United States
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
EPA/600/3-91/072
December 1991
&EPA
Data User's Guide to the
United States
Environmental Protection
Agency's Long-term
Monitoring Project:
Quality Assurance Plan
and Data Dictionary
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Front cover: Carolyn Peduzzi and Jim Kellogg,
of the Vermont Department of Environmental
Conservation, sampling Pigeon Pond, Vermont.
Photo by John Slade.
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EPA 600/3-91/072
December 1991
DATA USER'S GUIDE TO THE UNITED STATES
ENVIRONMENTAL PROTECTION AGENCY'S
LONG-TERM MONITORING PROJECT:
QUALITY ASSURANCE PLAN AND DATA DICTIONARY
PART I: QUALITY ASSURANCE PUN FOR THE LONG-TERM MONITORING PROJECT
By
Marilyn Morrison
ManTech Environmental Technology, Inc.
c/o U.S. EPA Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, OR 97333
PART II: LONG-TERM MONITORING PROJECT DATA DICTIONARY
By
Avis D. Newell and Randy Hjort
ManTech Environmental Technology, Inc.
c/o U.S. EPA Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, OR 97333
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Corvallis, OR 97333
Printed on Recycled Paper
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ACKNOWLEDGEMENTS
Many people have contributed tremendous effort to the completion of the Long-Term
Monitoring data base. These include all the cooperators who have contributed data, both those
identified in the text of this document, and those anonymous assistants who braved both the
elements of nature and the tedium of technology to produce the data in the Long-Term
Monitoring data base. The effort of all has been great, and the reward has been worthwhile. We
especially appreciate the efforts of Rich Van Dreason and Charles Driscoll of Syracuse University,
Kathy Webster of the Wisconsin Department of Natural Resources, Bruce Holdhusen and Patrick
Brezonik of the University of Minnesota, Jim Kellogg, Doug Burnham, and Carolyn Peduzzi of the
Vermont Department of Environmental Conservation, Steve Kahl of the University of Maine, Peter
Murdoch of the USGS in Albany, New York, and John Turk and Don Campbell of the USGS in
Denver, Colorado. Their efforts have entailed extremely tedious but fruitful ventures into the
catacombs of data analysis records to verify many of the reported data values. In addition, all
have contributed to the quality assurance plan. This data base, containing data from project
inception through 1989, has been preceded by a few false starts into the foray of data manage-
ment, Thus, thanks to all involved, the finalizatjon of this carefully groomed data base also
represents the adoption of a streamlined data management and quality assurance system, where
future additions of validated data of known quality will be an easy and mundane task.
Citations for these documents are:
Morrison, M.L 1991. Part I: Quality Assurance Plan for the Long-Term Monitoring Project.
In Data User's Guide for the U.S. EPA Long-Term Monitoring Project: Quality Assurance Plan
and Data Dictionary. EPA/600/3-91/072. U.S. EPA Environmental Research Laboratory,
Corvallis, Oregon.
Newell, A.D., and R. Hjort. 1991. Part II: Long-Term Monitoring Project Data Dictionary. In
Data User's Guide for the U.S. EPA Long-Term Monitoring Project: Quality Assurance Plan
and Data Dictionary. EPA/600/3-91/072. ;U.S. EPA Environmental Research Laboratory,
Corvallis, Oregon.
The research described in this QA plan and data dictionary has been funded in part by the
U.S. Environmental Protection Agency. This document has been prepared at the EPA
Environmental Research Laboratory in Corvallis, Oregon, through contract no. 68-C8-0006 with
NSI Technology Services Corporation. It has been subjected to the Agency's peer and
administrative review and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
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TABLE OF CONTENTS
PART I: QUALITY ASSURANCE PLAN FOR THE LONG-TERM MONITORING PROJECT
Section Page
1 PROJECT DESCRIPTION, ORGANIZATION, AND QUALITY ASSURANCE OBJECTIVES . . 1-1
1.1 Project Description -j.-j
1.2 Project Organization and Responsibilities 1-2
1.3 Quality Assurance Objectives 1 _2
1.3.1 Required Measurements 1-2
1.3.2 Quality Assurance (QA} Objectives 1-4
2 SAMPLING AND ANALYTICAL PROCEDURES ; 2-1
2.1 Sample Containers: Cleaning and Conductivity checks 2-1
2.2 Sample Collection 2-1
2.2.1 Lakes ' 2-1
2.2.2 Streams 2-2
2.2.3 Field Laboratory Notebook 2-2
2.3 Sample Custody, Preparation, and Preservation 2-2
2.3.1 Labels 2-2
2.3.2 Filtration and Preservation Protocol for Anion Analyses
(SO/', N03-, CO 2-2
2.3.3 Filtration and Preservation Protocol for Cation Analyses
(Ca, Mg, Na, K, and Al) 2-3
2.3.4 True Color and DOC '.'.'.'.'.'.'. 2-3
2.3.5 pH, ANC, and Specific Conductance ' 2-3
2.3.6 Sample Preservation and Holding Times 2-3
2.4 Analytical Methods 2-3
2.5 Calibration Procedures 2-6
2.5.1 pH '.'.'.'.'.'.'.'.'.'.'. 2-6
2.5.2 Atomic Absorption Spectrophotometer (AAS): Ca, Mg, Na, K, and Al 2-6
2.5.3 Ion Chromatograph (1C): SO42", Cr, and NO3" 2-7
2.5.4 Specific Conductance 2-7
3 QUALITY ASSURANCE AND QUALITY CONTROL PROCEDURES 3-1
3.1 Quality Control Procedures 3_1
3.1.1 Field Duplicates 3_-j
3.1.2 Field Blanks 3-1
3.1.3 Filter Blanks ! 3-1
3.1.4 Quality Control (QC) Samples .... 3-5
3.1.5 Laboratory Duplicates 3.5
3.1.6 Spiked Samples 3.5
3.1.7 Analytical Detection Limit 3.5
3.1.8 Preparation of Calibration Standards 3-6
in
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3.2 Procedures for Assessing Precision and Accuracy 3-6
3.2.1 Precision 3"7
3.2.2 Analytical Precision 3-7
3.2.3 Sampling and Analysis Precision 3-8
3.2.4 Accuracy and Bias 3-8
