for precision and bias switch from absolute ( transition value).
Two-tiered approach based on Hunt, D. T. E. and A. L. Wilson. 1986. The Chemical Analysis of Water: General Principles and Techniques.
2nd ed. Royal Society of Chemistry, London, England.
4. For standard samples, precision is estimated as the standard deviation of repeated measurements across batches at the lower
concentration range, and as percent relative standard deviation of repeated measurements across batches at the higher concentration
range.
For pH precision, the looser criteria applies to more highly alkaline samples. For NRSA, that is less of a concern than the ability to
measure acidic samples accurately and precisely. O
5. Bias (systematic error) is estimated as the difference between the mean measured value and the target value of a performance ^
evaluation and/or internal reference samples at the lower concentration range measured across sample batches, and as the percent p;
difference at the higher concentration range.
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Analyte
Units
Potential
Range of
Samples4
Lower Reporting
Limit5
Transitio
n Value6
Precision
Objective7
Bias
Objective8
Ammonia-N
(NHs-N)
mg N/L
Oto 17
0.02 (1.4 neq/L)
0.10
±0.01 or
±10%
±0.01 or
±10%
Nitrate-Nitrite
(NO3-NO2)
mg N/L
0 to 360 (as
nitrate)
0.02
0.10
±0.01 or
±10%
±0.01 or
±10%
Total Nitrogen
(TN)
mg/L
0.1 to 90
0.02
0.10
±0.01 or
±10%
±0.01 or
±10%
Total
Phosphorus (TP)
Hg P/L
0 to 22,000
4
20
± 2 or±10%
± 2 or±10%
Sulfate (SO4)
mg SO4/L
0 to 5,000
0.50 (10 neq/L)
2.5
±0.25 or
±10%
±0.25 or
±10%
Chloride (CI)
mg Cl/L
0 to 5,000
0.20 (6 neq/L)
1
±0.10 or
±10%
±0.10 or
±10%
Nitrate (NOs)
mg N/L
0 to 360
0.02 (4 neq/L)
0.1
±0.01 or
±10%
± 0.01 ±10%
Calcium (Ca)
mg Ca/L
0.04 to
5,000
0.10 (5 neq/L)
0.5
± 0.05 or
±10%
± 0.05 or
±10%
Magnesium (Mg)
mg Mg/L
0.1 to 350
0.10(8 neq/L)
0.5
± 0.05 or
±10%
± 0.05 or
±10%
Sodium (Na)
mg Na/L
0.08 to
3,500
0.10(4 neq/L)
0.5
± 0.05 or
±10%
± 0.05 or
±10%
Potassium (K)
mg K/L
0.01 to 120
0.10 (2 neq/L)
0.5
± 0.05 or
±10%
± 0.05 or
±10%
Silica (SiOz)
mg SiCh/L
0.01 to 100
0.10
0.5
± 0.05 or
±10%
± 0.05 or
±10%
Total Suspended
Solids (TSS)
mg/L
0 to 27,000
2
10
±1 or ±10%
±1 or ±10%
True Color
PCU
0 to 350
5
50
±5 or ±10%
±5 or ±10%
Chlorophyll a
M-g/L (in
extract)
0.7 to
11,000
0.5
15
± 1.5 or ±10%
± 1.5 or ±10%
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Laboratory Quality Control Samples Table 5.3 summarizes the pertinent laboratory quality control
samples for the water chemistry indicators.
Table 5.3 Laboratory quality control samples: water chemistry
QC Sample Analytes
Description
Frequency
Acceptance
Corrective Action
Type and
Criteria
Description
Laboratory/
All except TSS
Once per day
Control limits
Prepare and analyze new
Reagent
(ForTSS, the lab
prior to
< LRL
blank. Determine and
Blank
will filter a
sample
correct problem (e.g.,
known volume
analysis
reagent contamination,
of reagent water
instrument calibration, or
and process the
contamination introduced
filters per
during filtration) before
method)
proceeding with any
sample analyses.
Reestablish statistical
control by analyzing three
blank samples.
Filtration
All dissolved
ASTM Type II
Prepare once
Measured
Measure archived
Blank
analytes
reagent
per week
concentrations
samples if review of other
water
and archive
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QC Sample Analytes Description
Type and
Description
Frequency Acceptance Corrective Action
Criteria
Calibration
QCCS
For turbidity, a
QCCS is prepared
at one level for
routine analyses
(USEPA 1987).
Additional QCCSs
are prepared as
needed for
samples having
estimated
turbidities
greater than 20
NTU.
Before and
after sample
analyses
±10% or
method
criteria
Repeat QCCS analysis.
Recalibrate and analyze
QCCS.
Reanalyze all routine
samples (including PE and
field replicate samples)
analyzed since the last
acceptable QCCS
measurement.
Laboratory
Duplicate
Sample
All analyses
One per
batch
Control limits
< precision
objective
If results are below LRL:
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.
Standard
Reference
Material
(SRM)
When available
for a particular
analyte
One analysis
in a
minimum of
five separate
batches
Manufacturers
certified range
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 that are
acceptable. Qualify all
sample batches analyzed
since the last acceptable
reference standard
measurement for possible
reanalysis.
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QC Sample
Type and
Description
Analytes
Description
Frequency
Acceptance
Criteria
Corrective Action
Matrix
Only prepared
One per
Control limits
Select two additional
Spike
when samples
batch
for recovery
samples and prepare
Samples
with potential
cannot exceed
fortified subsamples.
for matrix
100±20%
Reanalyze all suspected
interferences are
samples in batch by the
encountered
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).
5.1.2.2 Data Reporting, Review, and Management
Checks made of the data in the process of review and verification is summarized in Table 5.4. Data
reporting units and significant figures are summarized in Table 5.5.
The Project QA Officer is ultimately responsible for ensuring the validity of the data, although
performance of the specific checks may be delegated to other staff members.
Table 5.4 Data validation quality control: water chemistry
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 <100 |aeq/L
%IBD < ±25%.
If total ionic strength > 100 |aeq/L
%IBD <±10%.
Determine which analytes, if any, are the largest contributors to
the ion imbalance. Review suspect analytes for analytical error
and reanalyze.
Flag = unacceptable %IBD
If analytical error is not indicated, qualify sample to attribute
imbalance to unmeasured ions. Reanalysis is not required.
Flag = %IBD outside acceptance criteria due to
unmeasured ions
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Activity or Procedure
Requirements and Corrective Action
Conductivity check:
Compare measured conductivity of each
sample to a calculated conductivity based
on the equivalent conductance of major
ions in solution (Hillman et al., 1987).
If measured conductivity < 25 nS/cm,
([measured - calculated] -r- measured) < ±25%.
If measured conductivity > 25 nS/cm,
([measured - calculated] -r- 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.
Review data from QA samples (laboratory
Performance evaluation (PE) samples,
and inter-laboratory comparison
samples)
Indicator QC Coordinator determines impact and possible
limitations on overall usability of data based on the specific issue.
Table 5.5 Data reporting criteria: water chemistry
Measurement
Units
No. Significant
Figures
Maximum No.
Decimal Places
DO
mg/L
2
1
Temperature
°C
2
1
PH
pH units
3
2
Carbon, total & dissolved organic
mg/L
3
1
ANC
|aeq/L
3
1
Conductivity
|j.S/cm at 25 °C
3
1
Calcium, magnesium, sodium, potassium,
ammonium, chloride, nitrate, and sulfate
|aeq/L
3
1
Silica
mg/L
3
2
Total phosphorus
m-§/l
3
0
Total nitrogen
mg/L
3
2
Nitrate-Nitrite
mg/L
3
2
Ammonia
mg/L
3
2
Turbidity
NTU
3
0
True color
PCU
2
0
TSS
mg/L
3
1
Chlorophyll a
Hg/I
3
2
QC
O
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: o
72
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Percent ion difference (%IBD)
(> cations -> anions)- ANC.
Equation 5.1 %IBD = ^ p 1
ANC + y,anions + 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 |a,eq/L); anions are the concentrations of chloride,
nitrate, and sulfate (converted from mg/L to |a,eq/L), and H+ is the hydrogen ion concentration calculated
from the antilog of the sample pH. Factors to convert major ions from mg/L to |a,eq/L are presented in
Table 5.6. For the conductivity check, equivalent conductivities for major ions are presented in
Table 5.7.
Table 5.6 Constants for converting major ion concentration from mg/L to |ieq/L
Analyte
Conversion from mg/L to
H.eq/L9
Calcium
49.9
Magnesium
82.3
Potassium
25.6
Sodium
43.5
Ammonia-N
71.39
Ammonium
55.4
Chloride
28.2
Nitrate-N
71.39
Nitrate
16.1
Sulfate
20.8
Table 5.7 Factors to calculate equivalent conductivities of major ions10
Ion
Equivalent Conductance per
mg/L (nS/cm at 25 °C)
Ion
Equivalent
Conductance per mg/L
(fAS/cm at 25 °C)
Calcium
2.60
Nitrate
1.15
Magnesium
3.82
Sulfate
1.54
Potassium
1.84
Hydrogen
3.5 x10s 11
Sodium
2.13
Hydroxide
1.92 x 10s
9 Measured values are multiplied by the conversion factor. For ammonia and nitrate, two factors are provided, one
if results are reported as mg N/L, the other if the ion is reported directly.
10 From Hillman et al. (1987).
11 Specific conductance per mole/L, rather than per mg/L.
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Ammonium
4.13
Bicarbonate
0.715
Chloride
2.14
Carbonate
2.82
5.1.3 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities.
Crews will measure water chemistry field measurements with a calibrated multiprobe. The crews will
calibrate the DO, pH, and conductivity prior to each sampling event in the field. Crews will test the
temperature meter against a thermometer that is traceable to the National Institute of Standards (NIST)
at least once per sampling season. Field crews will verify that all sample containers are uncontaminated
and intact, and that all sample labels are legible and intact. A summary of field quality control
procedures for water chemistry is presented in Table 5.8 and a visual description is laid out in Figure 5.1.
Before leaving the field, the crews will:
¦ Check the label to ensure that all written information is complete and legible.
¦ Place a strip of clear packing tape over the label, covering the label completely.
¦ Record the sample ID number assigned to the water chemistry sample on the Sample
Collection Form.
¦ Enter a flag code and provide comments on the Sample Collection Form if there are any
problems in collecting the sample or if conditions occur that may affect sample integrity.
¦ Store the sample on wet ice in a cooler.
¦ Recheck all forms and labels for completeness and legibility.
Table 5.8 Field quality control: water chemistry
Check Description
Frequency
Acceptance Criteria
Corrective Actions
Check calibration of
multiprobe
Prior to each
sampling day
Specific to instrument
Adjust and recalibrate, redeploy gear
Check calibrated
sounding rod
Each site
Depth measurements
for all sampling points
Obtain best estimate of depth where
actual measurement not possible
Check integrity of
sample containers and
labels
Each site
Clean, intact
containers and labels
Obtain replacement supplies
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FIELD MEASUREMENT PROCESS: WATER CHEMISTRY INDICATOR
PRE-DEPARTURE CHECK
Fail
Replace Probe
and/or Instrument
Probe Inspectrion
Electronic Checks
Test Calibration
Pass
1st time
FIELD CALIBRATION
QC CHECK
Fail
QC Sample Measurement
Performance Evaluation
Measurement ^
2nd time
Pass
CONDUCT
MEASUREMENTS
AND RECORD DATA
Qualify Data
QC CHECK
Fail
QC Sample Measurement
Duplicate Measurement
Qualify Data
Pass
Fail
REVIEW
DATA FORM
Qualify Data
Correct Errors
Pass
ACCEPT FOR DATA ENTRY
Figure 5.1 Field measurement process: water chemistry samples
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PREPARE QC SAMPLES
Laboratory Blank
Fortified Sample
Laboratory Split Sample
PREPARE QC SAMPLES
SAMPLEPROCESSING
QC Check Samples (QCCS)
Internal Reference Sample
CALIBRATION
Contamination
or Biased
Calibration
Laboratory\^
Blank >
Pass
Fail
LT-MDL
QCCS
Recheck
LT-MDL QCCS
Insert randomly
into sample batch
Pass
Fail
Calibration
QCCS
Pass ^
SAMPLES
Pass
Accept Batch
for Entry
and Verification
Pass
Fail
Review
Results
Re-Calibrate
Re-analyze
Previous Samples
Calibration
QCCS
Fail
Pass
Qualify batch
for possible
re-analysis
SAMPLES
Pass
Fail
Calibration
QCCS
Figure 5.2 Analysis activities: water chemistry samples
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5.2 Algal Toxins: Microcystin and Cylindrospermopsin
5.2.1 Sample Design and Methods
Detailed sample collection and handling procedures are found in the FOMs.
5.2.2 Pertinent QA/QC Procedures
5.2.2.1 Quality Assurance Objectives
MQOs for absorbances are given in Table 5.9. General requirements for comparability and
representativeness are addressed in Section 2.
Table 5.9 Measurement data quality objectives: microcystin and cylindrospermopsin
Variable or Measurement
Precision
Accuracy
Completeness
Algal Toxin Indicator
±15%12
±25%13
NA
5.2.2.2 QA Values and Objectives
Quality control for the microcystin and cylindrospermopsin indicators are listed in Table 5.10.
Table 5.10 Sample analysis quality control activities: microcystin and cylindrospermopsin
Quality Control
Description and Requirements
Corrective Action
Activity
Kit - Shelf Life
Is within its expiration date listed on kit box.
If kit has expired, then discard or
set aside for training activities.
Kit - Contents
All required contents must be present and in
acceptable condition. This is important
because Abraxis has calibrated the standards
and reagents separately for each kit.
If any bottles are missing or
damaged, discard the kit.
Calibration
All of the following must be met:
o Standard curve must have a
correlation coefficient of >0.99;
o Average absorbance value, A0, for SO
must be >0.80; and
o Standards S0-S5 (S6 for
cylindrospermopsin) must have
decreasing average absorbance
values. That is, if A, is the average of
the absorbance values for S,, then the
absorbance average values must be:
If any requirement fails:
• Results from the analytical run
are not reported.
• All samples in the analytical
run are reanalyzed until
calibtration provides
acceptable results.
