National Lakes Assessment 2022 Quality Assurance Project Plan Version 1.1, May 2022


&EPA

United States Environmental Protection Agency
Office of Water
Washington, DC
EPA 841-B-21-009

National Lakes Assessment 2022

Quality Assurance Project

Plan

Version 1.1, May 2022

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Quality Assurance Project Plan
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Approval Page

1 A REIKI A n\ IPKI7PI Di9italy signed by LAREINA GUENZEL
Lr\r\L_l INr\ vjvj L_l nZ-L-L riata- mum ?nno in-4<; .aim'

Lare.na Suenzel	Date

National Lakes Assessment 2022 Project Leader
U.S. EPA Office of Water

Cnr^k 1 okmtann	Digitally signed by Sarah Lehmann

3d I d 11 Lcl 11 I la I II I	Date;2022,05,20 10:42:39-04 00

Sarah lehmann	Date

National lakes Assessment Project Quality Assurance Coordinator

U.S. EPA Office of Water

Digitally signed by Sarah Lehmann

te: 2022.05.20 10:42:59 -04*00*

Sarah Lehmariri ^

Susan Hoidsworth	^ tr1 ~iIi f >''hrc!3nn Acting BC	Date

Chief, Monitoring Branch
U.S.EPA Office of Water

DCDMirC CN/IITLI Digitally signed by BERNICE SMITH

DtnlNILt JlVll I ri Date: 2022.05.2014:46:29-04'00'

Berni;~ „ :mrth	Bate

National Aquatic Resource Surveys Quality Assurance Coordinator
U.S.EPA Office of Water

Inhncnn Curit hia Kl Digitally signed by Johnson, Cynthia N.

jonnson, lyntnia pate:2022.0s.2sio:5i:i2^0400

Cynthia N. Johnson	Date

Office of Wetlands, Oceans, and Watersheds	Assurance Officer

U.S. ESA O'fice of Water

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Version History

Version

Date

Revisions or Comments

0.0

December 2021

Internal EPA version for project QAC review and comments

0.0

February 2022

•	Updated training schedule (Section 2.2)

•	Zooplankton QA/QC procedures (Section 3.2)

•	Updated Figure 5.1.

1.0

February 2022

Final approved QAPP; updated Table 2.1. Field training sessions for NLA 2022

1.1

May 2022

•	This version of the QAPP incorporates edits made to the NLA FOM
Version 1.2 and LOM Version 1.1 (see associated documents and
Appendix B)

•	Updated Appendix A

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Quality Assurance Project Plan Review & Distribution Acknowledgement
& Commitment to Implement the National Lakes Assessment 2022

l/We have read the Quality Assurance Project Plan and the methods manuals for the 2022 National
Lakes Assessment (NLA) listed below. Our agency/organization agrees to abide by its requirements for
work performed under the NLA 2022. Check appropriate boxes for the appropriate documents.

Quality Assurance Project Plan ~

Site Evaluation Guidelines ~

Field Operations Manual ~

Laboratory Operations Manual ~

Field Crew leaders: I also certify that I attended an EPA-sponsored NLA 2022 training and that all

p- members of my crew have received training in NLA protocols (check box) ~

"g Name (printed)

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E Title (Cooperator's Principal Investigator)

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NOTICE

The intention of the National Lakes Assessment 2022 (NLA 2022) is to provide a comprehensive "State of
the Lakes" assessment for lakes, ponds, and reservoirs across the United States. The complete
documentation of overall project management, design, methods, and standards is contained in this
Quality Assurance Project Plan and companion documents, including:

National Lakes Assessment 2022: Site Evaluation Guidelines (EPA 841-B-21-008)

National Lakes Assessment 2022: Field Operations Manual (EPA 841-B-21-011)

National Lakes Assessment 2022: Laboratory Operations Manual (EPA 841-B-21-010)

This document, the NLA 2022 Quality Assurance Project Plan (QAPP), contains elements of the overall
project management, data quality objectives, measurement and data acquisition, and information
management for NLA 2022. The complete QAPP includes this document and its associated Field
Operations Manual (FOM), Laboratory Operations Manual (LOM), and Site Evaluation Guidelines (SEG),
which together comprise the integrated set of QAPP documents. Methods described in this document
are to be used specifically in work relating to the NLA 2022. All project cooperators should follow these
guidelines. Mention of trade names or commercial products in this document does not constitute
endorsement or recommendation for use.

The suggested citation for this document is:

EPA. 2022. National Lakes Assessment 2022. Quality Assurance Project Plan. V.l.l. EPA 841-B-21-009.
U.S. Environmental Protection Agency, Washington, DC

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KITS

TITLE FRONT COVER

APPROVAL PAGE II

QUALITY ASSURANCE PROJECT PLAN REVIEW & DISTRIBUTION ACKNOWLEDGEMENT & COMMITMENT TO
IMPLEMENT THE NATIONAL LAKES ASSESSMENT 2022 IV

NOTICE V

TABLE OF CONTENTS VI

LIST OF TABLES VIII

LIST OF ACRONYMS IX

DISTRIBUTION LIST X

1 EXECUTIVE SUMMARY 1

1.1 Background 1

1.2 Project Organization 1

1.3 Quality Assurance Project Plan 1

1.4 Information Management Plan 1

1.5 NLA 2022 Design 2

1.6 Field Operations 2

1.7 Laboratory Operations 2

1.8 Peer Review 2

2 PROJECT PLANNING AND MANAGEMENT 4

2.1 Introduction 4

2.1.1 Project Organization 5

2.1.2 Project Schedule 10

2.2 Scope of QAPP 10

2.2.1 Field Operations 10

2.2.2 Overview of Laboratory Operations 13

2.2.3 Data Analysis and Reporting 15

2.2.4 Peer Review 15

3 DATA QUALITY OBJECTIVES 17

3.1 Data Quality Objectives 17

3.2 Measurement Quality Objectives 17

3.2.1 Laboratory Reporting Level (Sensitivity) 17

3.2.2 Field Measurements 19

3.2.3 Chemical Precision, Bias, and Accuracy 21

3.2.4 Taxonomic Precision and Accuracy ofBenthic Macroinvertebrates and Zooplankton 22

3.2.5 Precision of Physical Habitat Indicators 25

3.2.6 Completeness 26

f— 3.2.7 Comparability 27

Lu 3.2.8 Representativeness 27

O 4 SAMPLING DESIGN AND SITE SELECTION 28

q 4.1 Probability Based Sampling Design and Site Selection 28

lu 4.2 Reference (or Least-Disturbed) Site Selection 29

< 5 INFORMATION MANAGEMENT 30

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5.1 Roles and Responsibilities 30

5.1.1 State/Tribe-Based Data Management 33

5.2 Overview of System Structure 33

5.2.1 Data Flow 34

5.2.2 Simplified Description of Data Flow 34

5.2.3 Core Information Management Standards 35

5.2.4 Data Formats 36

5.2.5 Public Accessibility 36

5.3 Data Transfer Protocols 37

5.4 Data Quality and Results Validation 38

5.4.1 Design and Site Status Data Files 39

5.4.2 Sample Collection and Field Data 39

5.4.3 Laboratory Analyses and Data Recording 41

5.4.4 Data Review, Verification, and Validation Activities 42

5.5 Data Transfer 44

5.5.1 Database Changes 44

5.6 Metadata 44

5.7 Information Management Operations 44

5.7.1 Computing Infrastructure 44

5.7.2 Data Security and Accessibility 45

5.7.3 Life Cycle 45

5.7.4 Data Recovery and Emergency Backup Procedures 45

5.7.5 Long-Term Data Accessibility and Archive 45

5.8 Records Management 45

6 INDICATORS 46

6.1 Summary 46

6.1.1 Sampling Design 46

6.1.2 Sampling and Analytical Methods 46

6.1.3 Quality Assurance Objectives 46

6.1.4 Quality Control Procedures: Field Operations 46

6.1.5 Quality Control Procedures: Laboratory Operations 46

6.1.6 Data Management, Review, and Validation 46

7 ASSISTANCE VISITS 49

7.1 Field Evaluation and Assistance Visit Plan 49

7.2 Laboratory Evaluation and Assistance Visit Plan 49

8 DATA ANALYSIS PLAN 50

8.1 Data Interpretation Background 50

8.1.1 Scale of assessment 50

8.1.2 Selecting the best indicators 50

8.1.3 Defining least impacted (reference) condition 50

8.1.4 Determining thresholds for judging condition 50

8.2 Geospatial Data 51

8.3 Datasets Used for the Report 51 £

8.3.1 Trophic status and water quality 51

8.3.2 Ecological integrity 51 ^

8.3.3 Human health 52 O

8.4 Indicator Data Analysis 52 u.

8.4.1 Algal Toxins 52 2

8.4.2 Bacteria (Enterococci) 52 m

8.4.3 Benthic Macroinvertebrate and Zooplankton Assemblages 52 h

vii

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8.4.4 FisheDNA 52

8.4.5 Physical Habitat 53

8.4.6 Phytoplankton Assemblages 56

8.4.7 Fish Fillet Contaminants 56

8.4.8 Atrazine Pesticide Screen 56

8.4.9 Trophic Status 56

8.4.10 Water Chemistry, Chlorophyll a and Secchi Depth 56

9 LITERATURE CITED 58

APPENDIX A: LABORATORY LIST 61

APPENDIX B: REVISION HISTORY 62

LIST OF TABLES

Table 2.1 Field training sessions for NLA 2022 11

Table 2.2 Proposed peer review schedule for NLA 2022 report 16

Table 3.1 Important variance components for aquatic resource assessments 20

Table 5.1 Summary of IM responsibilities 30

Table 5.2 NLA 2022 Data submission software and associated file formats 37

Table 5.3 Summary sample and field data quality control activities 40

Table 5.4 Summary laboratory data quality control activities 41

Table 5.5 Data review, verification, and validation quality control activities 43

Table 6.1 Summary of indicator QA procedures and coordinators 47

Table 8.1 Physical habitat measurement data quality objectives 53

Table 8.2 Physical habitat field quality control 53

LIST

Figure 2-1 National Lakes Assessment 2022 project organization chart 9

Figure 2-2. Schedule for the NLA 2022 10

Figure 4-1 Design sites (base sites) for the 2022 National Lakes Assessment 29

Figure 5-1 Conceptual model of data flow into and out of the master SQL database for the NLA 2022... 35

LIST OF EQUATIONS

Equation 3-1. LT-MDL calculation for an individual analyte 18

Equation 3-2. Precision in absolute terms 21

Equation 3-3. Relative precision 21

Equation 3-4. Relative percent difference 22

Equation 3-5. Net bias 22

Equation 3-6. Bias in relative terms 22

Equation 3-7. Percent recovery 22

Equation 3-8. Percent taxonomic disagreement 23

Equation 3-9. Percent similarity 23

Equation 3-10. Percent difference in enumeration 24

Equation 3.11. Bray-Curtis Dissimilarity 24

Equation 3-12. Repeat visit variance 25

Equation 3-13. Signaknoise ratio 25

Equation 3-14. Source of variation in habitat variable 25

Equation 3-15. Percent completeness 26

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LIST OF ACRONYMS

ANC acid neutralizing capacity

ASTM American Society of Testing and Materials

CH4 methane

C02 carbon dioxide

CSDGM Content Standards for Digital Geospatial Metadata

DBH diameter at breast height

DO dissolved oxygen

DOC dissolved organic carbon

DQO Data Quality Objectives

eDNA Environmental deoxyribonucleic acid

EMAP Environmental Monitoring and Assessment Program

FGDC Federal Geographic Data Committee

FOIA Freedom of Information Act

FOM Field Operations Manual

GDIT General Dynamics Information Technology

GIS geographic information system

GRTS Generalized Random Tessellation Stratified (survey design)

HDPE high density polyethylene

H2S hydrogen sulfide

IM information management

LIMS Laboratory Information Management System

LOM Lab Operations Manual

LRL Laboratory Reporting Limit

LT-MDL target long-term Method Detection Limit

MDL Method Detection Limit

MQ/cm megaohms/centimeter

MMI multimetric indices

MQO Measurement Quality Objectives

NARS National Aquatic Resource Surveys

ND non-detect

NHD National Hydrography Dataset

NIST National Institute of Standards

NLA National Lakes Assessment

N20 nitrous oxide

OMB Office of Management and Budget

ORD EPA Office of Research and Development

OW EPA Office of Water

PESD EPA Office of Research and Development's Pacific Ecological Systems Division

PETG polyethylene terephthalate

QA quality assurance

QAPP Quality Assurance Project Plan

QA/QC quality assurance/quality control

QC quality control

QCS quality control sample

RL Reporting Limit ^

SEG Site Evaluation Guidelines 2

SOPs Standard Operating Procedures §

SQL Structured Query Language ^

TN total nitrogen g

TOC total organic carbon i_

TP total phosphorus zi

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EPA United States Environmental Protection Agency
USGS United States Geological Survey
WQX EPA Water Quality Exchange

DISTRIBUTION LIST

Quality Assurance Project Plan
Page x of xi

This QAPP, which includes the associated manuals or guidelines, is distributed to the following: EPA,
States, Tribes, universities, labs, and contractors participating in the National Lakes Assessment 2022
(NLA). EPA Regional Survey Coordinators are responsible for distributing the NLA QAPP to State and
Tribal Water Quality Agency staff or other cooperators who will perform the field sampling and
laboratory operations. The Logistics Coordinator distributes the QAPP and associated documents to
participating project staff at their respective facilities and to the project contacts at participating
laboratories, as they are determined. If the QAPP is updated, the project lead distributes the relevant
materials via email to necessary participants.