3.2.5 Certified Reference Material or QC Check Samples 3-8
3.2.6 Performance Evaluation Samples 3-9
3.2.7 Percent Recovery 3-10
3.3 Data Validation and Reporting 3-10
3.3.1 Cation-Anion Charge Evaluation 3-10
3.3.2 Specific Conductance Evaluation 3-10
3.3.3 Comparison with Previous Years' Data 3-14
3.4 Technical Systems Audits 3-14
3.5 Quality Assurance Reports • 3"15
4 MAINE REGION 4"1
5 VERMONT REGION 5"1
6 ADIRONDACK REGION : • 6'1
7 UPPER MIDWEST REGION 7"1
8 COLORADO REGION 8'1
9 CATSKILL REGION • 9'1
10 REFERENCES 10"1
APPENDIX A - Working Protocol for Sampling, Sample Analysis, and QA/QC for the USEPA
Long-Term Surface Water Monitoring Progrram A"1
APPENDIX B - Laboratory Notebook Guidelines B'1
IV
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LIST OF TABLES
Table Page
1-1 EPA Long-Term Monitoring Projects 1_3
1-2 Quality Assurance Objectives: Required Analytical Detection Limits,
within-Laboratory Relative Precision, and Accuracy Objectives 1-5
2-1 Sample Preservation and Holding Times for Required Measurements 2-4
2-2 Analytical Methods for Required Measurements 2-5
3-1 Summary of QA/QC Samples for LTM 3_2
3-2 Minimum Number of Duplicates per Sampling Interval 3.3
3-3 Guidelines for Determination of Contamination from Analysis of Blank Samples .... 3-4
3-4 Factors for Converting mg/L to ,«eq/L 3-12
3-5 Conductance Factors (F) of Ions 3_13
4-1 Analytical Methods for Maine Region 4.3
5-1 Analytical Methods for Vermont Region 5.4
6-1 Analytical Methods for Adirondack Region 6-3
7-1 Analytical Methods for Upper Midwest Region 7.4
8-1 Analytical Methods for Colorado Region 8-3
9-1 Analytical Methods for Catskill Region 9.3
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PART II: LONG-TERM MONITORING PROJECT DATA DICTIONARY
Section Pa9e
1 INTRODUCTION ......................... • .......................... • 1~1
1.1 Introduction
1.2 Data Dictionary Description
2 PROJECT DESCRIPTION ............................................... 2~1
3 DATA BASE QUALITY ................................................. 3"1
3.1 Quality Assurance/Quality Control (QA/QC) of LTM Data ...... . . ............. 3-1
3.2 Data Validation [[[ 3~3
3.2.1 Outlier Identification ....... .................................. 3"~
3.2.2 Tag Assignments ......... .................................. 3"5
4 REGIONAL DATA CHARACTERISTICS ............................ • ........ 4'1
4.1 Maine Data Characteristics
4.2 Vermont Data Characteristics
4.3 Adirondack Data Characteristics
4.4 Upper Midwest Data Characteristics
4.5 Colorado Data Characteristics
4.6 Catskill Stream Data Characteristics
5 DATA BASE DESCRIPTION .......... . .................................. 5'1
5.1 SAS Data Base
5.2 Variable Definitions
6 REFERENCES [[[ 6"1
APPENDIX A - Lakes included in the LTM Data Base ............................. A"1
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LIST OF FIGURES
Page
2-1 Location of the LTM Monitoring Sites 2-3
LIST OF TABLES
Table Page
2-1 EPA's Long-Term Monitoring (LTM) Project 2-2
3-1 Quality Assurance Objectives: Required Analytical Detection Limits,
within-Laboratory Relative Precision, and Accuracy Objectives 3-2
3-2 Relationships Used in Data Validation Procedures 3-4
3-3 Tag Values for Chemical Variables in the LTM Data Base 3-6
5-1 Names and Attributes of the Data Sets included in the LTM Data Base 5-2
5-2 Variables included in the Lake and Stream Data Sets 5-3
5-3 Variable Definitions and Reporting Units 5-6
VII
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PART I:
QUALITY ASSURANCE PLAN
FOR THE
LONG-TERM MONITORING PROJECT
December 1991
By
Marilyn Morrison
ManTech Environmental Technology, Inc.
U.S. EPA Environmental Research Laboratory
200 SW 35th Street
Corvallis, Oregon 97333
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CONCURRENCES:
Name:
Title:
Signature:
Name:
Title:
Jesse Ford
LTM Technical /Director
r1
Dixor
^HC^M
i Landers
EBA^ProiectX^fficer
Date: 6% A-£> ^ o
/ U
Signature:
Date:
Signature:
Robert Lackey
LTM Cooperators:
Name:
Charles Driscoll
LTM Region: Adirondacks
Affiliation:
Signature: (_
Syracuse University
Name:
Peter Murdoch
LTM Region: Catskills
Affiliation:
Signature:
Date: 1 1.
tt^
Name:
John Turk
LTM Region: Colorado
Affiliation:
U.S. Geological Survey. LakewooekCO
Signature:,
Name:
J. Steve Kahl
LTM Region: Maine
Affiliation:
Signature:
Name:
Patrick Brezonik
LTM Region: Upper Midwest Region
Affiliation:
Universlt^ of Minnesota
Signature:.
Date: /
Date:
Date: /..](•/..} cf_O
-------
Name: Katharine Webster
LTM Region: Upper Midwest Region
Affiliation: Wisconsin Department of Natural Resources
Name: Jim Kellogg
LTM Region: Vermont
Affiliation: ^errnqnt Department of Environmental Conservation
K - ifiM*' Date I < I <*
Name: Doug Bumham
LTM Region: Vermont
Affdiatlpni.--, Vermonjt Depafttpent of Environmental Conservation
Signature: A_ ^pfajefe*—]N/S^-^v^VX^^— Date:
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SECTION 1
PROJECT DESCRIPTION, ORGANIZATION, AND QUALITY ASSURANCE OBJECTIVES
1.1 PROJECT DESCRIPTION
The U.S. Environmental Protection Agency (EPA) Long-Term Monitoring (LTM) Project for
lakes and streams was initiated in 1983 within the National Acid Precipitation Assessment
Program (NAPAP) Task Group E organizational framework. The objectives were to detect and
measure trends in the chemistry of low acid neutralizing capacity (ANC) surface waters over
gradients of H+ and SO42" deposition in different geographic regions. The LTM Project consists
of cooperators affiliated with several federal agencies and universities in different regions. The
EPA Environmental Research Laboratory in Corvallis, Oregon (ERL-C), manages the LTM Project
and coordinates the LTM cooperators.