12 For algal toxins, the precision for a sample is reported in terms of the percent coefficient of variation (%CV) of its absorbance
values. For the %CV calculation, see the Laboratory Operations Manual. Relative Standard Deviation (RSD) is the same as the
%CV. Because many of the plate reader software programs provides the CV in their outputs, the procedure presents the quality g
control requirement in terms of %CV instead of RSD. I—
<
13 For algal toxins, accuracy is calculated by comparing the average concentration of the kit control with the required range ^
(0.75+/-0.185). Z
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A0 > Ai > A2 > A3 > A4 >A5 > As (for
cylindrospermopsin only)
Kit Control
The average concentration value of the
duplicates (or triplicate) must be within the
range of 0.75 +/- 0.185 ng/L for microcystin
kits and 0.75 +/- 0.15 ng/L for
cylindrospermopsin. That is, results must be
between 0.565 and 0.935 for microcystin and
between 0.60 and 0.90 for cylindrospermopsin
Negative Control
The values for the negative control replicates
must meet the following requirements:
o All concentration values must be <
0.15 ng/L (i.e., the reporting limit);
and
o One or more concentration results
must be nondetectable (i.e., <0.10
Hg/L for microcystin and <0.05 ng/L
for cylindrospermopsin)
If either requirement fails:
• Results from the analytical run
are not reported
• The lab evaluates its
processes, and if appropriate,
modifies its processes to
correct possible
contamination or other
problems.
• The lab reanalyzes all samples
in the analytical run until the
controls meet the
requirements.
Sample
Evaluations
All samples are run in duplicate. Each
duplicate pair must have %CV<15% between
its absorbance values.
If %CV of the absorbances for the
sample>15%, then:
• Record the results for both
duplicates.
• Report the data for both
duplicate results as Quality
Control Failure "QCF"; and
• Re-analyze the sample in a
new analytical run. No
samples are to be run more
than twice.
If the second run passes, then the
data analyst will exclude the data
from the first run. If both runs fail,
the data analyst will determine if
either value should be used in the
analysis (e.g., it might be
acceptable to use data if the CV is
just slightly over 15%).
Results Within
Calibration
Range
All samples are run in duplicate. If both of the
values are less than the upper calibration
range (i.e., 5.0 ng/Lfor undiluted microcystin
samples, 2.0 ng/L for cylindrospermopsin
samples), then the requirement is met.
If one or both duplicates register
as 'HIGH,' then the sample must
be diluted and re-run until both
results are within the calibration
range. No samples are to be run
more than twice.
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External Quality
External QC Coordinator, supported by QC
Based upon the evaluation, the
Control Sample
contractor, provides 1-2 sets of identical
External QC Coordinator may
samples to all laboratories and compares
request additional information
results.
from one or more laboratories
about any deviations from the
Method or unique laboratory
practices that might account for
differences between the
laboratory and others. With this
additional information, the
External QC Coordinator will
determine an appropriate course
of action, including no action,
flagging the data, or excluding
some or all of the laboratory's
data.
5.3 Periphyton
5.3.1 Introduction
Periphyton are diatoms and soft-bodied algae, as well as fungi and bacteria, that are attached or
otherwise associated with channel substrates. Periphyton, in general, can contribute to the physical
stability of inorganic substrate particles, and provide habitat and structure. Periphyton are useful
indicators of environmental condition because they respond rapidly and are sensitive to a number of
anthropogenic disturbances, including habitat destruction, contamination by nutrients, metals,
herbicides, hydrocarbons, and acidification.
5.3.2 Sampling Design and Methods
Detailed sample collection and handling procedures are described in FOM. Field collected periphyton
samples will be subdivided into four samples for diatom identification, chlorophyll a, ash free dry mass,
and metagenomic analysis.
Analysis: Diatom identification samples are preserved, processed, enumerated, and organisms identified
to the lowest possible taxonomic level (genus/species) using specified standard keys and references.
Processing and archival methods are based on a modified USGS NAWQA method (Charles et al. 2003).
Detailed procedures are contained in the LOM.
Chlorophyll a sub-samples will be filtered in the field and analyzed in the laboratory according to the
procedures outlined in the LOM.
AFDM subsamples will be filtered in the field and analyzed in the laboratory according to the procedures
outlined in the laboratory operations manual.
The periphyton metagenomic sample will be collected in the field and shipped to the lab as described in
the FOMs. These samples will be analyzed at an EPA ORD lab, and the ORD lab is developing a separate
QAPP for this work.
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5.3.3 Quality Assurance Objectives
MQOs are given in Table 5.11. The MQOs refer to the diatom ID samples. The QA procedures for
periphyton chlorophyll a and AFDM are identical to the water chemistry chlorophyll a procedures. The
water chemistry labs will perform the sample analysis for all of these samples and follow the same QA
procedures laid out in Section 5.1. General requirements for comparability and representativeness are
addressed in Section 2. The MQOs for the periphyton meta-genomics subsample will be found in the
ORD QAPP. Three different measurement data quality objectives are used in evaluating diatom data:
For diatoms - Percent Taxonomic Disagreement (PTD) and Percent Difference in Enumeration (PDE)).
Targets are shown in
Table 5.11
Table 5.11 Measurement data quality objectives: diatom periphyton
Variable or Measurement
Precision
Accuracy
Completeness
Enumeration
75% a
85% b
99%
Identification
75% a
85% b
99%
a As measured by (100%-PTD);b As measured by (100%-PDE)
5.3.4 Pertinent QA/QC Procedures for ID Periphyton Sample
Quality control activities and data validation are summarized in Table 5.12 and Table 5.13. Equations
used are presented below. Percent disagreement in enumeration (PDE): measure of taxonomic
precision for diatoms comparing the number of organisms, rii, counted in a sample by the primary
taxonomist with the number of organisms, n2, counted by the secondary taxonomist.
PDE =
Vh -«2
nl + n2
100
Percent taxonomic disagreement (PTD): measure of taxonomic precision for diatoms comparing the
number of agreements (positive comparisons, compp0s) of the primary taxonomist and internal or
external QC taxonomists. In the following equation, N is the total number of organisms in the larger of
the two counts.
PTD =
1-
compp
N
: 100
5.3.4.1 Internal Taxonomic QC
Before samples are counted and identified, a lead taxonomist will develop pre-count regional voucher
flora for the diatom taxa. This process is described in detail in section 10.7 of the NRSA LOM.
The internal QC taxonomist will randomly select 10% of the diatom slides for an independent count and
identification by another Internal QC Taxonomist. As appropriate, calculate the PctDiff, PDE, and PTD. If
any do not meet the QA requirements, perform a third count and reidentification for the sample. The
process for selecting, at random, which samples will be re-identified and counted is described in 10.7.3
in the LOM.
Table 5.12 Quality control: all activities
Check or Sample
Frequency
Acceptance Criteria
Corrective Action
i/i
QC
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Description
Internal QC Taxonomist
verifies that diatom
slide is appropriate for
diatom analysis
All samples
No obvious problems such
as bubbles under the
coverslip
Slide is discarded and replaced
with a new slide
Duplicate identification
for Internal QC
10% of samples per
taxonomistwill be re-
analyzed by a second
taxonomist. This
process randomly
selects those samples
to be re-identified.
PctDiff<50% (soft algae)
PDE < 15% (diatoms)
PTD < 25% (diatoms)
If any criterion is exceeded,
perform a third count and
reidentification for the sample.
Use of
widely/commonly
accepted taxonomic
references by all NRSA
labs
For all identifications
All keys and references
used by each lab must be
on bibliography prepared
by one or more additional
NRSA labs or in BioData
(see Section 10.7 in the
LOM for retrieval
instructions). This
requirement demonstrates
the general acceptance of
the references by the
scientific community.
If a lab proposes to use other
references, the lab must identify
them in the database.
Prepare reference
collection
Each new taxon per
laboratory
Complete reference
collection to be
maintained by each
individual laboratory
Internal Taxonomy QC Officer
periodically reviews data and
reference collection to ensure
reference collection is complete
and identifications are accurate
Table 5.13 Data validation: diatom
Check or Sample
Description
Frequency
Acceptance Criteria
Corrective Action
Taxonomic
All data
Taxa known to occur in given
Second or third identification
"reasonable-ness"
rivers or streams or geographic
by expert in that taxon
checks
area
5.3.5 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOM. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Flag codes are
recorded and comments provided on the Sample Collection Form to denote any problems encountered
in collecting the sample or the presence of any conditions that may affect sample integrity. A summary
of field quality control procedures for periphyton samples is presented in Table 5.14.
Table 5.14 Sample collection and field processing quality control: periphyton
i/i
QC
O
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Quality Control Activity
Description and Requirements
Corrective Action
Check integrity of sample
containers and labels
Clean, intact containers and labels
Obtain replacement
supplies
Sample Storage (field)
Store samples on wet ice and in a dark place (cooler)
Discard and recollect
sample
Homogenize composite
Thoroughly mix samples before processing to ensure that
the sample material is evenly distributed throughout the
composite.
Discard and recollect
sample
Processing samples in the
field
Use the appropriate filter or preservative for each type of
sample prepared from the composite.
Discard and prepare a
replacement subsample
from the composite
Holding times
The frozen chlorophyll and AFDM filters are shipped
immediately on wet ice. The ID sample preserved with
formalin solution is held in a refrigerator and must be
shipped on wet ice within 2 weeks of collection. The
FROZEN periphyton meta-genomic samples must be
shipped within 1 week of collection on dry ice.
Qualify samples
5.4 Benthic Macroinvertebrates
5.4.1 Introduction
The benthic macroinvertebrate assemblage found in sediments and on substrates of streams and rivers
reflect an important aspect of the biological condition of the stream or river. 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 macroinvertebrate indicators are to
detect stresses on community structure in rivers and streams and to assess and monitor the relative
severity of those stresses. The benthic macroinvertebrate indicator procedures are based on various
bioassessment literatures (Barbour et al. 1999, Hawkins et al. 2000, Peck et al. 2003).
5.4.2 Sampling Design and Methods
Detailed sample collection and handling procedures are described in the FOM.
Analysis: Community identification samples are preserved, processed, enumerated, and organisms
identified to the lowest possible taxonomic level (generally genus) using specified standard keys and
references. Detailed procedures are contained in the LOM.
5.4.3 Quality Assurance Objectives
Measurement quality objectives (MQOs) are given in
Table 5.15, Section 2.2. General requirements for comparability and representativeness are addressed
in Section 2. The MQOs given in Section 2.2represents 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 sorting and picking accuracy (defined and procedure in LOM Section 4) is
estimated from examinations (repicks) of randomly selected residues by experienced taxonomists.
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Table 5.15 Measurement data quality objectives: benthic macroinvertebrates
Variable or Measurement
Precision
Accuracy
Completeness
Sort and Pick
N/A
90% 14
99% 15
Identification
85% 16
95% 13
99%
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.
5.4.4 Pertinent QA/QC Procedures
5.4.4.1 Sorting and Subsampling QC
¦ A QC Analyst will use 6-10X microscopes to check all sorted grids from the first five samples
processed by a sorter to ensure that each meets the acceptable criteria for percent sorting
efficiency (PSE), which is 90%. This will not only apply to inexperienced sorters, but also to
those initially deemed as "experienced." Qualification will only occur when sorters achieve
PSE > 90% for five samples consecutively.
¦ The QC Officer will calculate PSE for each sample as follows:
Equation 5.2 Percent sorting efficiency (PSE).
PSE = ——— x 100
A + B
Where: A = number of organisms found by the primary sorter, and B = number of
recoveries (organisms missed by the primary sort and found during the QC check).
¦ If the sorting efficiency for each of these five consecutive samples is > 90% for a particular
individual, this individual is considered "experienced" and can serve as a QC Officer. In the
event that an individual fails to achieve > 90% sorting efficiency, he or she will be required
to sort an additional five samples to continue to monitor their sorting efficiency. However, if
he or she shows marked improvement in sorting efficiency prior to completion of the next
five samples, achieving > 90% sorting efficiency, the QA Officer may, at his/her discretion,
consider this individual to be "experienced". Do not calculate PSE for samples processed by
more than one individual.
14Taxonomic accuracy and sorting accuracy as calculated using equation 2.11 in Section 2.2
15 Sample completeness as calculated using equation 2.12 in Section 2.2
16 Taxonomic precision as calculated using equation 2.10 in Section 2.2
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¦ After individuals qualify, 10% (1 out of 10, randomly selected) of their samples will be
checked.
¦ If an "experienced" individual fails to maintain a > 90% PSE as determined by QC checks, a
QC Officer will perform QC checks on every grid of five consecutive samples until a > 90%
sorting efficiency is achieved on all five. During this time, that individual will not be able to
perform QC checks.
5.4.5 TaxonomicQC
5.4.5.1 Internal Taxonomic QC
As directed by the EPA QA Coordinator, an in-house QC Analyst will conduct an internal 10% re-
identification of all samples identified by that laboratory to ensure that each meets the acceptable
criteria for percent identification efficiency which is 85%.
If the individual fails to maintain a > 85% identification as determined by QC checks, previous samples
will be re-counted and identified.
5.4.5.2 External Taxonomic QC
¦ Upon receipt of the data, the EPA QA Coordinator for macroinvertebrates will randomly
select 10% of the samples. The EPA QA Coordinator will then have the original laboratory
send those samples to a QC taxonomist (another experienced taxonomist who did not
participate in the original identifications). The original laboratory will complete a chain-of-
custody form and send with the samples and notify NARS IM
¦ The QC taxonomist will perform whole-sample re-identifications, taking care to ensure
inclusion of all slide-mounted specimens and completing another copy of the Benthic
Macroinvertebrate Taxonomic Bench Sheet for each sample. The QC taxonomist will label
each bench sheet with the term "QC Re-ID." As each bench sheet is completed, the QC
taxonomist will fax it to the NARS QA Coordinator.
¦ The EPA QA Coordinator will compare the taxonomic results (counts AND identifications)
generated by the primary and QC taxonomists for each sample and calculate percent
difference in enumeration (PDE) and percent taxonomic disagreement (PTD) as measures of
taxonomic precision (Stribling et al. 2003) as follows:
Equation 5.3 Percent difference in enumeration (PDE).
k ~n0\
PDE = ^— —xlOO
nx + n2
Where: n 1 is the number of specimens counted in a sample by the first taxonomist and
n2 is the number of specimens counted by the QC taxonomist.
¦ The recommendation for PDE is 5% or less.
