United States Environmental Protection Agency
           Office of Water
           Washington, DC
           EPA841-B-11-006
    2012 National Lakes Assessment
Quality Assurance Project
               Plan
             Version 1.0, May 15, 2012

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Approval Page
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Amina Pollard                                                                      Date
2012 National Lakes Assessment Project Leader
U.S. EPA Office of Water
                                                                       T	/
      Holdsworth                                                                  Date
Chief, Monitoring Branch
U.S. EPA Office of Water
Sarah Lehrnann                                                                    Date
National Aquatic Resource Surveys Team Leader
U.S. EPA Office of Water
Virginia |y<-Norse                                                                  Date
Office of Wetlands, Oceans, and Watersheds Quality Assurance Officer
U.S. EPA Office of Water
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2012 National Lakes Assessment                                       Quality Assurance Project Plan
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Quality Assurance Project Plan Review & Distribution Acknowledgement
& Commitment to Implement the National Lakes Assessment 2012
We have read the Quality Assurance Project Plan, Site Evaluation Guidelines, Field Operations Manual,
and Laboratory Operations Manual for the 2012 National Lakes Assessment listed below. Our
agency/organization, agrees to abide by its requirements for work performed under the 2012 National
Lakes Assessment. Check appropriate boxes.
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Field Operations Manual             n                                                       ^

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2012 National Lakes Assessment
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NOTICE
Quality Assurance Project Plan
              Page vii of xvi
The intention of the 2012 National Lakes Assessment (NLA 2012) 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:
2012 National Lakes Assessment: Site Evaluation Guidelines (EPA841-B-11-005)
2012 National Lakes Assessment: Field Operations Manual (EPA841-B-11-003)
2012 National Lakes Assessment: Laboratory Operations Manual (EPA 841-B-11-004)
This document, Quality Assurance Project Plan (QAPP), contains elements of the overall project
management, data quality objectives, measurement and data acquisition, and information management
for NLA 2012. 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 2012. 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:
USEPA. 2012. 2012 National Lakes Assessment. Quality Assurance Project Plan. EPA 841-B-11-006. U.S.
Environmental Protection Agency, Washington, DC
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TITLE	FRONT COVER

APPROVAL PAGE	Ill
QUALITY ASSURANCE PROJECT PLAN REVIEW & DISTRIBUTION ACKNOWLEDGEMENT & COMMITMENT TO
IMPLEMENT THE NATIONAL LAKES ASSESSMENT 2012	V
NOTICE	VII

TABLE OF CONTENTS	IX

LIST OF TABLES	XI

LIST OF ACRONYMS	XII

DISTRIBUTION LIST	XIII
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 2012 DESIGN	2
   1.6     FIELD OPERATIONS	2
   1.7     LABORATORY OPERATIONS	2
   1.8     PEER REVIEW	3

2    PROJECT PLANNING AND MANAGEMENT	5

   2.1     INTRODUCTION	5
     2.1.1   Project Organization	6
     2.1.2   Project Schedule	8
   2.2     SCOPE OF QAPP	8
     2.2.1   Field Operations	8
     2.2.2   Overview of Laboratory Operations	12
     2.2.3   Data Analysis and Reporting	14
     2.2.4   Peer Review	14

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	18
     3.2.3   Chemical Precision, Bias, and Accuracy	20
     3.2.4   Taxonomic Precision and Accuracy of Zooplankton, Phytoplankton, Diatoms and Benthic
     Macroinvertebrates	21
     3.2.5   Completeness	23
     3.2.6   Comparability	23
     3.2.7   Representativeness	24
4    SAMPLING DESIGN AND SITE SELECTION	25

   4.1     PROBABILITY BASED SAMPLING DESIGN AND SITE SELECTION	25
   4.2     REFERENCE SITE SELECTION	26

5    INFORMATION MANAGEMENT	27
                                 8
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   5.1     ROLES AND RESPONSIBILITIES	27
     5.1.1   State/Tribe-Based Data Management	29
   5.2     OVERVIEW OF SYSTEM STRUCTURE	30
     5.2.1   Data Flow	30
     5.2.2   Simplified Description of Data Flow	30
     5.2.3   Core Information Management Standards	32
     5.2.4   Data Formats	32
     5.2.5   Public Accessibility	32
   5.3     DATA TRANSFER PROTOCOLS	33
   5.4     DATA QUALITY AND RESULTS VALIDATION	34
     5.4.1   Design and Site Status Data Files	34
     5.4.2   Sample Collection and Field Data	35
     5.4.3   Laboratory Analyses and Data Recording	36
     5.4.4   Data Review, Verification, and Validation Activities	37
   5.5     DATA TRANSFER	39
     5.5.1   Database Changes	39
   5.6     METADATA	40
   5.7     INFORMATION MANAGEMENT OPERATIONS	40
     5.7.1   Computing Infrastructure	40
     5.7.2   Data Security and Accessibility	40
     5.7.3   Life Cycle	40
     5.7.4   Data Recovery and Emergency Backup Procedures	41
     5.7.5   Long-Term  Data Accessibility and Archive	41
   5.8     RECORDS MANAGEMENT	41

6    INDICATORS	43

   6.1     SUMMARY	43
     6.1.1   Sampling Design	43
     6.1.2   Sampling and Analytical Methods	43
     6.1.3   Quality Assurance Objectives	43
     6.1.4   Quality Control Procedures: Field Operations	43
     6.1.5   Quality Control Procedures: Laboratory Operations	43
     6.1.6   Data Management, Review, and Validation	43

7    ASSISTANCE VISITS	45

   7.1     FIELD EVALUATION AND ASSISTANCE VISIT PLAN	45
   7.2     LABORATORY EVALUATION AND ASSISTANCE VISIT PLAN	45
8    DATA ANALYSIS PLAN	47

   8.1     DATA INTERPRETATION BACKGROUND	47
     8.1.1   Scale of assessment	47
     8.1.2   Selecting the best indicators	47
     8.1.3   Defining least impacted reference condition	47
     8.1.4   Determining thresholds for judging condition	47
   8.2     GEOSPATIAL DATA	48
   8.3     DATASETS USED FOR THE REPORT	48
     8.3.1   Trophic status	48
     8.3.2   Ecological integrity	48
     8.3.3   Recreational  value	48
   8.4     INDICATOR DATA ANALYSIS	49
     8.4.1   Algal Toxins	49
     8.4.2   Benthic Macroinvertebrate and Zooplankton Assemblages	49
     8.4.3   Physical Habitat	49

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     8.4.4   Phytoplankton Assemblages	52
     8.4.5   Sediment Dating	53
     8.4.6   Sediment Diatoms	53
     8.4.7   Sediment Mercury	53
     8.4.8   Thazine Pesticide Screen	53
     8.4.9   Trophic Status	53
     8.4.10     Water Chemistry, Chlorophyll a and Secchi Depth	53

9    LITERATURE CITED	54

APPENDIX A: LABORATORY LIST	58



I   '    '

Table 2.1 Field training sessions for NLA 2012	11
Table 3.1 Important variance components for aquatic resource assessments	19
Table 5.1 Summary of IM responsibilities	27
Table 5.2 Summary sample and field data quality control activities	35
Table 5.3 Summary laboratory data quality control activities	36
Table 5.4 Data review, verification, and validation quality control activities	38
Table 6.1 Summary of indicator QA procedures and coordinators	43
Table 8.1 Physical habitat measurement data quality objectives	49
Table 8.2 Physical habitat field quality control	50

   ••r  .    •     .  .-••
Figure 2.1 National Lakes Assessment 2012 project organization  chart	9
Figure 2.2 National Lakes Assessment 2012 project timeline	10
Figure 4.1 Design sites for the 2012 National Lakes Assessment	26
Figure 5.1 Conceptual model of data flow into and out of the master SQL database for the NLA 2012	31
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          ANC
          C02
          CPR
          DBH
          Dl
          DO
          DOC
          EMAP
          EPA
          ETON
          FOM
          CIS
          GPS
          HOPE
          H2S
          LOM
          MPCA
          NALMS
          NH4
          NIST
          NO3
          OSHA
          PCB
          P-Hab
          QA
          QAPP
          QA/QC
          QCCS
          QRG
          SEG
          SOPs
          TN
          TOC
          TP
          TSS
          TVS
          USGS
acid neutralizing capacity
carbon dioxide
cardiopulmonary resuscitation
diameter at breast height
deionized
dissolved oxygen
dissolved organic carbon
Environmental Monitoring and Assessment Program
Environmental Protection Agency
ethyl alcohol
Field Operations Manual
geographic information system
global positioning device
high density polyethylene
hydrogen sulfide
Lab Operations Manual
Minnesota Pollution Control Agency
North American Lakes Management Society
ammonium
National Institute of Standards
nitrate
Occupational Safety and Health Administration
polychlorinated biphenyl
physical habitat
quality assurance
Quality Assurance Project Plan
quality assurance/quality control
quality control check solution
Quick Reference Guide
Site Evaluation Guidelines
Standard Operating Procedures
total nitrogen
total organic carbon
total phosphorus
total suspended solids
total volatile solids
United States Geological Survey
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DISTRIBUTION LIST
This QAPP, which includes the associated manuals or guidelines, will be distributed to the following:
EPA, States, Tribes, universities, labs, and contractors participating in the 2012 National Lakes
Assessment (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 will distribute 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 will distribute the relevant
materials via email to necessary participants.
EPA HQ Project Lead
Amina Pollard, OW
                                                                  Contact Information
pollard.amina@epa.gov
202-566-2360
EPA Wetlands, Oceans, and
Watersheds
1200 Pennsylvania Ave NW (4503T)
Washington, DC 20460
EPAHQNARSQALead
Sarah Lehmann, OW
lehmann.sarah@epa.gov
202-566-1379
EPA Wetlands, Oceans, and
Watersheds
1200 Pennsylvania Ave NW (4503T)
Washington, DC 20460
E PA HQQA Officer
Virginia Fox-Norse, OW
fox-norse.virginia@epa.gov
202 566-1266
EPA Wetlands, Oceans, and
Watersheds
1200 Pennsylvania Ave NW (4504T)
Washington, DC 20460
E PA HQ Logistics Lead
Marsha Landis, OW
landis.marsha@epa.gov
202-564-2858
EPA Wetlands, Oceans, and
Watersheds
1200 Pennsylvania Ave NW (4503T)
Washington, DC 20460
EPA HQ Project Management Team   Richard Mitchell, OW
                                 mitchell.richard@epa.gov
                                 202-566-0644
                                 EPA Wetlands, Oceans, and
                                 Watersheds
                                 1200 Pennsylvania Ave NW (4503T)
                                 Washington, DC 20460
EPA ORD Technical Advisor
Steven G. Paulsen, ORD
paulsen.steve@epa.gov
541-754-4428
Freshwater Ecology Branch Western
Ecology Division, NHEERL, ORD, EPA
200 S.W. 35th St.
Corvallis, OR  97330
                                                                                                       CO
Contract Logistics Coordinator
Chris Turner, GLEC, Inc.
cturner@glec.com
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                                                                             715-829-3737
          NARS Information Management
          Coordinator
Marlys Cappaert, SRA International
Inc.
          EPA Regional NLA Coordinators
Hilary Snook, Region 1
cappaert.marlys@epa.gov
541-754-4467
541-754-4799 (fax)
snook.hilary@epa.gov
617-918-8670
EPA Region 1
11 Technology Drive
North Chelmsford, MA 01863
                                           Jim Kurtenbach, Region 2
                                  kurtenbach.james@epa.gov
                                  732-321-6695
                                  EPA Region 2
                                  2890 Woodbridge Avenue
                                  Edison, NJ 08837
                                           Frank Borsuk, Region 3
                                  borsuk.frank@epa.gov
                                  304-234-0241
                                  EPA Region 3
                                  1060 Chapline Street, Suite 303
                                  Wheeling, WV 26003
                                           Marion Hopkins, Region 4
                                  hopkins.marion@epa.gov
                                  404-562-9481
                                  EPA Region 4
                                  61 Forsythe Street SW
                                  Atlanta, GA 30303
                                           Mari Nord, Region 5
                                  nord.mari@epa.gov
                                  312-886-3017
                                  EPA Region 5
                                  77 West Jackson Blvd
                                  Chicago, IL 60604
                                           Mike Schaub, Region 6
                                  schaub.mike@epa.gov
                                  214-665-7314
                                  EPA Region 6
                                  1445 Ross Avenue, Suite 1200
                                  Dallas, TX 75202
                                           Gary Welker, Region 7
                                  welker.gary@epa.gov
                                  913-551-7177
                                  EPA Region 7
                                  901 N. Fifth Street
                                  Kansas City,  KS 66101
                                           Kris Jensen, Region 8
CO
                                           Jeff McPherson, Region 8
                                  jensen.kris@epa.gov
                                  303-312-6237
                                  EPA Region 8
                                  1595 Wynkoop Street
                                  Denver, CO 80202
                                  mcpherson.jeffrey@epa.gov
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                                                                     303-312-7752
                                                                     EPA Region 8
                                                                     16194 West 45th Drive
                                                                     Golden, CO 80403
                                  Sue Keydel, Region 9
keydel.susan@epa.gov
415-972-3106
EPA Region 9
75 Hawthorne Street
San Francisco, CA 9410
                                   Lil Merger, Region 10
herger.lillian@epa.gov
206-553-1074
EPA Region 10
1200 Sixth Avenue, Suite 900
Seattle, WA 98101
                                                                                                           CO
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1      EXECUTIVE SUMMARY