An ad hoc committee, with representation from the EPA, the U.S. Geological Survey
(USGS), the Tennessee Valley Authority (TVA), the U.S. Forest Service (USFS), the U.S. Fish and
Wildlife Service (USFWS), and the U.S. National Park Service (USNPS), developed a draft
sampling and analysis protocol in 1983 to standardize monitoring efforts among the Task Group
E agencies. In 1984, the EPA initiated the National Surface Water Survey (NSWS). The methods
manual developed for the NSWS (Hillman et al., 1986) was used, together with the 1983 Task
Group E sampling and analysis protocol draft, to produce the working protocol for sampling,
sample analysis, and quality assurance/quality control (QA/QC) for the EPA LTM Project
(Appendix A). The objective was to align the long-term monitoring methodology with that of
NSWS, without undue disruption of existing monitoring procedures. This protocol, completed in
May 1985, has served as the standard protocol for the LTM Project. Note that sampling for LTM
began in 1983, although the standardized protocol was not completed until 1985. This docu-
ment, the LTM QA Plan, is the latest revision of the original protocols.
The LTM Project originally was to be replaced in 1988 by the Temporally Integrated Moni-
toring of Ecosystems (TIME) Project, the long-term monitoring phase of NSWS. Implementation
of the TIME Project has been delayed, however, due to changing priorities within the EPA.
Sampling in several of the LTM regions has been extended because of this delay. A primary
justification for continuing the LTM Project was to maximize the length of record of the LTM data
set so it could be analyzed for trend information for the 1990 Assessment Report to NAPAP.
1-1 QAPIan
-------
A report analyzing the data from the LTM. Project, Analysis of Data from Long-Term
Monitoring of Lakes (Newell et al., 1987) was completed in 1987; it included LTM data collected
through 1985. One of the summary comments in the report noted the lack of adequate quality
assurance data for effectively describing the quality of the LTM data. It was suggested that the
number of duplicate samples be increased to improve the confidence of precision estimates and
that more stable performance audit samples be provided so interlaboratory comparisons could be
made. This revision of the QA protocols incorporates those suggestions, so that the quality of the
data collected in the coming years can be adequately described.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
The current LTM Project consists of cooperators located in Maine, Vermont, the Adirondacks
(New York), the Upper Midwest (Minnesota, Wisconsin, and Michigan), the Rocky Mountains
(Colorado), and the Catskills (New York). The target resource includes lakes in all regions except
the Catskills, where streams are monitored. Table 1-1 lists the current LTM cooperators, their
locations, the number of sites, the sampling schedules, and the dates when monitoring began at
those locations. Each cooperator is responsible for sampling, analysis, QA/QC procedures, data
validation, and data reporting to the U.S. EPA Environmental Research Laboratoiy in Corvallis,
Oregon. Sections 4 through 9 contain information specific to each LTM region. ERL-Corvallis is
responsible for coordination among the LTM projects, data management of the combined LTM
data set, coordination of the performance evaluation program, and final reporting of the LTM data.
The QA procedures described in this documerit make up the minimum requirements that each
cooperator must follow.
1.3 QUALITY ASSURANCE OBJECTIVES
1.3.1 Required Measurements
A set of required measurements was specified for the LTM Project that would provide
sufficient characterization of stream or lake water quality to assess the sensitivity and change
related to acidification; these measurements are:
• pH (field or field laboratory, and air-equilibrated)
• Acid neutralizing capacity (ANC)
• Specific conductance
• Temperature
1-2 QA Plan
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• Secchi disc transparency (lakes)
• True color
• Major cations (Ca, Mg, Na, K)
• Major anions (SO42~, NO3", CI")
• Total filtered aluminum
Additional measurements, including titrated acidity, dissolved inorganic carbon (DIG),
dissolved organic carbon (DOC), F, Fe, Mn, N^4+, SiO2, and total P, were made by NSWS;
some of these analyses, while not required, are also being performed by some cooperators in the
monitoring program.
1.3.2 Quality Assurance (QA) Objectives
Table 1-2 lists the required detection limits and QA objectives for intralaboratory precision
and accuracy. In addition, accuracy and bias are assessed through the Long-Range Transport of
Airborne Pollutants (LRTAP) Interlaboratory Comparability Studies. In these studies, a flag is
assigned to an audit sample result if the result exceeds the acceptable limit for difference from the
median value. Acceptable limits for each variable were established by the LRTAP program. Any
flags assigned are investigated. Bias for each variable is assessed by the LRTAP study with an
Interlaboratory ranking procedure. The objective for bias identified by LRTAP results is to avoid
having a bias class greater than "slightly low" or "slightly high." If a variable has a bias identified
for two consecutive LRTAP studies, the cause of the bias must be determined. Section 3.2
describes the specific procedures used for assessing precision, accuracy, and bias, and Section
3.2.6 describes the LRTAP Comparability Studies.
Specific objectives for LTM cooperators include comparability, completeness, and repre-
sentativeness. For comparability, data collected by each LTM cooperator should be comparable
from year to year and comparable with data from other laboratories performing acid precipitation
research. Comparability will be assessed with the LRTAP studies, by comparing each labora-
tory's performance index over time. The objective is to have similar performance indices from
year to year. For completeness, each cooperator should collect and analyze 90% of the samples
initially planned for collection. Finally, for representativeness, samples should be representative
of trends over time.
1-4 QA Plan
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TABLE 1-2. QUALITY ASSURANCE OBJECTIVES: REQUIRED ANALYTICAL DETECTION
LIMITS, WITHIN-LABORATORY RELATIVE PRECISION, AND ACCURACY
OBJECTIVES
Variable
Required measurements
pH, field or field lab
pH, air equilibrated
ANC
Conductivity
Color
S042"
N03-
cr
Ca
Mg
Na
K
Al, total dissolved
Additional Measurements:
Acidity
DIC
DOC
F
Fe
Mn
NH4+
Si02
P, Total
Reporting
Units
pH units
pH units
jieq/L
nS/cm
Pt-Co units
lieq/L
|ieq/L
peq/L
neq/L
u.eq/L
|ieq/L
neq/L
H9/L
Jieq/L
mg/L
mg/L
u.eq/L
mg/L
mg/L
|ieq/L
mg/L
ng/L
Required
Detection
Limit
—
—
-
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0
1.0
0.1
0.3
0.5
0.8
0.4
0.3
5
5
0.05
0.1
0.3
0.01
0.01
0.6
0.05
2
Intralab
Relative
Precision
(%)a
± 0.1 pH unit
± 0.05 pH unit
± 5 u.eq/L (if ANC<30)
10% (if ANC>30)
± 2 u.S/cm (if cond. <25)
5% (if cond. >25)
± 5 Pt-Co units
5
± 2 (ieq/L(if NO3'<15)
10% (if NO3">15)
5
5
5
5
5
20 (if Al<50 u.g/L) 20
10 (if AI>50|ig/L) 10
10
10
10 (if DOC<5 mg/L)
5 (if DOC>5 mg/L)
5
10
10
5
5
20 (if P<10 pg/L) 20
10 (if P>10 ng/L) 10
Accuracy (%)b
..