Title

Affiliation

EPA

First

Last Name Email

Phone

Region/

Name

Office

3
EG
oc
I—

EPA Project
Lead

EPA

Lareina

Guenzel

guenzel. Iareina(® eoa.gov

202-566-0455

EPA Project
OA

Coordinator

EPA

Sarah

Lehmann

lehmann.sarah(® eoa.gov

202-566-1379

EPA Project
OA

Coordinator

EPA

Bernice

Smith

smith.bernicelPeoa.gov

202-566-1244

EPA QA
Officer

EPA

Cynthia

Johnson

Johnson.cvnthiaN(® eoa.gov

202-566-1679

EPA Logistics
Lead

EPA

Brian

Hasty

hastv. brian(® eoa.gov

202-564-2236

EPA

Laboratory

Review

Coordinator

EPA

Kendra

Forde

forde. kendra(® eoa.gov

202-566-0417

Contract
Logistics
Coordinator

GLEC

Chris

Turner

cturner(®glec.com

715-829-3737

Fish Fillet
Contaminants
Indicator Co-
Leads

EPA

OST

Leanne

Stahl

leanne.stahKSeoa.gov

202-566-0404

EPA

OST

John

Healey

healev.iohn (Seoa.gov

202-566-0176

NARS IM
Coordinator

EPA

ORD

Karen

Blocksom

blocksom.karenPeoa.gov

541-754-4470

NARS IM

Coordinator

(Contractor)

GDIT

Michelle

Gover

gover. michelle(® eoa.gov

541-754-4793

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Title

Affiliation

EPA
Region/
Office

First
Name

Last Name

Phone

Regional
Coordinators

EPA

Region 1

Hilary

Snook

snook.hilarvPepa.gov

617-918-8670

EPA

Region 2

Emily

Nering

Nering.EmilvPepa.gov

732-321-6764

EPA

Region 3

Frank

Borsuk

borsuk.frankPepa.gov

304.234.0241

EPA

Region 3

Leah

Ettema

ettema.leahPepa.gov

304-234-0245

EPA

Region 4

Chris

McArthur

mcarthur.christopherPepa.gov

404-562-9391

EPA

Region 4

Jerry

Ackerman

Ackerman.JerrvPepa.gov

706-355-8721

EPA

Region 5

Mari

Nord

nord.mariPepa.gov

706-355-8721

EPA

Region 6

Rob

Cook

Cook.RobertPepa.gov

214-665-7141

EPA

Region 7

Gary

Welker

Welker.GarvPepa.gov

913-551-7177

EPA

Region 8

Liz

Rogers

Rogers.LizPepa.gov

303-312-6974

EPA

Region 8

Tom

Johnson

iohnson.tomPepa.gov

303-312-6226

EPA

Region 9

Matt

Bolt

Bolt.MatthewPepa.gov

415-972-3578

EPA

Region
10

Lil

Herger

Herger.LillianPepa.gov

206-553-
1074

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1 EXECUTIVE SUMMARY

1.1 Background

To address the need for improved water quality monitoring and analysis at multiple scales, the EPA
Office of Water (OW), in partnership with EPA's Office of Research and Development (ORD), EPA
regional offices, states and tribes and other partners, assesses the condition of the nation's waters via a
statistically valid approach. Often referred to as probability-based surveys, these assessments, known as
the National Aquatic Resource Surveys (NARS), report on core indicators of water condition using
standardized field and lab methods and utilize integrated information management (IM) plans to ensure
confidence in the results at national and ecoregional scales.

The National Lakes Assessment 2022 (referred to as NLA 2022 throughout this document), which builds
upon the previous NLA 2007, 2012 and 2017, aims to address three key questions about the quality of
the nation's lakes and reservoirs:

¦ What percent of the nation's lakes are least, moderately, and most disturbed for key
indicators of trophic state, ecological health, and human use (recreation)?

¦ What is the relative importance of key stressors such as nutrients and pathogens?

¦ What changes are occurring in the condition of the nation's lakes?

The surveys are also designed to help expand and enhance state and tribal monitoring programs.
Through these surveys, 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.

1.2 Project Organization

Overall project coordination is conducted by EPA's Office of Water in Washington, DC, with technical
support from the ORD's Pacific Ecological Systems Division (PESD) 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 field crews who collect the water and biological samples following NLA 2022
protocols. EPA began planning the NLA 2022 with state, tribal, and other federal partners in 2020 and is
continuing this partnership effort. EPA expects to make the raw data publicly available Spring of 2024
and report the results by December 2024 in compliance with the Data Quality Act.

1.3 Quality Assurance Project Plan

The purpose of this QAPP is to document the NLA 2022 project data quality objectives and quality
assurance/quality control measures needed to ensure that the data collected meets those objectives.
The plan contains elements of the overall project management, data quality objectives, measurement
and data acquisition, and information management for the NLA 2022 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
together make up the full QAPP for the NLA 2022.

1.4 Information Management Plan

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organization and ensured integrity of stored electronic data. IM is integral to all aspects of the NLA 2022
from initial selection of sampling sites through the dissemination and reporting of final, validated data.

A technical 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 plan section of this QAPP. Validated data are transferred to the central database managed
by information management support staff located at the EPA PESD 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 NLA 2022 are eventually transferred to EPA's Water
Quality Exchange (WQX) for archival in the Water Quality Portal for public accessibility.

1.5 NLA 2022 Design

EPA used an unequal probability design to select 904 lakes and reservoirs greater than 1 hectare (ha) in
size (note: in NLA 2007, the lower size limit was 4 ha) in the continental United States. The design
includes 2 revisits in each state resulting in a total of 1,000 site visits. Revisit samples are collected for
quality assurance purposes including evaluation of the ability of an indicator to distinguish among sites
from differences within individual sites. Of the 904 lakes, approximately 50% of the lakes are new lakes
selected for 2022 and 50% are previously sampled lakes as part of the NLA 2017. The NLA 2017 lakes
referred to as resample lakes.

1.6 Field Operations

Sample collection for NLA 2022 is designed to be completed during the index period of June through the
end of September 2022. Field data acquisition activities are implemented in a consistent manner across
the entire country. Each site is given a unique ID which identifies it throughout the pre-field, field, lab,
analysis, and data management phases of the project. Specific procedures for evaluating each sampling
location and for replacing non-sampleable sites are documented in NLA 2022 Site Evaluation Guidelines
(SEG, 841-B-21-008).

NLA 2022 indicators include: algal toxins (microcystins and cylindrospermopsin), benthic
macroinvertebrates, physical habitat, phytoplankton, atrazine pesticide screen, water chemistry and
chlorophyll-o, and zooplankton. Supplemental indicators include enterococci and fish fillet
contaminants. An additional research indicator is fish environmental DNA (eDNA). Field measurements
and sampling methods are outlined in the NLA 2022 Field Operations Manual (FOM, EP841-B-21-011).
Field crews are trained on these methods at a required EPA-sponsored training session. Field sampling
assistance visits are completed for each field crew for quality assurance.

1.7 Laboratory Operations

NLA 2022 laboratory analyses are conducted either by state/tribal-selected labs or "National
Laboratories" set up by the EPA to conduct analyses for any state/tribe which so elects. The designated
National Laboratories and state/tribal labs must comply with the QA/QC requirements described in this
document and in the National Lakes Assessment 2022: Laboratory Operations Manual (LOM, 841-B-21-
010). Any laboratory selected to conduct analyses with NLA 2022 samples must demonstrate that it can
meet the quality standards presented in this NLA 2022 QAPP and in the NLA 2022 LOM.

1.8 Peer Review

The NARS program, including the NLA utilizes a three-tiered approach for peer review of the Survey.
¦ internal and external review by the EPA, states, other cooperators and partners;

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¦ 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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2 PROJECT PLANNING AND MANAGEMENT
2.1 Introduction

In the early 2000s, several reports identified the need for improved water quality monitoring and
analysis at multiple scales. In 2000, the General Accounting Office (USGAO 2000) reported that the 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 the 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
inadequate data for national reporting on fresh water, coastal and ocean water quality indicators. The
National Association of Public Administrators (NAPA 2002) stated that improved water quality
monitoring is necessary to help states and tribes make more effective use of limited resources. EPA's
Report on the Environment 2003 (EPA 2003) stated that there is insufficient information to provide a
national answer, with confidence and scientific credibility, to the question, 'What is the condition of U.S.
waters and watersheds?'

In response to this need, OW, in partnership with states and tribes, began a program to assess the
condition of the nation's waters via a statistically valid approach. The current assessment, the National
Lakes Assessment 2022 (referred to as NLA 2022 throughout this document), builds upon the three prior
lake surveys (NLA 2007, 2012, and 2017) as well as other NARS surveys such as the National Rivers and
Streams Assessment, National Coastal Condition Assessment, and the National Wetland Condition
Assessment. The NLA 2022 effort will provide important information to states and the public about the
condition of the nation's lake resources and key stressors on a national and regional scale.

EPA developed this QAPP to support project participants and to ensure that the final assessment is
based on high quality data that is documented and appropriate for its intended use. The QAPP contains
elements of the overall project management, data quality objectives, measurement and data
acquisition, and information management for NLA 2022. 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 expects that any supplemental elements are addressed by the states, tribes, or
their designees, in a separate approved QAPP or an addendum to this QAPP. The NLA 2022 participants
have agreed to follow this QAPP and the protocols and design laid out in this document, and its
associated documents - the NLA 2022 FOM, LOM, and SEG.

I—

m 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 lakes with both 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 National Lakes Assessment 2022 has three main objectives:

Estimate the current status, trends, and changes in selected trophic, ecological, and human use
indicators of the condition of the nation's lakes with known statistical confidence.

< • Seek associations between selected indicators of natural and anthropogenic stresses and

indicators of ecological condition.

• Assess changes in population status between 2007 and 2022.

O
oc

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The NLA Steering Committee, comprised of EPA, state, and other partners, decided on a few
improvements and changes to the NLA indicators. Additions in the 2022 survey include a new human
health fish fillet contaminants indicator and enterococci (bacteria) indicators. The following research
indicators from NLA 2017 are not being sampled or analyzed in the NLA 2022: sediment contaminants,
sediment TOC, sediment grain size, and dissolved gases. These indicators will be evaluated for inclusion
in the next survey expected in 2027. New indicators in NLA 2022 that were not part of NLA 2017 include:
fish fillet contaminants, enterococci and a visual HABs assessment. While taxonomic information is
included as part of the laboratory work for the phytoplankton index site sample, the focus for NLA
(including QC and assessment) is on cyanobacteria. Finally, crews will only collect the microcystin sample
in a polyethylene terephthalate (PETG) sample container, which is recommended sampling container by
Abraxis, and will not be collecting samples in the high density polyethylene (HDPE) sample containers
used in previous survey. EPA determined this is an appropriate change because a comparison study in
which EPA and partners collected/analyzed microcystins using both types of bottles in 2017 did not
show differences in results between the two types.

2.1.1 Project Organization

The responsibilities and accountability of the various principals and cooperators are described here and
illustrated in Figure 2-1. Overall, the project is coordinated by the Office of Water (OW) in Washington,
DC, with support from EPA PESD 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 work with their Regional EPA Coordinator to address any technical issues. The NLA
implements a comprehensive quality assurance (QA) program to ensure data integrity and provide
support for the reliable interpretation of the findings from this project. The Project Lead convenes the
NLA Steering Committee and Technical Experts Workgroups to provide the team with support for
determining the best and most appropriate approaches for key technical issues, such as: (1) the
selection and establishment of reference conditions based on least-disturbed sites and expert consensus
for characterizing benchmarks for assessment of ecological condition; (2) selection and calibration of
ecological endpoints and attributes of the biota and relationship to stressor indicators; (3) a data
analysis plan for interpreting the data and addressing the objectives in a nationwide assessment; and (4)
a framework for the reporting of the condition assessment and conveying the information on the
ecological status of the nation's lakes.

Contractor support is provided for all aspects of this project. Contractors provide support ranging from
implementing the survey, sampling and laboratory processing, data management, data analysis, and
report writing. Cooperators 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:

Project Leader: Lareina Guenzel, EPA OW

¦ 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 Lakes Steering Committee and establishes needed technical workgroups.

¦ Interacts with the EPA Project Team on technical, logistical, and organizational issues on a
regular basis.

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EPA Project QA Coordinator: Sarah Lehmann, EPA OW

¦ Provides leadership, development, and oversight of project-level quality assurance for NLA.

¦ Assembles and provides leadership for a NLA 2022 Quality Team.

¦ Maintains official, approved QAPP.

¦ Maintains all training materials and documentation.

¦ Maintains all laboratory accreditation files.

EPA QA Officer, Office of Wetlands, Oceans and Watersheds: Cynthia N.Johnson, EPA OW

¦ 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 Field Logistics Coordinator: Brian Hasty, EPA OW

¦ 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.

QA Assistance Visit Coordinator: Brian Hasty, EPA Office of Water

¦ The EPA employee who will supervise the implementation of the QA audit program; and

¦ Directs the field and laboratory audits and ensures the field and lab auditors are adequately
trained to correct errors immediately to avoid erroneous data and the eventual discarding
of information from the assessment.

EPA Laboratory Review Coordinator: Kendra Forde, EPA OW

¦ Ensures participating laboratories have the appropriate technical competencies to process
samples.

¦ Ensures participating laboratories complete sample analysis following Laboratory Operations
Manual.

¦ Ensures participating laboratories follow QA activities.

National Laboratory Task Order Managers - one for each contract
!_ ¦ 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 the Project Leader and EPA Laboratory Review Coordinator on the sample

^ processing status of labs and any questions or concerns raised by participating labs in regard to

^ timelines and deliverables.

¦z.

^ Information Management Coordinator: Michelle Gover, GDIT

^ "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.

cl ¦ Oversees all sample shipments and receives data forms from the Cooperators.

b ¦ Oversees all aspects of data entry and data management for the project.

OC
CL

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Fish Fillet Contaminants Indicator Co-Leads: Leanne Stahl and John Healey, EPA OW

Organizes and oversees fish sampling training for the human health fish fillet contaminants
indicator (Leanne).

Coordinates and tracks fish sample collection for the human health fish fillet contaminants
indicator (Leanne)

Provides direction for development of the fish sample preparation QAPP and for preparation of
fillet tissue samples from fish composite samples (John).

Oversees laboratory solicitation process, sample analysis QAPP development, and analysis of
fillet tissue samples for target chemicals (John).

Interact with the EPA Project Leads, EPA regional coordinators, contractors and cooperators to
provide information and respond to questions related to the human health fish fillet
contaminants indicator (Leanne and John).

Endangered Species Act (ESA) Lead: Karolyn Lock, EPA OW

¦ Primary ESA contact for the U.S. Fish and Wildlife Service (FWS) and National Oceanic and
Atmospheric Administration, National Marine Fisheries Service (NOAA/NMFS).

¦ Works with the EPA Project Lead to ensure that survey manuals and protocols include
appropriate responses and reporting requirements in the event that a crew encounters federally
listed species when conducting field work.

¦ Prepares the Biological Evaluation to support Section 7 consultations.

¦ Works with the survey logistics lead to implement the conservation measures, reasonable and
prudent measures, and reporting requirements identified in the Biological Opinion.

¦ Maintains library of NLA ESA documents.

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, and 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 z

and interpretation to be scientifically valid. ^

Cooperator(s): States, Tribes, USGS, others <

¦z.