1.1    Background
Several recent reports have identified the need for improved water quality monitoring and analysis at
multiple scales. In response, the U.S. 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, has begun
a program to assess 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 NLA 2012, which builds upon the previous 2007 NLA aims to address two key questions about the
quality of the nation's lakes and reservoirs:
       •   What percent of the nation's lakes  are in good, fair, and poor condition for key indicators of
           water quality, ecological health, and recreation?
       •   What is the relative importance of key stressors such as nutrients?
The surveys are also designed to help expand and enhance state 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 (OW) in Washington,  DC, with
technical support from the ORD's Western Ecology Division (WED) in Corvallis, Oregon. Each of the EPA
Regional Offices has identified regional coordinators to assist in implementing the survey and coordinate
with the state/tribal crews who collect the water and sediment samples following NLA 2012 protocols.
The U.S. Geological  Survey is partnering with EPA on the analysis for several indicators. EPA began
planning the NLA 2012 with state, tribal, and other federal partners in 2010 and is continuing this
partnership effort. EPA expects to report the results in December 2014 in compliance with the Data
Quality Act.

1.3    Quality Assurance Project Plan
The purpose of this QAPP is to document the project data quality objectives and quality
assurance/quality control measures that will be implemented in order to ensure that the data collected
meets those needs. The plan contains elements of the overall project management, data quality
objectives, measurement and data acquisition,  and  information management for the NLA 2012 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 National Lakes Assessment
2012.

1.4    Information Management Plan
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
2012 employs a variety of well-tested information management (IM) strategies to aid in the functional
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organization and ensured integrity of stored electronic data. IM is integral to all aspects of the NLA 2012
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 EMAP information management support staff located at the Western Ecology Division facilities in
Corvallis. This database is known as the National Aquatic Resource Surveys Information Management
System (NARS IM). All validated measurement and indicator data from the NLA 2012 are eventually
transferred to EPA's Water Quality Exchange (WQX) for archival in  EPA's STORET warehouse for public
accessibility. NLA 2012 IM staff provides support and guidance to all program operations  in addition to
maintaining NARS IM.

1.5    NLA 2012 Design
EPA used an  unequal probability design to select 904 lakes and reservoirs greater than 1 hectare in size
(note: in NLA 2007, the lower size limit was 4 ha) in the continental United States. The design also
includes revisits to 96 lakes during the sampling season for quality  assurance purposes including
evaluation of the ability of an indicator to distinguish among sites from differences within individual
sites. To improve our ability to assess changes since  NLA 2007, the  design includes revisits to
approximately 50% (398  lakes) of the sites sampled in 2007 (resample lakes). Related designs were also
completed for sampling of lakes on Oahu, Hawaii and the North Slope of Alaska.

1.6    Field Operations
Sample collection for NLA 2012 is designed to be completed during the index period of May through the
end of September 2012.  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 2012 NLA Site Evaluation Guidelines
(SEG, EPA-841-B-11-005).
NLA 2012 indicators include: algal toxins (microcystins), benthic macroinvertebrates, physical habitat,
phytoplankton, sediment dating, sediment diatoms, triazine pesticide screen, water chemistry and
chlorophyll A, and zooplankton. Additional  research indicators include: dissolved carbon and methane,
macrophytes, and sediment mercury. Field measurements and sampling methods are outlined in the
2012 NLA Field Operations  Manual (FOM, EPA 841-B-11-004). Field crews are trained on these methods
at a required EPA-sponsored training session. Field sampling assistance visits will be completed for each
field crew for quality assurance.

1.7    Laboratory Operations
NLA 2012 laboratory analyses are conducted either by state/tribal-selected labs or "National
Laboratories" set up by 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 2012 National Lakes Assessment: Laboratory  Operations Manual (LOM, EPA 841-B-
11-004). Any laboratory selected to conduct analyses with NLA 2012 samples must demonstrate that  it
can meet the quality standards presented in this 2012 NLA QAPP and LOM.
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1.8   Peer Review
Quality Assurance Project Plan
               Page 3 of 61
Surveys undergo a thorough peer review process, where the scientific community and the public are
given the opportunity to provide comments on the report. Cooperators have been actively involved in
the development of the overall project management, design, indicator selection, and methods.
EPA utilizes a three-tiered approach for peer review of the Survey report:
           •   internal and external review by EPA, states, other cooperators and partners,
           •   external scientific peer review, and
           •   public review.
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
Several recent reports have identified the need for improved water quality monitoring and analysis at
multiple scales. In 2000, the General Accounting Office (USGAO 2000) reported that EPA, states, and
tribes collectively cannot make statistically valid inferences about water quality (via 305[b] reporting)
and lack data to support key management decisions. In 2001, the National Research Council (NRC 2000)
recommended EPA, states, and tribes promote a uniform, consistent approach to ambient monitoring
and data collection to support core water quality programs. In 2002, the H. John Heinz III Center for
Science, Economics, and the Environment (Heinz Center 2002) found that there is 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
(USEPA 2003) states that there is insufficient information to provide a national answer, with confidence
and scientific credibility, to the question, 'What is the condition of U.S. waters and watersheds?'
In response to this need, OW, in partnership with states and tribes, has begun a program to assess the
condition of the nation's waters via a statistically valid approach. The current assessment, the National
Lakes Assessment 2012  (referred to as NLA 2012 throughout this document), builds upon the 2007
National Lakes Assessment, Wadeable Streams Assessment implemented by EPA to monitor and assess
the condition of the nation's wadeable stream resources, as well as other NARS surveys such as the
National Coastal Condition Assessment, the 2008-2009 National Rivers and Streams Assessment, and
2011 National Wetland Condition Assessment. The NLA 2012 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 and  known quality for its intended use, and information. The QAPP contains
elements of the overall project management, data quality objectives, measurement and data
acquisition, and information management for NLA 2012. 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 2012 NLA participants
have agreed to follow this QAPP and the protocols and design laid out in this document, and its
associated documents-the 2012 NLA FOM, LOM, and SEG.
This cooperative effort between states, tribes, and federal agencies makes it possible to  produce a
broad-scale assessment of the condition of the Nation's 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 2012 National Lakes Assessment has three main objectives:

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

   •   Seek associations between selected indicators of natural and anthropogenic stresses and
       indicators of ecological condition.

   •   Assess changes between 2007 and 2012.
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A 2012 NLA workgroup, comprised of EPA, state, and other partners, decided on a few improvements to
the suite of original indicators. The additions include a triazine pesticide screen, a macrophyte indicator,
and dissolved carbon. Modifications include adding a draw down component to physical habitat, adding
littoral sampling for algal toxins, and using different zooplankton sampling nets. Finally, pathogens will
no longer be included. In order to ensure we can track changes for modified indicators, we conducted
pilot testing of the habitat and zooplankton protocols and will be collecting zooplankton using both the
NLA 2007 and the NLA 2012 nets at approximately 250 random sites.

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 will be coordinated by the Office of Water (OW) in
Washington, DC, with support from EPA Western Ecology Division (WED) in Corvallis, Oregon. Each EPA
Regional  Office has identified a Regional EPA Coordinator who is part of the EPA crew providing a critical
link with  state and tribal partners. Cooperators will work with their Regional EPA Coordinator to address
any technical issues. A comprehensive quality assurance (QA) program has been established to ensure
data integrity and provide support for the reliable interpretation of the findings from this project.
Technical Experts Workgroups will be convened to provide EPA 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 will provide support ranging
from implementing the survey, sampling and laboratory processing, data management, data analysis,
and report writing. Cooperators will interact with their Regional  EPA Coordinator and the EPA Project
Leader regarding contractual services.
The primary responsibilities of the principals and cooperators are as follows:
Project Leader. Amina Pollard
    *   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 EPA Project Team on technical, logistical, and organizational  issues on a regular
       basis.
EPA Field Logistics Coordinator: Marsha Landis
    *   EPA employee who functions to support implementation of the  project based on technical
       guidance established by the EPA Project Leader and serves as point-of-contact for questions
       from field crews and cooperators for all activities.
    •   Tracks progress of field sampling activities.
EPA Project QA Lead: Sarah Lehmann
    *   Provides leadership, development, and oversight of project-level quality assurance for NARS.