Equation 5.4 Percent taxonomic disagreement (PTD).
PTD =
1-
comPPoS
N
: 100
Where: comppos is the number of agreements (positive comparisons) and N is the total h
number of specimens in the larger of the two counts. y
84
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¦ A PTD of 15% or less is recommended for taxonomic difference (overall mean < 15% is
acceptable). The NRSA QA Officer will examine individual samples exceeding 15% for
taxonomic areas of substantial disagreement, and investigate the reasons for disagreement.
A reconciliation call between the primary and secondary taxonomist will facilitate this
discussion of samples that do not meet specified criteria.. The NRSA QA officer, along with
the QC taxonomist and the primary taxonomist, will investigate results greater than this
value and they will note them for indication of error patterns or trends.
¦ Corrective actions include determining problem areas (taxa) and consistent disagreements
and addressing problems through taxonomist interactions. These actions help to rectify
disagreements resulting from identification to a specific taxonomic level.
5.4.6 Taxonomic QC Review & Reconciliation
The EPA QA Coordinator prepares a report or technical memorandum to quantify aspects of taxonomic
precision, assess data acceptability, highlight taxonomic problem areas, and provide recommendations
for improving precision. This report is submitted to the HQ Project Management Team, with copies sent
to the primary and QC taxonomists. Another copy is maintained in the project file. Significant
differences may result in the re-identification of samples by the primary taxonomist and a second QC
check by the secondary taxonomist.
Each laboratory prepares reference/ voucher samples. These samples will be identified and digitally
referenced (a photograph with taxonomic information superimposed on the photograph and in the file
name) and will be included in an electronic file folder on the NARS Sharefile. All samples are stored at
the laboratory until the Project Lead notifies the lab regarding disposition.
Table 5.16 Laboratory quality control: benthic macroinvertebrates
Check or Sample Frequency Acceptance Criteria Corrective Action
Description
SAMPLE PROCESSING AND SORTING
Sample pickate
examined by
different analyst
within lab
10% of all
samples
completed per
analyst
PSE > 90%
If < 90%, examine all residuals
of samples by that analyst and
retrain analyst
Sorting QC Officer
counts number of
organisms not
found in sorted
grids
All samples
Sorter achieves PSE >90% in
5 consecutive samples.
Sorter is now considered
"experienced"
Sorting QC Officer checks all
samples until acceptance criteria
met
Sorting QC Officer
counts number of
organisms not
found in sorted
grids for
experienced sorters
1 in 10 samples
completed per
sorter
Sorter achieves PSE >90%
If <90%, examine all sorted grids in
samples assigned to sorter since
last achieving proficiency (i.e.,
PSE>90%). Sorter loses
"experienced" status and must
again show proficiency by
achieving PSE >90% in 5
consecutive samples. If the sorter
shows marked improvement in
their sorting efficiency prior to
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completion of the next five
samples, the Sorting QC Officer
may, at his/her discretion, consider
this individual to be "experienced"
and check only 1 in the next 10
samples.
External QC
Coordinator
evaluates grid and
quarter data to
determine if the
sample was well
mixed as
demonstrated by
consistency in
counts between
grids (or quarters)
All grids and
quarters within
each sample
Sorter demonstrates relative
consistency for 90% of
assigned samples
If <90%, evaluate whether: 1) the
sorter's consistency is similar to
other sorters; or 2) few samples
were assigned the sorter. If neither
explanation applies, EPA's External
QC Coordinator contacts the
laboratory to discuss possible
corrective action (e.g., resorting of
sorter's samples)
IDENTIFICATION
Duplicate
identification by
Internal Taxonomy
QC Officer
1 in 10 samples per
taxonomist
PTD <15%
If PTD >15%, reidentify all samples
completed by that taxonomist
since last meeting the acceptance
criteria, focusing on taxa of
concern
Independent
identification by
outside, expert,
taxonomist
All uncertain taxa
Uncertain identifications to
be confirmed by expert in
particular taxa
Record both tentative and
independent IDs
External QC
10% of all
samples
completed per
laboratory
PDE <5%
PTD < 15%
If PDE > 5%, implement
recommended corrective
actions.
If PTD > 15%, implement
recommended corrective
actions.
Use of
widely/commonly
accepted taxonomic
references by all
NRSA labs
For all
identifications
All keys and references used
by each lab must be on
bibliography prepared by
one or more additional
NRSA labs or in WQX (see
Section 4.4.1 for retrieval
instructions). This
requirement demonstrates
the general acceptance of
the references by the
scientific community.
If a lab proposes to use other
references, the lab must obtain
prior permission from Project QA
Officer before submitting the data
with the identifications based upon
the references.
Prepare reference
collection
Each new taxon
per laboratory
Complete reference
collection to be maintained
by each individual
Internal Taxonomy QC Officer
periodically reviews data and
reference collection to ensure
reference collection is complete
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laboratory
and identifications are accurate
DATA VALIDATION
Taxonomic
"reasonable-
ness" checks
All data sheets
Taxa known to occur in
given rivers or streams or
geographic area
Second or third identification by
expert in that taxon
5.4.7 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Field Crews enter a
flag code and provide comments on the Sample Collection Form if there are any problems in collecting
the sample or if conditions occur that may affect sample integrity. Specific quality control measures for
field operations are listed in Table 5.17.
Table 5.17 Sample collection and field processing quality control: benthic macroinvertebrates
Quality Control Activity
Description and Requirements
Corrective Action
Check integrity of
sample containers and
labels
Clean, intact containers and labels
Obtain replacement
supplies
Sample Collection
Keep the individual benthic macroinvertebrate subsamples wet
while in the sieve bucket as each subsequent subsample is
collected.
Discard and recollect
sample if sample is not
preserved
Sample Collection
Carry a small amount of ethanol to immediately preserve larger
predaceous invertebrates to reduce the chance that other
specimens will be consumed or damaged.
Qualify samples
Sample Processing
(field)
Preserve with 95% ethanol. Fill jarsl/3 full of material to reduce
the chance of organisms being damaged.
Qualify sample. If
sample is deteriorated,
discard sample and
recollect.
Sample Storage (field)
Store benthic samples in a cool, dark place until shipment to
analytical lab
Discard and recollect
sample
Holding time
Preserved samples can be stored indefinitely; periodically check
jars and change the ethanol if sample material appears to be
degrading.
Qualify samples
5.5 Fish Assemblage
5.5.1 Introduction
Monitoring of the fish assemblage is an integral component of many water quality management
programs. The assessment will measure specific attributes of the overall structure and function of the
ichthyofaunal community to evaluate biological integrity and water quality.
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5.5.2 Sampling Design and Methods
Detailed sample collection and handling procedures are described in the FOMs.
Analysis: Community identification samples are preserved, processed, enumerated, and organisms
identified to the lowest possible taxonomic level (generally genus) using specified standard keys and
references. Detailed procedures are contained in the LOM.
5.5.3 Quality Assurance Objectives
MQOs are given in Table 5.18. General requirements for comparability and representativeness are
addressed in Section 2. Precision is calculated as percent efficiency, estimated from independent
identifications of organisms in randomly selected samples. The MQO for accuracy is evaluated by having
individual specimens representative of selected taxa identified by recognized experts.
Table 5.18 Measurement data quality objectives: fish community
Variable or Measurement
Precision
Accuracy
Completeness
Identification
85%
85%17
99%
5.5.4 Pertinent QA/QC Procedures
¦ The EPA Project QA Officer will randomly select 10% of the samples for QA analysis. The EPA
Project QA Officer will then have the field crews voucher samples in the field and send them
to a QCtaxonomist (another experienced taxonomist who did not participate in the original
identifications). The field crew and laboratory will complete a chain-of-custody form and
send with the samples.
¦ The QC taxonomist will perform whole-sample re-identifications, taking care to ensure
inclusion of all samples and completing fish voucher Taxonomic Bench Sheet for each
sample. As each bench sheet is completed, email it to the Project Lead.
¦ The EPA Project QA officer will compare the taxonomic results (counts AND identifications)
generated by the field crews and QC taxonomists for each sample and calculate percent
difference in enumeration (PDE) and percent taxonomic disagreement (PTD) as measures of
taxonomic precision (Stribling et al. 2003) as follows:
Equation 5.5 Percent difference in enumeration (PDE).
In, -nn\
PDE=^— —xlOO
Oj + n2
Where: rii is the number of specimens counted in a sample by the field taxonomist
and n2 is the number of specimens counted by the QC taxonomist.
Equation 5.6 Percent taxonomic disagreement (PTD).
PTD =
COmPpos
1-
N
: 100
17 Taxonomic accuracy as calculated as described in 3.2.3
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Where: compp0s is the number of agreements (positive comparisons) and N is the
total number of specimens in the larger of the two counts.
¦ The recommendation for PDE is 5% or less.
¦ A PTD of 15% or less is recommended for taxonomic difference (overall mean < 15% is
acceptable). Individual samples exceeding 15% are examined for taxonomic areas of
substantial disagreement, and the reasons for disagreement investigated. A reconciliation
call between the primary and secondary taxonomist will facilitate this discussion. Results
greater than this value are investigated and logged for indication of error patterns or trends.
¦ Corrective actions include determining problem areas (taxa) and consistent disagreements
and addressing problems through taxonomist interactions. These actions help to rectify
disagreements resulting from identification to a specific taxonomic level.
5.5.5 Taxonomic QC Review & Reconciliation
The EPA Project QA Officer prepares a report or technical memorandum to quantify aspects of
taxonomic precision, assess data acceptability, highlight taxonomic problem areas, and provide
recommendations for improving precision. This report is submitted to the HQ Project Management
Team, with copies sent to the field and QC taxonomists. Another copy is maintained in the project file.
Significant differences may result in the re-identification of samples by the primary taxonomist and a
second QC check by the secondary taxonomist.
Each laboratory prepares reference/ voucher samples. These samples will be identified and digitally
referenced (a photograph with taxonomic information superimposed on the photograph and in the file
name) and will be included in an electronic file folder on the NARS Sharefile. All samples are stored at
the laboratory until the Project Lead notifies the lab regarding disposition.
5.5.6 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities.
An experienced fish taxonomist will identify the collected fish specimens in the field. All specimens must
be identified by common name as listed in Appendix D of the FOMs. The biologist may choose to retain
certain specimens for identification or verification in the laboratory. These samples are retained at the
discretion of the fish taxonomist and are separate from the official voucher specimens that must be
collected at 10% of each field crews' sites to be re-identified by an independent taxonomist.
A summary of field quality control procedures for the fish community indicator is presented in Table
5.19.
Table 5.19 Sample collection and field processing quality control: fish community
Quality Control Activity
Description and Requirements
Corrective Action
Check integrity of
Clean, intact containers and labels
Obtain replacement supplies
sample containers and
labels
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Quality Control Activity Description and Requirements Corrective Action
Set up electrofishing
equipment
An experienced fisheries biologist sets up the
unit. Determine if appropriate fish capture
results are achieved.
If results are poor, adjustments are
made to the pulse width and voltage
to sample effectively and minimize
injury/mortality.
Determine if electroshocker is
functioning properly; if not use
backup (e.g. generator).
Comparable effort
Reset unit clock to document button time (700
seconds per transect).
If button time is not metered,
estimate it with a stop watch and
flag the data.
Comparable effort
No more than 1 person is netting at any one
time.
Limit number of crew members with
nets.
Field Processing
Fish should be released in a location that
prevents the likelihood of their recapture.
Flag data if fish were released in area
where recapture was possible
Field Processing
The fisheries biologist will identify specimens in
the field using a standardized list of common
names.
Indicator lead will contact crews to
resolve discrepancies in names.
Sample Collection
The biologist may retain uncertain specimens for
ID or verification in the laboratory. These
samples are retained at the discretion of the
biologist and are separate from the official
voucher specimens that must be collected at
10% of each field crews' sites to be re-identified
by an independent taxonomist.
Flag data. If crew does not collect
voucher at specified site, NRSA QA
Officer will identify additional QA
sites for collection.
Sample Collection -
Taxonomic QC samples
EPA selected sites designated for independent,
taxonomic confirmation offish assemblage
taxonomy. A minimum of 1 complete voucher is
required for each field taxonomist and will
consist of either preserved specimen(s) or digital
images representative of all species in the
sample, even common species.
If crew does not collect voucher at
specified site, EPA Project QA Officer
will identify additional QA sites for
collection.
Sample Preservation
Fish retained for laboratory ID or vouchers are
preserved with 10% buffered formalin. All
personnel must read and should follow the
appropriate guidelines for handling formalin in
the field. An MSDS can be found at the following
website.
(http://www.osha.gov/pls/oshaweb/owadisp.sh
ow document?p id=10076&p table=standards)
If vouchers are not adequately
preserved, new vouchers must be
collected at the next field site.
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5.5.6.1 Voucher Specimens
Approximately 10% of each field crews' sites will be randomly pre-selected for re-identification by an
independent QA/QC taxonomist. These samples will be selected in coordination from the EPA Project
QA Officer. A minimum of one complete voucher is required for each person performing field taxonomy
and will consist of either preserved specimen(s) or digital images representative of all species in the
sample, even common species. Multiple specimens per species can be used as vouchers, if necessary
(i.e., to document different life or growth stages, or sexes). Note that a complete sample voucher does
not mean that all individuals of each species will be vouchered, only enough so that independent
verification can be achieved.
For species that are retained, specimen containers should be labeled with the sample number, site ID
number, site name, and collection date. There should be no taxonomic identification labels in or on the
container.
Digital images should be taken as voucher documentation for species that are recognized as Rare.
Threatened, or Endangered (RTE) they should not be harmed or killed. Very common and well-known, or
very large-bodied species may also be recorded by digital images; however, these can be preserved at
the discretion of the taxonomist. Labeling, within the image, should be similar to that used for preserved
samples and not include taxonomic identification. Guidance for naming photo files is provided below in
the photovouchering section.
5.5.6.2 Photovouchering
Digital imagery should be used for fish species that cannot be retained as preserved specimens (e.g., RTE
species; very large bodied; or very common). Views appropriate and necessary for an independent
taxonomist to accurately identify the specimen should be the primary goal of the photography.