¦ Under the scope of their assistance agreements, plans and executes their individual studies as ^
part of the cross jurisdictional NLA 2022 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. z

Field Sampling Crew Leader 2j

¦ Functions as the senior member of each Cooperator's field sampling crew and the point of t
contact for the Field Logistics Coordinator. O

¦ Provides training and oversight to their field crew as needed. cl

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¦ Accompanies and oversees other members of the sampling crew in the field.

¦ Responsible for overseeing all activities of the field sampling crew and ensuring that the Project
field method protocols are followed during all sampling activities.

Contractor Field Logistics Coordinator: Chris Turner¦, GLEC

¦ 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.

EPA Technical Advisor: Steven Paulsen, EPA ORD

¦ Advises the Project Leader on the relevant experiences and technology developed within ORD
that may be used in this project.

¦ Facilitates consultations between NLA personnel and ORD scientists.

EPA Study Design Manager: Tony Olsen, EPA ORD

¦ Provides leadership and oversight of Design Team

¦ Coordinates w/ Project Manager and Field Logistics Coordinator to develop and manage the
Sampling Frame, select sampling locations, and track field evaluation and site reconnaissance.

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Project Management

Project Lead - Lareina Guenzel, EPA OW
Project QA - Sarah Lehmann, EPA OW
Technical Advisor - Steve Paulsen, EPA ORD

Study Design

Tony Olsen, EPA ORD

Quality Assurance

Cynthia N. Johnson
EPA OW

Field Protocols

NLA 2022 Steering
Committee

Field Logistics Coordinator

Brian Hasty, EPA OW
Training

EPA HQ EPA ORD, EPA Regions, Contractors
Field Implementation
EPA HQ EPA Regions, States, Tribes, Contractors
Indicator Team
NLA 2022 Steering Committee

Sample Flow: EPA TOCORS

Algal Toxins

Atrazine Pesticide

Benthic Macroinvertebrates

Chlorophyll a

Phytoplankton

Fecal Indicator (Enterococci)

Fish eDNA

Chemistry

Field Data

Zooplankton

Laboratory Processing Oversight

EPA Laboratory Review Coordinator
EPA Lab Task Order Managers

Information Management

EPA ORD and Contractor - Karen Blocksom and
Michelle Gover

Final Data

Fish Tissue Contaminants

Laboratory Processing
Oversight & Information
Management

EPA OST Fish Tissue
Coordinator

Assessment

EPA ORD

Assessment

OW - Project Lead
EPA ORD, EPA Regions, States, Tribes, Federal
Partners, Cooperators

Figure 2-1 National Lakes Assessment 2022 project organization chart.

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2.1.2 Project Schedule

Training and field sampling are conducted in spring and summer of 2022. The team needs to complete
sample processing and data analysis by 2023 in order to publish a planned report in FY 2024. The full
schedule of the NLA 2022 is presented in Figure 2-2.

2020 2021 2022 2023 2024

research

design

Field

lab / data

report

survey planning

select indicators

design frame

select sites

implementation
manuals

field training

sampling season

sample processing

data analysis

draft results and/or
report

peer review as needed

final results and/or
report

Figure 2-2. Schedule for the NLA 2022

2.2 Scope of QAPP

This QAPP addresses the data acquisition efforts of the NLA 2022, which focuses on the sampling of
lakes across the United States in 2022. Data from approximately 1,000 site visits (selected with a
^ probability design) located within the contiguous 48 states provide a comprehensive assessment of the

^ nation's lakes. Quality information, requirements, and procedures are contained in the QAPP and its

o accompanying documents: the SEG, FOM, and LOM. Much of the detailed quality assurance information

< is in the companion documents to avoid redundancy. In these cases, the QAPP directs readers to the

^ primary sources of this information.

2.2.1 Field Operations

All field operations information is available in the FOM. Field operations are implemented for the NLA
2022 based on guidance developed by EMAP (Baker and Merritt 1990), experience from NLA 2007,

O 2012, and 2017 advice from the NARS Team, and through consultation with a steering committee

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comprised of various state, tribal, federal, and regional agencies. Funding for states and tribes to
conduct field data collection activities is provided by EPA under Section 106 of the Clean Water Act. The
project lead initiates field operations preparation by working with the Design Team (led by ORD in
Corvallis) to revise, as needed, the target population and sample frame and to identify state/tribal or
other organization-requested intensifications/modifications. The Design Team selects sampling
locations. The Project Lead distributes the list of sampling locations to the EPA Regional NLA
Coordinators, states, and tribes and to other partners. See the Site Evaluation Guidelines for the
detailed design documentation.

With the sampling location list, state and tribal field crews can begin site reconnaissance on the primary
sites and alternate replacement sites and begin work on obtaining permission to access each site.
Specific procedures for evaluating each sampling location and for replacing non-sampleable sites are
documented in the NLA 2022 SEG. Field crews procure scientific collecting permits from State, Tribal,
and Federal agencies, as needed. The field crews use standard field equipment and supplies. Field Crew
Leaders from states and tribes work with EPA Regional Coordinators and the NARS Information
Management (IM) Center to coordinate equipment and supply requirements. This helps to ensure
comparability of protocols across states. Detailed lists of equipment required for each field protocol, as
well as guidance on equipment inspection and maintenance, are contained in the FOM.

Trained crews collect field measurements and samples. Each Field Crew Leader must be trained at an
EPA-sponsored training session prior to the start of the field season (see Table 2.1), along with as many
crew members as possible. EPA will provide the two-day in-person training sessions in a number of
locations around the country for cooperators and contractors. It is strongly encouraged that field crews
attend all days of training. 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 NLA 2022 must adhere to the provisions of this integrated QAPP, FOM, and SEG.
Trainers maintain a list of all personnel trained and provide the information to the NLA Project Lead and
the OA Project Lead.

The Project QA Coordinator or their designated member of the Quality Team maintains training
documentation in NLA 2022 QA files. Field crews may not operate without a trained field crew leader
present.

Table 2.1 Field training sessions for NLA 2022.

Date* Training Location

Primary Trainees**

March 8-10

Nashville, TN

Survey trainers including EPA
OW and Regional staff and
contractors

April 12-14

Dallas, TX

LA, AR, OK, NM, TX

April 19-20

Trenton, NJ

April 19-20

Moss Landing, CA

AZ, CA, HI, NV

April 26-28

Athens, GA

TN, AL, GA, FL, SC, NC

May 3-4

Cacapon State Park, WV

PA, WV, VA, DE, MD, DC,

Mayl0-12

Albany, NY

May 10-12

Frankfort, KY

KY, MS

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Date*

Training Location

Primary Trainees**

May 17-19

Denver, CO

CO, MT, ND, SD, UT, WY,
Standing Rock Sioux Tribe

May 24-26

Kenosha, Wl

IL, IN, Ml, MN, OH, Wl, Leech
Lake Tribe, Fond du Lac Tribe

May 24-26

Lenexa, KS

IA, MO, KS, NE

June 1-2

Traverse City, Ml

Contracted field crews

June 7-9

North Chelmsford, MA

CT, ME, MA, NH, Rl, VT, NY

June 14-16

Olympia, WA

ID, OR, WA

*The in-person portion of the NLA training will be a two day event. Exact dates are still to be determined for the
trainings with more than two days identified.

**Actual trainees will change based on training dates and who is conducting the sampling; COVID and state travel
restrictions required a few smaller, state-specific training opportunities.

Trained evaluators conduct evaluation and assistance visits with each Field Crew early in the sampling
and data collection process. Evaluators provide corrective actions in real time. These visits provide EPA
with a 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 are based on the uniform training, plans, and checklists. For more
information on field assistance visits see Section 8 of the FOM.

Crews may use a variety of methods to access a lake. Some sampling locations require crews to hike in,
transporting all equipment in backpacks. For this reason, EPA and the steering committee considered
ruggedness and weight as 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.

The site verification process is outlined in the NLA 2022 SEG and FOM. EPA fully documented all
methods used in the field in step-by-step procedures in the NLA 2022 FOM. The manual also contains
detailed instructions for completing documentation, labeling samples, any field processing
requirements, and sample storage and shipping. Field communications is through Field Crew Leaders,
and involves regularly scheduled conference calls or contacts with the NLA 2022 logistics staff

Standardized field data forms are the primary means of data recording. For NLA 2022, crews are using
electronic field forms (NLA eforms application). Back-up paper forms are available if needed. On
^ completion, a field crew member other than the person who initially entered the information reviews

^ the data forms. 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 is done for either form of data collection (electronic or paper). Field crews are provided

^ an iStick to back-up electronic field data in case data are lost from the tablet.

After field sampling is complete (and wifi available), crews will submit all completed data forms in the
NLA App. If still reviewing data forms, the Site Verification and Tracking Forms (for any shipped samples)
must be submitted if samples are shipped. All submitted data will be sent back to the field crew in a
summary email from the database to the field crew's iPad. The NLA App is the required format for field
§ data submission. If a field crew needed to use paper forms, the crew is required to transfer all data to

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the App for submission and submit the original paper forms to NARS IM (crews should also retain a copy
of the forms for their own files) (see Section 5.4.4).

Field crews store or package samples for shipment in accordance with instructions contained in the NLA
2022 Field Operations Manual, including taking precautions so holding times are not exceeded. Field
crews deliver samples which must be shipped to a commercial carrier; crews maintain copies of bills of
lading or other documentation. Using the tracking form, crews notify the NARS IM Center about sample
shipment; thus, NARS IM and Logistics staff can initiate tracking procedures quickly in the event samples
are not received. Crews complete chain-of-custody forms for all transfers of samples, with copies
maintained by the field crew. The Logistics staff follows 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.

2.2.2 Overview of Laboratory Operations

Holding times for surface water samples vary with the sample types and analytes. Some analytical
measurements begin during sampling (e.g., in situ profiles) while others are not initiated until sampling
has been completed (e.g., phytoplankton and zooplankton). Analytical methods are summarized in the
NLA 2022 LOM.

Chemical, physical, or biological analyses may be performed by cooperator or contractor laboratories.
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 ASTM class 1 or 2 weights which have
National Institute of Standards and Technology (NIST) traceable certificates.

• Verification of the calibration of top-loading balances using NIST-certified ASTM class 4 weights.

• Checking and recording the composition of fresh calibration standards against the previous lot.
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 safety
data sheets for all required materials.

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• An overall program of hazardous waste management and minimization, and evidence of proper
waste handling and disposal procedures (e.g., 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 resistivity (>18 megaohms/cm (MQ/cm; at 25 °C; ASTM D1193-
6) 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
(zooplankton and benthic macroinvertebrates) as appropriate.

• Labeling all containers used in the laboratory with date prepared contents, and initials of the
individual who prepared the contents.

• Dating and storing all chemicals safely upon receipt. Chemicals are disposed of properly when
the expiration date has expired.

• Using a laboratory information management system to track the location and status of any
sample received for analysis.

• Reporting results electronically using standard formats and units compatible with NARS IM (see
NLA 2022 LOM for data templates). These files are labeled properly by referencing the indicator
and/or analyte and date.

All laboratories providing analytical support to NLA 2022 must adhere to the provisions of this
integrated QAPP and LOM. Laboratories provide information documenting their ability to conduct the
analyses with the required level of data quality prior to data analysis. EPA provides different
requirements based on the type of analysis being done by the lab (i.e., chemistry vs. biological analyses).

Labs send the documentation to the Laboratory Review Coordinator at EPA Headquarters (or other such
designated parties) to maintain in the NLA 2022 OA files. Such information may include the following,
depending on the evaluation by the Quality Assurance Project Coordinator and the Laboratory Review
Coordinator:

• Signed Quality Assurance Project Plan by the laboratory performing the analysis.

• Signed Laboratory Form.

• Valid Accreditation or Certification.

• Laboratory's Quality Manual and/or Data Management Plan.

• Method Detection Limits (MDL).

• Demonstration of Capability.

• Results from inter-laboratory comparison studies.

• Analysis of performance evaluation samples.

• Control charts and results of internal QC sample or internal reference sample analyses to
document achieved precision, bias, accuracy.

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Other laboratory 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.

• Participation in inter-laboratory sample exchange.

All qualified laboratories shall work with the NARS IM Center to track samples as specified in Section 1 of
the LOM.

2.2.2.1 Biological Laboratory Quality Evaluation

The NLA 2022 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.

2.2.3 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 managed by NARS IM support staff located at PESD in Corvallis. Information management
activities are discussed further in Section 4. Data in the PESD database are available to Cooperators for
use in development of indicator metrics. All validated measurement and indicator data from NLA 2022
are eventually transferred to EPA's Water Quality Exchange (WQX) and then the Water Quality Portal
(WQP).

2.2.4 Peer Review

If deemed necessary, the NLA 2022 report will undergo a thorough peer review process. Cooperators
have been actively involved in the development of the overall project management, design, methods,
and standards including the drafting of four key project documents:

Quality Assurance Project Plan.

• Site Evaluation Guidelines.

• Field Operations Manual.

• Laboratory Operations Manual.

The EPA NARS program, including the NLA 2022, 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 NLA team reviews

comments and feedback from the cooperators and incorporate such feedback into the draft report, ^

when appropriate. The NLA 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 NLA may undergo public z

and scientific peer review. ^

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Below are the proposed measures EPA plans for engaging in the peer review process:

¦ Follow the EPA's Information Quality Guidelines and complete the 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 (if applicable) and produce a final report.

The proposed peer review schedule is provided below in Table 2.2 and is contingent upon timeliness of
data validation and schedule availability for regional meetings and experts for data analysis workshop.

Table 2.2 Proposed peer review schedule for NLA 2022 report.

Proposed Schedule

Activity

June 2022-July 2023

Data validation

July - November 2023

Internal data analysis and review meetings (e.g., web conferences); release of
preliminary data to state/tribal/EPA partners

November - January 2024

Report development

February 2024

Draft released for external peer review (if applicable)

July 2024

Draft released for public review (if applicable)

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3 DATA QUALITY OBJECTIVES

It is a policy of the 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 (EPA 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
(EPA 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 (EPA 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 (EPA 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 (EPA 2006).

3.1 Data Quality Objectives

Target DQOs established for the NLA 2022 relate to the goal of describing the current status of selected
indicators of the condition of lakes 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 lakes (± 5%) in the conterminous U.S. that fall below the designated
threshold for good conditions or other applicable benchmarks/criteria for selected measures
with 95% confidence.

For the ecoregions of interest, the DQO is:

• Estimate the proportion of lakes (± 15%) in a specific ecoregion that fall below the designated
threshold for good conditions or other applicable benchmarks/criteria for selected measures
with 95% confidence.

For estimates of change, the DQOs are:

• Estimate the proportion of lakes (± 7%) in the conterminous U.S. that have changed condition
classes for selected measures with 95% confidence.