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    •   Assembles and provides leadership for a NLA 2012 Quality Team.
    •   Maintains official, approved QAPP
    •   Maintains all training materials and documentation
    •   Maintains all laboratory accreditation files
EPA Technical Advisor: Steven Paulsen
    *   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.
Information Management Coordinator: Marlys Cappaert
    *   A contractor who functions to support implementation of the project based on technical
       guidance established by the EPA  Project Leader and Alternate EPA Project Leader oversees all
       sample shipments and receives data forms from the Cooperators.
    •   Oversees all aspects of data entry and data management for the project.
EPA QA Officer, Office of Wetlands, Oceans and Watersheds: Virginia Fox-Norse
    *   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.
Regional EPA Coordinators
    *   Assists EPA Project Leader with regional coordination activities.
    •   Serves on the Technical  Experts Workgroup and interacts with Project Facilitator on technical,
       logistical, and organizational issues on a regular basis.
    •   Serves as primary point-of-contact for the Cooperators.
Steering Committee (Technical Experts Workgroup): States, EPA, academics, other federal agencies
    *   Provides expert consultation on key technical issues as identified by the EPA Coordination crew
       and works with Project Facilitator to resolve approaches and strategies to enable data analysis
       and interpretation to be scientifically valid.
Cooperator(s): States, Tribes, USGS, others
    *   Under the scope of their assistance agreements, plans and executes their individual studies as
       part of the cross jurisdictional NLA 2012 and adheres to all QA requirements and standard
       operating procedures (SOPs).
    •   Interacts with the Grant Coordinator, Project Facilitator and EPA Project Leader regarding
       technical, logistical, organizational issues.
Field Sampling Crew Leader
    *   Functions as the senior member of each Cooperator's field sampling crew and the point of
       contact for the Field Logistics Coordinator.
    •   Responsible for overseeing all activities of the field sampling crew and ensuring that the Project
       field method protocols are followed during all sampling activities.
Field Logistics Coordinator: Chris Turner, Great Lakes Environmental Center (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
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             •   Serves as point-of-contact for questions from field crews and cooperators for all activities.
             •   Tracks progress of field sampling activities.

         2.1.2   Project Schedule
         Training and field sampling will be conducted in 2012. Sample processing and data analysis will be
         completed by 2013 in order to publish a report in 2014. Figure 2.2 gives an overview of the major tasks
         leading up to the final report.

         2.2   Scope of QAPP
         This QAPP addresses the data acquisition efforts of the 2012 NLA, which focuses on  the sampling of
         lakes across the United States in 2012. Data from approximately 1000 lakes (selected with a probability
         design) located within the contiguous 48 states will provide a comprehensive assessment of the nation's
         lakes. Quality information, requirements, and procedures are contained in the QAPP and its
         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 you 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 2012 based on guidance developed by EMAP (Baker and
         Merritt 1990), experience from NLA 2007, advice from the NARS team, and through  consultation with a
         steering committee 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 (e.g. the Forest Service per the 2011 MOA). 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.
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                                   Project Management
                            Project Lead - Amina Pollard, EPA OW
                            Project QA-Sarah Lehmann, EPAOW
                        Technical Advisor - Steve Paulsen, EPA ORD
  •    s
     Tony
5>tudy Design
 Olsen, EPAORD
                                                                  .uality Assurance
                                                            *   Virginia Fox-Norse,
                                                                     EPAOW
                        Field Protocols
                      NLA 2012 Steering
                          Committee
                                         Training
                         EPA HQ, EPA ORD, EPA Regions, Contractors
                                       Field Logistics
                                Implementation Coordinator
                                   Field Implementation
                             EPA HQ, EPA Regions, States, Tribes,
                                        Contractors
                                      Indicator Team
                                NLA 2012 Steering Committee
    Algal Toxins
                Sample Flow
Benthic Macroinvertebrates
                                               Chemistr
    Sediment Mercury
                     Field Data
             listry         Chlorophyll A

Phytoplankton         Sediment Diatoms
                         riazine Pesticide Screen        Zooplankton
                                   iformation Manageme
                                 EPA WED - Marlys Cappaert
                                         Final Data
                                  Web, STORET/WQX-OW
                                        Assessment
                                        OW - Lead
                         EPA ORD, EPA Regions, States, Tribes, Federal
                                   Partners, Cooperators
Figure 2.1 National Lakes Assessment 2012 project organization chart.
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2010 2011 2012 2013 2014

survey planning
pilot studies
select indicators
design frame
select sites
implementation
manuals
field training
sampling season
sample processing
data analysis
draft report
peer review
final report
research
-
-
-
-
-









design
-

-


-
-







field





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lab /data









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report
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         Figure 2.2 National Lakes Assessment 2012 project timeline.
         Specific procedures for evaluating each sampling location and for replacing non-sampleable sites are
         documented in the 2012 NLA SEG. Field crews will procure scientific collecting permits from State,
         Tribal, and Federal agencies, as needed. The field crews will use standard field equipment and supplies.
         Field Crew Leaders from states and tribes will 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.
         Field measurements and samples are collected by trained crews. Each Field Crew Leader must be
         trained at an EPA-sponsored training session prior to the start of the field season, along with as many
         crew members as possible. EPA will provide the three-day training sessions in a number of locations
         around the country for cooperators and contractors. It is strongly encouraged that  field crews attend all
         three 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 2012 must adhere to the provisions of this integrated QAPP, FOM, and SEG.
         Trainers will maintain a list of all personnel trained and provide the information to  the NLA Project Lead
         and the QA Project Lead.
         Training documentation will be maintained by the NARS QA Lead in NLA 2012 QA files. Field crews may
         not operate without a trained field crew leader present.
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Table 2.1 Field training sessions for NLA 2012
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Date
13-15 MAR 2012
26-28 MAR 2012

3-5 APR 2012
10-12 APR 2012

17-19 APR 2012
24-26 APR 2012
8- 10 MAY 2012

12-14 JUN 2012
Training Location
Unicoi State Park, Helen, GA
Cacapon State Park, Berkeley Springs, WV

Lake Murray State Park, Ardmore, OK
Golden, CO

Lawrence, KS
Moss Landing, CA
North Chelmsford, MA
Madison, Wl
Olympia, WA
Primary Trainees
Train the trainer
DE, DC, MD, PA, VA, WV, NJ

LA, AR, OK, NM,TX
CO, MT, ND, SD, UT, WY

IA, KS, MO, NE
AZ, CA, HI, NV
CT, ME, MA, NH, Rl, VT, NY
IL, IN, Ml, MN, OH,WI
AK, ID, OR, WA
Evaluation and assistance visits will be conducted with each Field Crew early in the sampling and data
collection process, and corrective actions will be conducted in real time. These visits provide EPA with a
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 visits evaluations will be based on the uniform training, plans, and checklists. For
more information on field assistance visits see Section 8 of the FOM.
A variety of methods may be used to access a lake. Some sampling locations require crews to hike in,
transporting all equipment in backpacks. For this reason, ruggedness and weight are important
considerations in the selection of equipment and instrumentation. Crews may need to camp out at the
sampling location and may need to provide themselves with the necessary camping equipment.
The site verification process is outlined in the NLA 2012 SEG and FOM. All methods used in the field are
fully documented in step-by-step procedures in the NLA 2012 FOM. The manual also contains detailed
instructions for completing documentation, labeling samples, any field processing requirements, and
sample storage and shipping. Field communications will  be through Field Crew Leaders, and will involve
regularly scheduled conference calls or contacts with the NLA 2012 Communications Center.
Standardized field data forms are the primary means of data recording. For NLA 2012 crews will have
the option to use paper or electronic forms. On completion, the data forms are reviewed by a person
other than the person who initially entered the information. Prior to departure from the field site, the
field crew leader reviews all forms and labels for completeness and legibility and ensures that all
samples are  properly labeled and packed. This review process will be done for either form of data
collection (paper or electronic).
Upon return from field sampling to the office, field crews using paper forms send completed data forms
to the information management staff at WED in  Corvallis, Oregon for entry into a computerized
database. Field crews using electronic forms send completed forms via email as soon as they have
access to email. At WED, the IM team review electronic data files independently  to verify that values are
consistent with those recorded on the field data form or original field data file (see Section 4.1.4).
Field crews store or package samples for shipment in accordance with instructions contained in the NLA
2012 Field Operations Manual, including taking precautions so holding times are  not exceeded. Samples
which must be shipped are delivered by field crews to a commercial carrier; copies of bills of lading or
other documentation are maintained by the crew. Using the tracking form, crews notify the NARS IM
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         Center about sample shipment; thus, tracking procedures can be initiated quickly in the event samples
         are not received. Chain-of-custody forms are completed by the crews for all transfers of samples, with
         copies maintained by the field crew. The IM team will follow up with field crews about any missing
         samples and/ or incomplete files.
         The field operations phase is completed with collection of all  samples or expiration of the sampling
         window.

         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, zooplankton). Analytical methods are summarized in the NLA
         2012 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 class "S" weights which are certified by
                the National Institute of Standards and Technology (NIST).

            •   Verification of the calibration of top-loading balances using NIST-certified class "P" weights.

            •   Checking and recording the composition of fresh calibration standards against the previous lot.
                Acceptable comparisons are less than or equal to two percent of the theoretical value. (This
                acceptance is tighter than the method calibration criteria.)

            •   Recording all analytical data in bound logbooks in ink, or on standardized recording forms.

            •   Verification of the calibration of uniquely identified daily use thermometers using NIST-certified
                thermometers.

            •   Monitoring and recording (in a logbook or on  a recording form) temperatures and performance
                of cold storage areas and freezer units (where samples, reagents, and standards  may be stored).
                During periods of sample collection operations, monitoring must be done on a daily basis.

            •   An overall program of laboratory health and safety including periodic inspection  and verification
                of presence and  adequacy of first aid and spill kits; verification of presence and performance of
                safety showers, eyewash  stations, and fume hoods; sufficiently exhausted reagent storage units,
                where applicable; available chemical and hazardous materials inventory; and accessible material
                safety data sheets for all required materials.

            •   An overall program of hazardous waste management and minimization, and evidence of proper
                waste handling and disposal procedures (90-day storage, manifested waste streams, etc.).

            •   If needed, having a source of reagent water meeting American Society of Testing and  Materials
                (ASTM) Type I specifications for conductivity (< 1 u.S/cm at 25 ฐC; ASTM 1984) available in
                sufficient quantity to support analytical operations.
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    •  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, phytoplankton, diatoms, benthic macroinvertebrates) as appropriate

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

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

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

    •  Reporting results electronically using standard formats and units compatible with NARS IM (see
       LOM for data templates). These files will be labeled properly by referencing  the indicator
       and/or analyte and date.
All laboratories providing analytical support to NLA 2012 must adhere to the provisions of this
integrated QAPP and LOM. Laboratories will  provide information documenting their  ability to conduct
the analyses with the required level of data quality prior to data analysis. Different requirements will be
provided based on the type of analysis being done by the lab (i.e. chemistry vs. biological analyses).
Labs will send the documentation to the Quality Assurance Lead at EPA Headquarters (or other such
designated parties) in NLA 2012 QA files.  Such information may include the following, depending on the
evaluation by the Quality Assurance Lead:

    •  Signed Quality Assurance Project Plan by the Lab performing analysis
    •  Signed Laboratory Form
    •  Valid Accreditation or Certification
    •  Laboratory's Quality Manual and/or Data Management Plan
    •  Method Detection Limits (MDL)
    •  Demonstration of Capability (DOC)
    •  Results from  inter-laboratory comparison studies
    •  Analysis of performance evaluation samples
    •  Control charts and results of internal QC sample or internal reference sample analyses to
       Document achieved precision, bias, accuracy
Other requirements may include:

    •  Participation in calls regarding lab procedures and processes with participating labs
    •  Participation in a lab technical assessment or audit
    •  Participation in performance evaluation studies
    •  Participation in inter-laboratory sample exchange
All qualified labs shall work with the NARS IM Center to track samples as specified in Section 5 of the
LOM.