Additional detail for these guidelines is provided in Stauffer et al. (2001), and the Field Logistics
Coordinator will distributed to all field crews electronically via the sharefile site. The recommended
specifications for digital images to be used for photovouchering include: 16-bit color at a minimum
resolution of 1024x768 pixels; macro lens capability allowing for images to be recorded at a distance of
less than 4 cm; and built-in or external flash for use in low-light conditions. Specimens should occupy as
much of the field of view as possible, and the use of a fish board is recommended to provide a reference
to scale (i.e., ruler or some calibrated device) and an adequate background color for photographs.
Information on Station ID, Site Name, Date and a unique species ID (i.e., A, B, C, etc.) should also be
captured in the photograph, so that photos can be identified if file names become corrupted. All
photovouchered species should have at least a full-body photo (preferably of the left side of the fish)
and other zoom images as necessary for individual species, such as lateral line, ocular/oral orientation,
fin rays, gill arches, or others. It may also be necessary to photograph males, females, or juveniles.
Images should be saved in medium- to high-quality jpeg format, with the resulting file name of each
picture noted on the Fish Collection Form. It is important that time and date stamps are accurate as this
information can also be useful in tracking the origin of photographs. It is recommended that images
stored in the camera be transferred to a PC or storage device at the first available opportunity. At this
time, rename the original files to include the site ID, visit number, voucher specimen tag number, and
photo sequence (e.g., NRS18_WY_10001_Vl_tag01a.jpg). Field crews should maintain files for the
duration of the sampling season. Notification regarding the transfer of all images to the existing
database will be provided at the conclusion of the sampling.
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5.5.7 Quality Control Procedures: Laboratory Operations (Voucher Specimens)
5.5.7.1 Sample Receipt and Processing
QC activities associated with sample receipt and processing are presented in
Table 5.20. The communications center and information management staff is notified of sample receipt
and any associated problems as soon as possible after samples are received.
Table 5.20 Sample receipt and processing quality control: fish community
Quality Control
Activity Description and Requirements Corrective Action
Sample Log-in
Upon receipt of a sample shipment, laboratory
personnel check the condition and identification of
each sample against the sample tracking record.
Discrepancies, damaged, or missing
samples are reported to the IM
staff and indicator lead
Sample Storage
Samples Stored in formalin in dark room or
photovouchers kept on external hard drive
Qualify sample as suspect for all
analyses
Holding time
Not Applicable
Qualify samples
Preservation
Vouchers are stored on formalin
Qualify samples
5.5.7.2 Analysis of Samples
Specific quality control measures for laboratory operations are listed in Table 5.21 and
Table 5.22.
Table 5.21 Laboratory quality control: fish voucher taxonomic identification
Check or Sample
Frequency
Acceptance Criteria
Corrective Action
Description
Use
widely/commonly
accepted taxonomic
references
All identifications
All keys and references
used must be on
bibliography prepared
by the field and QC
taxonomists
For all field crew
identifications, EPA will
convert field crew's use of
common names to
taxonomic references
Independent
identification by
outside, expert,
laboratory fish
taxonomist ("QC
taxonomist")
When field
taxonomist cannot
identify specimen
Identification by QC
taxonomist (who must
be a different individual
than the field
taxonomist)
Replace field crew's
"unknown" identification
with determination by QC
taxonomist
External QC
Approximately 10% of
all sites sampled by
each field taxonomist
PTD < 15%
If PTD > 15%, review data
for possible explanations;
otherwise, insert data
qualifier for field crew
identifications
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Calculate average
PTD for field
taxonomist
Each sample
submitted to the QC
taxonomist
PTD < 15%
If PTD > 15%, consult with
NARS QA Officer for
appropriate action.
Conduct assistance
visit
EPA may choose to
visit any laboratory
Visit conducted using
checklist
Performance and any
recommended
improvements described in
debrief with laboratory
staff
Table 5.22 Data validation: fish voucher taxonomic identification
Check or Sample
Description
Frequency
Acceptance Criteria
Corrective Action
Data Validation:
All data sheets
Genera known to occur
Data qualifiers on data
Taxonomic
in given rivers/streams
that fail reasonableness
"reasonable-ness"
or geographic area
check. No further
checks
corrective action steps.
5.6 Physical Habitat Quality
5.6.1 Introduction
Naturally occurring differences in physical habitat structure and associated hydraulic characteristics
among surface waters contributes too 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.
For the NRSA, indicators derived from data collected on physical habitat quality will be used to help
explain or characterize stream and river conditions relative to biological response and trophic state
indicators. Specific groups of physical habitat attributes important in stream and river 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 rivers and to monitor biologically relevant changes
in habitat quality and intensity of disturbance.
5.6.2 Sampling Design and Methods
As the physical habitat indicator is based on field measurements and observations, there is no sample
collection associated with this indicator. At NRSA 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 and river 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.
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Field measurements, observations, and associated methodology for the protocol are summarized in
Table 5.23. Detailed procedures for completing the protocols are provided in the FOM.
There are no sample collections or laboratory analyses associated with the physical habitat
measurements.
Table 5.23 Field measurement methods: physical habitat
Variable or
Measurement Units Summary of Method References
THALWEG PROFILE
Thalweg depth
cm
Measure max depth at 100-150 points for wadeable or 200
points for non-wadeable along reach with surveyor's rod
or sonar equipment
US EPA
http://www.epa.g
ov/emap/
Wetted width
0.1m
Measure wetted width with range finder or measuring
tape on perpendicular line to mid-channel line
US EPA
http://www.epa.g
ov/emap/
Habitat class
none
Visually estimate channel habitat using defined class
descriptions
Frissell et al.,
1986
WOODY DEBRIS TALLY
Large woody
debris
# of pieces
Use pole drag and visually estimate amount of woody
debris in baseflow channel using defined class descriptions
Robison and
Beschta, 1990
CHANNEL AND RIPARIAN CROSS-SECTIONS
Slope and
bearing
%/ degrees
Backsight between cross-section stations using clinometer,
rangefinder, compass, surveyor's level & tripod
Robison &
Kaufmann, in
prep.; Stack, 1989
Substrate size
mm
At 5 points on cross section, estimate size of one selected
particle using defined class descriptions
Wolman, 1954;
Bain et al., 1985;
Plafkin et al.,
1989
Bank angle
degrees
Use clinometer and surveyors rod to measure angle
Platts et al., 1983
Bank incision
0.1m
Visually estimate height from water surface to first terrace
of floodplain
US EPA
http://www.epa.g
ov/emap/
Bank undercut
cm
Measure horizontal distance of undercut
US EPA
http://www.epa.g
ov/emap/
Bankfull width
0.1m
Measure width at top of bankfull height
US EPA
http://www.epa.g
ov/emap/
Bankfull height
0.1m
Measure height from water surface to estimated water
surface during bankfull flow
US EPA
http://www.epa.g
ov/emap/
Canopy cover
points of
inter-
section
Count points of intersection on densiometer at specific
points and directions on cross-section
Lemmon, 1957;
Mulvey et al.,
1992
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Variable or
Measurement
Units
Summary of Method
References
Riparian
vegetation
structure
percent
Observations of ground cover, understory, and canopy
types and coverage of area 5 m on either side of cross
section and 10 m back from bank
US EPA
http://www.epa.g
ov/emap/
Fish cover, algae,
macrophytes
percent
Visually estimate in-channel features 5 m on either side of
cross section
US EPA
http://www.epa.g
ov/emap/
Human influence
none
Estimate presence/absence of defined types of
anthropogenic features
US EPA
http://www.epa.g
ov/emap/
STREAM DISCHARGE
Discharge
m/s or
L/min.
Velocity-Area method, Portable Weir method, timed
bucket discharge method
Linsley et al.,
1982
5.6.3 Quality Assurance Objectives
Measurement data quality objectives (measurement DQOs or MQOs) are given in Table 5.24. General
requirements for comparability and representativeness are addressed in Section 2. The MQOs given in
Table 5.24 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., NRSA sites,
revisit sites, state comparability sites). 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.
Table 5.24 Measurement data quality objectives: physical habitat
Variable or Measurement
Precision
Accuracy
Completeness
Field Measurements and Observations
±10%
NA
90%
Map-Based Measurements
±10%
NA
100%
NA = not applicable
5.6.4 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Specific quality control
measures are listed in Table 5.25 for field measurements and observations.
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Table 5.25 Field quality control: physical habitat
Check Description
Frequency
Acceptance Criteria
Corrective Actions
Check totals for cover class
Each
Sum must be reasonable (best
Repeat observations
categories (vegetation type, fish
transect
professional judgment)
cover)
Check completeness of thalweg
depth measurements
Each site
Depth measurements for all
sampling points
Obtain best estimate of
depth where actual
measurement not
possible
5.7 Fecal Indicator: Enterococci
5.7.1 Introduction
The primary function of collecting water samples for Pathogen Indicator Testing is to provide a relative
comparison of fecal pollution indicators for national rivers and streams. The concentration of
Enterococci (the current bacterial indicator for fresh and marine waters) in a water body correlates with
the level of more infectious gastrointestinal pathogens present in the water body. While some
Enterococci are opportunistic pathogens among immuno-compromised human individuals, the presence
of Enterococci is more importantly an indicator of the presence of more pathogenic microbes (bacteria,
viruses and protozoa) associated with human or animal fecal waste.
5.7.2 Sampling Design and Methods
Detailed sample collection and handling procedures are described in the FOMs.
5.7.3 Pertinent QA/QC Procedures
5.7.3.1 Quality Assurance Objectives
Measurement quality objectives (MQO) are given in Table 5.26. General requirements for comparability
and representativeness are addressed in Section 2.
Table 5.26 Measurement data quality objectives: pathogen-indicator DNA sequences
Variable or Measurement18
Method Precision
Method Accuracy
Completeness
SPC & ENT DNA sequence numbers of
Calibrators & Standards by AQM
RSD=50%
50%
95%
ENT CCEs by dCt RQM
RSD = 70%
35%
95%
ENT CCEs by ddCt RQM
RSD = 70%
50%
95%
18 AQM = Absolute Quantitation Method; dCt=delta (change) of control treated; RQM = Relative Quantitation
Method; SPC = Sample Processing Control (Salmon DNA/Sketa) (note - Sketa is a reagent); CCEs = Calibrator Cell
Equivalents; RSD= Relative Standard Distribution; ENT=Enterococci
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5.7.3.2 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Specific quality control
measures are listed in
Table 5.27 for field measurements and observations.
Table 5.27 Sample collection and field processing quality control: fecal indicator
Quality Control Activity Description and Requirements Corrective Action
Check integrity of sample
containers and labels
Clean, intact containers and labels
Obtain replacement
supplies
Sterility of sample
containers
Sample collection bottle and filtering apparatus are sterile
and must be unopened prior to sampling. Nitrile gloves
must be worn during sampling and filtering
Discard sample and
recollect in the field.
Sample Collection
Collect sample at the last transect to minimize holding time
before filtering and freezing
Discard sample and
recollect in the field.
Sample holding
Sample is held in a cooler on wet ice until filtering
Discard sample and
recollect in the field.
Field Processing
Sample is filtered and filters are frozen on dry ice within 6
hours of collection
Discard sample and
recollect in the field
Field Blanks
Field blanks must be filtered at 10% of sites
Review blank data and
flag sample data.
5.7.3.3 Quality Control Procedures: Laboratory Operations
Specific quality control measures for laboratory operations are listed in Table 5.28.
Table 5.28 Laboratory quality control: pathogen-indicator DNA sequences
Check or Sample Frequency Acceptance Criteria Corrective Action
Description
SAMPLE PROCESSING
Re-process sub-
samples
(Lab Duplicates)
10% of all
samples
completed per
laboratory
Percent Congruence <70%
RSD
If >70%, re-process additional sub-
samples
qPCR ANALYSIS
Duplicate analysis
by different
biologist within lab
10% of all
samples
completed per
laboratory
Percent Congruence <70%
RSD
If >70%, determine reason and if
cause is systemic, re-analyze all
samples in question.
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Check or Sample
Description
Frequency
Acceptance Criteria Corrective Action
Use single stock of
E. faecalis calibrator
For all qPCR
calibrator
samples for
quantitation
All calibrator sample Cp (Ct)
must have an RSD < 50%.
If calibrator Cp (Ct) values exceed an
RSD value of 50% a batch's
calibrator samples shall be re-
analyzed and replaced with new
calibrators to be processed and
analyzed if RSD not back within
range.
DATA PROCESSING & REVIEW
100% verification
and review of qPCR
data
All qPCR
amplification
traces, raw and
processed data
sheets
All final data will be checked
against raw data, exported
data, and calculated data
printouts before entry into
LI MS and upload to Corvallis,
OR database.
Second tier review by contractor
and third tier review by EPA.
5.7.4 Data Management, Review, and Validation
Checks made of the data in the process of review, verification, and validations are summarized in Table
5.29. 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 NRSA Project
Coordinator. Once data have passed all acceptance requirements, data is submitted to NARS IM and
coordinated with the NRSA data Information Coordinator.
Table 5.29 Data validation quality control: fecal indicator
Check Description
Frequency
Acceptance Criteria
Corrective Action
Duplicate sampling
Duplicate composite
samples collected at
10% of sites
Measurements should be
within 10 percent
Review data for reasonableness;
determine if acceptance criteria
need to be modified
Field filter blanks
Field blanks filtered at
10% of sites
Measurements should be
within 10 percent
Review data for reasonableness;
determine if acceptance criteria
need to be modified
5.8 Whole Fish Tissue Samples for Fillet Analysis
5.8.1 Introduction
Fish are time-integrating indicators of persistent pollutants, and contaminant bioaccumulation in fish
tissue has important human health implications for people who consume fish. The objective for whole
fish tissue sampling is to collect one whole fish sample from each of the 478 target river sites selected
for whole fish tissue sampling. Analysis of fillet tissue samples prepared from the whole fish samples will
provide information on the national distribution of toxic chemicals (mercury, PCBs, and PFCs) in fish
from rivers of the contiguous United States.
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5.8.2 Sampling Design and Methods
Detailed whole fish tissue sample collection and handling procedures are described in the FOMs. These
procedures are based on methods applied in EPA's National Study of Chemical Residues in Lake Fish
Tissue (USEPA 2009) and described in EPA's Guidance for Assessing Chemical Contaminant Data for Use
in Fish Advisories, Volume 1 (Third Edition) (USEPA 2000).