3.2 Measurement Quality Obj ectives

For each parameter, performance objectives (associated primarily with measurement error) are
established for several different data quality indicators (following EPA Guidance for Quality Assurance
Plans, EPA 2002). Specific Measurement Quality Objectives (MQOs) for each parameter are presented in
the indicator section of the LOM or FOM as appropriate. The following sections define the data quality
indicators and present approaches for evaluating them against acceptance criteria established for the
program.

3.2.1 Laboratory Reporting Level (Sensitivity)

For water chemistry measurements, requirements for the method detection limit (MDL) are typically
established and the process used by the national laboratory is described here. See indicator specific

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information in the LOM and related QAPPs for specifics on what is used for each indicator. The MDL is
defined as the lowest level of analyte that can be distinguished from zero with 99 percent confidence
based on seven measurements (40CFR136 App. B). USGS NWQL has developed a variant of the MDL
called the long-term MDL (LT-MDL) to capture greater method variability (Oblinger Childress et al.
1999). Unlike MDL, it is designed to incorporate more of the measurement variability that is typical for
routine analyses in a production laboratory, such as multiple instruments, operators, calibrations, and
sample preparation events (Oblinger Childress et al. 1999). Because the LT-MDL addresses more
potential sources of variability than the MDL, the NLA uses the LT-MDL for water chemistry indicator
parameters.

For the NLA, target long-term MDL (LT-MDL, following Oblinger-Childress et al., 1999) values were
established for each chemical analyte based on the anticipated range of concentrations expected, values
required as thresholds for assigning lake condition based on chemical stressors (e.g., nutrients,
acidification, salinity, etc.) or trophic state (oligotrophic vs. mesotrophic vs. eutrophic), and the
capability of analytical laboratories to measure an analyte at low concentrations over time given
available methods.

The LT-MDL determination ideally employs at least 24 blanks and spiked samples prepared and analyzed
by multiple analysts on multiple instruments over a 6- to 12-month period at a frequency of about two
samples per month (EPA 2004). The LT-MDL uses "F-pseudosigma" (F0) in place of s, the sample standard
deviation, used in the EPA MDL calculation. F-pseudosigma is a non-parametric measure of variability
that is based on the interquartile range of the data (EPA 2004). The LT-MDL is calculated using either the
mean or median of a set of long-term blanks, and from long-term spiked sample results (depending on
the analyte and specific analytical method). The LT-MDL for an individual analyte is calculated as:

Equation 3-1. LT-MDL calculation for an individual analyte.

LT -MDL =M + (t0 99 x Fa)

where:

M = the mean or median of blank results

n = the number of spiked sample results

F0 = F-pseudosigma, a nonparametric estimate of variability calculated as:

a-a

1.349

where:

Q3 = the 75th percentile of spiked sample results
Qi = the 25th percentile of spiked sample results

LT-MDL is designed to be used in conjunction with a laboratory reporting level (LRL; Oblinger Childress
et al. 1999).

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The lab monitors performance using the determined/calculated LT-MDL values, but uses the MDLs as
determined based on 40CFR136 App. B to establish MDLs and Reporting Levels for reporting purpose,
estimates and flagging (RLs are also known as minimal reporting levels). The RL values are designed to
achieve a risk of <1% for both false negatives and false positives (Oblinger- Childress et al., 1999). The
Laboratory Reporting Limit (LRL) is set as two times higher than the target LT-MDL value. Therefore,
multiple measurements of a sample having a true concentration at the RL should result in the
concentration being detected and reported 99 percent of the time (Oblinger- Childress et al., 1999).

Target MDL and RL values are based on the presumption that a laboratory receives samples from across
the United States. Laboratories analyzing NLA samples from a more restricted region may have modified
target RL values based on the range of expected concentrations and required thresholds values. A
modified RL for a "regional" laboratory cannot be greater than a required threshold value used in the
NLA assessment. The objective for NLA is to minimize the number of values reported as "estimated" by
an individual laboratory (i.e., between an estimated MDL and the laboratory RL).

For chemical analyses, all participating laboratories will monitor their target RL values by one (or both)
of the following approaches:

1) For every calibration curve, include a calibration standard with an analyte concentration equal to
the RL.

2) Monitor the RL by including a Quality Control Sample (QCS) with a concentration equal to the RL
with each analytical batch. Results of each QCS analysis must meet the acceptance criteria
established for precision and bias (Section 3.2.3).

Laboratories are encouraged to conduct evaluations of analytical performance using samples at the
target RLs established based on a "national" laboratory (receiving samples from across the US). These
studies provide an indication of the confidence that can be placed on "estimated" results reported by
the laboratory.

Laboratories must submit estimates of 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.

3.2.2 Field Measurements

Since analytical (or field) precision, bias, and accuracy of field measurements is not monitored
separately during the NLA 2022, a revisit site approach is implemented to help evaluate the quality of
data (revisiting sites within the NLA 2022 index period). The survey design also incorporates a plan for
resampling a subset of sites from previous NLAs (including a subset of 226 lakes that were originally
sampled in NLA 2007 and 218 lakes that were originally sampled in NLA 2012). Data from these repeat
visits provide estimates of important components of variance to evaluate the performance of ecological
indicators, evaluate the capability of the survey design to estimate status vs. detect trend, and to
potentially reduce bias in the population estimates by "de-convoluting" the variance. These variance
components are presented in Table 3.1. If estimates of these components are available from other
studies, they are used in conjunction with the project requirements to evaluate alternative design
scenarios (Larsen et al., 1995, 2001, 2004). Status estimates are influenced most by the interaction (if

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multiple years are required to complete sampling) and residual variance components. Residual variance
is composed of temporal variance within a sampling period confounded with measurement error of
various types. If the magnitude of residual variance is sufficiently large to impact status estimates (see
above), then relative magnitudes of the interaction variance and various components of residual
variance are examined to determine if any reduction can be achieved in the future. Interaction variance
can only be reduced by increasing the sample size. Index variance can be reduced by either increasing
the number of sites, increasing the number of times a site is visited within a year, reducing the length of
the index period, or by reducing measurement error. Trend detection is evaluated using the equation to
determine the variance in the slope of the trend (Table 3.1). In the equation, residual variance also
includes the interaction component. For multi-site networks such as the NARS assessments, trend
detection is most sensitive to coherent year variance, which can only be reduced by extending the time
period for monitoring (Larsen et al., 1995, 2001, 2004). If residual variance is large relative to the
coherent year variance, then trend detection within a fixed time period can be improved by increasing
the number of sites sampled each year, increasing the number of times each site is sampled within a
year, or by reducing measurement error.

Table 3.1 Important variance components for aquatic resource assessments.

Model for status estimation Model for trend detection

i/i
LU
>
I—

O
>
H

<
3

<
i—
<
Q

2 = 2. / 2 + 2 + 2.

O"total O"sjtss V year sitex year O"residual'

2 ( 2 \

a sites .^2 S residual \
xt vsor u

var(slope) = ^ ''

yens

y -y)

i=l

and

T T T

J. X J.

residual ^within-year error

And

r 2 2 ^residual
* residual ~ ^ site x veirr ,r

visit

Components in parentheses represent "extraneous" variance

Variance
Component

Description

^ sites

Observed variance among all sites or streams sampled over multiple-year sampling cycle.
If sites are revisited across years, this effect can be eliminated.

^ year

Coherent variance across years that affects all sites equally, due to regional-scale factors such
as climate or hydrology.

Principal effect on trend detection, reduced only by increasing number of years

^ site xyear

"Interaction" variance occurring at each site across years that affects each site independently.
Principal effect on status, reduce by increasing number of sites.

^residual

"Residual" variance: Includes temporal variance at each site within a single index period
(o2within-year) confounded with measurement error (a2error) due to acquiring the data from the
site (e.g., sample collection and analysis)

Principal effect on status,

If o2mdex » o2error reduce by increasing number of sites or altering index period.

If o2error is large relative to a2ndex, then modify sampling and analysis procedures.

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For NLA 2022, 10 percent of all sample sites receive repeat visits to determine temporal variability plus
analytical variability within the index period. Revisit sites must be sampled at least 2 weeks apart and as
long as possible within the index period to ensure that temporal variability is assessed. The NLA team
implements control measures to minimize measurement error among crews and sites. These control
measures include the use of standardized field protocols provided in the FOM, consistent training of all
crews, field assistance visits to all field crews, and availability of experienced technical personnel during
the field season to respond to site-specific questions from field crews as they arise.

3.2.3 Chemical Precision, Bias, and Accuracy

The information in this section is particularly relevant to analysis of water chemistry precision, bias and
accuracy . See more specifics for how these are applied in the relevant sections of the LOM. See
additional information on QC procedures for other indicators in the relevant sections of the LOM and in
the fillet tissue sample analysis QAPP developed by OST for the human health fish fillet contaminants
indicator.

Precision and bias are estimates of random and systematic error in a measurement process (Kirchmer,
1983; Hunt and Wilson, 1986; EPA, 2002). 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 methods and standardized procedures across all
laboratories. 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, MQOs for precision and bias are established in both absolute and relative
terms, following the approach outlined in Hunt and Wilson (1986). At lower concentrations, MQOs are
specified in absolute terms. At higher concentrations, MQOs are stated in relative terms. The point of
transition between an absolute and relative MQO 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%).

Precision in absolute terms is estimated as the sample standard deviation(s) when the number of
measurements is greater than two:

Equation 3-2. Precision in absolute terms.

5 =

t = jT(x,-x)2

n-1

where x, 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 3-3. Relative precision.

RSD = —

where s is the sample standard deviation of the set of measurements, and X equals the mean value for
the set of measurements. Both RSD and CV can be expressed as percentages by multiplying by 100.

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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 3-4. Relative percent difference.

RPD =

f \A-B\ ^
(A + B)/2

x 100

vv "r ~ j

where A is the first measured value, and B is the second measured value.

For repeated measurements of samples of known composition, net bias (6) is estimated in absolute
terms as:

Equation 3-5. Net bias.

B = ~x — T

where X equals the mean value for the set of measurements, and T equals the theoretical or
target value of a performance evaluation sample.

Bias in relative terms (B[%]) is calculated as:

Equation 3-6. Bias in relative terms.

B{ %) = x 100

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 (%recovery) is calculated as:

Equation 3-7. Percent recovery.

%recovery = Cb~Ca x 100
Cs

where Cis is the measured concentration of the spiked sample, C„ is the concentration of the unspiked
sample, and Cs is the concentration of the spike.

NLA 2022 includes two layers of quality assurance for biological data: internal and external.

oo 3.2.4 Taxonomic Precision and Accuracy of Benthic Macroinvertebrates and Zooplankton

LU
>

I—

g 3.2.4.1 Internal quality assurance and quality control for biological data

Each laboratory conducts internal, or within laboratory, quality assurance and quality control activities.

< Each laboratory must evaluate the sorting efficiency of the NLA 2022 laboratory analysts. All laboratory
^ analysts responsible for taxonomic identification must participate in an internal taxonomic verification

< check. The details of the sorting and taxonomic verifications can be found in the indicator-specific
cj sections of the NLA 2022 LOM.

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3.2.4.2 External quality assurance for biological data

Each laboratory participates in external, or among laboratory, quality assurance. In general, external
quality assurance takes two forms: (1) an independent taxonomist re-analyzes 10% of samples or (2) all
of the laboratories participate in a round robin, where they swap 10% of samples among laboratories
and re-analyze them. The details of the external quality assurance requirements (e.g., taxonomic
resolution, calculations) are found in the indicator-specific sections of the NLA 2022 LOM.

3.2.4.3 External quality assurance for benthic macroinvertebrate data

For benthic macroinvertebrates , accuracy of taxonomy is 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). To calculate taxonomic
precision, EPA randomly selects 10% of the samples for re-identification by an independent, outside
taxonomist or laboratory. Comparison of the results of whole sample re-identifications provides a
Percent Taxonomic Disagreement (PTD) calculated as:

Equation 3-8. Percent taxonomic disagreement.

where comppos is the number of agreements, and N is the total number of individuals in the larger of the
two counts. The lower the PTD, the more similar the taxonomic results and the greater the overall
taxonomic precision. An MQO of 15% 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.

In addition, percent similarity (PSC) is calculated between the taxonomic laboratories. Percent similarity
is a measure of similarity between two communities or two samples (Washington, 1984). Values range
from 0% for samples with no species in common, to 100% for samples which are identical. It is
calculated as follows:

Equation 3-9. Percent similarity.

where: a and b are, for a given species, the relative proportions of the total samples A and B,
respectively, which that species represents. An MQO of >85% is recommended for percent similarity of
taxonomic identification. If the MQO is not met, the reasons for the discrepancies between analysts is
discussed. If a major discrepancy is found in how the two analysts have been identifying organisms, the
last batch of samples that have been counted by the analyst under review may have to be re-counted.

Sample enumeration is another component of 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:

comp

PTD = 1 ^ x 100

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Equation 3-10. Percent difference in enumeration.

r\Lab\- Labi^

PDE - J l- x 100

Labi + Labi

An MQO of 5% is recommended (overall mean of <5% is acceptable). Individual samples exceeding 5%
are examined to determine reasons for the exceedance.

Corrective actions for samples exceeding these MQOs can include defining the taxa for which re-
identification may be necessary (potentially even by third party), for which samples (even outside of the
10% lot of QC samples) it is necessary, and where there may be issues of nomenclatural or enumeration
problems. Specific corrective actions are identified in the indicator sections of the LOM.

Taxonomic accuracy is evaluated by having individual specimens (representative of selected taxa)
identified by recognized experts. Samples are identified using the most appropriate technical literature
that is accepted by the taxonomic discipline and that reflects the accepted nomenclature including the
NLA taxonomic lists from past surveys. Specific references are identified in the indicator sections in the
LOM. Any laboratory or taxonomist who believes these are not sufficient must contact the EPA NLA
Project Leader and Project QA Coordinator to discuss options. The internal NLA taxonomic lists are used
to verify nomenclatural validity and spelling. A reference collection is compiled as the samples are
identified. If necessary, specialists in several taxonomic groups verify selected individuals of different
taxa, as determined by the NLA workgroup.

3.2.4.4 External quality assurance for zooplankton data

Because the laboratory and QC taxonomist will be looking at different subsamples from the original field
collected samples, the QC process for zooplankton will utilize the relative abundance of each taxon
identified by both the laboratory and QC for each sample to determine the precision of taxonomic
identifications. To determine the precision of taxonomic identifications the Indicator QC Coordinator will
utilize a Bray-Curtis Dissimilarity index to compare taxonomic results from two independent
taxonomists, using the formula:

y \%. %. I

Equation 3.11 Bray-Curtis Dissimilarity (BCd).BCd = —r-1—'-r

2a\xi+Xj)

where X/andX/are the specific counts from two different taxonomists for each taxon identified in each
subsample.