2.2.2.1 Water Chemistry and Chlorophyll A Lab Quality Evaluation
The NLA 2012 Quality Team has enlisted the  expertise of the Region 3 Environmental Science Center for
expert review of the water chemistry laboratories.  Participating labs will send requested documentation
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         to the Region 2012 NLA QA Team for evaluation of qualifications. The NLA 2012 Quality Team will
         maintain these records in the project QA file.

         2.2.2.2 Biological Lab Quality Evaluation
         The NLA 2012 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 information management support staff located at WED in Corvallis. Information
         management activities are discussed further in Section 4. Data in the WED database are available to
         Cooperators for use in development of indicator metrics. All validated measurement and indicator data
         from NLA 2012 are eventually transferred to EPA's Water Quality Exchange (WQX) and then the National
         STORET warehouse.

         2.2.4   Peer Review
         The NLA 2012 report will undergo a thorough peer review process, where the  scientific community and
         the public will be given the  opportunity to provide comments. Cooperators have been actively involved
         in the development of the overall project management, design, methods, and standards including the
         drafting of four key project  documents:

            •   Quality Assurance Project Plan

            •   Site Evaluation Guidelines

            •   Field Operations Manual

            •   Laboratory Operations Manual
         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.
         EPA will utilize 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, and (3) public review.
         Once data analysis has been completed, Cooperators will examine the results.  Comments and feedback
         from the Cooperators will be incorporated into the draft report. Following review by Cooperators, the
         scientific peer review will occur. The public comment period will take place following incorporation of
         scientific peer review comments and other EPA and cooperator reviews. This public comment period is
         important to the process and will allow us to garner a broader perspective for clarifying the results
         before the final report is issued. The public peer review is consistent with the Agency and the Office of
         Management and Budget's  (OMB's) revised  requirements for peer review.
         Below are the proposed measures EPA will implement for engaging in the peer review process:
            •   Follow the Agency's Information Quality Guidelines (IQG) and complete the IQG checklist
            •   Develop and maintain a public website with links to standard operating procedures, quality
                assurance  documents, fact sheets, scientific peer review feedback, and final report
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    •   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
    •   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)
    •   Consider public comments and produce a final  report
The proposed peer review schedule is provided below and is contingent upon timeliness of data
validation and schedule availability for regional meetings and experts for data analysis workshop.
Proposed Schedule Activity
May - December 2013
March 2014
May -August 2014
August 2014
October 2014
Data validation
Data analysis workshop
Internal peer review meetings (e.g., web conferences) with states, cooperators,
participants
Draft released for external peer review
Draft released for public review
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3      DATA QUALITY OBJECTIVES
It is a policy of the U.S. EPA that Data Quality Objectives (DQOs) be developed for all environmental data
collection activities following the prescribed DQO Process. DQOs are qualitative and quantitative
statements that clarify study objectives, define the appropriate types of data, and specify the tolerable
levels of potential decision errors that will be used as the basis for establishing the quality and quantity
of data needed to support decisions (USEPA 2006). Data quality objectives thus provide the criteria to
design a sampling program within cost and resource constraints or technology limitations imposed upon
a project or study. DQOs are typically expressed in terms of acceptable uncertainty (e.g., width of an
uncertainty band or interval) associated with a point estimate at a desired level of statistical confidence
(USEPA 2006). The DQO Process is used to establish performance or acceptance criteria, which serve as
the basis for designing a plan for collecting data of sufficient quality and quantity to support the goals of
a study (USEPA 2006). As a general rule, performance criteria represent the full set of specifications that
are needed to design a data  or information collection effort such that, when implemented, it will
generate newly-collected data that are of sufficient quality and quantity to address the project's goals
(USEPA 2006). Acceptance criteria are specifications intended to evaluate the adequacy of one or more
existing sources of information or data as being acceptable to support the project's intended use (USEPA
2006).

3.1   Data Quality Objectives
Target DQOs established for the NLA 2012 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 (ฑ 5%)  in the conterminous U.S. that fall below the designated
       threshold for good conditions for selected measures with 95% confidence.
For the ecoregions of interest the DQO is:

    •  Estimate the proportion of lakes (ฑ 15%) in a specific ecoregion that fall below the designated
       threshold for good conditions for selected measures with 95% confidence.
For estimates of change, the DQOs are:

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

3.2   Measurement Quality Objectives
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 USEPA 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 chemical measurements, requirements for the method detection limit (MDL) are typically
established. The MDL is defined as the lowest level of analyte that can be distinguished from zero with
99 percent confidence based on a single measurement (Glaser et al., 1981). For the NLA, target long-
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         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 target LT-MDL values
         were then used as the basis for establishing reporting levels (RL, 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 etal. 1999).
         Target MDL and RL values are based on the presumption that a laboratory will be  receiving samples
         from across the US. 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 that
         will be 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 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.
         Estimates of RLs (and how they are determined) are required to be submitted with analytical results.
         Analytical results associated with  RLs that exceed the objectives are flagged as being associated with
         unacceptable RLs. Analytical data that are below the estimated RLs, but above the laboratory's MDL are
         reported, but are flagged as "estimated" values (detected but not quantified). Values below the MDL
         should be reported (if possible), but flagged 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 will not be monitored
         separately during the NLA 2012, a revisit site approach will be taken to help evaluate the quality of data.
         The survey design incorporates a plan for repeated sampling  of a subset of sites (including a subset of
         398 lakes that was originally sampled in NLA 2007). 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 "deconvoluting" 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
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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 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 national aquatic resource 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
2 - 2 + ( 2 +- 2 + 2
(Distal (j sites ' fj year & jits* year tT residual'
and
11 1
^ residual D within-year *-* error

0 'sites . ^.1 ^residual '
v . Vy* v
iV sites \ ^ sites }
vears ,,
2>,-rr
i=l
and
r1 1 a residual
"residual ^iits'-year ' -T
** visit
Components in parentheses represent "extraneous" variance
Variance
Component
ฐi*,
&y*ar
asit*Y-pear
^residual
Description
Observed variance among all sites or streams sampled over multiple-year sampling cycle.
If sites are revisited across years, this effect can be eliminated
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
"Interaction" variance occurring at each site across years that affects each site independently.
Principal effect on status, reduce by increasing number of sites
"Residual" variance: Includes temporal variance at each site within a single index period (a2withm-
year) confounded with measurement error (o2error) due to acquiring the data from the site (e.g.,
sample collection and analysis)
Principal effect on status,
If a2index ป a2error reduce by increasing number of sites or altering index period
If o2error is large relative to o2index, then modify sampling and analysis procedures
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         For NLA 2012, 10 percent of all sample sites will receive repeat visits to determine temporal variability
         plus analytical variability within the index period. Revisit sites must be sampled at least 2-4 weeks apart
         to ensure that we are assessing temporal variability. Control measures to minimize measurement error
         among crews and sites will be employed. 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
         Precision and bias are estimates of random and systematic error in a measurement process (Kirchmer,
         1983;  Hunt and Wilson, 1986, USEPA 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, MQOsfor  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 when the number of
         measurements is greater than two:
         Equation 3.1
s =
                                         n-\
         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 3.2
                                      —
                                      X
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         where s is the sample standard deviation of the set of measurements, and X  equals the mean value for

         the set of measurements.
         Precision based on duplicate measurements is estimated based on the range of measured values (which
         equals the difference for two measurements).
         The relative percent difference (RPD) is calculated as:
                                          A-B
         Equation 3.3
RPD =
xlOO
         where A is the first measured value, 6 is the second measured value.
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For repeated measurements of samples of known composition, net bias (6) is estimated in absolute
terms as:

Equation 3.4     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 3.5     B(%) = - - x 1 00


where X equals the mean value for the set of measurements, and 7 equals the theoretical or target
value of a performance evaluation sample.
Accuracy is estimated for some analytes from fortified or spiked samples as the percent recovery.
Percent recovery is calculated as:

                             C -~C-
Equation 3.6     %re cov ery = — - — - x 1 00
                              Cs

where Cis is the measured concentration of the spiked sample, Ca is the concentration of the unspiked
sample, and Cs is the concentration of the spike.
For NLA 2012 each laboratory must monitor precision and bias for every sample batch by the analysis of
internal QC samples. Labs will also report on percent recovery to determine accuracy. Labs must review
and re-analyze samples associated with unacceptable QC sample results within one week. Labs should
consult with the Project QA manager about any unacceptable results within one week and to verify that
the appropriate corrective actions are taken.

3.2.4  Taxonomic Precision and Accuracy of Zooplankton, Phytoplankton, Diatoms and
       Benthic Macroinvertebrates
NLA 2012 will include two layers of quality assurance for biological assemblages: internal and external.

3.2.4.1 Internal quality assurance and quality control for biological data
Each laboratory will conduct internal, or within lab, quality assurance and quality control activities. Each
lab must evaluate sorting efficiency of the NLA 2012 lab analysts.  All lab 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 sections of the NLA 2012 LOM.
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3.2.4.2 External quality assurance for biological data                                               ^
                                                                                                   u
Each laboratory will participate in external, or among lab, quality assurance. In general, external quality       ^
assurance takes two forms: (1) an independent taxonomist will re-analyze 10% of samples or (2) all of        O
the labs participate in a round robin, where they swap 10% of samples among labs and re-analyze them.       b
The details of the external quality assurance requirements (e.g., taxonomic resolution, calculations) are       <
found in the indicator-specific sections of the NLA 2012 LOM.                                             d
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         For benthic invertebrates, sediment diatoms, phytoplankton, and zooplankton accuracy of taxonomy
         will be qualitatively evaluated through specification of target hierarchical levels (e.g., family, genus, or
         species); and the specification of appropriate technical taxonomic literature or other references (e.g.,
         identification keys, voucher specimens). To calculate taxonomic precision, EPA will randomly select 10
         percent of the samples for re-identification by an independent, outside taxonomist or laboratory.
         Comparison of the results of whole sample re-identifications will provide a Percent Taxonomic
         Disagreement (PTD) calculated as:
         Equation 3.7    PTD =
 1-

                                              xlOO
         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, we will calculate percent similarity between the taxonomic labs. 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:
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         Equation 3.8
         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 will
         be 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:
         Equation 3.9
PDE =
                                        Lab\-Lab2}
                                                     x 100
         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 will be identified using the most appropriate technical
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literature that is accepted by the taxonomic discipline and reflects the accepted nomenclature. Specific
references are identified in the indicator sections in the LOM. Any lab or taxonomist who believes these
are not sufficient must contact the EPA NLA Project Leader and Project QA Coordinator to discuss
options. The Integrated Taxonomic Information System (ITIS, http://www.itis.usda.gov/) will be used to
verify nomenclatural validity and spelling. A reference collection will be compiled as the samples are
identified. If necessary,  specialists in several taxonomic groups will verify selected individuals of
different taxa, as determined by the NLA workgroup.