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—
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10
10
10
10
10
10
(if Al<50 ng/L)
(ifAI>50jig/L)
10
10
10
10
10
10
10
10
(if P<10 ng/L)
(ifP>10lxg/L)
Expressed as percent relative standard deviation (standard deviation divided by the mean) when concentrations measure at
least 10 times above instrumental detection limits (unless concentration range is noted), or, if ± units appear, as
, plus or minus the specified number of units.
Expressed as percent difference from a reference value.
0 Blank must be < 2.0 jiS/cm.
1-5
QA Plan
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SECTION 2
SAMPLING AND ANALYTICAL PROCEDURES
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
Sample containers for required variables should be composed of high-density linear poly-
ethylene. Containers to be used for pH, ANC, and anion analyses shall be rinsed three times with
deionized water, filled with deionized water, and allowed to stand for at least 48 hours, then
emptied and rinsed with sample water in the field.
Soak the sample containers for cations and metals in 10% HCI for 12 hours, then rinse them
6 times with deionized water. Next, fill the containers with deionized water and allow them to
stand for 48 hours, then empty and refill them with deionized water until they are used for col-
lecting samples. The containers should not be allowed to dry between acid washing and sample
collection.
At least 50% of the cleaned (selected randomly) containers must be given a conductivity
check. Measure the conductivity of the deionized water in the container after the 48-hour period.
If the conductivity is > 2.0 |iS/cm, rerinse all the containers in that batch. Record in the labora-
tory notebook the highest conductivity value for each batch. Since container contamination is
random and is most likely to be caused by incomplete rinsing after acid washing, increasing the
number of conductivity checks will give better assurance that each container has been thoroughly
cleaned and rinsed prior to being used to collect samples.
2.2 SAMPLE COLLECTION
2.2.1 Lakes
Lakes should be sampled near their deepest points, at least 20 m from shore if possible. If
the water column is not thermally stratified, that is, if the temperature difference between the top
and bottom of the water column is < 4°C (Drouse et al., 1985), one sample should be collected
approximately one-half meter beneath the water surface. If the water body is stratified, an
epilimnetic sample should be collected approximately one-half meter beneath the water surface
and a hypolimnetic sample collected one or two meters above the bottom. These two samples
are to be analyzed separately, and not mixed. A plastic Van Dorn type sampling device should
2-1 QA Plan
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be used to obtain samples at depth; do not us£ a metal sampler. Samples should be collected
from the sampling device in plastic bottles that; have been prepared as described in Section 2.1.
See Section 3.1.1 for guidance in collecting duplicate samples.
2.2.2 Streams
Samples should be obtained by hand as near mid-stream as possible, using a properly
cleaned and rinsed plastic container. Keep hands away from mouth of container. See Section
3.1.1 for guidance in collecting duplicate samples.
2.2.3 Field Laboratory Notebook
Carefully record in field notes or a sampling log any observed conditions that might affect
analysis or interpretation of samples, for example, weather conditions or recent shore activities.
Key project personnel who are responsible for sample integrity must be identified in the notebook.
Guidelines for laboratory notebooks are given jn Appendix B.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.1 Labels
Labels on all containers should include sufficient information to permit tracing the sample
back to the point and time of sample collection: lake name or ID, collection date, aliquot name,
and sample preservation. ;
2.3.2 Filtration and Preservation Protocol for Anion Analyses (SO42', NO3", CI")
Filter aliquots for anion analyses as soon as possible after sample collection. Rinse a mem-
brane filter of 0.4-iim pore size (e.g., polycarbpnate or cellulose-based), with approximately
100 mL deionized water and two 20-mL aliquots of sample. Rinse the sample container with the
two filtered sample water rinses, then discard each rinse. Filter the required amount of sample
(60 to 100 mL) into the container. If more than one filter is used for a sample, rinse each filter
before use. Ice or refrigerate the filtered sample.
2-2
QA Plan
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2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, Na, K, and Al)
Filter aliquots for cation analyses as soon as possible after sample collection. Rinse the
filters as described for anions: rinse a membrane filter of 0.4-nm pore size (e.g., polycarbonate or
cellulose-based) with approximately 100 ml_ of deionized water and two 20-mL aliquots of sample.
Rinse the acid washed sample container with the two filtered sample water rinses, then discard
each rinse. Filter the required amount of sample (60 to 100 ml_) into the container, and add ultra-
pure or an equivalent nitric acid to acidify the sample to < pH 2.0. If more than one filter is used
for a sample, rinse each filter before use. Ice or refrigerate the filtered sample.
2.3.4 True Color and DOC
Aliquots for true color are either centrifuged or filtered. Aliquots for DOC are filtered. All
cooperators should continue to follow the protocols for color and DOC that they have followed in
the past; these protocols are listed in Sections 4 through 9.
2.3.5 pH, ANC, and Specific Conductance
Aliquots for pH, alkalinity, and specific conductance are not filtered or acidified.
2.3.6 Sample Preservation and Holding Times
Table 2-1 lists the preservation and storage requirements for each of the required variables,
and the maximum allowable holding times. The holding time is the time between sample collec-
tion and sample analysis. Records must be kept of the holding time for each variable for each
sample. A list of each sample by variable with any holding time exceeding those in Table 2-1
must be included when data are reported to ERL-Corvaliis. Decisions to use the data for trend
detection are not made until all data for that sample (e.g., ion balances, conductivity checks) have
been evaluated.
2.4 ANALYTICAL METHODS
Table 2-2 lists the analytical methods for each required measurement.
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TABLE 2-1. SAMPLE PRESERVATION AND ;HOLDING TIMES FOR REQUIRED
MEASUREMENTS
Variable
pH, field
pH, air equilibrated
ANC
Color
Conductivity
S042'
cr
Sample Preservation
closed container
store at 4°C
store at 4°C
filtered or centrifuged,
store at 4°C
store at 4°C
filter thru 0.4 \im,
store at 4°C
filter thru 0.4 |im,
Maximum
Holding Time3
measured on site
7 days
14 days
48 hours
14 days
28 days
28 days
Ca
Mg
Na
K
AI
store at 4°C
filter thru 0.4 jim,
store at 4°C
filter thru 0.4 urn,
store at 4°C, acidify with
HNO3 to < pH 2.0.
filter thru 0.4 jim,
store at 4°C, acidify with
HNOg to < :pH 2.0.
filter thru 0.4 jim,
store at 4°C, acidify with
HNOg to < ;pH 2.0.
filter thru 0.4 nm,
store at 4°C, acidify with
HNOg to < ;pH 2.0.
filter thru 0.4 jim,
store at 4°C, acidify with
HNOg to < .pH 2.0.
7 days
6 months
6 months
6 months
6 months
6 months
Holding times from Drouse et al., 1985.