Given the fact that a one for one reidentification of specimens cannot occur, as is done for the benthics
macroinvertebrate samples, a BCd of 0.25 or less is recommended for taxonomic difference (overall
mean < 0.25 is acceptable). Individual samples exceeding 0.25 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.

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3.2.5 Precision of Physical Habitat Indicators

In a regional or national assessment of status, differences among lakes are the signal of interest, but real
differences can be obscured by noise variance (Paulsen et al. 1991, Kaufmann et al. 1999). The habitat
variables (metrics) of interest are lake summary variables based on measurements at 10 randomized,
equidistant nearshore stations employing measurements and observations at littoral, riparian, and
drawdown zone plots at each of those stations.

Measures of variance between repeat visits within the sampling season of the same year provide
accurate estimates of the variances in individual lake habitat metrics that would be encountered in a
spatially extensive survey carried out over a typical summer field season. Repeat visit variance includes
the combined effects of within-season habitat variation, measurement variation, changes in the
locations of sampling plots between visits to individual lakes, and variation in estimates obtained by
different field crews. Analysts employed variance components analysis to estimate repeat visit variance
and the signaknoise (S/N) ratio which is one expression of the relative precision of habitat metrics
(Kaufmann et al. 1999).

Equation 3-12. Repeat visit variance.

rep

Equation 3-13. Signaknoise ratio.

S/ N — 0"? lakel 0"? rep

Analysts used the general random-effects model of Kincaid, et al. (2004) to model the sources of
variation in any habitat variable, V, as

Equation 3-14. Source of variation in habitat variable.

Yijk = H + L, + Tj + LTij + Ejjk,

Here V^is the measured metric value for the kth visit to lake i within the jth year. The grand mean value is
H, and L and T are random lake and year effects, respectively. For the NLA, data came from a single year,
so the year (7) and lake:year interaction (LT) terms in Equation 3-13 are zero, and the model simplifies
to the form /,* = £/ + £,- + £,*. The residual error (Eik) of the simplified model represents within-year
variation at any single lake, which we estimated from a subset of the lakes that were resampled the
same summer. Analysts assume that Z., and £,* are normally-distributed random effects, with variances of
a2take and <7%,, respectively. The combined data set containing samples from different lakes as well as
revisits to same lakes, enabled estimation of both among-lake variance (cr2;^) and repeat visit variance
{cfrep) using restricted maximum likelihood (Littell et al. 2006).

In the synoptic survey context, variance among lakes (a2/^) is the signal of interest, and the variance in
revisits within the index period from all sources (
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relevance to many types of statistical analysis and detecting differences in subpopulation means (Zar
1999). High noise in habitat descriptions relative to the signal (i.e., low signal: noise ratio, S/N)
diminishes statistical power to detect differences among lakes or groups of lakes. Imprecise data limit
the ability to detect temporal trends (Larsen et al. 2001, 2004). Noise variance also limits the maximum
amount of variance that can be explained by models such as multiple linear regression (Van Sickle et al.
2005, Kaufmann and Hughes 2006). By reducing the ability to quantify associations between variables
(Allen et al. 1999, Kaufmann et al. 1999), imprecision compromises the usefulness of habitat data for
discerning likely controls on biota and diagnosing probable causes of impairment. The adverse effects of
noise variance on these types of analysis are negligible when S/N >10; becoming minor as S/N decreases
to 6, increasing to moderate as S/N decreases to 2, and finally becoming severely limiting as S/N
approaches 0 (Paulsen et al. 1991, Kaufmann et al. 1999). At S/N=0, all the metric variance observed
among lakes in the survey can be attributed to measurement "noise". Based on these guidelines, the
effects of imprecision are minor for all the indicators except for the Littoral Cover index, for which the
effects are minor-to-moderate.

Kaufmann et al. (2014a) explain that the S/N ratio may not always be a good measure of the potential of
a given metric to discern ecologically important differences among sites. For example, a metric may
easily discriminate between sparse and abundant littoral cover for fish, but S/N for the metric would be
low in a region where littoral cover does not vary greatly among lakes. In cases where the signal
variance (a2/^) observed in a regional survey reflects a large range of habitat alteration or a large range
in natural habitat conditions, S/N would be a good measure of the precision of a metric relative to what
we want it to measure. However, in random surveys or in relatively homogeneous regions, cr2;^ and
consequently S/N, may be less than would be calculated for a set of sites specifically chosen to span the
full range of habitat conditions occurring in a region. To evaluate the potential usefulness of metrics,
Kaufmann et al. (2014a) suggested that an alternate measure of relative precision, orep divided by its
potential or observed range (Rgpot or Rg0bs) offers additional insight. The minimum detectable
difference in means between 2 lakes (or between two times in one lake) is given by Dmin = 1.96CTrep(2n)1/2
= l.llcjrep , using a 2-sided Z-test with a = 0.05 (Zar 1999). Thus, to detect any specified difference
between 2 lakes in a metric relative to its potential or observed range (Rgpot or Rg0bs, the standardized
within-lake standard deviation, orep/Rg, cannot exceed {Dmi„/Rg)/2.77. By the criteria in Kaufmann et al.
(2014a, Table 2), the key NLA physical habitat indices were precise or moderately precise, with orep/Rgobs
between 0.052 - 0.107 (Table 7, EPA 2016). Depending on the index, they have the potential to discern
differences between single lakes (or one lake at two different times) that are between l/3rd and l/8th
the magnitude of the observed ranges of these indices.

3.2.6 Completeness

Completeness requirements are established and evaluated from two perspectives. First, valid data for
individual parameters 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 1000 initial sampling sites. Percent completeness
(%C) is calculated as:

Equation 3-15. Percent completeness.

where V is the number of measurements/samples judged valid, and T is the total number of planned
measurements/samples. Within each indicator, completeness objectives are also established for
individual samples or individual measurement variables or analytes. These objectives are estimated as

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the percentage of valid data obtained versus the amount of data expected based on the number of
samples collected or number of measurements conducted. Where necessary, supplementary objectives
for completeness are presented in the indicator-specific sections of the LOM.

In addition to evaluating completeness for each laboratory, 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 sampling (10% of samples collected)
and revisit samples (10% of sites visited) 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.

3.2.7 Comparability

Comparability is defined as the confidence with which one data set can be compared to another
(EPA,2002). A performance-based methods approach is being utilized for water chemistry and
chlorophyll a analyses that defines a set of laboratory method performance requirements for data
quality. Following this approach, participating laboratories may choose which analytical methods they
use for each target analyte as long as they are able to achieve the performance requirements as listed in
Table 10.4 of the LOM. Requirements for reporting limits may be modified for regional laboratories
based on the expected range of concentrations for samples they may receive and required threshold
values for assessing condition. For all parameters, comparability is addressed by the use of standardized
sampling procedures and analytical methods by all sampling crews and laboratories. Comparability of
data within and among parameters is also facilitated by the implementation of standardized quality
assurance and quality control techniques and standardized performance and acceptance criteria. For all
measurements, reporting units and format are specified, incorporated into standardized data recording
forms, and documented in the information management system. Comparability is also addressed by
providing results of QA sample data, such as estimates of precision and bias, conducting methods
comparison studies when requested by the grantees and conducting inter-laboratory performance
evaluation studies among state, university, and NLA 2022 contract laboratories. See indicator specific
sections of the LOM for more information when appropriate.

3.2.8 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" (EPA, 2002). At one level, representativeness is affected by problems in any or all
of the other data quality indicators.

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 response design, (which includes when, where, and how to collect a sample at each
site). Holding time requirements for analyses ensure analytical results are representative of conditions
at the time of sampling. See indicator specific sections of the LOM for more information and Appendix B
of the FOM for more information.

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4 SAMPLING DESIGN AND SITE SELECTION

The overall sampling program for the NLA 2022 project requires a randomized, probability-based
approach for selecting lakes where sampling activities are to be conducted. Details regarding the specific
application of the probability design to surface waters resources are described in Paulsen et al., (1991),
Peck et al., (2013), and Stevens (1994).

4.1 Probability Based Sampling Design and Site Selection

The target population of "lakes" includes permanent natural and man-made freshwater lakes, ponds,
and reservoirs greater than one hectare (approximately 2.5 acres), greater than 1,000 square meters of
open water, and greater than one meter in depth. Lakes that are saline due to tidal influence are
excluded as are those used for aquaculture, disposal-tailings, sewage treatment, evaporation, or other
unspecified disposal use. The National Hydrography Dataset Plus High Resolution (NHDPIus HR) data
layer was employed by EPA to derive a list of lakes for potential inclusion in the survey. The overall
sample size was set to include 1000 lake sampling events. In NLA 2022, 904 lakes will be sampled; and
96 of the lakes will be sampled twice for a total of 1000 lake visits. The 904 lakes consist of two sets of
lakes. The first set is 451 lakes that were included in the NLA 2017 design and includes the 96 lakes that
will be sampled twice. The second is 453 new lakes that will be sampled for the first time in NLA 2022.
This design provides a robust number of sites that we will use to evaluate change between the 2017 and
the 2022 lakes assessments. Figure 4-1 displays the distribution of the 904 base sites from the original
NLA 2022 design.

A Generalized Random Tessellation Stratified (GRTS) survey design for a finite resource was used for site
selection. Lake selection for the survey provided for four size class categories (1-4 hectares (ha), 4-10 ha,
20-50 ha, >50 ha), as well as spatial distribution across the lower 48 states and nine aggregated Omernik
Level 3 ecoregions (for more information on Omernik ecoregions see https://www.epa.gov/eco-
research/ecoregions). Additional lakes were selected as potential replacement lakes (oversample sites).
The oversample is used to replace a candidate lake that is determined to be non-target or to replace a
target lake that is not accessible due to landowner denials, physical barriers, or safety concerns. Crews
must take replacement sites from the Oversample List in the order that they appear in the site list
(numerically by SITEJD). Skipping over sites on the list compromises the integrity of the survey design
and complicates the assessment analyses. It is important that crews assign a final status to all sites on
the list regardless of whether they end up being sampled.

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Design Sites for the National Lakes Assessment 2022

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NLA2022 Base Sites - Fish
NLA 2022 Base Sites - No Fish

165 330

Figure 4-1 Design sites (base sites) for the 2022 National Lakes Assessment.

Complete documentation is included in Appendix C in the Site Evaluation Guidelines document,

4.2 Reference (or Least-Disturbed) Site Selection

A set of reference lakes (least disturbed lakes), i.e., those that EPA will use to inform ecoregional
benchmarks 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 NLA 2017 (EPA, unpublished).
Analysts will consider whether information from these sites, combined with information from past
surveys, indicates a need to revise thresholds used in the NLA 2017.

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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 NLA
2022 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 NLA 2022
from initial selection of sampling sites through the dissemination and reporting of final, validated data.
And, by extension, all participants in the NLA 2022 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 NLA 2022 cooperators.

¦ Increase the quality and relevance of accumulated data.

¦ Ensure the flexibility and sustainability of the NLA 2022 IM structure.

This IM strategy provides a congruent and scientifically meaningful approach for maintaining
environmental monitoring data that satisfies both the scientific and technological requirements of the
NLA 2022.

5.1 Roles and Responsibilities

At each point where data and information are generated, compiled, or stored, the NLA 2022 team must
manage the information. 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 NLA
2022 play an integral part within the IM system. Table 5.1 provides a summary of the IM responsibilities
identified by the NLA 2022 IM group. Specific information on the field crew responsibilities for tracking
and sending information is found in the FOM.

Table 5.1 Summary of IM responsibilities.

NLA 2022

Contact

Primary Role

Responsibility

Group

i—

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.

Ship/email 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 NLA 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

Review all electronic data transmittal files for
completeness and accuracy (as identified in the QAPP).

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NLA 2022 Contact Primary Role Responsibility

Group

manner

appropriate to

acquire

biotic/abiotic

indicators/measur

ements

requested.

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 NARS 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 QAPP or as negotiated
with the NLA Project Leader.

Maintain open communications with NARS IM Center
regarding any data issues.

IM Center
staff

EPAORD

NHEERL

PESD-

Corvallis

Partners and

Contractors

Provides support
and guidance for
all IM operations
related to
maintaining a
central data
management
system for NLA
2022

Develop/update field data forms and NLA App.

Plan and implement electronic data flow and management
processes.

Manage the centralized database and implement related
administration duties.

Receive electronic submissions of field data forms via the
NLA App.

Monitor and track samples from field collection, through
shipment to appropriate laboratory.

Receive data submission packages (analytical results and
metadata) as compiled by the NLA 2022 Quality Team from
each laboratory or directly (e.g., national water chemistry
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.

Implement backup and recovery support for central
database.

Implement data version control as appropriate.

Project
Quality
Assurance
Coordinator

EPA Office of
Water

Review and
evaluate the
relevancy and
quality of
information/data
collected and
generated

Oversee NLA 2022 Quality Team including initial review of
laboratory electronic data deliverables, quality checks and
submission of compiled datasets to the NARS IM Center

Monitor quality control information.

Evaluate results stemming from field and laboratory audits.

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NLA 2022

Contact

Primary Role

Responsibility

Group

i—

through the NLA
2022 survey.

Investigate and take corrective action, as necessary, to
mitigate any data quality issues.

Issue guidance to NLA 2022 Project Leader and IM Center
staff for qualifying data when quality standards are not met
or when protocols deviate from plan.

Steering
Committee

NLA 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

EPA Office of
Water, ORD
PESD,
Partners

Provide the data
analysis and
technical support
for NLA 2022
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

EPA Office of
Water, ORD
PESD,

Provides data
librarian support

Prepare NLA 2022 data for transfer to EPA public web-
servers).

Generate data inventory catalog record (Science Inventory
Record).

Ensure all metadata is consistent, complete, and compliant
with EPA standards.

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5.1.1 State/Tribe-Based Data Management

Some state or tribal partners manage activities for both field sampling and laboratory analyses. While
the NARS program encourages states to use these in-house capabilities, it is imperative that NLA 2022
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
performs all of the functions associated with the following roles:

• Field Crew—including submitting field data forms to the IM Coordinator (NLA 2022 electronic
field forms must be used except in rare instances when paper forms might be necessary; and
the original field forms must be sent to the NARS IM Center as outlined in the NLA 2022 FOM).