Whole fish tissue samples will be collected with the same gear used to collect the fish assemblage
samples. Collection of individual specimens for whole fish samples occurs anywhere in the sample reach
during the fish assemblage sampling. Ideally, each fish sample will contain 5 fish of the same species that
are similar in size. Depending on the size of the fish, fewer than 5 fish may be acceptable or more than 5
fish will be necessary to meet the 500-gram fillet tissue requirement for chemical analysis and archived
tissue. Recommended target species are given in Table 5.30. If the target species are unavailable, the
fisheries biologist will select an alternative species to obtain a whole fish sample (i.e., a species that is
commonly consumed by humans, with specimens that are of harvestable or consumable size and are in
sufficient numbers to yield a fish sample with adequate tissue for analysis). If sufficient fish are not
collected during the fish assemblage sampling, sample for up to one additional hour (collections can
occur in areas/subreaches not otherwise sampled if desired). If no fish can be collected, call the Contract
Field Logistics Coordinator at the end of the day and record "no sample collected" on the whole fish
tissue collection form, along with the reason in the comments section of the form.
Table 5.30 Recommended target species: whole fish tissue collection
Family name
Common name
Scientific name
Length Guideline
(Estimated
Minimum)
Spotted bass
Micropterus punctulatus
~280 mm
Largemouth bass
Micropterus salmoides
~280 mm
Centrarchidae
Smallmouth bass
Micropterus dolomieu
~300 mm
Black crappie
Pomoxis nigromaculatus
~330 mm
U)
0J
White crappie
Pomoxis annularis
~330 mm
(J
a>
a.
to
Channel catfish
Ictalurus punctatus
~300 mm
4-»
a>
M
*_
Ictaluridae
Blue catfish
Ictalurus furcatus
~300 mm
(0
l-
Flathead catfish
Pylodictis olivaris
~300 mm
Sauger
Sander canadensis
~380 mm
Percidae
Walleye
Sander vitreus
~380 mm
Yellow perch
Perca flavescens
~330 mm
Moronidae
White bass
Morone chrysops
~330 mm
Esocidae
Northern pike
Esox lucius
~430 mm
Chain pickerel
Esox niger
~430 mm
Salmonidae
Brown trout
Salmo trutta
~300 mm
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Cutthroat trout
Oncorhynchus clarkii
~300 mm
Rainbow trout
Oncorhynchus mykiss
~300 mm
Brook trout
Salvelinus fontinalis
~330 mm
5.8.2.1 Sampling and Analytical Methodologies
Detailed sampling methods and procedures for handling and shipping whole fish tissue samples for fillet
analysis are found in the NRSA 2018/19 FOM.
5.8.3 Pertinent QA/QC Procedures
5.8.3.1 Quality Assurance Objectives
General requirements for completeness, comparability, and representativeness are addressed in Section
2. The relevant quality objectives for fish fillet tissue indicator sample collection activities are primarily
related to completeness (collecting the target number of samples) and sample handling issues. Types of
field sampling data needed for the fish fillet tissue indicator are listed in Table 5.31. Methods and
procedures described in this QAPP and the FOMs are intended to reduce the magnitude of the sources
of uncertainty (and their frequency of occurrence) by applying:
¦ standardized sample collection and handling procedures, and
¦ use of trained scientists to perform the sample collection and handling activities.
Table 5.31 Field data types: whole fish tissue samples for fillet analysis
Variable or Measurement Measurement Endpoint or Unit
Fish specimen
Species-level taxonomic identification
Fish length
Millimeters (mm), total length
Unique composite identifier
Sample identification number
Specimen count classification
Specimen number
5.8.3.2 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Specific quality control
measures are listed in Table 5.32 for field measurements and observations.
Table 5.32 Field quality control: whole fish tissue samples for fillet analysis
Quality Control Activity Description and Requirements Corrective Action
Check integrity of sample
containers and labels
Clean, intact containers and labels
Obtain replacement
supplies
Set up electrofishing
equipment
An experienced fisheries biologist sets up the unit. If
results are poor, adjustments are made to the pulse
width and voltage to sample effectively and minimize
injury/mortality.
Adjust voltage in field
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Quality Control Activity
Description and Requirements
Corrective Action
Field Processing
The fisheries biologist will identify specimens in the
field using a standardized list of common names (App.
D of the FOMs).
Labs verify. If not same
species, appropriate
adjustments are made to
the sample composite
Sample Collection
The biologist will retain 5 specimens of the same
species to form the composite sample.
Labs verify. If not same
species, appropriate
adjustment are made to
the sample composite
Sample Collection
The length of the smallest fish must be at least 75% of
the length of the longest fish.
If fish out of length range
requirement, EPA will
evaluate the extent of the
deviation and generally
reject undersize fish
specimens
5.8.4 Data Management, Review, and Validation
Checks made of the data during the process for review, verification, and validation are summarized in
Table 5.33. For the whole fish tissue data, 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 EPA OST
Fish Tissue Coordinator. Once data have passed all acceptance requirements, the data are submitted to
the EPA OST Fish Tissue Coordinator.
Table 5.33 Data validation quality control: whole fish tissue samples for fillet analysis
Check Description
Frequency
Acceptance Criteria
Corrective Action
Composite validity
check
All composites
Each routine composite
sample must have 5 fish of
the same species
For non-routine composite samples, EPA
indicator lead (OST Fish Tissue
Coordinator) contacted for instructions
before processing begins
75% rule
All composites
Length of smallest fish in
the composite must be at
least 75% of the length of
the longest fish.
For non-routine composite samples, EPA
indicator lead (OST Fish Tissue
Coordinator) contacted for instructions
before processing begins
5.9 Fish Tissue Plugs
5.9.1 Introduction
Fish are time-integrating indicators of persistent pollutants, and contaminant bioaccumulation in fish
tissue has important human health implications for people who consume fish. The objective for fish plug
sampling is to collect one plug sample for mercury analysis at all river and stream sites where suitable
fish species and lengths are available except the 478 river sites selected for whole fish tissue sampling. A
plug sample consists of two fish tissue plugs collected from two fish of the same species (one plug per
i/i
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O
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fish). Analysis of the NRSA fish tissue plug samples will provide information on the national distribution
of mercury in fish from streams and rivers of the contiguous United States.
5.9.2 Sampling Design and Methods
Detailed fish tissue plug sample collection and handling procedures are described in the FOMs.
Collection of individual fish specimens for the fish tissue plug samples occurs in the sample reach during
the fish assemblage sampling effort, using the same gear used to collect the fish assemblage samples.
Fish tissue plug samples should be taken from the species identified in the target list found in Table
5.34. If the target species are unavailable, the fisheries biologist will select an alternative species (i.e., a
species that is commonly consumed in the study area, with specimens of harvestable or consumable
size) to obtain a plug sample. Some recommended alternative species are included in Table 5.34.
Table 5.34 Recommended target and alternate species: fish tissue plug collection
Family name
Common name
Scientific name
Length Guideline
(Estimated
Minimum)
Spotted bass
Micropterus punctulatus
~280 mm
Largemouth bass
Micropterus salmoides
~280 mm
Centrarchidae
Smallmouth bass
Micropterus dolomieu
~300 mm
Black crappie
Pomoxis nigromaculatus
~330 mm
White crappie
Pomoxis annularis
~330 mm
Channel catfish
Ictalurus punctatus
~300 mm
Ictaluridae
Blue catfish
Ictalurus furcatus
~300 mm
Flathead catfish
Pylodictis olivaris
~300 mm
Sauger
Sander canadensis
~380 mm
Percidae
Walleye
Sander vitreus
~380 mm
Yellow perch
Perca flavescens
~330 mm
Moronidae
White bass
Morone chrysops
~330 mm
Esocidae
Northern pike
Esox lucius
~430 mm
Chain pickerel
Esox niger
~430 mm
Brown trout
Salmo trutta
~300 mm
qj
u
ai
Salmonidae
Cutthroat trout
Oncorhynchus clarkii
~300 mm
Q.
to
+-»
0)
Rainbow trout
Oncorhynchus mykiss
~300 mm
DO
1_
(0
1—
Brook trout
Salvelin us fon tin alis
~330 mm
4*4
o
£
Cyprinidae
Northern pikeminnow
Ptychocheilus oregonensis
~300 mm
a
5 8
Centrarchidae
Bluegill
Lepomis macrochirus
~200 mm
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Rock bass
Ambloplites rupestris
~200 mm
Redbreast sunfish
Lepomis auritus
~200 mm
5.9.2.1 Sampling and Analytical Methodologies for Field Operations and Laboratory Analyses
Detailed sampling methods and procedures for handling and shipping fish plugsamples are found in the
FOMs. The laboratory method for mercury analysis offish plug samples is performance based. Example
standard operating procedures are provided in Appendix F of the LOM.
5.9.3 Pertinent QA/QC Procedures
5.9.3.1 Quality Assurance Objectives
The relevant quality objectives for fish tissue plug sample collection activities are primarily related to
sample handling issues. Types of field sampling data needed for the fish tissue plugs are listed in Table
5.35. Methods and procedures described in this QAPP and the FOMs are intended to reduce the
magnitude of the sources of uncertainty (and their frequency of occurrence) by applying:
¦ standardized sample collection and handling procedures, and
¦ use of trained scientists to perform the sample collection and handling activities.
Table 5.35 Field data types: fish tissue plug
Variable or Measurement
Measurement Endpoint or Unit
Fish specimen
Species-level taxonomic identification
Fish length
Millimeters (mm), total length
Fish weight
Grams (g)
5.9.3.2 Quality Control Procedures: Field Operations
Field data quality is addressed, in part, by application and consistent performance of valid procedures
documented in the standard operating procedures detailed in the FOMs. That quality is enhanced by the
training and experience of project staff and documentation of sampling activities. Specific quality control
measures are listed in Table 5.36 for field measurements and observations.
Table 5.36 Field quality control: fish tissue plug
Quality Control Activity Description and Requirements Corrective Action
Check integrity of sample
containers and labels
Clean, intact containers and labels
Obtain replacement supplies
Set up electrofishing
equipment
An experienced fisheries biologist sets up the
unit. If results are poor, adjustments are made to
the pulse width and voltage to sample effectively
and minimize injury/mortality.
Adjust voltage in field
Field Processing
The fisheries biologist will identify specimens in
the field using a standardized list of common
names (App. D of the FOMs).
Labs verify. If not same species,
sample not composited
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Quality Control Activity Description and Requirements Corrective Action
Sample Collection
The fisheries biologist will retain 2 specimens of
the same species to form the composite sample
If not the same species, sample
not composited
Sample Collection
The length of the smallest fish must be at least
75% of the length of the longest fish.
If fish out of length range
requirement, EPA contacted for
instructions
5.9.4 Data Management, Review, and Validation
Checks made of the data in the process of review, verification, and validation is summarized in Table
5.37. 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 NRSA Project Coordinator. Once data have
passed all acceptance requirements, data submitted to EPA Coordinator.
Table 5.37 Data validation quality control: fish tissue plug
Check Description
Frequency
Acceptance Criteria
Corrective Action
75% rule
All composites
Length of smallest fish in the
Indicator lead will review
composite must be at least 75% of
composite data and advise the lab
the length of the longest fish.
before processing begins
5.9.5 Quality Control Procedures: Laboratory Operations
Table 5.38 Measurement data quality objectives: fish tissue plug
Variable or Measurement
MDL
Quantitation Limit
Mercury
0.47 ng/g
5.0 ng/g
Table 5.39 Lab quality control: fish tissue plug
Activity
Evaluation/Acceptance Criteria
Corrective Action
Demonstrate competency
for analyzing fish samples
to meet the performance
measures
Demonstration of past
experience with fish tissue
samples in applying the
laboratory SOP in achieving the
method detection limit
EPA will not approve any laboratory
for NRSA sample processing if the
laboratory cannot demonstrate
competency. In other words, EPA will
select another laboratory that can
demonstrate competency for its
NRSA samples.
Check condition of
sample when it arrives.
Sample issues, such as punctures
or rips in wrapping; missing label;
temperature; adherence to
holding time requirements;
sufficient volume for test. All
Assign appropriate condition code
identified in Appendix 3.
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' samples should arrive at the
laboratory frozen.
Store sample appropriately. Check the temperature of the
While stored at the freezer per laboratory's standard
laboratory, the sample must operating procedures,
be kept at a maximum
temperature of -20° C.
Record temperature of sample upon
arrival at the laboratory. If at any other
time, samples are warmer than required,
note temperature and duration in
comment field.
Analyze sample within
holding time
The test must be completed within
the holding time (i.e., 1 year). If the
original test fails, then the retest also
must be conducted within the
holding time.
Perform test, but note reason for
performing test outside holding time.
EPA expects that the laboratory will
exercise every effort to perform tests
before the holding time expires.
Maintain quality control Data meet all QC specifications in
specifications from the selected method/SOP.
selected method/SOP
(that meets the
measurement data
quality objectives)
If data do not meet all QC
requirements, data must be flagged.
Maintain the required
MDL
Evaluate for each sample
If MDL could not be achieved, then
provide dilution factor or QC code
and explanation in the comment
field.
Use consistent units for Verify that all units are
QC samples and field consistently provided in wet
samples weight units
If it is not possible to provide the
results in the same units as most
other analyses, then assign a QC code
and describe the reason for different
units in the comments field of the
database.
Maintain completeness
Completeness objective is 95%
for all parameters.
Contact the EPA Survey QA Lead
immediately if issues affect
laboratory's ability to meet
completeness objective.
i/i
QC
O
Q
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6 FIELD AND BIOLOGICAL LABORATORY QUALITY EVALUATION AND
ASSISTANCE VISITS
6.1 National Rivers and Streams Assessment Field Quality Evaluation and
Assistance Visit Plan
Evaluation and assistance visits (AV) will be conducted with each field crew early in the sampling and
data collection process, if possible, and corrective actions will be conducted in real time. These visits
provide both a quality check for the uniform evaluation of the data collection methods and an
opportunity to conduct procedural reviews, as required, minimizing data loss due to improper technique
or interpretation of field procedures and 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 visit also provides
the field crews with an opportunity to clarify procedures and offer suggestions for future improvements
based on their sampling experience preceding the visit. The field 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 field
crew collecting and contributing data under this program; hence no data will be recorded to the project
database that was produced by an 'unaudited' process or individual. The field evaluations will be based
on the evaluation plan and field evaluation checklist.