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TABLE 2-2. ANALYTICAL METHODS FOR REQUIRED MEASUREMENTS
Variable
Method
Reference
pH, field
pH, air equilibrated
ANC
Conductivity
True Color
SO/', Cf, N03'
Ca, Mg
Electrode
Electrode, aeration
with 300 ppm CO2
Titration with Gran plot
Conductivity cell
Comparison of centrifuged
or filtered samples with
platinum-cobalt color standards
Ion chromatography
Atomic absorption
spectrophotometry (AAS),
add La or use N2O flame
Section 2.5.1
Hillman et al., 1986
Gran, 1950, 1952;
Hillman et al., 1986
U.S. EPA, 1979
U.S. EPA, 1979
Hillman et al., 1986;
O'Dell et al., 1984
U.S. EPA, 1979
Na, K
Al, total
AAS
Graphite furnace AAS
U.S. EPA, 1979
U.S. EPA, 1979;
Hillman et al., 1986
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2.5 CALIBRATION PROCEDURES
2.5.1 pH i
pH should be measured to the hundredths unit using a high-quality pH meter with an
expanded or digital scale. An electrode designed for low ionic strength solutions, such as the
Orion Ross Combination Model 81-02, should be used. The electrode should be calibrated with
pH 4.0 and 7.0 buffer solutions and then checked with a dilute acid solution. The dilute acid
solutfon can be made by diluting the acid used for ANC titrations 1:1000. For example, a 1:1000
dilution of 0.02 N H2SO4 or HCI will produce a solution with a theoretical pH of 4.70. Rinse the
electrode copiously with sample or deionized water before immersing in the sample. A duplicate
should be measured after every 10 samples, and the dilute acid solution should be measured at
the completion of a sample batch. Two types of pH measurements are to be performed, one on
an unagitated sample in the field or field laboratory, and another on an air-equilibrated sample in
the laboratory.
Measure field pH as soon after collection as possible. The electrode should remain in the
unagitated sample until there is no discernible drift in the pH reading, but no longer than 15
minutes.
Air-equilibrated pH is measured in the laboratory for intercomparison of the pH values
obtained by various participating investigators. Equilibration is achieved by bubbling samples
with standard air containing 300 ppm CO2 for 20 minutes while stirring on a magnetic stirrer. A
fritted plastic diffuser is used for dispersal of air in the sample. Measure pH immediately following
equilibration. A duplicate should be measured, after every 10 samples, and the dilute acid solu-
tion should be measured at the completion of £ sample batch.
2.5.2 Atomic Absorption Spectrophotometer (AAS): Ca, Mg, Na, K, and Al
Calibrate the AAS with standards made from American Chemical Society (ACS) reagent
grade chemicals or from atomic absorption reference standards. At least three standards
spanning the concentration range of the samples must be used for calibration. Measure QC
samples after the instrument has been calibrated and before the samples are analyzed, and after
every 10 samples. At a minimum, the QC samples should be analyzed three times in each batch:
at the beginning, in the middle, and at the end of the batch. A batch is the set of samples
2-6
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analyzed with the same calibration curve. QC samples are prepared from a source independent
of the calibration standards. Ideally, there should be two QC samples at two different
concentrations in the working range; if only one QC sample is used, one or more of the
calibration standards should also be rerun once every 10 samples.
QC samples are used by the analyst to keep the analytical instrument in control. The
acceptable range of measured QC sample values for Ca, Mg, Na, and K is a 5% difference from
the theoretical value; the range for Al is a 10% difference from the theoretical value. If the QC
sample is out of this range, the source of the problem must be determined and the situation
corrected before more samples are analyzed. The set of samples analyzed after the last accep-
table QC value was obtained are reanalyzed.
2.5.3 Ion Chromatograph (1C): SO42', Cf, and NO3'
Calibrate the 1C with standards made from ACS reagent grade chemicals or from 1C refer-
ence standards. Three to seven standards spanning the concentration range of the samples
must be used for calibration. The same QC sample procedure described for the AAS in Section
2.5.2 should be used for the 1C. Measure QC samples or standards at least every 10 samples to
check the calibration. The acceptable range of measured QC values for SO42" and CI" is a 5%
difference from the theoretical value; for NO3", the range is a 10% difference from the theoretical
value.
2.5.4 Specific Conductance
Check the calibration of the conductivity meter daily with a standard KCI solution with a
conductance of < 50 (iS/cm and calibrate if necessary (if the meter can be calibrated), or
recalculate the cell constant. Before measuring the first sample, measure the conductance of a
QC standard. The standard should have a theoretical or certified conductance in the conductivity
range of the samples. If the measured conductivity is not within ± 5% of the certified
value, then restandardize the meter and cell and repeat the measurement. Remeasure the
conductance of the QC standard at least once every 20 samples. One sample per batch must be
measured in duplicate.
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SECTION 3
QUALITY ASSURANCE AND QUALITY CONTROL PROCEDURES
Table 3-1 contains a summary of quality assurance and quality control (QA/QC) samples for
the LTM Project.
3.1 QUALITY CONTROL PROCEDURES
3.1.1 Field Duplicates
Collect field duplicates as collocated samples, that is, one after another at the same point in
the lake or stream. After collecting the samples into separate containers, filter and analyze the
field duplicates as two discrete samples. Collect a field duplicate once for every 10 lakes
sampled, with a minimum of 2 pairs of field duplicates during each sampling round. Table 3-2
lists the number of field duplicates to be collected by each project. Field duplicates are used to
estimate the precision of the sampling process, including analytical precision.
3.1.2 Field Blanks
Collect a field blank by bringing deionized water (with conductivity < 2.0 jiS/cm) to the field
site and transferring the deionized water into containers normally used to collect the sample from
the Van Dorn sampler. From that point on, process the blank as if it were a regular sample; blank
aliquots are to be (1) unfiltered for pH, ANC, and conductivity, (2) filtered for anions, (3) filtered
and acidified for cations, and (4) filtered for DOC and true color. Field blanks should be collected
once for every 10 sites sampled, or a minimum of 2 per sampling round. Analysis of the blanks
serves as a check on the presence of contamination from the sampling process. Table 3-3 pro-
vides guidelines for determination of contamination. Contamination should be assumed if anal-
ysis of a field blank yields values equal to or higher than those listed in Table 3-3.