• Laboratory quality assurance including responding to the NLA 2022 Quality Team questions after
submitting data

• Submission of data from the state or tribe to the Laboratory Review Coordinator or other
designated member of the Quality Team (who submit to the NARS IM Center). Typically, the
state or tribe must provide a single point of contact for all activities related to NLA 2022 data.
However, it may be advantageous for the Laboratory Review Coordinator to have direct
communication with the state or tribe-participating laboratories to facilitate the transfer of
data. This is a point that may be negotiated between the primary state or tribal contact, the
regional coordinator and the Laboratory Review Coordinator.

• Data transfers to the NARS IM Center must be timely. States 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 the Laboratory Review Coordinator by
March 2023, in order to meet NLA 2022 product deadlines (unless otherwise indicated for a
contract/grant requirement).

• Data transfers must be complete. For example, laboratory analysis results submitted by the
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 must ensure that data meet minimum quality standards and that data transfer
files meet negotiated content and file structure standards.

The Laboratory Review Coordinator communicates the necessary guidance for data management and
submission requirements (i.e., data templates). Each group that performs in-house IM functions
incorporates these guidelines as is practicable or as previously negotiated.

5.2 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, map 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 NLA data.

The central repository for data and associated information collected for use by NLA 2022 is a secure,
access-controlled server located at EPA PESD-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 SAS or R
language software packages.

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5.2.1 Data Flow

The NLA 2022 will accumulate large quantities of observational and laboratory analysis data. To
appropriately manage this information, 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
5-1) helps focus efforts on maintaining organizational and custodial integrity, ensuring that data
available for analyses are of the highest possible quality.

5.2.2 Simplified Description of Data Flow

There are several components associated with the flow of information. These are described below and
also shown in Figure 5-1:

¦ Communication—between the NARS IM Center and the various data contributors (e.g., field
crews, the NLA Quality Team, 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., GIS
data)—encompassing an array of data types: site characterization; biotic assessment; fish fillet
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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Field Data Collection

Paper forms
(backup only)

I Pads/NLA App
Field Forms
Tracking Forms

Raw Data
Submission Review

Office QA/QC
Review

Raw Data Files
(NARS IM Spec)

Verified Data Files

Sample Collection and
Tracking

Labels

Packing Slips

Information Management Center
EPAORD PESD

Data Entry

NARS IM SQL
Server

Final Indicator Data
Files

Data Analysis

Assessment Data
Files

Create flat files
for use with R
or SAS ^

Update records
in SQL tables ¦

Relational
1 record per datum

Data
Table
1

Data
Table
4

Data
Table
2

Data
Table
3

Data
Table
n

Laboratory ^

Sample Receipt

Sample Analysis

QA/QC Review

Laboratory Information
Management System

Raw Data
Submission Package

Office QA/QC Review

Other Data Files

(e.g.. Survey design, GfS
attribute data)

Final Data Records

(Flat Files) Posted to
Webpage or ftp site

Final Data Records

(Water Quality Portal)
Permanent Archival

Figure 5-1 Conceptual model of data flow into and out of the master SQL database for the NLA 2022.

The following sections describe core information management standards, data transfer protocols, and
data quality and results validation. Additionally, Section 5.4 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.

5.2.3 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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NLA 2022 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 NLA 2022
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.

5.2.4 Data Formats

5.2.4.1 Attribute Data

• SQL Tables

• SAS Data Sets

• Ra Workspaces

• American Standard Code for Information Interchange (Ascii) Files: Comma-Separated values, or
space-delimited, or fixed column

5.2.4.2 GISData

¦ ARC/INFO native and export files; compressed .tar file of ARC/INFO workspace

5.2.4.3 Standard Coding Systems

Sampling Site: (EPA National Locational Data Policy; EPA, 2004)

Coordinates: Latitude and Longitude in decimal degrees (±0.002)

Datum: NAD83

Chemical Compounds: Chemical Abstracts Service (CAS, 1999)

Species Codes: Integrated Taxonomic Information System when possible
Land cover/land use codes: Multi-Resolution Land Characteristics; National Hydrography
Dataset Plus Version 1.0 (NHDPIus, 2005)

5.2.5 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.

¦ Program must comply with Data Quality Act requirements before making any data available to
z the public and the person generating data must fill out and have a signed the Information

^ Quality Guidelines package available before any posting to the Web or distribution of any kind.

(J ¦ Data and metadata files are made available to the contributor or participating group for review

<£.

z or other project-related use from NARS IM or in flat files before moving to an EPA-approved

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¦ Only "final" data (those used to prepare the final project report) are readily available through an
EPA-approved public websiteb.

As new guidance and requirements are issued, the NARS IM staff assess the impact upon the IM system
and develop plans for ensuring timely compliance.

5.3 Data Transfer Protocols

Field crews are expected to use the provided electronic field forms containing in situ measurement and
event information to the NARS IM Center defined in the FOM for submission. If crews need to use paper
forms, they must transfer the data from the hard copies of the field forms to the NLA App for electronic
submission within two weeks of sampling. The paper forms must also be scanned, emailed to the NLA
Logistics Lead and retained by the field crew for 2 years . Laboratories must 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 presented in Table 5.2:

Table 5.2 NLA 2022 Data submission software and associated file formats.

Software

Export Options (file extensions)

Microsoft Excel*

xls, xlsx, csv, formatted txt delimited

SAS*

csv, formatted txt delimited

csv, formatted txt delimited, R

workspaces (.Rdata)

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. Laboratories may send files periodically, before all samples are
analyzed, but EPA must be informed that more data are pending if a partial file is submitted0. All data
files sent by the laboratories 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 or state-
based IM group should consider when packaging data for electronic transfer to the NLA team and that is
captured in the applicable data submission templates using row/column data file/table structure (see
Appendix C in the LOM for templates).

¦ Include NLA 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.

¦ Use a consistent set of column labels.

¦ Use file structures consistently.

¦ Use a consistent set of data qualifiers.

¦ Use a consistent set of units.

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Include method detection limit (MDL) as part of each result recordd.

Include reporting limit (RL) as part of each result record.

Provide a description of each result/QC/QA qualifier.

Provide results/measurements/MDL/RL in numeric form.

Maintain result qualifiers (e.g., <, ND) in a separate column.

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.

¦ Include laboratory sample identifier.

¦ Include batch numbers/information so results can be paired with appropriate QA/QC
information.

¦ Include "true value" concentrations, if appropriate, in QA/QC records.

¦ Include a short description of preparation and analytical methods used (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.

¦ 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.

¦ 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).

¦ Data results must complement expectations (analysis results) as specified by contract or
agreement.

¦ Identify and qualify missing data (why are the data missing?).

¦ 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 lab SOP.

The Laboratory Review Coordinator works with the NARS IM Coordinator to establish a data load
process into NARS IM.

!_ 5.4 Data Quality and Results Validation

-Z.

^ Data quality is integrated throughout the life cycle of the data. This includes development of appropriate

g forms, labels etc. for capturing data as well as verifying data entry, results, and other assessments.

< Indicator workgroup experts and the data analysis and reporting teams submit any recommended

< changes to the Project QA Coordinator who recommends and submits any changes (deletions, additions,

z corrections) to the NARS IM data center for inclusion in the validated data repository. The NARS IM

Q Center includes all explanation for data changes in the record history.

d National lab to provide MDL with each result, and may provide an "estimate" comment for each result below the
RL but above the MDL, and a flag when a result is below the MDL.

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5.4.1 Design and Site Status Data Files

The site selection process described in Section 4 produces a list of candidate sampling locations,
inclusion probabilities, and associated site classification data (e.g., target status, ecoregion, etc.). The
Design Team provides this file to the NLA 2022 Project Leader, who in turn distributes to the IM 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 FOM. The site
evaluation spreadsheets and verification forms are submitted to the Project Lead by the field crews via
SharePoint. The Contractor Field Logistics Coordinator and the NARS 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 identified and a request is made by
Contractor Field Logistics Coordinator to the field crew (or site evaluator) to complete the record.
Revised information is then submitted to the NARS IM Center.

5.4.2 Sample Collection and Field Data

Field crews record sampling event observational data in a standard and consistent manner using field
data collection forms in the NLA App. 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.

NLA 2022 provides two options for completing field forms: electronic data entry using pre-developed
forms on a tablet or smart phone or "traditional" paper.

• Electronic Field Forms: This form of data collection will be collected through an Apple iPad
which will be provided for all state, tribal, and EPA crews. Each of the field forms are separated
into sections for easier data entry. Field crews are to familiarize themselves with the App prior
to field sampling. Each individual field form must be submitted by only one device. For example,
if there are 5 field forms (A,B,C,D,E) and iPad 1 submits forms A, B, and D, then iPad 2 should not
submit those 3 forms or data will be overwritten. In this example, iPad 2 could still submit forms
C and E with no issues. While a data or Wi-Fi connection is required to submit the data, no data
connection is required for the data collection process.

• Paper Field Forms: Error! Reference source not found.Error! Reference source not found.Extra
paper field forms will only be provided to field crews to serve as backup copies in case of
problems with electronic field forms. As soon as possible, the completed paper field forms
should be transcribed to the NLA 2022 App for data submission. The original completed version
of the forms must also be scanned, emailed to the EPA Logistic Lead and stored by the field crew
for two years.

Recorded data in the NLA App 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 all
forms to the NARS IM Staff by selecting the "submit" button in the NLA App as described in the NLA
2022 FOM.

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All samples are tracked from the point of collection. Field crews ensure that copies of the shipping and
custody record accompany all sample transfers; other copies are transmitted to the NARS IM Center.
The NARS 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 NLA 2022 FOM.

Procedures for completion of sample labels and electronic field data forms 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 5.3.

Additional QA/QC checks or procedures specific to individual indicators are described in the NLA 2022
LOM.

Table 5.3 Summary sample and field data quality control activities.

Quality Control
Activity

Description and/or Requirements

i—

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

Data Qualifiers

Defined qualifier codes used on data form; qualifiers explained in comments
section on data form

Sample Custody

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 ORD Technical Lead 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 their 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

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5.4.3 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. Any discrepancies, damaged samples, or missing samples are
reported to the NARS IM staff and NLA 2022 Project Lead electronically.

Most of the laboratory analyses for the NLA 2022 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 NLA 2022 indicators
are described in Table 5.4. Additional QA/QC procedures specific to individual indicator and parameter
analyses are described in the LOM. Biological sample analyses are generally based on current acceptable
practices within the particular biological discipline. QC checks and procedures applicable to most NLA
2022 biological samples are described in the LOM.

Table 5.4 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 according to manufacturer's recommendations; recalibrate or replace
before analyzing any samples if producing erratic results

QC Data

Maintain control charts, determine LT-MDLs and achieved data attributes; include
QC data summary (narrative and compatible electronic format) in submission
package

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

<
¦z.

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 P

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 by o

the laboratory personnel with a flag variable. The laboratory explains all flagged data in a comments z

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section. Private contract laboratories generally have a laboratory Quality Management Plan(QMP) or
equivalent document and established standard operating procedures (SOPs) for recording, reviewing,
and validating analysis data.

Once analytical data have passed all of the laboratory's internal review procedures, the laboratory
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 may be transferred to EPA's designated laboratory or facilities as directed by
the NLA 2022 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. EPA's project records are scheduled 501 and are also
permanent.

5.4.4 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.

5.4.4.1 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.

5.4.4.2 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 OA manager and
individuals responsible for collecting the data for resolution.

The NLA Quality Team evaluates 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

i_ standard deviations.

-z.

^ The NLA Quality Team 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

^ 5.5. QA staff use automated review procedures. The primary purpose of the initial checks is to confirm

O 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,

O frequency tabulations of coded or alphanumeric variables to identify erroneous codes or misspelled

5 entries, and summations of variables reported in terms of percent or percentiles. In addition, associated

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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 are either 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. 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 are flagged for data qualification.

The NARS IM staff, with the support of the NLA 2022 Quality Team, correct and qualify all questionable
data. Copies of the raw data files are maintained in NARS IM, generally in active files until 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 headquarters shared G: drive system.

Table 5.5 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 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 h

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 lu

are considered to be unacceptable for use in any analysis or interpretation activities and are generally <

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 NARS IM system. O

I—

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 q

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activities are collectively referred to as "data enhancement." In order to develop indicator metrics from
one or more variables, data files may be restructured so as to provide a single record per lake.

5.5 Data Transfer

Field crews must transmit all field collected data and sample tracking information electronically via the
NLA App. Copies of raw, verified, and validated data files are transferred from the Project QA
Coordinator (or designee) to the NARS IM staff for inclusion in the NARS IM system. All transfers of data
are conducted using a means of transfer, file structure, and file format that has been approved by the
NARS IM staff. Data files that do not meet the required specifications are not incorporated into the
centralized data access and management system.

5.5.1 Database Changes

The NARS IM Center staff complete data corrections at the lowest level to ensure that any subsequent
updates contain only the most correct data. The NARS IM Center alerts the Laboratory Review
Coordinator if a laboratory result is found to be in error. The Laboratory Review Coordinator, or other
identified member of the NLA team, sends the laboratory results found to be in error to the originator
(lab) for correction. After the originator makes any corrections, the Laboratory Review Coordinator
resubmits the entire batch or file to the NARS IM Center (unless otherwise discussed with the NARS IM
staff). 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. 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 take place. The NARS IM staff works with the Quality team to
address significant problems with formatting. The original raw data remains 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 reruns the validation programs to
re-inspect the data.

The NARS IM Center may implement database auditing features to track changes.

5.6 Metadata

All metadata will be documented following the procedures outlined by the Federal Geographic Data
Committee, Content standard for digital geospatial metadata, version 2.0. FGDC-STD-001-1998 (FGDC,
1998).

5.7 Information Management Operations
5.7.1 Computing Infrastructure

The NARS IM Center collects and maintains electronic data within a central server housed at PESD 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. The NARS IM Center
conducts official IM functions in a centralized environment.

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5.7.2 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. The NARS IM Center follows guidance
and policy documents of EPA and management policies established by the IM Technical Coordination
Group for data access and data confidentiality. Raw and verified data files are accessible only to the NLA
2022 collaborators. Validated data files are accessible only to users specifically authorized by the NLA
2022 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.

The NARS IM Center routinely stores and archives on redundant systems the data generated, processed,
and incorporated into the IM system. 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 NLA 2022 IM meet EPA's standard security
authentication (i.e., username, password) process in accordance with EPA's Information Security Policy
(EPA Order 2150). Any data sharing requiring file transfer protocol (FTP) or internet protocol is provided
through an authenticated site.