3.2.5  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 is
calculated as:
Equation 3.10
                          -V,
:100
where V is the number of measurements/samples judged valid, and 7 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
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 lab, 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.

3.2.6  Comparability
Comparability is defined as the confidence with which one data set can be compared to another (USEPA
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 will use for each
target analyte as long as they are able to achieve the performance requirements as listed in Table 9.4 of
the LOM. Requirements for Reporting limits may be modified for regional laboratories based on the
expected range of concentrations 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,
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         university, and NLA 2012 contract laboratories.  See indicator specific sections of the LOM for more
         information when appropriate.

         3.2.7  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" (USEPA 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 D
         of the FOM for more  information.
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4     SAMPLING DESIGN AND SITE SELECTION
The overall sampling program for the NLA 2012 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)
and Stevens (1994).

4.1    Probability Based Sampling Design and Site Selection
The target population for this project includes all lakes, reservoirs, and ponds within the 48 contiguous
United States greater than 1 hectare (2.5 acres) in surface area that are permanent water bodies. 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
(NHD, 1:100,000 scale) 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, of which 904 are discrete
lake samples and 96 are revisits. 398 of these sites were selected from the sites sampled in 2007 to
provide a robust number of sites that we will use to evaluate change between the 2007 and the 2012
lakes assessments.
A Generalized Random Tessellation Stratified (GRTS) survey design for a finite resource was  used for site
selection. Lake selection for the survey provided for six size class categories (1-4 ha, 4-10 ha, 10-20 ha,
20-50 ha, 50-100 ha, >100 ha), as well as spatial distribution across the lower 48 states and nine
aggregated Omernik Level 3 ecoregions (for more information see
www.epa.gov/wed/pages/ecoregions/level_iii_iv.htm). EPA developed another subset of lakes for states
that may want to do state level assessments (to increase the overall sample size to 50 per state).  An
additional 5,221 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 to 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
                                 2012 National Lakes Assessment
                                                               r3^%ฎ-..j
              Legend
              . 2012 NLA Base Site; (904)
               2012 NLA State Supplemental (2182)
               2012 NLA Oversample (5221)
{ Osfir*^
— -
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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
2012 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 2012
from initial selection of sampling sites through the dissemination and reporting of final, validated data.
And, by extension, all participants in the NLA 2012 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 2012 cooperators.
    •    Increase the quality and relevance of accumulated data.
    •    Ensure the flexibility and sustainability of the NLA 2012 IM structure.

This IM strategy provides a congruent and scientifically meaningful approach for maintaining
environmental monitoring data that will satisfy both the scientific and technological requirements of the
NLA 2012.

5.1    Roles and Responsibilities

At each point where data and information are generated,  compiled, or stored, the NLA 2012 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
2012 play an integral part within the IM system. The following table provides a summary of the IM
responsibilities identified by NLA 2012 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 2012
Group
Field Crews
               Contact
               State/tribal
               partners and
               contractor or
               other field
               crews
               (regional
               EPA,etc.)
Primary Role       Responsibility
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/fax field and sample tracking forms to NARS IM Center
so information can be integrated into the central database

Work with the NARS IM Center staff to develop acceptable
file structures and electronic data transfer protocols should
there be a need to transfer and integrate data into the
central database

Provide all data as specified in FOM, SEG or as negotiated
with the NLA Project Leader.

Maintain open communications with NARS IM Center
regarding any data issues
                                                                                                      <
                                                                                                      z
                                                                                                      <
                                                                                                      ^
                                                                                                      z
                                                                                                      o
                                                                                                      QC
                                                                                                      o
Analytical       State/tribal     Analyze samples    Review all electronic data transmittal files for
Laboratories    partners and    received from      completeness and accuracy (as identified in the QAPP).
               contractors     field crews in the                MADCIH/I/-   *   *• ซ*•  j   i   *•<
                                              Work with the NARS IM Center staff to develop file
                                              structures and electronic data transfer protocols for
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                                        appropriate to
                                        acquire
                                        biotic/abiotic
                                        indicators/measur
                                        ements
                                        requested.
                                 electronically-based data.

                                 Submit completed sample tracking forms to NLA 2012 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 NLA 2012 IM Center
                                 regarding any data issues.
          IM Center
          staff
USEPAORD
NHEERL
Western
Ecology
Division-
Corvallis,
Contractors
Provides support
and guidance for
all IM operations
related to
maintaining a
central data
management
system for NLA
2012
                   Develop/update field data forms.

                   Plan and implement electronic data flow and management
                   processes.

                   Manage the centralized database and implement related
                   administration duties.

                   Receive, scan, and conduct error checking of field data
                   forms.

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

                   Receive data submission packages (analytical results and
                   metadata) from each laboratory.
                   Run automated error checking, e.g., formatting differences,
                   field edits, range checks, logic checks, etc.

                   Receive verified, validated, and final indicator data files
                   (including record changes and reason for change) from QA
                   reviewers. Maintain history of all changes to data records
                   from inception through delivery to WQX.

                   Organize data in preparation for data verification and
                   validation analysis and public dissemination.

                   Implement backup and recovery support for central
                   database.

                   Implement data version control as appropriate.
          Project
          Quality
          Assurance
          Manager
<
z
<
^
z
O
US EPA Off ice
Of Water
Review and
evaluate the
relevancy and
quality of
information/data
collected and
generated
through the NLA
2012 surveys.
                   Monitor quality control information.

                   Evaluate results stemming from field and laboratory audits.

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

                   Issue guidance to NLA 2012 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,
Provide technical
recommendations
related to data
analysis, reporting
                   Provide feedback and recommendations related to QA,
                   data management, analysis, reporting and data
                   distribution issues
                   Review and comment on QA and information management
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               EPA Regional
               and ORD
               staff, States,
               tribes, other
               federal
               agencies
              and overall
              implementation
                  documentation (QAPP, data templates, etc).
Data Analysis
and Reporting
Team
USEPA Office
of Water,
ORD WED,
Partners
Provide the data
analysis and
technical support
for NLA 2012
reporting
requirements
Provide data integration, aggregation and transformation
support as needed for data analysis.

Provide supporting information necessary to create
metadata.

Investigate and follow-up on data anomalies using
identified data analysis activities.

Produce estimates of extent  and ecological condition of the
target population of the resource.

Provide written background information and data analysis
interpretation for report(s).
Document in-depth data analysis procedures used.

Provide mapping/graphical support.

Document formatting and version control.

Develops QA report for management.
Data
Finalization
Team
TBD
Provides data
librarian support
Prepare NLA 2012 data for transfer to USEPA public web-
server(s).

Generate data inventory catalog record (Science Inventory
Record).

Ensure all metadata is consistent, complete, and compliant
with USEPA standards.
5.1.1   State/ Tribe-Based Data Management
Some state or tribal partners will be managing activities for both field sampling and laboratory analyses
and would prefer to handle data management activities in-house. While the NARS program encourages
states to use these in-house capabilities, it is imperative that NLA 2012 partners understand their
particular role and responsibilities for executing these functions within the context of the national
program. If a state chooses to do IM in-house, the state will perform all of the functions associated with
the following roles:

    •   Field Crew—including shipping/faxing of field data forms to the IM Coordinator (NLA 2012
        paper or electronic field forms must be used and the original field forms must be sent to the
        NARS IM Center as outlined in the NLA 2012 FOM).
    •   Quality Control Team for laboratory data
    •   To some extent, Quality Assurance Manager for laboratory results
    •   All data will flow from the state to the NARS IM Center. Typically, the state will provide a single
        point of contact for all things related to NLA 2012 data. However, it may be advantageous for
        the NARS IM Center staff to have direct communication with the state-participating laboratories
        to facilitate the transfer of data—a point that may be negotiated between the primary state
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                contact, the regional coordinator and the NLA 2012 Project Leader (with input from the NARS
                IM Center staff).
             •   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 NARS IM Center by May 2013, in order
                to meet NLA 2012 product deadlines.
             •   Data transfers must be complete. For example, laboratory analysis results submitted by the
                state must be accompanied by related quality control and quality assurance data, qualifiers code
                definitions, contaminant/parameter code cross-references/descriptions, test methods,
                instrumentation information and any other relevant laboratory-based assessments or
                documentation related to specific analytical batch runs.
             •   The state or Tribe will ensure that data meet minimum quality standards and that data transfer
                files  meet negotiated content and file structure standards.
         The NARS IM Center will provide the necessary guidance for IM requirements. Each group that will
         perform in-house IM functions will incorporate these guidelines as is practicable or as previously
         negotiated.

         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 2012 is a secure,
         access-controlled server located at WED-Corvallis.
         This database is known as the NARS IM. Data are stored and managed on this system using the
         Structured Query Language (SQL). Data review (e.g., verification and validation) and data analysis (e.g.,
         estimates of status and extent) are accomplished primarily using programs developed in either (SAS)  or
         R language software packages.

         5.2.1   Data Flow
         The NLA 2012 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:
             •   Communication—between the NARS IM Center and the various data contributors (e.g., field
                crews,  laboratories and the data analysis and reporting team)—is vital for maintaining an
                organized, timely, and successful flow of information and data.
             •   Data are captured or acquired from four basic sources — field data transcription, laboratory
                analysis reporting, automated data capture, and submission of external data files (e.g., GIS
                data)—encompassing an array of data types: site characterization; biotic assessment; sediment
                and tissue contaminants; and water quality analysis. Data capture generally relies on the
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       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).
            ECOLOGICAL INDICATOR FIELD AND LABORATORY DATA FLOW
                                        INFORMATION
                                     MANAGEMENTCENTE
                                        (WED-Corvallis)
          ASSESSMENT DATA FILES
         (Extent and status estimates)
FINAL DATA
 RECORDS
 (Flat files)
 Posted to
Webpage or
 FTP site
FINAL DATA
 RECORDS
(EPA WATER
 QUALITY
EXCHANGE
  [WQX])
 Permanent
 Archival
Figure 5.1 Conceptual model of data flow into and out of the master SQL database for the NLA 2012.
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
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         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.
         NLA 2012 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 2012
         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

             •   R Data Sets

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

         5.2.4.2 CIS Data
             *   ARC/INFO native and export files; compressed .tar file of ARC/INFO workspace
             •   Spatial Data Transfer Standard  (SDTS; FGDC 1999) format available  upon request

         5.2.4.3 Standard Coding Systems
             •   Sampling Site: (EPA Locational Data Policy; EPA 1991)
             •   Coordinates: Latitude and Longitude in decimal degrees (ฑ0.002)
             •   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.
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    •   Program must comply with Data Quality Act before making any data available to the public and
       person generating data must fill out and have a signed Information Quality Guidelines package
       before any posting to the Web or distribution of any kind.
    •   Data and metadata files are made available to the contributor or participating group for review
       or other project-related use from NARS IM or in flat files before moving to an EPA-approved
       public website.
    •   Data to be placed on a public website will undergo QA/QC review according to the approved
       QAPP.
    •   Only "final" data (those used to prepare the final project report) are readily available through an
       EPA-approved public website.
As new guidance and requirements are issued, the NARS IM staff will assess the impact upon the IM
system and develop  plans for ensuring timely compliance.