3.1.3 Filter Blanks
Prepare filter blanks by filtering deionized water (with conductivity < 2.0 |iS/cm) into
properly cleaned anion and cation containers. Preserve the filter blanks in the same manner as
for regular samples. A set of filter blanks (one for anions and one for cations) should be collected
once for every 10 lakes sampled. Filter blanks are archived until after the field blanks and regular
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TABLE 3-1. SUMMARY OF QA/QC SAMPLES FOR LTM
Sample Type
Frequency
Purpose
Field Duplicates
Lab Duplicates
Field Blanks
Filter Blanks
QC Sample
Spiked Samples
Certified Reference
Material
Performance
Evaluation Samples:
LRTAP Round Robin
and synthetic Al audits
Minimum of 2 p| er sampling
round; or 1 per 10 sites
Minimum of 2 per analytical
batch
Minimum of 2 per sampling
round; or 1 per 10 sites
1 set (1 each for cations,
anions, DOC) per 10 sites;
only analyzed if problem
with field blanks
Measured 3 times per
analytical batch
1 per analytical batch
(optional3) '.
1 per analytical
batch (optional3)
3 times per year
Estimate sampling and
analytical precision
Estimate analytical
precision
Detect contamination
from sample processing,
including filtration
Detect contamination
from filtering process
Check instrument
performance and
calibration; estimate
analytical precision and
accuracy
Estimate instrument
performance, % recovery,
and matrix effects
Estimate accuracy
Detect lab bias,
estimate accuracy,
evaluate lab performance
over time
Note: For estimates of accuracy, either calibration QC samples, spiked samples, or certified reference material must be
used.
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TABLE 3-2. MINIMUM NUMBER OF DUPLICATES PER SAMPLING INTERVAL
Region
Number of
Duplicates
Sampling Intervals
Upper Midwest
Colorado
Adirondacks
Maine
Vermont
Catskills
3
2
2
2
2
2
spring, summer, fall
monthly for 3 months
monthly
spring, summer, fall
spring, summer, fall, winter
9 times per year
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TABLE 3-3. GUIDELINES FOR DETERMINATION OF CONTAMINATION FROM ANALYSIS OF
BLANK SAMPLES
Variable
lieq/La
mg/La
ANC
PH
Conductivity
10
Ca
K
Mg
Na
1.0
0.6
2.0
0.8
0.02
0.02
0.02
0.02
CT
1.0
0.2
2.0
0.02
0.01
0.1
Color
DOC
0 Pt-Co Units
0.2
Al
F
P, total
SiO0
0.6
0.01 (10
0.01
0.004 (4 jig/L)
0.1
a These values are obtained by approximately doubling the required detection limit values listed in Table 1-2. They are
meant as guidelines to the analyst, to expedite the detection of contamination and analytical problems. Contamination
Is assumed if analysis of a field blank yields values equal to or higher than the values listed here. For most lakes,
these values are well below the expected values for most variables, although nitrate and phosphorus values are often
at or below these values and the detection limits. Blank values for ANC and pH are difficult to quantify, yet blanks can
still give Information about contamination of these variables as well. Keep in mind that these values are presented as
guidelines, and use common sense and prior knowledge about the systems in question to help determine the quality
of data at hand.
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samples have been analyzed, the data have been analyzed, and the data have been evaluated for
suspected problems. The filter blanks need to be analyzed only if a contamination problem is
indicated by the field blanks or the analysis of the lake data.
3.1.4 Quality Control (QC) Samples
QC samples are used to check the calibration of analytical instruments. QC samples are
analyzed a minimum of three times in each analytical batch. A complete description of the use of
QC samples is given in Section 2.5.2, Calibration Procedures for AAS.
3.1.5 Laboratory Duplicates
Laboratory duplicates are samples split into separate containers after filtration (if
appropriate) but prior to analysis, and analyzed as separate samples within the same batch.
There should be a laboratory duplicate for every 10 samples, with a minimum of 2 pairs per
batch. Laboratory duplicates are used to estimate within-batch analytical precision.
3.1.6 Spiked Samples
The use of spiked samples is optional, but if they are used to estimate within-batch
accuracy, a spiked sample should be prepared for each batch for each analyte being measured.
Prepare a spiked sample by adding a known quantity of analyte to an aliquot of a sample, then
analyzing the analyte in the spiked and unspiked aliquots. A percent recovery can then be
calculated (see equation 3 in Section 3.2.7) and used as an estimate of accuracy. The spike
concentration should be at least 10 times the detection limit for the analyte, and should keep the
measured value of the spiked sample within the linear range of the analytical instrument. The
volume of the spike added should be negligible.
3.1.7 Analytical Detection Limit
Measurement of analytical detection limit was not required in the 1985 Working Protocol for
Sampling, Sample Analysis, and QA/QC for LTM (Appendix A). However, measurement of the
analytical detection limit on a regular basis is necessary for monitoring programs in order to
provide regular assessment of instrument performance, as well as a quantifiable concentration
that will indicate when a measured value is above zero and is in fact detectable by the analytical
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instrument. The analytical detection limit can be defined as three times the standard deviation of
a low-level check standard (Taylor, 1987). The-concentration of the low-level check standard
should be three to five times the required analytical detection limit as listed in Table 1 -2. The low-
level check standard should be used to monitor batch-to-batch detection limits. In addition, LTM
cooperators should measure the actual instrument detection limit quarterly or semiannually by
preparing a series of dilutions of the lowest calibration standard. The dilutions are analyzed from
the lowest concentration to the highest, with the' objective of determining which standard yields a
detectable response. :
3.1.8 Preparation of Calibration Standards
Analytical balances should be serviced at regular intervals. Weights certified by the National
Institute of Standards and Technology (NIST), formerly the National Bureau of Standards, class
"S" or better, should be used to check the accuracy of the balance prior to each use for preparing
standards. If pipets are used in the preparation of standards, the accuracy of each pipet should
be verified by weighing the volume of deionized water delivered by the pipet. One ml_ of
deionized water weighs one gram at 25°C.
When new calibration standards are prepared, they must be compared to the standard
being replaced and to the other standards for that variable. Never allow standards to be
completely used up until a replacement standard has been prepared and compared. Acceptable
limits for comparison are within 2% of the theoretical value and of the measured value of the
previous standard. The comparison must be recorded. If the 2% limit is not obtained, then a new
standard must be prepared and compared with ihe old standard.
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
An estimate of precision and accuracy must be made for each analytical batch of samples,
so that the quality of the data can be adequately described. If an analytical batch includes
samples other than LTM samples, but similar toiLTM samples, duplicates and spikes of the other
samples can be used to estimate precision and^accuracy for that batch.
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3.2.1 Precision
Precision is the degree of mutual agreement characteristic of independent measurements
resulting from repeated application of the process under specified conditions (Taylor, 1987). In
this program, we estimate (1) analytical and (2) sampling and analysis precision:
Analytical precision refers to the precision of the analysis performed by analytical
laboratory instruments; it is estimated by laboratory duplicates or replicates.