5.7.3 Life Cycle

Data may be retrieved electronically by the NLA 2022 team, partners and others throughout the records
retention and disposition lifecycle or as practicable (See Section 5.7.5). Data in the NARS IM database
are subject to EPA Record Schedule 0089 as described in the NARSPROC-003 standard operating
procedure.

5.7.4 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. The NARS IM Center maintains backups of the entire system off-site. The IM
process used by the NARS IM Center for NLA 2022 also uses system backup procedures. The NARS IM
Center backs up and archives the central database according to procedures already established for PESD
and NARS IM. All laboratories generating data and developing data files are expected to establish
procedures for backing up and archiving computerized data.

5.7.5 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 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 NLA 2022 project are run through an
Interface Module in an Excel format and uploaded to WQX by the WQX team. Once uploaded, states and z

tribes and the public can download data. Data are also provided in flat files on the NARS website. ^

5.8 Records Management ^

The NARS IM Center maintains removable storage media (i.e., CDs, thumb drives) and paper records in a ^

centrally located area at the NARS IM Center. Paper records are returned to OW once the assessment is q

complete or destroyed per records retention schedules. The NARS IM staff identifies and maintains files ^

using standard divisional procedures. Records retention and disposition comply with EPA directive 2160 ^

Records Management Manual (July, 1984) in accordance with the Federal Records Act of 1950. O

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6 INDICATORS
6.1 Summary

The NLA Project Team provides detailed, indicator-specific design, collection method, sample handling,
and quality control procedures for field operations in the National Lakes Assessment 2022 Field
Operations Manual. Similarly, the team provides detailed, indicator-specific sample handling, laboratory
procedure, and quality control procedures for laboratory operations in the National Lakes Assessment
2022 Laboratory Operations Manual. Quality assurance objectives for physical habitat, which does not
collect samples or have laboratory analysis associated with its measurements, are in the data analysis
plan of this document. A summary of the QA procedures and the Indicator QA Coordinators is shown in
Table 6.1.

6.1.1 Sampling Design

Field crews collect samples from an index site and/or littoral sites on each lake as described in the
National Lakes Assessment 2022 Field Operations Manual.

6.1.2 Sampling and Analytical Methods

6.1.2.1 Sample Collection

Detailed sample collection and handling procedures are described in the National Lakes Assessment
2022 Field Operations Manual.

6.1.2.2 Analysis

Detailed analysis procedures are described in the National Lakes Assessment 2022 Laboratory
Operations Manual.

6.1.3 Quality Assurance Objectives

Quality assurance objectives are described in detail in the National Lakes Assessment 2022 Laboratory
Operations Manual.

6.1.4 Quality Control Procedures: Field Operations

Detailed design, collection, sample handling and quality control procedures for field operations are
described in the National Lakes Assessment 2022 Field Operations Manual.

6.1.5 Quality Control Procedures: Laboratory Operations

Specific information about sample receipt, processing, and analysis are in the National Lakes Assessment
2022 Laboratory Operations Manual.

6.1.6 Data Management, Review, and Validation

Detailed information about data management, review, and validation are in the National Lakes
Assessment 2022 Laboratory Operations Manual.

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Table 6.1 Summary of indicator QA procedures and coordinators.

Indicator

Lab

Method Lab Analyses QA

Taxa

Indicator QA QA Analyst

Verification

Verificati

Require

Coordinator(s)

ments

Algal Toxins

Documentation

Methods

Interlab comparison

N/A

Kendra Forde

Danielle

(microcystins

review (e.g.,

Call

Lab blanks,

Grunzke

and

SOPs, lab

duplicates and spiked

cylindrosperm

certifications,

samples

opsin)

prior experience)

Audit

documentation

(if applicable)

Bacteria

Documentation

Methods

Interlab comparison

N/A

Kendra Forde

Lareina

(Enterococci)

review (e.g.,

Call

Lab reagent blanks

Guenzel

SOPs, lab

and duplicates

certifications,

prior experience)

Audit

documentation

(if applicable)

Benthic

Taxa QC samples

Methods

Outside Lab QA

Genus or

Brian Hasty

Garrett

Macro-

Documentation

Call

Taxonomist to review

Family

Stillings

invertebrates

review (e.g.,

10% of samples -

(see

SOPs, lab

photos

LOM)

certifications,

Reconciliation calls

prior experience)

Audit

documentation

(if applicable)

Fish eDNA

N/A

Erik Pilgrim

Fish Fillet

John Healey

Leanne Stahl

Contaminants

Physical

N/A

Phil Kaufmann

Phil

Habitat

Kaufmann

Phytoplankton

Taxa QC samples

Methods

Outside Lab QA

Species

Brian Hasty

Richard

Documentation

Call

Taxonomist round

Mitchell

review (e.g.,

robin

SOPs, lab

Reconciliation calls

certifications,

prior experience)

Audit

documentation

(if applicable)

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Indicator

Lab

Method

Lab Analyses QA

Taxa

Indicator QA

QA Analyst

Verification

Verificati

Require

Coordinator(s)

ments

Atrazine

Documentation

Methods

Duplicates

N/A

Kendra Forde

Lareina

Pesticide

review (e.g.,

Call

Standard Solution

Guenzel

Screen

SOPs, lab

certifications,

prior experience)

Audit

documentation

(if applicable)

Water

Documentation

Methods

Lab blanks,

N/A

Dave Peck

Chemistry and

review (e.g.,

Call

duplicates and spiked

Alan Herlihy

Chlorophyll-o

SOPs, lab

samples (as

certifications,

appropriate)

prior experience)

Audit

documentation

(if applicable)

Zooplankton

Taxa QC samples

Methods

Outside Lab QA

Species

Lareina

Richard

Lab blanks,

Call

Taxonomist to review

Guenzel

Mitchell

duplicates and

10% of samples -

spiked samples

photos

(as appropriate)

Reconciliation calls

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7 ASSISTANCE VISITS

Assistance visits are a component of the QA program for the NLA 2022. Both these sections have been
explained clearly in the National Lakes Assessment 2022 FOM and LOM and therefore are not included
here.

7.1 Field Evaluation and Assistance Visit Plan

Please see the NLA 2022 Field Operations Manual for details.

7.2 Laboratory Evaluation and Assistance Visit Plan

Please see the NLA 2022 Lab Operations Manual for details.

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8 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 lakes and identify the relative impact of stressors
on this condition. Results from the analysis are included in the final report and used in future analysis.
The data analysis plan may be refined and clarified as the data are analyzed by EPA and states.

8.1 Data Interpretation Background

The basic intent of data interpretation is to evaluate the occurrence and distribution of parameters
throughout the population of lakes in the United States within the context of regionally relevant
expectations for least disturbed reference conditions. This is presented using a cumulative distribution
function or similar graphic. For most indicators the analysis categorizes the condition of water as least,
moderately, or most disturbed. 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.

8.1.1 Scale of assessment

This is the third national report on the ecological condition of the nation's lakes 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 lakes that would provide for statistically valid conclusions about the condition of the population
of lakes 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.

8.1.2 Selecting the best indicators

Indicators should be applicable across all reporting units and must be able to differentiate a range of
conditions. EPA formed a steering committee for these discussions. The Committee, comprised of state
representatives from each of the EPA regions, provides advice and recommendations to EPA on matters
related to the NLA 2022. This committee was able to develop and refine indicators and sampling
methodologies.

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.

8.1.3 Defining least impacted (reference) condition

Reference condition data are necessary to describe expectations for biological conditions under least
disturbed settings. Analysts expect to use an approach similar to that used in NLA 2012, which is
described in detail in the NLA 2012 Technical Report (EPA 841-R-16-114) (EPA 2016). Analysts will
consider whether data from additional 2022 reference sites indicate that NLA 2012 thresholds need to
be updated or not.

8.1.4 Determining thresholds for judging condition

This reference site approach is then used to set expectations and benchmarks for interpreting the data
on lake 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

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benchmarks used to define distinct condition classes (e.g., least disturbed, moderately, most disturbed)
are drawn from this reference distribution. 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. Using the 5th percentile means that lakes 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 lakes reported as least disturbed are as good as 75% of the sites used to
define reference condition. Thresholds may also be adjusted following the process in Herlihy et al.,
(2008). For some indicators, analysts use literature or other established values.

8.2 Geospatial Data

Geospatial data is an integral part of data analysis for the NLA 2022, as it has been for all other surveys.
The following activities are anticipated: review of coordinate data and corrections, pourpoint (the outlet
of the lake) identification, watershed delineations, and computing landscape metrics. Through the site
evaluation process, lakes that have changed or are inaccurately represented in the National
Hydrography Dataset High Resolution (NHD HR) will be noted and provided to those that update the
NHD.

8.3 Datasets Used for the Report

The datasets available for use in the report were developed based on analytical methods selected during
the NLA data analysis workshop. Many of the analytical methods used in the survey stem from
discussions, input, and feedback provided by the NLA Steering Committee. Many of the methods are an
outgrowth of the testing and refinement of the existing and developed methods and the logistical
foundation constructed during the implementation of the Environmental Monitoring and Assessment
Program (EMAP) studies from 1991 through 1994 (Whittier et al., 2002), from a New England pilot study
conducted in 2005, from focused pilot studies for methods development, and from various state water
quality agency methods currently in use.

The survey uses indicators to assess trophic status and water quality, ecological integrity, and the human
use of lakes.

8.3.1 Trophic status and water quality

Lakes are typically classified according to their trophic state. Three variables, chlorophyll a, Secchi disk
depth, and total phosphorus, are used by EPA to estimate algal biomass and define the trophic state of a
particular lake. Other variables are measured in conjunction with the trophic state variables to
supplement and enhance understanding of lake processes that affect primary productivity.

8.3.2 Ecological integrity

Ecological integrity describes the ecological condition of a lake based on different assemblages of the
aquatic community and their physical habitat. The biological and physical indicators include
zooplankton, benthic macroinvertebrates, and the physical habitat of the shoreline and littoral zone.
The chemical indicator (atrazine) provides a measurement of risk to aquatic life. Analysts will also
examine a research indicator-fish eDNA.

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8.3.3 Human health

Human health 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 (microcystins and cylindrospermopsin), bacteria
(enterococci), and contaminants in fish fillet tissue will serve as the primary indicators of human health.

8.4 Indicator Data Analysis

8.4.1 Algal Toxins

Cyanobacterial (blue-green algal) blooms are common midsummer to late fall events that occur in many
lakes and reservoirs 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.

Laboratories analyze the total (whole water) concentrations of microcystins and cylindrospermopsin in
lakes and reservoirs throughout the United States using a standardized immunoassay test. The data
analysis team compares these concentrations to national or other literature values. In addition, the data
analysis team analyzes and interpret the data for microcystin occurrence and concentration in the
context of other environmental data that is collected as part of the lake assessment (e.g. nutrients,
phytoplankton, chlorophyll a, turbidity, specific conductance, pH).

8.4.2 Bacteria (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 NLA, water samples are analyzed using a process known as
quantitative polymerase chain reaction, or qPCR, a methodology that facilitates the detection of DNA
sequences unique to these bacteria. Analysts compare the NLA results to a new EPA qPCR threshold for
protecting human health in ambient waters designated for swimming.

8.4.3 Benthic Macroinvertebrate and Zooplankton Assemblages

The data analysis team calculates benthic macroinvertebrate and zooplankton assemblage will be
analyzed using multimetric indices (MMI). 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
resource.

8.4.4 Fish eDNA

Water samples will be analyzed for fish environmental DNA. This is a supplemental research indicator
and may not result in an assessment endpoint. NLA will use this sample to evaluate whether general fish
occurrence information can be determined from this sample.

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8.4.5 Physical Habitat

8.4.5.1 Quality assurance objectives and procedures

MQOs are presented in Table 8.1. General requirements for comparability and representativeness are
addressed in Section 3.2. The MQOs given in Table 8.1 represent the maximum allowable criteria for
statistical control purposes. Precision is determined from results of revisits (field measurements) taken
on a different day and by duplicate measurements taken on the same day.

Table 8.1 Physical habitat measurement data quality objectives.

Variable or

Precision

Accuracy

Completeness

Measurement

Field Measurements and Observations

±10%

90%

Specific quality control measures are listed in Table 8.2 for field measurements and observations.
Table 8.2 Physical habitat field quality control.

Check Description

Frequency

Acceptance Criteria

Corrective Actions

QUALITY CONTROL

Check totals for cover
class categories
(vegetation type,
substrate, cover)

Each
station

Sum must be reasonable

Repeat observations

Check completeness of
station depth
measurements

Each
station

Depth measurements for
all stations

Obtain best estimate of depth where
actual measurement not possible

DATA VALIDATION

Estimate precision of
measurements based on
repeat visits

2 visits

Measurements should be
within 10 percent

Review data for reasonableness;
Determine if acceptance criteria need
to be modified

8.4.5.2 Shoreline human disturbances

Crews record the presence or absence of 12 predefined types of human land use or disturbance for each
of the 10 stations. As part of the NLA 2022, crews separately identify additional human disturbances
outside of, but adjacent to, the plots. For each of the 12 disturbance categories, the data analysis team
calculates the proportion of lakeshore stations where the disturbance is observed on each lake.
Proportions are weighted according to the proximity of the disturbance before computing the whole-
lake metrics. Weightings are 1.0 for disturbance observations within the riparian sample plots and 0.33
for those behind or adjacent to the plots. Two types of summary metrics are calculated by synthesizing
all the human disturbance observations. The first, a measure of the extent of shoreline disturbance, is
calculated as the proportion of stations at which one or more human disturbances were observed. The
second, a measure of disturbance intensity, is calculated as the mean number of human disturbance
types observed at each of the 10 shoreline stations.

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8.4.5.3 Riparian vegetation

Crews visually estimate riparian vegetation type and areal cover in three layers: the canopy (>5 m high),
mid-layer (0.5-5 m high) and ground cover (<0.5 m high). Coniferous and deciduous vegetation is
distinguished in the canopy and mid-layer; woody and herbaceous vegetation is distinguished in the
mid-layer and ground cover. As was done in NLA 2007 and NLA 2012, crews estimate cover in four
classes: absent (0), sparse (0-10%), moderate (10-40%), heavy (40-75%) and very heavy (>75%). The data
analysis team calculates simple whole-lake metrics by assigning the cover class mid-point value to each
station's observations and then averaging those cover values across all 10 stations. The data analysis
team calculates summary metrics for each lake by summing the areal cover or tallying the presence of
defined combinations of riparian vegetation layers or vegetation types.

8.4.5.4 Aquatic macrophytes

Using the same cover classes as for riparian vegetation, crews estimate areal covers of nearshore
emergent, floating, and submerged aquatic macrophytes visually. The data analysis team calculates
simple and summary aquatic macrophyte metrics for each lake in the same fashion as for riparian
vegetation.