5.3    Data Transfer Protocols

Field crews are expected to send in hard copies of field forms or use the provided electronic field
forms containing in situ measurement and event  information  to the NARS IM Center defined in the FOM
for submission. Laboratories will submit electronic data files. Field crews and labs 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:
Software
Export Options (file extensions)
Microsoft Excel
              .
xls, xlsx, csv, formatted txt delimited
Microsoft Access     mdb, csv, formatted txt delimited
SAS
 csv, formatted txt delimited
R
csv, formatted txt delimited
All electronic files must be accompanied by appropriate documentation (e.g., metadata, laboratory
reports, QA/QC data and review results). This documentation must contain sufficient information to
identify field contents, field formats, qualifier codes, etc. It is very important to keep EPA informed of
the completeness of the analyses. Labs may send files periodically, before all samples are analyzed, but
EPA must be informed that more data are pending if a partial file is submitted. All data files sent by the
labs must be accompanied by text documentation describing the status of the analyses, any QA/QC
problems encountered during processing, and any other information pertaining to the quality of the
data. Following is a list of general transmittal requirements each laboratory or state-based IM group
should consider when  packaging data for electronic transfer to the IM Center:

    •   Provide  data  in row/column data file/table structure - see attachment D in LOM for
       templates.  All  cooperators and contractors should further consider the following:
    •   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.
    •   Include method detection limit (MDL) as part of each result record.
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             •   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 to (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.
         Labs will work with the NARS IM Coordinator to establish a data load process into NARS IM.

         5.4   Data Quality and Results  Validation
         Data quality is integrated throughout the life cycle of the data. This includes development of appropriate
         forms, labels etc. for capturing data as well as verifying data entry, results, and other assessments.
         Indicator workgroup experts, the data analysis and reporting team submit any recommended changes to
         the Project QA Coordinator who recommends and submits any changes (deletions, additions,
         corrections) to the  NARS IM data center for inclusion in the validated data repository. All  explanation for
         data changes is included in the record history.

         5.4.1   Design and Site Status Data Files
         The site selection process described in Section 3 produces a  list of candidate sampling locations,
         inclusion probabilities, and associated site classification data (e.g., target status, ecoregion, etc.). The
         Design Team provides this file to the NLA 2012 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 Site Evaluation Guidelines
         (SEG)  and the Field  Operations Manual (FOM). The site evaluation spreadsheets and verification forms
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are submitted to the Project Lead by the field crews. 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 the NARS IM
Center to the field crew (or site evaluator) to complete the record.

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 (Appendix E of the NLA 2012 FOM). Prior to initiation of field activities, the NARS
IM staff works with the indicator leads and analytical support laboratories to develop standardized field
data forms and sample labels. Adhesive labels, completed by the field crews, have a standard  recording
format and are affixed to each sample container. Field protocols include precautions to ensure that label
information remains legible and the label remains attached to the sample.
Table 5.2 Summary sample and field data quality control activities.
Quality Control
Activity
Contamination
Prevention
Sample Identification
Data Recording
Data Qualifiers
Sample Custody
Sample Tracking
Data Entry
Data Submission
Data Archival
Description and/or Requirements
All containers for individual site sealed in plastic bags until use; specific
contamination avoidance measures covered in training
Pre-printed labels with unique ID number on each sample
Data recorded on pre-printed forms of water-resistant paper; field sampling
crew reviews data forms for accuracy, completeness, and legibility
Defined qualifier codes used on data form; qualifiers explained in comments
section on data form
Unique sample ID and tracking form information entered in LIMS; sample
shipment and receipt confirmed
Sample condition inspected upon receipt and noted on tracking form with
copies sent to ORD Technical Lead and/or IM
Data entered using customized entry screens that resemble the data forms;
entries reviewed manually or by automated comparison of double entry
Standard format defined for each measurement including units, significant
figures, and decimal places, accepted code values, and required field width
All data records, including raw data, archived in an organized manner. For
example, following verification/validation of the last submission into the
NARS database, it is copied to a terabit external hard drive and sent to the
Project Leader for inclusion in his project file, scheduled as 501, permanent
records.
Processed samples and reference collections of taxonomic specimens
submitted for cataloging and curing at an appropriate museum facility
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NLA 2012 provides two options for completing field forms: electronic data entry using pre-developed
forms on your laptop or smart phone or "traditional" paper. Paper forms are printed for field crews on
water resistant paper. Copies of the field data forms and instructions for completing each form are
documented in the NLA 2012 FOM. Recorded data - whether through e-forms or paper - are reviewed
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         upon completion of data collection and recording activities by the Field Crew Leader. Field crews check
         completed data forms and sample labels before leaving a sampling site to ensure information and data
         were recorded legibly and completely. Errors are corrected by field crews if possible, and data
         considered as suspect are qualified using a flag variable. The field sampling crew enters explanations for
         all flagged data in a comments section. Field crews transmit e-forms to the NARS IM Staff by selecting
         the "submit" button as described in the FOM. Field crews ship completed paper field data forms to the
         NARS IM staff for entry into the central database management system.
         All samples are tracked from the point of collection. Field crews ensure that copies of the shipping and
         custody record accompany all sample transfers; other copies are transmitted to the IMC. The IMC 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 Field Operations Manual.
         Procedures for completion of sample labels and field data forms and use of PCs are covered extensively
         in training sessions. General QC checks and procedures associated with sample collection and transfer,
         field measurements, and field data form completion for most indicators are listed in Table 5.3.
         Additional QA/QC checks or procedures specific to individual indicators are described in the NLA 2012
         Lab Operations Manual.

         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 2012 Project Lead electronically.
         Most of the  laboratory  analyses for the NLA 2012 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 2012 indicators
         are described in. 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. Some QC checks and procedures applicable to  most
         NLA 2012 biological samples are described in the LOM.
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         Table 5.3 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
         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.
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Data Qualifiers
Data Entry

Submission Package
                         Review other QA data (e.g., condition upon receipt, etc.) for possible problems with
                         sample or specimen.
                         Use defined qualifier codes; explain all qualifiers
                         Automated comparison of double entry or 100% manual check against original data
                         form
                         Includes:
                         •   Letter by laboratory manager
                         •   Data
                         •   Data qualifiers and explanations
                         •   Electronic format compatible with NARS IM
                         •   Documentation of file and database structures
                         •   Metadata: variable descriptions and formats
                         •   Summary report of any problems and corrective actions implemented

A laboratory's IM system may consist of only hardcopy records such as bench sheets and logbooks, an
electronic laboratory information management system (LIMS), or some combination of hardcopy and
electronic records. Laboratory data records are reviewed at the end of each analysis day by the
designated laboratory onsite QA coordinator or by supervisory personnel. Errors are corrected by
laboratory personnel if possible, and data considered as suspect by laboratory analysts are qualified
with a flag variable. All flagged  data are explained in a comments section. Private contract laboratories
generally have a laboratory Quality Assurance Plan and established procedures for recording, reviewing,
and validating analysis data.
Once analytical data have passed all of the laboratory's internal review procedures, the lab prepares and
transfers a submission package using the prescribed templates in the LOM. The contents of the
submission package are largely dictated by the type of analysis (physical, chemical, or biological.
Remaining sample material and voucher specimens may be transferred to EPA's designated laboratory
or facilities as directed by the NLA 2012 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 Paper Forms
The NARS IM  Center either optically scans or transcribes information from field collection forms into an
electronic format (sometimes using a combination of both processes). During the scanning process,
incoming data are subjected to a number of automated error checking routines. Obvious errors are
corrected immediately at the time of scanning. Suspected errors that cannot be confirmed at the time of
scanning are qualified for later  review by someone with the appropriate background and experience
(e.g., a chemist or aquatic ecologist). The process continues until the transcribed data are 100% verified
or no corrections are required.
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         5.4.4.2 Electronic Forms
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         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.

         5.4.4.3 Additional Review
         Additional validation is accomplished by the NARS IM Center staff using a specific set of guidelines and
         executing a series of programs (computer code) to check for: correct file structure and variable naming
         and formats, outliers, missing data, typographical errors and illogical or inconsistent data based on
         expected relationships to other variables. Data that fail any check routine are identified in an "exception
         report" that is reviewed by an appropriate scientist for resolution.
         The NARS IM Center brings any remaining questionable data to the attention of the QA manager and
         individuals responsible for collecting the data for resolution.
         The HQ QA lead 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 standard
         deviations.
         The HQ QA lead 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
         5.4. Automated review  procedures may be  used. The primary purpose of the initial checks is to confirm
         that each data value present in an electronic data file is accurate with respect to the value that was
         initially recorded on a data form or obtained from an analytical instrument. In general, these activities
         focus on  individual variables in the raw data file and may  include range checks for numeric variables,
         frequency tabulations of coded or alphanumeric variables to identify erroneous codes or misspelled
         entries, and summations of  variables reported in terms of percent or percentiles. In addition, associated
         QA information (e.g., sample holding time)  and QC sample data are reviewed to determine if they  meet
         acceptance criteria. Suspect values are assigned a data qualifier. They will either be corrected, replaced
         with a new acceptable value from sample reanalysis, or confirmed suspect after sample reanalysis. For
         biological samples, species identifications are corrected for entry errors associated with incorrect or
         misspelled codes. Errors associated with misidentification of specimens are corrected after voucher
         specimens have been confirmed and the results are available.  Files corrected for entry errors are
         considered to be raw data files. Copies of all raw data files are maintained in the centralized NARS  IM
         System.
         Any suspect data will be flagged for data qualification.
         The NARS IM staff, with the  support of the NLA 2012 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.
         Table 5.4 Data review, verification, and validation quality control activities.
         Quality Control Activity
                                                            Description and/or Requirements
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Review any qualifiers associated with variable

Determine if MQOs and project DQOs have been achieved


Exploratory data analyses (univariate, bivariate,
multivariate) utilizing all data