• Sampling and analysis precision refers to the precision of the entire sampling process,
from sample collection through analysis; it encompasses analytical precision. It is
estimated by field duplicates or replicates.
Both analytical precision and sampling and analysis precision are estimates of intralabora-
tory precision. Laboratory and field duplicates can be measured within the same analytical batch
to estimate within-batch precision, or in different analytical batches to estimate among-batch
precision. Among-batch precision includes more sources of error than within-batch precision.
QA objectives for precision (Table 1-2) are compared to within-batch analytical precision, although
it is desirable for all estimates of precision (i.e., from among-batches and field duplicates) to meet
these QA objectives.
Precison is expressed in terms of the coefficient of variation (CV) or percent relative
standard deviation (%RSD):
CV = %RSD = s/X (100) (1)
where: s = standard deviation
X = arithmetic mean
3.2.2 Analytical Precision
Analytical precision is determined by analyzing an individual sample in replicate. There are
two ways we can measure analytical precision:
• With laboratory duplicates, which are samples split in the laboratory (see Section 3.1.5).
A minimum of two pairs of laboratory duplicates per batch should be analyzed for each
variable measured. These kinds of duplicates can, in some cases, be blind to the
analyst.
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• With QC check samples, which are prepared from a source independent of the
calibration standards (see Section 2.5.2). QC samples are analyzed after the
instrument has been calibrated and before samples are analyzed, and then once after
every 10 samples. At a minimum, the QC samples will be analyzed three times in each
batch: at the beginning, in the middle, and at the end of the batch. QC samples are
used by the analyst to keep the analytical instrument in control; if a QC sample is out
of the acceptable range, the problem must be corrected before more samples are
analyzed.
3.2.3 Sampling and Analysis Precision
Sampling and analysis precision can be estimated from the analysis of the duplicate
samples collected in the field (see Section 3.1.1). One field duplicate is collected for every 10
lakes sampled, with a minimum of at least 2 pairs of field duplicates per batch. The %RSD
should be calculated for each pair of duplicates.
3.2.4 Accuracy and Bias
Accuracy is the degree of agreement of a measured value with the true or expected value of
the quantity of concern (Taylor, 1987). Accuracy is expressed as the percent difference from the
reference value, or as percent recovery if spiked samples have been used. Accuracy can be esti-
mated by measuring: (1) certified reference material or QC check samples, (2) performance eval-
uation samples, and/or (3) percent recovery on spiked samples. Certified reference materials, QC
check samples, and spiked samples will give the analyst an immediate estimate of accuracy,
whereas performance evaluation samples will provide an assessment of accuracy and basis for
comparison with the other LTM laboratories. Either certified reference materials, QC check
samples, or spiked samples should be used with each batch, to estimate accuracy.
Bias is a systematic error inherent in a rnethod or caused by some artifact or idiosyncrasy of
the measurement system (Taylor, 1987). The LRTAP Intel-laboratory Comparability Studies are
used to identify bias in the LTM laboratories. Section 3.2.6 describes the LRTAP Studies.
3.2.5 Certified Reference Material or QC Check Samples
Certified reference material or a QC chebk sample should be measured in each batch of
samples; then the percent difference should be calculated.
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% difference = [true value - measured value|
true value x 10° (2>
The % difference should be within the QA objectives for accuracy (Table 1-2). If not, corrective
action should be taken before samples are analyzed, such as correcting the instrument calibra-
tion, or the instrument settings.
Reference materials can be obtained from the National Institute of Standards and
Technology (MIST), formerly the National Bureau of Standards, or from commercial firms that
produce "U.S. EPA Certified" chemical reference materials in cooperation with the EPA. The
American Association for Laboratory Accreditation (A2LA) also has a certification program for
chemical reference materials that is acceptable to the EPA. Only the terms "U.S. EPA Certified" or
"A2LA Certified" indicate certification that has been approved by the EPA. QC check samples can
also be prepared from other sources, as long as the source and preparation are different from
those used to prepare calibration standards.
3.2.6 Performance Evaluation Samples
Three times a year (approximately April, August, and December) LTM laboratories par-
ticipate in the Canada Centre for Inland Waters LRTAP (Long Range Transport of Airborne
Pollutants) Interlaboratory Comparability Studies (Aspila, 1989). The purpose of the LRTAP
studies is to monitor laboratory performance over time. Forty to 50 laboratories participate in
each study and analyze 10 natural water samples. A median value for each variable for each
sample is determined. Flags (low, very low, extremely low, high, very high, or extremely high) are
assigned to variables whose values are outside the acceptable limits for difference from the
median value. The acceptable limits for each flag class for each variable are based on results
from earlier LRTAP studies.
Laboratory rankings of the results from the 10 samples in each study are used to identify
bias for each variable for each laboratory. Bias classes (slightly low, low, slightly high, high) are
assigned to a variable based on the procedure described by Youden (1969).
A summary sheet is prepared for each laboratory after a study, indicating the results (flag
classes or satisfactory rating, and if ranking indicates a bias) for each variable. If a variable is
flagged, first check to see if the value was reported correctly (e.g., that there are no transcription
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errors and that unit conversions were made cdrrectly). Results should be discussed with the
analyst to identify the source of a flagged result (e.g., calibration errors, dirty equipment, old
electrodes, or errors in calibration standards). If a variable is identified as biased in one study,
potential sources of bias should be investigated. If a variable is biased two times in a row,
special attention should be given to identifying^ and correcting the source of the bias.
[
Aluminum is not always included in the ILRTAP studies, so occasionally audit samples for
analysis of Al will be distributed to the LTM laboratories. The median value of results from all LTM
labs will be used to calculate percent difference.
3.2.7 Percent Recovery
Spike an aliquot of a sample with a knoWn amount of analyte (see Section 3.1.6), analyze
the spiked and unspiked sample, then calculate the percent recovery. A blank should also be
spiked at the same time.
% Recovery = [(S - X) / A] 100 (3)
where: S = value of sample plus spike
X = value of unspiked sample ;
A = value of spike added ;
3.3 DATA VALIDATION AND REPORTING
Once each variable in a sample has been determined, several procedures are used to pro-
vide a check on the analyses. These validation checks are completed as soon as possible after
analyses are finished, so problems can be detected and samples can be reanalyzed, if necessary,
before holding times are exceeded. Validation checks include: (1) cation-anion charge evalu-
ation, (2) specific conductance evaluation, and (3) comparison with previous years' data.