One or more designated EPA or Contractor staff members who are qualified (i.e. have completed
training) in the procedures of the NRSA 2018/19 field sampling operations will visit trained state, tribal,
contractor, and EPA field sampling crews during sampling operations on site. If membership of a field
crew changes, and at least two of the members have not been evaluated previously, the field crew must
be evaluated again during sampling operations as soon as possible to ensure that all members of the
field crew understand and can perform the procedures. If a deviation is needed from the process
described here, the staff member conducting the AV must contact the NRSA Project Lead. The NRSA
Project Lead will contact the NRSA Project QA Officer to determine an acceptable course of action.
The purpose of this on-site visit will be to identify and correct deficiencies during field sampling
operations. The process will involve preparation activities, field day activities and post field day activities
as described in the following sections. Additionally, conference calls with crews may be held
approximately every two weeks to discuss issues as they come up throughout the sampling season.
6.1.1 Preparation Activities
¦ Each Field Crew Evaluator will schedule an assistance visit with their designated crews in
consultation with the Contractor Field Logistics Coordinator, Regional NRSA Coordinator,
and respective Field Sampling Crew Leader. Ideally, each Field Crew will be evaluated within
the first two weeks of beginning sampling operations, so that procedures can be corrected
or additional training provided, if needed.
¦ Each Evaluator is responsible for providing their own field gear sufficient to accompany the
Field Sampling Crews during a complete sampling cycle. Schedule of the Field visits will be
made by the Evaluator in consultation with the respective Field Crew Leader. Evaluators
should be prepared to spend additional time in the field if needed (see below).
¦ Each Field Crew Evaluator will ensure that field crews are aware of their visit plans and all
capacity and safety equipment will be provided for the Field Crew Evaluator.
¦ Each Field Crew Evaluator will need to bring the items listed in Table 6.1.
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Table 6.1 Equipment and supplies: field evaluation and assistance visits
Type Item Quantity
Form
Appendix B (see FOM 2018/19)
1
Documentation
NRSA 2018/19 Field Operations Manuals
1
NRSA 2018/19 Quality Assurance Project Plan
1
Clipboard
1
Pencils (#2, for data forms)/Pen (or computer for electronic versions)
1
Field notebook (optional)
1
Gear
Field gear (e.g., protective clothing, sunscreen, insect repellent, hat, water,
food, backpack, cell phone)
As
needed
6.1.2 Field Day Activities
¦ The Field Crew Evaluator will review the Field Evaluation & Assistance Visit Checklist with
each crew during the field sampling day and establish and plan and schedule for their
evaluation activities for the day.
¦ The Field Crew Evaluator will view the performance of a field crew through one complete
set of sampling activities as detailed on the checklist.
¦ Scheduling might necessitate starting the evaluation midway on the list of tasks at a site, ^
instead of at the beginning. In that case, the Field Crew Evaluator will follow the crew to the
next site to complete the evaluation of the first activities on the list. ^
¦ If the field crew misses or incorrectly performs a procedure, the Field Crew Evaluator will ^
note this on the checklist and immediately point this out so the mistake can be corrected on h
the spot. The role of the Field Crew Evaluator is to provide additional training and guidance i/i
so that the procedures are being performed consistent with the FOM, all data are recorded <
correctly, and paperwork is properly completed at the site. z
¦ When the sampling operation has been completed, the Field Crew Evaluator will review the ^
results of the evaluation with the field crew before leaving the site (if practicable), noting Q
positive practices and problems (i.e., weaknesses [might affect data quality]; deficiencies <
[would adversely affect data quality]). The Field Crew Evaluator will ensure that the field ^
crew understands the findings and will be able to perform the procedures properly in the 15
future. ^
¦ The Field Crew Evaluator will review the list and record responses or concerns from the field <
crew, if any; on the checklist (this may happen throughout the field day). ^
¦ The Field Crew Leader will sign the checklist after this review. ^
O
6.1.3 Post Field Day Activities <
o
¦ The Field Crew Evaluator will review the checklist that evening and provide a summary of co
findings, including lessons learned and concerns. ^
¦ If the Field Crew Evaluator finds major deficiencies in the field crew operations (e.g., less ^
than two members, equipment, or performance problems) the Field Crew Evaluator must g
contact the EPA NRSA 2018/19 Project Lead. The EPA NRSA 2018/19 Project Lead will ^
contact the EPA NRSA 2018/19 Project Officer to determine the appropriate course of m
action. Data records from sampling sites previously visited by this Field Crew will be checked §
to determine whether any sampling sites must be redone.
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¦ The Field Crew Evaluator will retain a copy of the checklist and submit to the EPA NRSA QA
Officer either via Fed-Ex or electronically.
¦ The EPA NRSA 2018/19 Project Lead and EPA NARS QA Project Officer or authorized
designee will review the returned Field Evaluation and Assistance Visit Checklist, note any
issues, and check off the completion of the evaluation for each field crew.
6.1.4 Summary
Table 6.2 summarizes the plan, checklist, and corrective action procedures.
Table 6.2 Summary: field evaluation and assistance visits
Field
Evaluation
Plan
The Field Crew Evaluator:
• Arranges the field evaluation visit in consultation with the QA Officer, Regional NRSA
Coordinator, and respective Field Sampling Crew Leader, ideally within the first two weeks of
sampling
• Observes the performance of a crew through one complete set of sampling activities
• Takes note of errors the field crew makes on the checklist and immediately point these out to
correct the mistake
• Reviews the results of the evaluation with the field crew before leaving the site, noting positive
practices, lessons learned, and concern
Field
Evaluation
Checklist
The Field Crew Evaluator:
• Observes all pre-sampling activities and verifies that equipment is properly calibrated and in
good working order, and protocols are followed
• Checks the sample containers to verify that they are the correct type and size, and checks the
labels to be sure they are correctly and completely filled out
• Confirms that the field crew has followed NRSA protocols for locating the river/stream X point
• Observes the index site sampling, confirming that all protocols are followed
• Observes the littoral sampling and habitat characterization, confirming that all protocols are
followed
• Records responses or concerns, if any, on the Field Evaluation and Assistance Checklist
Corrective
Action
Procedures
• If the Field Crew Evaluator's findings indicate that the Field Crew is not performing the
procedures correctly, safely, or thoroughly, the Evaluator must continue working with this Field
Crew until certain of the crew's ability to conduct the sampling properly so that data quality is
not adversely affected.
• If the Field Crew Evaluator finds major deficiencies in the Field Crew operations the Evaluator
must contact the EPA NRSA 2018/2019 Project Lead.
6.2 National Rivers and Streams Assessement Laboratory Quality Evalution
and Assistance Visit Plan
As part of the NRSA 2018/19, field samples will be collected at each assessment site. These samples will
be sent to laboratories cooperating in the assessment. To ensure quality, each Project Cooperator
laboratory analyzing samples from the NRSA 2018/19 will receive an evaluation from an NRSA Lab
Evaluator. All Project Cooperator laboratories will follow these guidelines.
No national program of accreditation for laboratory processing for many of our indicators currently
exists. For this reason, a rigorous program of laboratory evaluation has been developed to support the
NRSA 2018/19.
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Given the large number of laboratories participating in the NRSA 2018/19, it is not feasible to perform
an assistance visit19 (AV) on each of these laboratories. An AV would include an on-site visit to the
laboratory lasting at least a day. As a result, the EPA Headquarters Project Management Team will
conduct remote review of laboratory certifications and accreditations of all laboratories. Additionally,
EPA will include an inter-laboratory comparison between some laboratories (mainly for biological
indicators). If issues arise from the remote review or inter-laboratory comparison that cannot be
resolved remotely then the EPA Quality Team and/or contractors will perform an on-site visit to the
laboratory. This process is in keeping with EPA's Policy to Assure Competency of Laboratories, Field
Sampling, and Other Organizations Generating Environmental Measurement Data under Agency-Funded
Acquisitions.
6.2.1 Remote Evaluation/Technical Assessment
A remote evaluation procedure has been developed for performing assessment of all laboratories
participating in the NRSA 2018/19.
The NRSA QA Team will conduct laboratory evaluation prior to data analysis to ensure that the
laboratories are qualified and that techniques are implemented consistently across the multiple
laboratories generating data for the program. The EPA National Aquatic Resource Surveys team has
developed laboratory evaluation plans to ensure uniform interpretation and guidance in the procedural
reviews.
The NRSA Quality Team is using a procedure that requests the laboratory to provide documentation of
its policies and procedures. For the NRSA 2018/19 project, the Quality Team is requesting that each
participating laboratory provide the following documentation:
¦ The laboratory's Quality Manual, Quality Management Plan or similar document.
¦ Standard Operating Procedures (SOPs) for each analysis to be performed.
¦ Long term Method Detection Limits (MDLs) for each instrument used and Demonstration of
Capability for each analysis to be performed.
¦ A list of the laboratory's accreditations and certifications, if any.
¦ Results from Proficiency Tests for each analyte to be analyzed under the NRSA 2018/19
If a laboratory has clearly documented procedures for sample receiving, storage, preservation,
preparation, analysis, and data reporting; has successfully analyzed Proficiency Test samples (if required
by EPA, EPA will provide the PT samples); has a Quality Manual that thoroughly addresses laboratory
quality including standard and sample preparation, record keeping and QA non-conformance;
participates in a nationally recognized or state certification program; and has demonstrated ability to
perform the testing for which program/project the audit is intended, then the length of an on-site visit
will be minimum, if not waived entirely. The QA Team will make a final decision on the need for an
actual on-site visit after the review and evaluation of the documentation requested.
If a laboratory meets or exceeds all of the major requirements and is deficient in an area that can be
corrected remotely by the lab, suggestions will be offered and the laboratory will be given an
opportunity to correct the issue. The QA Team will then verify the correction of the deficiency remotely.
project.
19 The evaluation of the labs is being considered an Assistance Visit rather than an audit because the evaluation is
designee! to provide guidance to the labs rather than as "inspection" as in a traditional audit.
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The on-site visit by EPA and/or a contractor should only be necessary if the laboratory fails to meet the
major requirements and is in need of help or fails to produce the requested documentation.
In addition, all labs must sign a Lab Signature Form (see NRSA 2018/19 LOM) indicating that they will
abide by the following:
¦ Utilize procedures identified in the NRSA 2018/19 LOM (or equivalent). If using equivalent
procedures, please provide procedures manual to demonstrate ability to meet the required
MQOs.
¦ Read and abide by the NRSA 2018/19 Quality Assurance Project Plan (QAPP) and related
Standard Operating Procedures (SOPs).
¦ Have an organized IT system in place for recording sample tracking and analysis data.
¦ Provide data using the template provided in the Lab Operations Manual.
¦ Provide data results in a timely manner. This will vary with the type of analysis and the
number of samples to be processed. Sample data must be received no later than May 1,
2019 for samples collected in 2018 and May 1, 2020 for samples collected in 2019 or as
otherwise negotiated with EPA.
¦ Participate in a lab technical assessment or audit if requested by EPA NRSA staff (this may
be a conference call or on-site audit).
If a lab is participating in biology analyses, they must, in addition, abide by the following:
¦ Use taxonomic standards outlined in the NRSA 2018/19 Lab Manual.
¦ Participate in taxonomic reconciliation exercises during the field and data analysis season,
which include conference calls and other lab reviews (see more below on Inter-laboratory
comparison).
6.2.2 Water Chemistry Laboratories
The water chemistry laboratory approval process which is outlined on in the previous paragraphs of this
section is deemed appropriate because many laboratories participate in one or more national laboratory
accreditation programs such as the National Environmental Laboratory Accreditation Program (NELAP),
International Organization for Standardization (ISO-17025) as well as various state certification
programs which include strict requirements around documentation and procedures as well as site visits
by the accrediting authority. It is built off of the process used by the NLA 2012. The laboratories
participating in NRSA 2018/19 meet these qualifications and as such have demonstrated their ability to
function independently. This process is one that has been utilized in Region 3 for many years and is
designed around the national accrediting programs listed above.
6.2.3 Inter-laboratory Comparison
The NRSA QA plan includes an inter-laboratory investigation for the laboratories performing analysis on
benthic macroinvertebrates, and periphyton data for the NRSA 2018/19. This process is defined as an
inter-laboratory comparison since the same protocols and method will be used by both laboratories as
described in this manual. The QA plan also includes an independent taxonomist (EPA Contractor) to re-
identify 10% of the samples from each laboratory. No site visit is envisioned for these laboratories
unless the data submitted and reviewed by EPA does not meet the requirements of the inter-laboratory
comparison described.
6.2.4 Assistance Visits
Assistance Visits will be used to:
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¦ Confirm the NRSA 2018/19 Laboratory Operations Manual (LOM) methods are being
properly implemented by cooperator laboratories.
¦ Assist with questions from laboratory personnel.
¦ Suggest corrections if any errors are made in implementing the lab methods.
Evaluation of the laboratories will take the form of administration of checklists which have been
developed from the LOM to ensure that laboratories are following the methods and protocols outlined
therein. The checklist will be administered on-site by a qualified EPA scientist or contractor.
See LOM for copies of the Document Request form used for both the Biological laboratories and the
Chemical laboratories.
6.2.5 NRSA 2018/19 Document Request Form Chemistry Laboratories
EPA and its state and tribal partners will conduct a survey of the nation's rivers and streams. This
National River and Streams Assessment (NRSA), is designed to provide statistically valid regional and
national estimates of the condition of rivers and streams. Consistent sampling and analytical procedures
ensure that the results can be compared across the country. As part of the NRSA 2018/19, the Quality
Assurance Team will conduct a technical assessment to verify quality control practices in your laboratory
and its ability to perform chemistry analyses under this project. Our review will assess your laboratory's
ability to receive, store, prepare, analyze, and report sample data generated under EPA's NRSA 2018/19.
The first step of this assessment process will involve the review of your laboratory's certification and/or
documentation. Subsequent actions may include (if needed): reconciliation exercises and/or a site visit.
All laboratories will need to complete the following forms:
If your lab has been previously approved within the last 5 years for the specific parameters:
¦ A signature on the attached Laboratory Signature Form indicates that your laboratory will
follow the quality assurance protocols required for chemistry laboratories conducting
analyses for the NRSA 2018/19. A signature on the QAPP and the LOM Signature Form
indicates that you will follow both the QAPP and the LOM.