Confirm assumptions regarding specific types of statistical
techniques being utilized in development of metrics and
indicators
                   Quality Assurance Project Plan
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Determine if value is suspect or invalid; assign
validation qualifiers as appropriate

Determine potential impact on achieving research
and/or program objectives

Identify outlier values and determine if analytical
error or site-specific phenomenon is responsible

Determine potential impact on achieving research
and/or program objectives
In the final stage of data verification and validation, exploratory data analysis techniques may be used to
identify extreme data points or statistical outliers in the data set. Examples of univariate analysis
techniques include the generation and examination of box-and-whisker plots and subsequent statistical
tests of any outlying data points. Bivariate techniques include calculation of Spearman correlation
coefficients for all pairs of variables in the data set with subsequent examination of bivariate plots of
variables having high correlation coefficients. Multivariate techniques have also been used in detecting
extreme or outlying values in environmental data sets (Meglen, 1985; Garner et al., 1991; Stapanian et
al., 1993).
The Quality Team reviews suspect data to determine the source of error, if possible. If the error is
correctable, the data set is edited to incorporate the correct data. If the source of the error cannot be
determined, the Quality Team qualifies the  data as questionable or invalid. Data qualified as
questionable may be acceptable for certain types of data analyses and interpretation activities. The
decision to use questionable data must be made by the individual data users. Data qualified as invalid
are considered to be unacceptable for use in any analysis or interpretation activities and will generally
be removed from the data file and replaced with a missing value code and explanatory comment or flag
code. After completion of verification and validation activities, a final data file is created, with copies
transmitted for archival and for uploading to the centralized IM system.
Once verified and validated, data files are made available for use in various types of interpretation
activities;  each activity may require additional restructuring of the data files. These restructuring
activities are collectively referred to as "data enhancement." In order to develop indicator metrics from
one or more variables, data files may be restructured so as to provide a single record per lake.

5.5     Data Transfer
Field crews may transmit data electronically; hardcopies of completed data and sample tracking forms
may be sent via express courier service. Copies of raw, verified, and validated data files are transferred
from the QA lead to the IM staff for inclusion in the central  IM system. All transfers of data are
conducted using a means of transfer, file structure, and file  format that has been approved by the IM
staff. Data files that do not meet the required specifications will not be incorporated into the centralized
data access and management system.

5.5.1   Database Changes
The NARS IM  Center staff complete data corrections at the lowest level to ensure that any subsequent
updates will contain only the most correct data. The NARS IM Center sends back laboratory results
found to be in error to the originator (lab) for correction. After the originator makes  any corrections, the
entire batch or file is resubmitted to the NARS IM Center.  The NARS IM Center uses these resubmissions
to replace any previous versions of the same data.
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         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 will take place. The original raw data will remain unchanged. This
         practice further ensures the integrity of the data and provides an additional data recovery avenue,
         should the need arise.
         All significant changes are documented by the NARS IM Center staff. The NARS IM Center includes this
         information in the final summary documentation for the database (metadata).
         After corrections have been applied to the data, the NARS IM Center will rerun the validation programs
         to re-inspect the data.
         The NARS IM Center may implement database auditing features to track changes.

         5.6    Metadata
         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
         Electronic data are collected and maintained within a central server housed at WED  using a Windows
         Server 2003 R2 (current configuration) or higher computing platform in SQL native tables for the primary
         data repository and SAS* native data sets or R datasets for data analysis. Official IM functions are
         conducted in a centralized environment.

         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. Guidance and policy documents of
         EPA and management policies established by the IM Technical Coordination Group for data access and
         data confidentiality are followed. Raw and verified data files are accessible only to the NLA2012
         collaborators. Validated data files are accessible only to users specifically authorized by the NLA 2012
         Project Leader. Data files in the central repository used for access and dissemination are marked as
         read-only to prevent corruption by inadvertent editing, additions, or deletions.
         Data generated, processed, and incorporated into the IM system are routinely stored as well as archived
         on redundant systems by the NARS IM Center. This ensures that if one system is destroyed or
         incapacitated, IM staff can reconstruct the databases. Procedures developed to archive the data,
         monitor the process, and recover the data are described in IM documentation.
         Data security and accessibility standards implemented for NLA 2012 IM meet EPA's  standard security
         authentication (i.e., username, password) process in accordance to EPA's Information Management
         Security Manual (1999; EPA Directive 2195 Al) and EPA Order 2195.1 A4 (2001D). Any data sharing
         requiring file transfer protocol  (FTP) or internet  protocol is provided through an authenticated site.

         5.7.3  Life Cycle
         Data may be retrieved electronically by the NLA 2012 team, partners and others throughout the records
         retention and disposition lifecycle or as practicable (See section 4.8).
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5.7.4  Data Recovery and Emergency Backup Procedures
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The NARS IM Center maintains several backup copies of all data files and of the programs used for
processing the data are maintained. Backups of the entire system are maintained off-site by the NARS
IM Center. The IM process used by the NARS IM Center for NLA 2012 uses system backup procedures.
The NARS IM Center backs up and archives the central database according to procedures already
established for WED 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 U.S. EPA's agency-wide WQX data management system for archival purposes. WQX is a repository for
water quality, biological, and physical data and is used by state environmental agencies, EPA and other
federal agencies, universities, and private citizens.  Data from the NLA 2012 project will be run through
an Interface Module in an Excel format and uploaded to WQX by the WQX team. Once uploaded, states
and tribes and the public will be able to download  data (using Oracle software) from their region. Data
will also be provided in flat files on the NARS website.

5.8   Records Management
Removable storage media  (i.e., CDs, diskettes, tapes) and paper records are maintained in a centrally
located area at the NARS IM Center. Paper records will be returned to OW once the assessment is
complete. The IM Team identifies and maintains files using standard divisional procedures. Records
retention and disposition comply with U.S. EPA directive 2160 Records Management Manual (July, 1984)
in accordance with the Federal Records Act of 1950.
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6     INDICATORS

6.1    Summary
You will find detailed, indicator-specific design, collection method, sample handling, and quality control
procedures for field operations in the Lakes Assessment 2012 Field Operations Manual. Similarly, you
will find detailed, indicator-specific sample handling, laboratory procedure, and quality control
procedures for laboratory operations in the Lakes Assessment 2012 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 will collect samples from an index site and/or littoral sites on each lake as described in the
National Lakes Assessment 2012 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
2012 Field Operations Manual.

6.1.2.2 Analysis
Detailed analysis procedures are described in the National Lakes Assessment 2012 Laboratory
Operations Manual.

6.1.3  Quality Assurance Objectives
Quality assurance objectives are described in detail in the National Lakes Assessment 2012 Laboratory
Operations Manual.

6.1.4  Quality Control Procedures: Field Operations
You will find detailed design, collection, sample handling and quality control procedures for field
operations in the Lakes Assessment 2012 Field Operations Manual.

6.1.5  Quality Control Procedures: Laboratory Operations
Specific information about sample receipt, processing,  and analysis are in National Lakes Assessment
2012 Laboratory Operations  Manual.

6.1.6  Data Management, Review, and Validation
Detailed information about data management, review, and validation are in National  Lakes Assessment
2012 Laboratory Operations  Manual.
Table 6.1 Summary of indicator QA procedures and coordinators.
Indicator
              Verification
Method    Lab Analyses QA
Verificati
Taxa      Indicator QA   QA
Require    Coordinator   Analyst
QC
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          Algal Toxins
          (microcystin)


          Benthic
          Macro-
          invertebrates
          Phytoplankton
          Sediment
          Carbon
          Sediment
          Dating
          Sediment
          Diatoms
Audit
documentation
SOPs
Taxa QC samples
Audit
documentation
SOPs

Taxa QC samples
Audit
documentation
SOPs

Responsibility of
USGS
SOPs

SOPs
Methods
Call


Methods
Call
Methods
Call
Sediment
Mercury
Triazine
Pesticide
Screen
Water
Chemistry and
Chlorophyll A
Zooplankton

Responsibility of
uses
Prior Experience
SOPs
Review by EPA
Region III
Environmental
Science Center
Taxa QC samples
Audit
documentation
SOPs

Methods
Call
Methods
Call
Methods
Call

Interlab comparison
Lab duplicates

Outside Lab QA
Taxonomist to review
10% of samples-
photos
Reconciliation calls
Outside Lab QA
Taxonomist to review
10% of samples-
photos
Reconciliation calls
Methods
Call
Methods   Outside Lab QA
Call        Taxonomist to review
           10% of samples-
           photos
           Reconciliation calls
                                                     Duplicates
                                                     Standard Solution
N/A
Species
Species
                     N/A
                     Species
                                                     Outside Lab QA
                                                     Taxonomist to review
                                                     10% of samples -
                                                     photos
                                                     Reconciliation calls
                                                 N/A
                                                                          N/A
                                                 Species
Amina
Pollard


Richard
Mitchell
Richard
Mitchell
                                          Amina
                                          Pollard
                                          Marsha
                                          Landis

                                          Dave Peck
                                          Richard
                                          Mitchell
TBD
TBD
TBD
Amina
Pollard
Marsha
Landis
Richard
Mitchell
TBD

TBD

TBD

                                              TBD

                                              TBD


                                              TBD




                                              TBD
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7     ASSISTANCE VISITS
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             Page 45 of 61
Assistance visits will be a component of the QA program for the 2012 NLA. Both these sections have
been explained clearly in the 2012 National Lakes Assessment FOM and LOM and therefore are not
included here.

7.1    Field Evaluation and Assistance Visit Plan
Please see the Field Operations Manual for details.

7.2    Laboratory Evaluation and Assistance Visit Plan
Please see the 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 will be included in the final report and used in future
analysis. The data analysis plan will likely 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 will also categorize the condition of water as
good, fair, or poor. 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 will be the second 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. The Agency formed a steering committee for these discussions. The Committee, comprised
of state representatives from each of the EPA regions, provides advice and recommendations to the
Agency on matters related to the NLA 2012. 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 setting. We will use an approach similar to that used in NLA 2007, which is described in detail
in the NLA 2007 Technical Appendix (EPA 841-R-09-001a) (USEPA 2009).

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
benchmarks used to define distinct condition classes (e.g., good, fair, poor / least disturbed,
intermediate, most disturbed) will be drawn from this reference distribution. EPA's approach is to
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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.

8.2     Geospatial Data
Geospatial data is an integral part of data analysis for the NLA 2012, 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.  We anticipate
that through the site evaluation process, lakes that have changed or are inaccurately represented in the
National Hydrography Dataset 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 base 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 National Lakes Assessment 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, 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 will use indicators to assess trophic  status, ecological integrity, and the recreational value of
lakes.

8.3.1   Trophic status
Lakes are typically classified according to their trophic state. Three variables, chlorophyll, Secchi disk
depth, and total phosphorus, will be used by EPA to estimate biomass and define the trophic state of a
particular lake. Other variables will be 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 indicators include plankton  (phytoplankton and
zooplankton), benthic macroinvertebrates, diatoms, and the physical  habitat of the shoreline and littoral
zone.