8.4.5.5 Fish concealment features

Crews record the presence or absence of eight specified types of fish concealment features within each
10-m x 15-m littoral plot. Crews assign the areal cover of each type to one of the same cover classes
listed above. Simple metrics for each type of fish concealment feature are calculated as the proportion
of littoral stations with the particular concealment feature present. The data analysis team calculates
summary metrics as the mean number of concealment types per station. The team then uses the areal
cover class designation to unweight very sparse cover in the calculation of both simple and summary fish
cover metrics.

8.4.5.6 Shoreline and littoral bottom substrate

Crews make visual estimates of areal cover of 9 defined substrate types (bedrock, boulders, cobble,
gravel, sand, silt/clay/muck, woody debris, organic matter, and vegetation) separately for the 1-m
shoreline band and the bottom within the 10-m x 15-m littoral plot. Cover classes are the same as for
riparian vegetation, with the same modification to include an additional higher cover class. In cases
where the bottom substrate cannot be observed directly, crew observers use a clear plastic viewing
bucket, a 3-m plastic (PVC) sounding tube, or an anchor to examine or obtain samples of bottom
sediments.

The data analysis team obtains simple metrics describing the lake-wide mean cover of littoral and
shoreline substrate in each cover class size category by averaging the cover estimates at each station,
using the cover class midpoint approach described for riparian vegetation. The team then calculates
three substrate summary metrics for both shoreline and littoral bottom substrates. First is the mean
cover of the dominant substrate type. Second and third are measures of the central tendency and
variety of substrate size. Because the size categories are approximately logarithmic, the data analysis
team calculates a cover-weighted mean substrate size class and its standard deviation; ranks the
substrate classes by size from 1 to 6, weighting them by their lakewide mean cover, and then averages
weighted cover or computes its variance across size classes.

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8.4.5.7 Littoral depth, bank characteristics and other observations

Crews measure lake depth 10 m offshore using SONAR, sounding line, or sounding rod. Field crews
estimate the bank angle based on high and low water marks and the vertical and lateral range in lake
water level fluctuation. They also note the presence of water surface scums, algal mats, oil slicks, and
sediment color and odor. The data analysis team calculates whole-lake metrics for littoral depth and
water level fluctuations as arithmetic averages and standard deviations. For bank angle classes and
qualitative observations of water surface condition, sediment color, and odor, the team calculates the
proportion of stations where the described features are present.

8.4.5.8 Human Disturbances in Riparian/Littoral

12 Simple metrics describe presence (proportion of shore) with: buildings, commercial land use, lawns,
developed parkland, roads/railroads, docks/boats, trash/landfill, seawalls/revetments, row crop
agriculture, pasture, orchards, and other human activities.

2 Summary metrics describe mean number of disturbance types observed per station and proportion of
shoreline with human disturbance of any type.

8.4.5.9 Riparian Vegetation Structure

8 Simple metrics describe areal cover of trees >0.3 m diameter at breast height (DBH) and <0.3 m DBH in
canopy layer; woody and herbaceous vegetation in mid-layer; barren ground and woody, herbaceous,
and inundated vegetation in ground cover layer.

6 Summary metrics describe aggregate covers in canopy + mid-layer, woody vegetation in canopy + mid-
layer, and canopy + mid-layer + ground cover layers; presence of vegetation in canopy layer; presence in
both canopy and mid-layer.

8.4.5.10 Littoral Aquatic Macrophytes

Simple metrics describe cover of emergent, floating, and submergent macrophytes; and presence of
macrophytes lakeward from the shoreline observation plot.

2 Summary metrics describe mean combined cover and proportion of shoreline with macrophytes
present.

8.4.5.11 Shoreline and Littoral Substrate Type and Size

14 Simple metrics separately describing shoreline and littoral substrate: areal cover estimates of bedrock
(>4000 mm), boulder (250-4000 mm), cobble (64-250 mm), gravel (2-64 mm), sand (0.06-2.0 mm), soil
or silt/clay/muck (<0.06 mm), and vegetation or woody debris (if concealing substrate).

6 Summary metrics (3 for shore and 3 for littoral bottom) estimating cover-weighted mean size class,
size class variance, and the areal cover of the dominant substrate type.

8.4.5.12 Littoral Fish Cover

8 Simple metrics estimating proportion of shore zone with various fish cover types: boulder, rock ledge,
brush, inundated live trees, overhanging vegetation, snags >0.3 m diameter, aquatic macrophytes, and
human structures (e.g., docks, enhancement structures).

Summary metrics describing the mean number of fish cover types.

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8.4.5.13 Littoral Depth, Banks, and Level Fluctuations

7Simple metrics describing mean depth and depth variation among sampling station, bank angle, and
apparent height and extent of vertical and horizontal lake water level fluctuations.

1 Summary metric describing spatial variation of station depths on lake.

8.4.5.14 Miscellaneous Habitat Variables

7Simple metrics describing proportion of sampling sites with sediment odor (petrol, H2S,) sediment
colors (black, brown, other), and water surface films (oil, algal mat, other).

1 Summary metric describing proportion of sampling sites with surface film of any type.

8.4.6 Phytoplankton Assemblages

Phytoplankton will be collected as an integrated sample from the euphotic zone in open water. Both
abundance and biovolume on a species-specific basis will be determined. The data will be used in to
calculate cyanobacteria cell density, which will be compared to algal toxin benchmarks established by
the World Health Organization.

8.4.7 Fish Fillet Contaminants

Crews collect whole fish composite samples for the human health fish fillet contaminants indicator. A
laboratory prepares fillet samples from each fish composite sample by scaling each fish in the sample,
removing skin-on fillets from both sides of each fish, homogenizing fillets from all fish in the sample, and
dividing the homogenized fillet tissue into individual fillet tissue samples for chemical analyses. Fillet
tissue samples will be analyzed for mercury (total), 40 per- and polyfluoroalkyl substances (PFAS), and all
209 polychlorinated biphenyl (PCB) congeners. Analytical results (e.g., detection frequencies and fillet
tissue concentration ranges) and statistical results (e.g., percentiles, including median fillet tissue
concentrations, and percentage of lakes containing fish with fillet tissue concentrations above chemical-
specific fish tissue screening levels for protection of human health) will be reported for this indicator.

8.4.8 Atrazine Pesticide Screen

Analysts plan to determine atrazine occurrence and concentration from lake water samples.
Comparisons will be made among lakes, relative to land use in the watershed and other water quality
characteristics (e.g., nutrient concentrations) and changes in detection will be tracked overtime.
Atrazine concentrations will also be compared to EPA's level of concern for plant communities
(https://www.epa.gov/ingredients-used-pesticide-products/atrazine).

8.4.9 Trophic Status

The trophic state of lakes is analyzed using chlorophyll a concentrations, which is considered the most
accurate estimator of tropic state. Trophic state is assessed using chlorophyll a concentration
thresholds, as follows: oligotrophic, <2 ng/L; mesotrophic, 2 to 7 ng/L; eutrophic, 7 to <30 ng/L; and
hypereutrophic, >30 ng/L. These categories will be used to rank the condition of lakes relative to their

< trophic state.

^ 8.4.10 Water Chemistry, Chlorophyll a and Secchi Depth

< Laboratories measure a wide array of water chemistry parameters, including DO, pH, total nitrogen (TN),

< total phosphorus (TP), clarity, DOC, color, ANC, primary productivity, and other analytes. The data

h analysis team plans to assess some of these parameters using the reference based approach and some

o using nationally-consistent values. Additionally, the team reports on values for these parameters and

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their distribution. Water chemistry analysis is critical for interpreting the biological indicators.
Temperature profiles are used to determine degree of lake stratification.

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9 LITERATURE CITED

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Carlson, R.E. 1977. A trophic state index for lakes. Limnology and Oceanography 22(2):361-369.

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Garner, F.C., M.A. Stapanian, and K.E. Fitzgerald. 1991. Finding causes of outliers in multivariate
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Heinz Center. 2002. The State of the Nation's Ecosystems. The Cambridge University Press.

Herlihy, A. T., S. G. Paulsen, J. V. Sickle, J. L. Stoddard, C. P. Hawkins, and L. L. Yuan. 2008. Striving for
consistency in a national assessment: the challenges of applying a reference-condition approach at a
continental scale. Journal of the North American Benthological Society 27:860-877.

Hunt, D.T.E and A.L. Wilson. 1986. The chemical analysis of water: general principles and techniques. 2nd
edition. Royal Society of Chemistry, London, England.

Kaufmann PR, Hughes RM. 2006. Geomorphic and anthropogenic influences on fish and amphibians in
Pacific Northwest coastal streams. Pages 429-455 in Hughes RM, Wang L, Seelbach PW (editors).
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Symposium 48, Bethesda, Maryland.

Kaufmann PR, Hughes RM, Van Sickle J, Whittier TR, Seeliger CW, Paulsen SG. 2014a. Lake shore and
littoral habitat structure: Afield survey method and its precision. Lake and Reservoir
Management. 30:157-176.

Kaufmann PR, Hughes RM, WhittierTR, Bryce SA, Paulsen SG. 2014b. Relevance of lake physical habitat
assessment indices to fish and riparian birds. Lake and Reservoir Management. 30:177-191.

Kaufmann PR, Levine P, Robison EG, Seeliger C, Peck DV. 1999. Quantifying physical habitat in wadeable
streams. EPA/620/R-99/003. U.S. Environmental Protection Agency, Office of Research and
Development, Washington, DC. Available at

http://www.epa.gov/emap/html/pubs/docs/groupdocs/surfwatr/field/phyhab.html. Accessed
April 2011.

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Lakeshore and littoral physical habitat structure in a national lakes assessment. Lake and Reservoir
Management. 30:192-215.

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Kincaid TM, Larsen DP, Urquhart NS. 2004. The structure of variation and its influence on the estimation
of status: indicators of condition of lakes in the Northeast USA. Environmental Monitoring and
Assessment 98:1-21.

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181A.

Klemm, D.J., P.A. Lewis, F. Fulk, and J.M. Lazorchak. 1990. Macroinvertebrate Field and Laboratory
Methods for Evaluating the Biological Integrity of Surface Waters. EPA 600/4-90/030. U.S.
Environmental Protection Agency, Cincinnati, Ohio.

Larsen DP, Kinkaid TM, Jacobs SE, Urquhart NS. 2001. Designs for evaluating local and regional scale
trends. Bioscience 51(12):1069-1078.

Larsen DP, Kaufmann PR, Kincaid TM, Urquhart NS. 2004. Detecting persistent change in the habitat of
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Larsen, D. P., N. S. Urquhart, and D. L. Kugler. 1995. Regional-scale trend monitoring of indicators of
trophic condition of lakes. Water Resources Bulletin 31:117-139.

Lemmon, P.E. 1957. A new instrument for measuring forest overstory density. J. For. 55(9): 667-669.

Littel RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O. 2006. SAS for mixed models, Second
Edition. Cary, N.C. SAS Institute, Inc. 814p.

Meglen, R.R. 1985. A quality control protocol for the analytical laboratory. Pg. 250-270. IN: J.J. Breen and
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NHDPIus 2005. NHD - National Hydrography Dataset Plus Version 1.0
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Oblinger Childress, C.J., Foreman, W.T., Connor, B.F. and T.J. Maloney. 1999. New reporting procedures
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Peck, D. V., and R. C. Metcalf. 1991. Dilute, neutral pH standard of known conductivity and acid
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APPENDIX A: LABORATORY LIST

National Lakes Assessment 2022 Laboratory List

Analysis

Contacts

Contractor Lab/EPA Lab

Algal Toxins (microcystins and
cylindrospermopsin)

Danielle Grunzke (OW)

Avanti Corporation

Bacteria (Enterococci)

Rich Haugland (ORD)
Kendra Forde (OW)

Center for Environmental
Measurement and
Modeling (CEMM),
EPA ORD - Cincinnati

Benthic Macroinvertebrates

Garrett Stillings (OW)

EcoAnalysts

eDNA

Erik Pilgrim (ORD)
Richard Mitchell (OW)

Center for Environmental
Measurement and
Modeling (CEMM),
EPA ORD - Cincinnati

Fish Fillet Contaminants

John Healey (OW)
Leanne Stahl (OW)

TBD

Atrazine Pesticide Screen

Kendra Forde (OW)

GLEC

Water Chemistry

Dave Peck (ORD)

PESD Laboratory, USEPA
ORD-Corvallis

Zooplankton and Phytoplankton

Brian Hasty (OW)
Lareina Guenzel (OW)

EcoAnalysts

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APPENDIX B: REVISION HISTORY

This appendix documents changes made to approved final versions of the NLA
2022 FOM, LOM, or SEG.

NLA FOM Version History

Version

Date

Revisions and Comments

0.0

December 2021

Internal EPA version for project QAC review and comments

0.0

February 2022

• Updated introduction to be consistent with other NLA 2022 manuals

• Corrected typos, formatting, numbering of tables and figures

• Updated field data and tracking form names to be consistent with the
NLA App

• Updated sample bottle types

• Added ESA conservation measures (Section 6.4, Section 10 and
Appendix C)

1.0

February 2022

Final approved document

• Corrected NLA App entry inconsistencies

• Clarified whole fish sample storage and shipping (Section 7.3)

• Revised ESA conservation measures (Section 6.4, Section 10 and
Appendix C)

• Updated base kit list

1.1

March 2022

• Added a project identifier (NLA2022) for bloomWatch reports
(Section2.2.4.5)

• Clarified DO probe calibration requirements (Section 5.2.2.3)

• Added direction to homogenize last integrated sample if the last pull
does not fit in container (Section 5.5.3)

• Updated Figure 5-4 with attachment bridle length

• Corrected shipping timeframe for T-2 samples in Table B-l

• Corrected cross reference errors, formatting and typos

1.2

May 2022

• Corrected atrazine preservation and shipping. Atrazine samples are to
be kept chilled until analysis (Section 5.5.3.2, Section 8.4 and Appendix B
(T-2 Frozen Batched Samples))

• Added Appendix D: NLA Handpicked Sites: Resampling of the National
Eutrophication Study Lakes

NLA LOM Version History

>
LU
CC

CD
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Z

LU
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D_
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Version

Date

Revisions or Comments

0.0

February 2022

Internal EPA version for project QAC review and comments

1.0

February 2022

Final approved document

1.1

May 2022

• Corrected the atrazine sample preservation and shipping. Atrazine
samples should be kept chilled until analysis (Section 8)

• Corrected typos in Tables 5.3, 7.2 and 11.2

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