If you have not been approved within the last 5 years for the specific parameters in order for us to
determine your ability to participate as a laboratory in the NRSA, we are requesting that you submit
the following documents (if available) for review:
¦ Documentation of a successful quality assurance audit from a prior National Aquatic
Resource Survey (NARS) that occurred within the last 5 years (if you need assistance with
this please contact the individual listed below).
¦ Documentation showing participation in a previous NARS for Water Chemistry for the same
parameters/methods.
Additionally, we request that all laboratories provide the following information in support of your
capabilities, (these materials are required if neither of the two items above are provided):
¦ A copy of your Laboratory's accreditations and certifications if applicable (i.e. NELAC, ISO,
state certifications, North American Benthological Society (NABS), etc.).
¦ An updated copy of your Laboratory's QAPP.
¦ Standard Operating Procedures (SOPs) for your laboratory for each analysis to be performed
(if not covered in 2018/19 NRSA LOM).
¦ Documentation attesting to experience running all analytes for the 2018/19 NRSA, including
chlorophyll a and Ash Free Dry Mass (AFDM).
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This documentation may be submitted electronically via e-mail to forde.kendra@epa.gov. Questions
concerning this request can be submitted forde.kendra@epa.gov (202-566-0417) or
mitchell.richard@epa.gov (202-566-0644).
6.2.6 NRSA 2018/19 Document Request Form Biology Labs
EPA and its state and tribal partners will conduct a survey of the nation's rivers and streams. This
National River and Streams Assessment (NRSA), is designed to provide statistically valid regional and
national estimates of the condition of rivers and streams. Consistent sampling and analytical procedures
ensure that the results can be compared across the country. As part of the 2018/19 NRSA, the Quality
Assurance Team will conduct a technical assessment to verify quality control practices in your laboratory
and its ability to perform biology analyses under this project. Our review will assess your laboratory's
ability to receive, store, prepare, analyze, and report sample data generated under EPA's 2018/19 NRSA.
The first step of this assessment process will involve the review of your laboratory's certification and/or
documentation. Subsequent actions may include (if needed): reconciliation exercises and/or a site visit.
All laboratories will need to complete the following forms:
¦ If your laboratory has been previously approved within the last 5 years for the specific
parameters: A signature on the attached Laboratory Signature Form indicates that your
laboratory will follow the quality assurance protocols required for biology laboratories
conducting analyses for the 2018/19 NRSA.A signature on the QAPP and the LOM Signature
Form indicates you will follow both the QAPP and the LOM.
If you have not been approved within the last 5 years for the specific parameters, in order for us to
determine your ability to participate as a laboratory in the NRSA, we are requesting that you submit
the following documents (if available) for review:
¦ Documentation of a successful quality assurance audit from a prior National Aquatic
Resource Survey (NARS) that occurred within the last 5 years (if you need assistance with
this please contact the individual listed below).
¦ Documentation showing participation in previous NARS for this particular indicator.
Additionally, we request that all laboratories provide the following information in support of your
capabilities, (these materials are required if neither of the two items above are provided):
¦ A copy of your Laboratory's accreditations and certifications if applicable (i.e. NELAC, ISO,
state certifications, NABS, etc.).
¦ Documentation of NABS (or other) certification for the taxonomists performing analyses (if
applicable).
¦ An updated copy of your Laboratory's QAPP.
¦ Standard Operating Procedures (SOPs) for your lab for each analysis to be performed (if not
covered in NRSA 2018/19 LOM).
This documentation may be submitted electronically via e-mail to forde.kendra@epa.gov. Questions
concerning this request can be submitted forde.kendra@epa.gov (202-566-0417) or
mitchell.richard@epa.gov (202-566-0644).
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7 DATA ANALYSIS PLAN
The Data Analysis Plan describes the general process used to evaluate the data for the survey. It outlines
the steps taken to assess the condition of the nation's rivers and streams and identify the relative
impact of stressors on this condition. Results from the analysis will be included in the final report and
used in future analyses. The data analysis plan will likely be refined and clarified as the data are analyzed
by EPA and states.
7.1 Data Interpretation Background
The basic intent of data interpretation is to evaluate the occurrence and distribution of parameters
throughout the population of rivers and streams in the conterminous United States within the context of
regionally relevant expectations for least disturbed reference conditions. This is analyzed using a
cumulative distribution function (CDF). Based on information from the cumulative distribution function,
the analysis will also categorize the condition of water for most indicators as good, fair, poor, and
unassessed (for various reasons such as samples not collected, quality assurance issues, etc.). Because of
the large-scale and multijurisdictional nature of this effort, the key issues for data interpretation are
unique and include: the scale of assessment, selecting the best indicators, defining the least impacted
reference conditions, and determining thresholds for judging condition.
7.1.1 Scale of assessment
This will be the third national report on the ecological condition of the nation's rivers and streams (and
the fourth for wadeable systems) using comparable methods. EPA selected the sampling locations for
the assessment using a probability based design, and developed rules for selection to meet certain
distribution criteria, while ensuring that the design yielded a set of rivers and streams that would
provide for statistically valid conclusions about the condition of the population of rivers/streams across
the nation. A challenge that this mosaic of waterbodies poses is developing a data analysis plan that
allows EPA and other partners to interpret data and present results at a large, aggregate scale.
Additional information on data analysis procedures used for NRSA 2008/09 and proposed for NRSA
2018/19 can be found in the NRSA 2008-2009 Technical Report
(http://water.epa.gov/type/rsl/monitoring/riverssurvey/index.cfm).
7.1.2 Selecting the best indicators
Indicators should be applicable across all reporting units, and must be able to differentiate a range of
conditions. The Agency formed a steering committee for these discussions. Starting with the NRSA
2008/09 indicators, the Committee, comprised of EPA, state and other representatives provided advice
and recommendations to the Agency on indicator selection/refinement.
EPA developed screening and evaluation criteria which included indicator applicability on a national
scale, the ability of an indicator to reflect various aspects of ecological condition, and cost-effectiveness.
7.1.3 Defining least impacted reference condition
Reference condition data are necessary to describe expectations for biological conditions under least
disturbed setting. The NRSA 2018/19 project team will use an approach similar to that used in NRSA
2008/09, which is described in detail in the NRSA 2008-2009 Technical Appendix
http://water.epa.gov/type/rsl/monitoring/riverssurvey/index.cfm.
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7.1.4 Determining thresholds for judging condition
This reference site approach is used to set expectations and benchmarks for interpreting the data on
river/stream condition. The range of conditions found in the reference sites for an ecoregion describes a
distribution of those biological or stressor values expected for least disturbed condition. The
benchmarks used to define distinct condition classes (e.g., good, fair, poor / least disturbed, moderately
disturbed, most disturbed) will be drawn from this reference distribution. Typically, EPA's approach is to
examine the range of values for a biological or stressor indicator in all of the reference sites in a region,
and to use the 5th percentile of the reference distribution for that indicator to separate the most
disturbed of all sites from moderately disturbed sites. (Note: depending on the indicator, data analysis
groups and indicator leads may recommend alternative percentiles which will be reviewed by EPA).
Using the 5th percentile means that rivers/streams in the most disturbed category are worse than 95% of
the best sites used to define reference condition. Similarly, the 25th percentile of the reference
distribution can be used to distinguish between moderately disturbed sites and those in least disturbed
condition. This means that rivers/streams reported as least disturbed are as good as 75% of the sites
used to define reference condition.
7.2 Geospatial Data
Geospatial data is an integral part of data analysis for the NRSA 2018/19, as it has been for all other
surveys. The following activities are anticipated: review of coordinate data and corrections, watershed
delineations, and computing landscape metrics. Through the site evaluation process, rivers/streams that
have changed or are inaccurately represented in the National Hydrography Dataset (NHD) will be noted
and provided to EPA's NHD team.
7.3 Datasets Used for the Report
The datasets available for use in the report will be developed based on the data collected during
2018/2019, data from the NRSA 2013/14 report, data from the NRSA 2008/09 report, and data from the
WSA report (the NRSA 13/14, NRSA 08/09, and WSA data will be used for trends/change analyses, as
part of reference condition development, and for defining taxonomic names and autecology records).
Additionally, threshold values based on EPA water quality criteria and World Health Organization values
will be applied to the NRSA 2018/19 data for the human health related indicators. Geospatial files will
include river/stream coverage and watershed delineations based on NHD+, the National Land Cover
Dataset (NLCD), and Parameter-elevation Regressions on Independent Slopes Model (PRISM).
The survey will use indicators to assess ecological integrity; extent of stressors impacting integrity; and
the recreational value of rivers/streams.
7.3.1 Ecological Integrity
Ecological integrity describes the ecological condition of rivers/streams based on different assemblages
of the aquatic community and their physical habitat. The indicators include benthic macroinvertebrates,
periphyton and fish assemblages.
7.3.2 Stressor Status/Extent
Stressor indicators describe the extent of key parameters on the condition of rivers/streams as well as
the relative risk and attributable risk associated with stressors. The indicators include nutrients, physical
habitat (the riparian and instream zones) and excess sediments among others.
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7.3.3 Recreational value
Recreational indicators address the ability of the population to support recreational uses such as
swimming, fishing and boating. The protection of these uses is one of the requirements in the Clean
Water Act under 305(b). The extent of algal toxins (microcystin and cylindrospermopsin), the extent of
fish tissue concentrations above screening values for protection of human health, and Enterococci levels
will serve as the primary indicators of recreational value.
7.4 Indicator Data Analysis
7.4.1 Water Chemistry and Chlorophyll a
A wide array of water chemistry parameters will be measured, including DO, pH, total N, total P, ANC,
DOC, NH4, NO3-NO2, S04, CI, N03, Ca, Mg, Na, K, Si02, TSS, True Color, and chlorophyll-o. Values for these
parameters and their distribution will be reported. Water chemistry analysis is critical for interpreting
the biological indicators. Chlorophyll-o, and nutrient measurements will be used to determine the
extent of these key stressors on aquatic life and to assess relative risk/attributable risk.
7.4.2 Algal Toxins
Cyanobacteria (blue-green algae) blooms are common midsummer to late fall events that occur in many
waters throughout the United States. Algal toxin production has been identified as a significant potential
human health problem that has been associated with many of these bloom events. However, little is
known about the general occurrence of algal toxins in the pelagic zones of these water bodies, where
extensive blooms are less likely to occur than in near-shore areas.
The data analysis team will analyze the total (whole water) concentrations of microcystins and
cynlindrospermopsin in rivers/streams throughout the United States using a standardized immunoassay
test. In addition, the data analysis team will analyze and interpret the data for microcystin and
cylindrospermopsin occurrence and concentration in the context of other environmental data that is
collected as part of the NRSA assessment (e.g. nutrients, chlorophyll, turbidity, specific conductance,
7.4.3 Benthic Macroinvertebrate, Periphyton and Fish assemblages
Benthic macroinvertebrate and fish assemblage will be analyzed using both multimetric indices (MMI)
(modeled for all assemblages; and potentially modeled and traditional for benthic macroinvertebrates)
and observed/expected indices (O/E) models. The MMI approach summarizes various assemblage
attributes, such as composition, tolerance to disturbance, trophic and habitat preferences, as individual
metrics or measures of the biological community. Candidate metrics are evaluated for aspects of
performance and a subset of the best performing metrics are combined into an index known as a
Macroinvertebrate Index of Biotic Condition. This index is then used to rank the condition of the
The predictive model or O/E approach estimates the expected taxonomic composition of an assemblage
in the absence of human stressors, using a set of least-disturbed sites and other variables related to
natural gradients, such as elevation, stream size, latitude and longitude. The resulting models are then
used to estimate the expected taxa composition (taxa richness) at each site sampled. The number of
expected taxa actually observed at a site is compared to the number of expected taxa as an Observed
Expected ratio or index. Departure from a ratio of one indicate that the taxonomic composition in the
sample differs from that expected under least -disturbed conditions. The greater the departure from
one, the greater the sample differs from the least disturbed condition.
PH).
resource.
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EPA scientists will develop a separate data analysis plan for research related to the periphyton meta-
genomics indicator.
7.4.4 Physical Habitat
An assessment of river and stream (fluvial) physical habitat condition is a major component of the NRSA.
The assessment focuses on streambed stability and excess fine sediments, instream habitat cover
complexity, riparian vegetation, and riparian human disturbances. These four indicators are generally
important throughout the U.S. Furthermore, the project team had reasonable confidence in factoring
out natural variability to determine expected values and the degree of anthropogenic alteration of the
habitat attributes represented by these indicators.
7.4.4.1 Relative Bed Stability and Excess Fines
Streambed characteristics (e.g., bedrock, cobbles, silt) are often cited as major controls on the species
composition of macroinvertebrate, periphyton, and fish assemblages in streams (e.g., Hynes 1970,
Cummins 1974, Platts et al. 1983, Barbour et al. 1999, Bryce et al., 2008, 2010). Along with bedform
(e.g., riffles and pools), streambed particle size influences the hydraulic roughness and consequently the
range of water velocities in a stream channel. It also influences the size range of interstices that provide
living space and cover for macroinvertebrates and smaller vertebrates. Accumulations of fine substrate
particles (excess fine sediments) fill the interstices of coarser bed materials, reducing habitat space and
its availability for benthic fish and macroinvertebrates (Hawkins et al. 1982 Platts et al. 1983, Rinne
1988). In addition, these fine particles impede circulation of oxygenated water into hyporheic habitats
reducing egg-to-emergence survival and growth of juvenile salmonids (Suttle et al. 2004). Streambed
characteristics are often sensitive indicators of the effects of human activities on streams (MacDonald et
al. 1991, Barbour et al. 1999, Kaufmann et al. 2009). Decreases in the mean particle size and increases in
streambed fine sediments can destabilize stream channels (Wilcock 1997, 1998) and may indicate
increases in the rates of upland erosion and sediment supply (Lisle 1981, Dietrich et al. 1998).