8.3.3   Recreational value
Recreational indicators address the ability of the population to support recreational uses such as
swimming, fishing and boating. The protection  of these uses is one of the requirements in the Clean
Water Act under 305(b). The  extent of algal toxins (microcystin) and mercury will serve as the primary
indicators of recreational value.
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8.4   Indicator Data Analysis
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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.
The data analysis team will analyze the total (whole water) concentrations of microcystins (total) in lakes
and reservoirs throughout the United States using a standardized immunoassay test. In addition, the
data analysis team will analyze 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, turbidity, specific conductance, pH).

8.4.2  Benthic Macroinvertebrate and Zooplankton Assemblages
Benthic macroinvertebrate and zooplankton assemblage will be analyzed using both multimetric indices
(MMI) and observed/expected indices (O/E) models. The MMI approach summarizes various assemblage
attributes, such as composition, tolerance to disturbance, trophic and habitat preferences, as individual
metrics or measures of the biological community. Candidate metrics are evaluated for aspects of
performance and a subset of the best performing metrics are combined into  an  index known as a
Macroinvertebrate Index of Biotic Condition. This index is then used to rank the condition of the
resource.
The predictive model or O/E approach estimates the expected taxonomic composition of an assemblage
in the absence of human stressors,  using a set of least-disturbed sites and other variables related to
natural gradients, such as elevation, lake size, latitude and longitude. The resulting models are then used
to estimate the expected taxa composition (taxa richness) at each site sampled. The number of expected
taxa actually observed at a site is compared to the number of expected taxa as an Observed Expected
ratio or index. Departure from a ratio of one indicate that the taxonomic composition in the sample
differs from that expected under least -disturbed conditions. The greater the departure from one, the
greater the sample differs from the least disturbed condition.

8.4.3  Physical Habitat

8.4.3.1 Quality assurance objectives and procedures
MQOs are presented in Table 8.1. General requirements for comparability and representativeness are
addressed in Section 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
Measurement
Field
Measurements and Observations
Precision
ฑ10%
Accuracy
NA
Completeness
90%
1
i
Specific quality control measures are listed in Table 8.2 for field measurements and observations.
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         Table 8.2 Physical habitat field quality control
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         Check Description
         QUALITY CONTROL
                         Frequency
Acceptance Criteria
                                                                        Corrective Actions
         Check totals for cover class
         categories (vegetation type,
         substrate, cover)
                         Each station   Sum must be reasonable
                          Repeat observations
         Check completeness of       Each station   Depth measurements for all
         station depth measurements               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
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8.4.3.2 Shoreline human disturbances
The presence or absence of 12 predefined types of human land use or disturbance will be recorded for
each of the 10 stations. As part of the NLA 2012, additional human disturbances will be separately
identified outside of, but adjacent to, the plots. For each of the 12 disturbance categories, we will
calculate the proportion of lakeshore stations where the disturbance is observed on each lake.
Proportions will  be weighted according to the proximity of the disturbance before computing the whole-
lake metrics. Weightings will be 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 will be calculated by
synthesizing all the human disturbance observations. The first, a measure of the extent of shoreline
disturbance, will be calculated as the proportion of stations at which one or more human disturbances
were observed. The second, a measure of disturbance intensity, will be calculated as the mean number
of human disturbance types observed at each of the 10 shoreline stations.

8.4.3.3 Riparian vegetation
Riparian vegetation type and areal cover will be visually estimated 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, cover will be estimated in four classes: absent
(0), sparse (0-10%), moderate (10-40%) and heavy (40-75%) and very heavy (>75%). Simple whole-lake
metrics are calculated by assigning the cover class mid-point value to each station's observations and
then averaging those cover values across all 10 stations. Summary metrics are calculated for each lake
by summing the areal cover or tallying the presence of defined combinations of riparian vegetation
layers or vegetation types.

8.4.3.4 Aquatic macrophytes
Using the same cover classes as for riparian vegetation, areal covers of nearshore emergent, floating,
and submerged aquatic macrophytes are each  estimated visually. Simple and summary aquatic
macrophyte metrics will be calculated for each lake in the same fashion as for riparian vegetation.
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8.4.3.5 Fish concealment features
The presence or absence of eight specified types of fish concealment features will be recorded within
each 10-m x 15-m littoral plot. The areal cover of each type will be assigned to one of three cover
classes (0, 0-10%, >10%). Simple metrics for each type of fish concealment feature are calculated as the
proportion of littoral stations with the particular concealment feature present. Summary metrics will be
calculated as the mean number of concealment types per station. We will use the areal cover class
designation to unweight very sparse cover in the calculation of both simple and summary fish cover
metrics (i.e., the areal cover designations of 0. 0-10% and >10% were respectively assigned values of 0,
0.2, and 1.0).

8.4.3.6 Shoreline and littoral bottom substrate
Visual estimates of areal cover of 9 defined substrate types (bedrock, boulders, cobble, gravel, sand,
silt/clay/muck, woody debris, organic matter, and vegetation) will be made 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, observers will use a clear plastic viewing
bucket, a 3-m plastic (PVC) sounding tube, or an anchor to examine or obtain samples of bottom
sediments.
Simple metrics describing the lake-wide mean cover  of littoral and shoreline substrate in each cover
class size category will be obtained by averaging the  cover estimates at each station, using the cover
class midpoint approach described for riparian vegetation. Three substrate summary metrics will be
calculated for both shoreline and littoral bottom substrates. First will be the mean cover of the
dominant substrate type. Second and third will be measures of the central tendency and variety of
substrate size. Because the size categories are approximately logarithmic, we will calculate a cover-
weighted mean substrate size class and its standard deviation; we will rank the substrate classes by size
from 1 to 6, weighting them by their lakewide mean  cover, and then averaging weighted cover or
computing its variance across size classes.

8.4.3.7 Littoral depth, bank characteristics and other observations
Lake depth 10 m offshore will be measured using SONAR, sounding line, or sounding tube. Field crews
will estimate the bank angle based on high and low water marks, the vertical  and lateral range in lake
water level fluctuation. They will also note the presence of water surface scums, algal mats, oil slicks,
and sediment color and odor. Whole-lake metrics for littoral depth and water level fluctuations are
calculated as arithmetic averages and standard deviations. For bank angle classes and qualitative
observations of water surface condition, sediment color, and odor, we will calculate the proportion of
stations where the described features are present.

8.4.3.8 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, rowcrop
agriculture, pasture, orchards, 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.
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         8.4.3.9 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.
         8.4.3.10
              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.
         8.4.3.11
              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.
         8.4.3.12
              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.

8.4.3.13       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.

8.4.3.14       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.

8.4.4  Phytoplankton Assemblages
Phytoplankton will be collected as an integrated sample in open water. Both abundance and biovolume
on a species-specific basis will be determined. The raw data will be used  in multiple data analysis
techniques, metrics and indices, such as Centrales/ Pennales ratios, Palmer's WQ Index and other
diversity indices.
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8.4.5  Sediment Dating
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Lead-210 (210-Pb) concentrations will be assessed in sediment samples from the bottom 2-cm of
sediment cores in natural lakes, which provides a relative estimate of the time of deposition of
sediment. Decay of uranium in the earth's crust releases the gas radon. This gas produces 210Pb by
decay in the atmosphere. The lead isotope enters the water through precipitation. In the water phase
210Pb is adsorbed to particulate matter and together they are deposited in the sediment of lakes and
ponds. 2012Pb decays with a half life of approximately 22 years. The remaining amount of 210Pb in
these sediment samples will reveal its relative age. Owing to the 22-year half life, this 2012Pb method
covers the past period of 75-100 years.

8.4.6  Sediment Diatoms
Sediment diatoms will be sampled in the deepest part of the lake (up to 50 meters), or the midpoint of a
reservoir, using a sediment core sampling device. Diatoms will be analyzed/ identified in the sediment
surface fraction and in a deep fraction (i.e., 35 to 45 cm). Comparison of these fractions provides an
indication of both current and historical lake condition with respect to stressors such as nutrients
(phosphorus) and sediment loadings. Comparison of the diatoms found in deep and surface fractions
can also provide insight to the structure and composition of algal communities under pristine conditions
as well as inform on the temporal and spatial trends of eutrophication.

8.4.7  Sediment Mercury
Mercury levels (total and methyl) will be determined from the sediment core samples to compare to
existing national mercury distribution databases. These samples will be collected from the sediment
diatom core samples, from the upper portion of the core. Comparisons will be made among lakes,
relative to their mercury concentrations.

8.4.8  Triazine Pesticide Screen
Triazine occurrence and concentration will be determined 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).

8.4.9  Trophic Status
The trophic state of lakes will be analyzed using chlorophyll a concentrations,  which is considered the
most accurate estimator of tropic state. Trophic state will be assessed using chlorophyll a concentration
thresholds, as follows: oligotrophic, <2 u.g/L; mesotrophic, 2 to 7 u.g/L; eutrophic, 7 to <30 u.g/L; and
hypereutrophic, >30 u.g/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
A wide array of water chemistry parameters will be  measured, including DO, pH, total N, total P, clarity,
TOC/DOC, color, ANC and primary productivity. Values for these parameters and their distribution will
be reported. Water chemistry analysis is critical for interpreting the biological indicators. Chlorophyll a,
Secchi depth and nutrient measurements will be used to determine trophic level indices, such as the
Carlson Index. Temperature profiles will be used to determine degree of lake stratification.
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       APPENDIX A: LABORATORY LIST
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        National Lakes Assessment 2012 Contract Laboratory List
Analysis
Algal Toxins
(microcystin)
Benthic
Macroinvertebrates
Phytoplankton
Sediment Dating
Sediment Diatoms
Triazine Pesticide
Screen
Water Chemistry
Zooplankton
Contact
Richard
Mitchell
EPA/HQ
202-566-0644
Richard
Mitchell
EPA/HQ
202-566-0644
Richard
Mitchell
EPA/HQ
202-566-0644
Marsha Landis
EPA/HQ
202-564-2858
Richard
Mitchell
EPA/HQ
202-566-0644
Marsha Landis
EPA/HQ
202-564-2858
Dave Peck
EPA/COR
541-754-4463
Richard
Mitchell
EPA/HQ
202-566-0644
Contractor



Wisconsin State
Lab of Hygiene
2601 Agriculture
Drive
Madison, Wl 53718

Wisconsin State
Lab of Hygiene
2601 Agriculture
Drive
Madison, Wl 53718
Dynamac Corp.
c/o U.S. EPA
200 SW 35th St.
Corvallis, OR 97333
541-754-4463

Contractor No.
& Task No.






EP-D-06-013
Work
Assignment 1-06

Project Officer






Kathy Martin
U.S. EPA
NHEERL-WED
200 S.W. 35th St.
Corvallis, OR 97333
541-754-4502

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National Lakes Assessment 2012 Federal Partners Laboratory List
Analysis
Contact
Dissolved
Carbon
Sediment
Mercury
USGS
USGS
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