The scaled median streambed particle size is expressed as Relative Bed Stability (RBS), calculated as the
ratio of the geometric mean diameter, Dg, divided by Dcbf, the critical diameter (maximum mobile
diameter) at bankfull flow (Gordon et al., 1992), where Dg is based on systematic streambed particle
sampling ("pebble counts") and Dcbf is based on the estimated streambed shear stress calculated from
slope, channel dimensions, and hydraulic roughness during bankfull flow conditions
7.4.4.2 Instream Habitat Cover Complexity
Although the precise mechanisms are not completely understood, the most diverse fish and
macroinvertebrate assemblages are usually found in streams that have complex mixtures of habitat
features: large wood, boulders, undercut banks, tree roots, etc. (Kovalenko et al. 2011). When other
needs are met, complex habitat with abundant cover should generally support greater biodiversity than
simple habitats that lack cover (Gorman and Karr 1978, Benson and Magnuson 1992). Human use of
streams and riparian areas often results in the simplification of this habitat, with potential effects on
biotic integrity (Kovalenko et al., 2011). For this assessment, EPA proposes to continue the use of a
measure (XFC_NAT in Kaufmann et al., 1999) that sums the amount of instream habitat consisting of
undercut banks, boulders, large pieces of wood, brush, and cover from overhanging vegetation within a
meter of the water surface, all of which are estimated visually by NRSA field crews.
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7.4.4.3 Riparian Vegetation
The importance of riparian vegetation to channel structure, cover, shading, inputs of nutrients and large
wood, and as a wildlife corridor and buffer against anthropogenic disturbance is well recognized
(Naiman et al. 1988, Gregory et al. 1991). Riparian vegetation not only moderates stream temperatures
through shading, but also increases bank stability and the potential for inputs of coarse and fine
particulate organic material. Organic inputs from riparian vegetation become food for stream organisms
and provide structure that creates and maintains complex channel habitat.
EPA proposes to continue evaluating the cover and complexity of riparian vegetation based on the
metric XCMGW, which is calculated from visual estimates made by field crews of the areal cover and
type of vegetation in three layers: the ground layer (<0.5m), med-layer (0.5-5.0 m) and upper layer (>5.0
m). The separate measures of large and small diameter trees, woody and non-woody mid-layer
vegetation, and woody and non-woody ground cover are all visual estimates of areal cover. XCMGW
sums the cover of woody vegetation summed over these three vegetation layers, expressing both the
abundance of vegetation cover and its structural complexity. Its theoretical maximum is 3.0 if there is
100% cover in each of the three vegetation layers. XCMGW gives an indication of the longevity and
sustainability of perennial vegetation in the riparian corridor (Kaufmann et al. 1999).
7.4.4.4 Riparian Human Disturbances
Agriculture, roads, buildings, and other evidence of human activities in and near the stream and river
channel may exert stress on aquatic ecosystems and may also serve as indicators of overall
anthropogenic stress. EPA's 1992 stream monitoring workshop recommended field assessment of the
frequency and extent of both in-channel and near-channel human activities and disturbances (Kaufmann
1993). The vulnerability of the stream network to potentially detrimental human activities increases
with the proximity of those activities to the streams themselves. NRSA follow Stoddard et al. (2005b)
and U.S. EPA (2006) in using a direct measure of riparian human disturbance that tallies 11 specific
forms of human activities and disturbances (walls, dikes, revetments or dams; buildings; pavement or
cleared lots; roads or railroads; influent or effluent pipes; landfills or trash; parks or lawns; row crop
agriculture; pasture or rangeland; logging; and mining) at 22 separate locations along the stream reach,
and weights them according to how close to the channel they are observed (W1_HALL in Kaufmann et
al. 1997). Observations within the stream or on its banks are weighted by 1.5, those within the 10 x 10
meter plots are weighted by 1.0, and those visible beyond the plots are weighted by 0.5. The index
W1_HALL ranged from 0 (no observed disturbance) to ~7 (e.g., equivalent to four or 5 types of
disturbance observed in the stream, throughout the reach; or seven types observed within all 22
riparian plots bounding the stream reach). Although direct human activities certainly affect riparian
vegetation complexity and layering measured by the Riparian Vegetation Index (previous paragraph),
the Riparian Disturbance Index is more encompassing, and differs by being a direct measure of
observable human activities that are presently or potentially detrimental to streams.
7.4.5 Enterococci
Enterococci are bacteria that live in the intestinal tracts of warm-blooded animals, including humans,
and therefore indicate possible contamination of streams and rivers by fecal waste. Epidemiological
studies conducted at beaches affected by human sources of fecal contamination have established a
relationship between the density of enterococci in ambient waters and the elevated incidence of
gastrointestinal illness in swimmers. For the NRSA, water samples are analyzed using a process known as
quantitative polymerase chain reaction, or qPCR, a methodology that facilitates the detection of DNA
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sequences unique to these bacteria. Analysts compare the NRSA results to a new EPA qPCR threshold
for protecting human health in ambient waters designated for swimming.
7.4.6 Fish Tissue Indicator (fillets and plugs)
Mercury is widely distributed in the environment, due to both natural processes and human activities.
Measuring mercury levels in fish tissue is critical because about 80% of all fish consumption advisories
currently involve mercury. Analysts compare mercury results for each of the fish tissue indicator
analyses (fillets and plugs) to EPA's human health screening value for mercury of 300 ppb that, if
exceeded, can be harmful to human health. Other chemical-specific human health screening values are
applied to fillet results for PCBs and PFCs to evaluate potential health risks.
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8 REFERENCES
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l/l
LU
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en
en
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9 Appendix A: FOM and LOM Revision History
Wadeable Field Operations
Manual (FOM) Version
Date Approved
Changes Made
1.0
8/28/2017
Not Applicable
1.1
5/8/2019
Added final document numbers
throughout
Removed unnecessary acronyms
and made minor edits to acronyms
list
Distribution list: Updated contact
names and contact information
Minor editorial changes
throughout
Section 1.2: Clarified target
population
Table 1.1: Editorial changes
Section 1.6: Clarification of
protocol for data review on field
forms and app
Table 1.2: Editorial changes
Updated forms, labels, and tags
throughout
Section 2.1: Clarification of crew
group tasks
Figure 2.1: updated
Table 2.1: Added Bleach (10%)
solution and removed QCS solution
Table 2.2: Editorial changes
Section 2.2.1.3: Clarification of
supply request process; removed
requirement of submitting
tentative sampling schedule
Section 2.4: Added description of
electronic field forms and packing
slips; Added request form items list
and descriptions
Section 3.2.1: Clarification of
determining sampling status
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Section 3.3.1: Added Transect K
elevation measurement
Tables: Minor editorial changes
Figure 3.3: Revised with better
quality graphic
Throughout: Add instructions for
submitting data via the NRSA app
Section 4.1.2.1: Added Note that
DO should be calibrated at the site
Section 4.2: Clarification of water
sampling protocol
Throughout protocols: Added if
samples not collected, fill in the
"No Sample Collected" bubble
Section 7: Clarification of
periphyton sampling method
Section 7: Added bleach clean up
procedure for periphyton
equipment
Section 8: Clarification of physical
habitat protocols
Table 8.4: Condensed pool types
into single category
Figure 8.3: Revised with higher
quality graphic
Section 8.6.1: Clarification of
methods of measuring slope and
bearing
Figure 8.9: Revised with higher
quality graphic
Table 8.14: Changed C(Close) to
C(Contained)
Added Section 8.15: Elevation at
Transect K
Section 9: Slight
clarification/editorial changes to
Enterococci method
Section 10: Modification offish
sampling method to two protocols
(small and large streams) instead of
three protocols (small, medium,
large streams).
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Figure 10.3: Replaced with updated
figure
Section 10: Clarification offish
sampling protocols and vouchering;
instruction to indicate whether
conditions allowed for sufficient
sampling on the fish gear form and
response to final electrofishing
settings
Section 10.5.6: Clarification on fish
collection revision form guidance
Section 11: Clarification to
collection offish tissue plugs
Section 12: Clarification of
collection of whole fish; minor
editorial changes
Section 13: Minor editorial changes
to final site activity procedures
Section 13.3: Added "Fecal
Indicator" to description of
Enterococci throughout;
modifications of enterococci
sample packaging procedure;
modification to periphyton
processing procedure; changed
PMETto PDNA
Section 13.3.6.5: Added Cleaning of
Periphyton Equipment section.
Section 14 and Figure 14.2: Added
clarification on when to collect fish
plugs and whole fish tissue for
revisit sites
Section 14.2: Clarification for
Revisit Sampling Sites
Section 15: Added necessary and
deleted unnecessary reference
citations
1.2
5/8/2019
Pagex: Changed Indicators to Index
under MMI
Figure 1.1: Updated example labels
for sampling and tracking and
identification
Section 8.7: Clarified how to assess
embeddedness of substrate;
<
126
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Minor editorial change
Table 8.9: Clarified how to assess
embeddedness of substrate
Table 11.2: Uodated information
on size and recommended target
and alternate species for fish tissue
plug collection
Table 12.2: Updated information
on size and recommended target
and alternative species for whole
fish tissue collection
Non-Wadeable FOM Version
Date Approved
Changes Made
1.0
8/28/2017
Not Applicable
1.1
6/11/2018
Added final document numbers
throughout
Removed unnecessary acronyms
and made minor edits to acronyms
list
Distribution list: Updated contact
names and contact information
Minor editorial changes
throughout
Section 1.2: Clarified target
population
Table 1.1: Editorial changes
Section 1.6: Clarification of
protocol for data review on field
forms and app
Table 1.2: Editorial changes
Updated forms, labels, and tags
throughout
Section 2.1: Clarification of crew
group tasks
Figure 2.1: updated
Section 2.2.1.3: Clarification of
supply request process; removed
requirement of submitting
tentative sampling schedule
Table 2.1: Added Bleach (10%)
solution and removed QCS solution
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Table 2.2: Editorial changes
Section 2.4: Added description of
electronic field forms and packing
slips; Added request form items list
and descriptions
Section 3.2.1: Clarification of
determining sampling status
Section 3.3.1: Added Transect K
elevation measurement
Tables: Minor editorial changes
Figure 3.3: Revised with better
quality graphic
Throughout: Add instructions for
submitting data via the NRSA app
Section 4: Clarification of water
sampling protocol
Section 4.1.2.1: Added Note that
DO should be calibrated at the site
Throughout protocols: Added if
samples not collected, fill in the
"No Sample Collected" bubble
Figure 6.3: Revised with better
quality graphic
Section 7: Clarification of
periphyton sampling method
Section 7: Added bleach clean up
procedure for periphyton
equipment
Section 8: Clarification of physical
habitat protocols
Figure 8.1: Revised with higher
quality graphic
Figure 8.4: Revised with higher
quality graphic
Table 8.11: Changed C(Close) to
C(Contained)
Added Section 8.12: Elevation at
Transect K
Section 9: Slight
clarification/editorial changes to
Enterococci method
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Section 10: Modification offish
sampling method to two protocols
(small and large rivers) instead of
three protocols (small, medium,
large rivers).
Figure 10.3: Replaced with updated
figure
Section 10: Clarification offish
sampling protocols and vouchering;
instruction to indicate whether
conditions allowed for sufficient
sampling on the fish gear form and
response to final electrofishing
settings
Section 10.4.6: Clarification on fish
collection revision form guidance
Section 11: Minor clarifications to
collection offish tissue plugs
Section 12: Clarification of
collection of whole fish; minor
editorial changes
Section 13: Minor editorial changes
to final site activity procedures
Section 13.3: Modifications to
enterococci sample packaging
procedure; modification to
periphyton processing procedure;
changed PMET to PDNA
Section 13.3.6.5: Added Cleaning of
Periphyton Equipment section
Section 14 and Figure 14.2: Added
clarification on when to collect fish
plugs and whole fish tissue for
revisit sites
Section 14.2: Clarification for
Revisit Sampling Sites
Section 15: Added necessary and
deleted unnecessary reference
citations
1.2
5/8/2019
Page x: Changed Indicators to Index
under MMI
Figure 1.1: Updated example labels
for sampling and tracking and
identification
<
129
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Section 8.5.2.2: Clarified how to
assess and identify snags during
thalweg assessment
Table 8.4: Clarified how to assess
and identify snags
Table 10.2: Clarified how to fish
when island encountered
Table 11.2: Updated information
on size and recommended target
and alternative species for whole
fish tissue collection
FOM Appendices Version
Date Approved
Changes Made
1.0
8/28/2017
Not Applicable
1.1
6/11/2018
Appendix A: Updated equipment &
supplies
Appendix B: Updated forms and
labels
Appendix C: Updated Shipping
Guidelines
Added Appendix E: Example
Electrofishing Settings
1.2
5/8/2019
Updated the version number and
date
LOM Version
Date Approved
Changes Made
1.0
8/28/2017
Not Applicable
1.1
6/11/2018
Minor editorial changes
throughout
Section 3: Clarified calibration
range definition
Replaced Table 3.1 with Required
data elements for
cylindrospermopsin
Section 3.4: Changed code for
warm sample (>8C) to "NF: Sample
is not frozen"
Section 3.5.3: Clarified QC
evaluation for cylindrospermopsin
standards, controls, and samples;
updated code names
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National Rivers and Streams Assessment 2018/19
Version 1.2, May 2019
Quality Assurance Project Plan
Page 131 of 132
Section 3.6.1: Clarified that QC
samples are labeled as
performance test (PT) samples
Table 3.2: Changed 'The laboratory
reports both the original and
diluted sample results" to "If
samples are re-run, do not enter
concentration information of the
first run" in Results Within
Calibration Range row
Removed Table 4.1: Microcystin
required data elements - login;
Table 4.2 and Table 4.3 now
labeled as Table 4.1 and Table 4.2,
respectively.
Removed Figure 4.1: Abraxis
microcystin text kit image
Section 4.7.2: Clarified that QC
samples are labeled as
performance test (PT) samples
Table 4.1 (was Table 4.2): Clarified
field and column headings; Added
"Condition Comment", "Batch
Identification", and "Date
Analyzed" rows; removed LOGIN
and ANALYSIS column
Section 10: Heading changed from
Periphyton to Diatoms
Added Section 11: Periphyton
Biomass
Table 13.4: Added ANC
performance requirements
Table 13.8: Added Ammonia-N and
Nitrate-N conversions
Section 13.5: Added additional
references
Appendix B: Removed
cylindrospermopsin and
enterococci from chemistry lab
signature form.
Added Appendix F: Example SOP
for Ash Free Dry Mass Analysis of
Periphyton Biomass
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National Rivers and Streams Assessment 2018/19
Version 1.2, May 2019
Quality Assurance Project Plan
Page 132 of 132
Moved to Appendix G: Example
SOPs for Mercury in Fish Tissue
Plug Analysis
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