3.3.1 Cation-Anion Charge Evaluation :
Theoretically, the sum of anion equivalents equals the sum of cation equivalents in a
sample. In practice, this rarely occurs, due to ions that are present but not measured. For each
sample, the sums of the measured anion and cation equivalents and the ion ratio are calculated
as follows: ]
3-10 QAPIan
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2 anions = [Cr] + [F] + [NO3'] + [SO/'] + [HCO3'] + [CO32~] (4)
2 cations = [Na+] + [K+] + [Ca2+] + [Mg2+] + [NH4+] + [H+] (5)
Sum of ions = 2 anions + 2 cations (7)
Note: Omission of F", CO32", and NH4+ will not significantly affect results. ANC plus H+
(calculated from pH) may be used for HCO3", based on the following equation:
ANC = HC03- + 2CO32" + OH' - H+ (8)
when pH < 7.0, CO32" and OH+ are negligible, therefore the equation becomes:
ANC = HCO3' - H+; or HCO3' = ANC + H+ (9)
A percent ion difference can also be calculated instead of an ion ratio to evaluate cation and
anion charges:
% ion difference = ^ anjons ~ * 'c^°™ (10)
2 anions + 2 cations v '
All concentrations are expressed as microequivalents/liter (p.eq/L). Table 3-4 lists factors for
converting mg/L to jieq/L for each of the variables. Each region has specified the criteria, given
as a range of acceptable ion ratios or percent ion differences, that are used to decide if a sample
should be reanalyzed. These criteria are given in Sections 4 through 9.
3.3.2 Specific Conductance Evaluation
An estimate of the specific conductance of a sample can be calculated by summing the
equivalent conductance values for each measured ion at infinite dilution. The calculated
conductance is determined by multiplying the concentration of each ion (in jieq/L) by the
appropriate factor (F) in Table 3-5.
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TABLE 3-4. FACTORS FOR CONVERTING mg/L TO yeq/L
Factor
lona (iJ-eq/L per mg/L)
Ca2+
cr
COg2'
F :
K+
Mg2+
Na+
NH4+ (as ammonium)
NH4+ (as nitrogen)
NO3" (as nitrogen)
NO3~ (as nitrate)
SO42' (as sulfate)
ANC (as CaCO3) ;
49.9
28.2
33.3
52.6
25.6
82.3
43.5
55.4
71.4
71.4
16.1
20.8
20.0
Although total forms of Ca, Mg, Na, and K are measured, we assume that all are in ionic form for conversion to
mlcroequlvalents. '.
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TABLE 3-5. CONDUCTANCE FACTORS (F) OF IONS
lona
Ca2+
Mg2+
Na+
K+
H+
NH4+
a H+and
Conductance
Factor lona
59.47 NO3"
53.0 cr
50.08 SO42'
73.48 HCO3-
349.65 OH'
73.50
OH' calculated as: H+ = lO"**1 x 106 |ieq/L
OH' = lO14-"3" x 106 |»eq/L
Conductance
Factor
71.42
76.31
80.0
44.5
198
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The calculated conductance for the entire sample is obtained from the relationship
_ , , . . . . 2 (F x Concentration in ueq/L)
Calculated conductance = —* ^Q e_a—i
The percent difference between measured conductance and calculated conductance is given by
Calculated - Measured „ Hr,n /-,,«
% conductance difference = —Measured x 100 p^j
Or, the ratio of calculated to measured conductance can be determined by
* ., x *• Calculated conductance /13\
Conductance rat,o = Measured conductance (13)
Each region has specified the criteria, given as a range of acceptable percent conductance
differences or conductance ratios, that are used to decide if a sample should be reanalyzed.
These criteria are given in Sections 4 through 9. The value in error may be difficult to identify, as
several numbers are part of the calculated conductance estimate.
3.3.3 Comparison with Previous Years' Data
AH newly acquired data should be plotted and compared to historical data from the same
lakes or streams within the holding time requirements, if possible, to further assist the detection of
any analytical or contamination problems.
3.4 TECHNICAL SYSTEMS AUDITS .
On-site technical systems audits are conducted by EPA and technical support QA staff
during sampling and analytical activities to ensure that: (1) protocols are being followed properly,
(2) each laboratory follows and documents the:QA/QC procedures described in this QA plan, (3)
the laboratory facilities, personnel, and equipment are capable of continued operations, and (4)
problems are being identified and resolved quibkly. On-site audits are conducted in each region
approximately once every two years.
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3.5 QUALITY ASSURANCE REPORTS
Data quality must be indicated whenever data are reported. Data quality is most easily
indicated by estimates of precision and accuracy and by the results of blank analyses. Each
analytical batch should have an estimate of precision and accuracy, as described in Section 3.2.
When raw sample data are reported to ERL-Corvallis, raw data used in estimates of precision and
accuracy and in results of blank analyses should also be reported. Summaries of estimates of
precision and accuracy for a sampling period can be used when reports on the data are pre-
pared. Precision data can be presented by listing the range of precision values obtained in
%RSD by variable for each year or sampling period, noting the number of duplicates, and the
number of duplicates that exceeded the QA objectives. Accuracy data can be presented by
listing the range of accuracy values in % difference by variable for each year or sampling period,
noting the number and type of samples used to determine accuracy, and noting the number of
samples that did not meet the QA objectives. Similarly, summaries of blank analyses can be
included by listing the range of blank values by variable for each sampling period, the number
and concentration of blanks that exceeded the concentration values listed in Table 3-3, and the
total number of blank analyses. If the total number of duplicates or blanks is 10 or less, report
results from all duplicates or blanks, instead of writing a summary.
3-15 QA Plan
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SECTION 4
MAINE REGION
This section contains information about specific procedures and methods used in the Maine
region through 1989 and was prepared by Jeffrey S. Kahl, Sawyer Environmental Research
Center, University of Maine, Orono, Maine, 04469. This information supplements the information
in Sections 1 to 3, thus only sections in which region-specific information is required are listed.
These sections include project organization and responsibilities (1.2), sample containers (2.1),
filtration and preservation protocols (2.3.2 to 2.3.4), analytical methods (2.4), calibration
procedures (2.5), procedures for assessing precision and accuracy (3.2.2 to 3.2.4), and data vali-
dation criteria (3.3.1 and 3.3.2). The following section numbers and titles in italics correspond to
numbers in Sections 1 to 3.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
The Maine LTM program is conducted at the University of Maine, Orono, Maine. The pro-
gram is operated by Terry A. Haines, U.S. Fish and Wildlife Service, and Jeffrey S. Kahl, Depart-
ment of Geological Sciences, and Director of the Environmental Chemistry Laboratory (ECL).
Terry Haines is responsible for all aspects of the fisheries efforts; Steve Kahl is responsible for
field and laboratory activities, QC/QA, data validation, and data reporting in aquatic chemistry.
One or two regular staff are utilized for field sampling, and the regular laboratory staff in the ECL
analyze the samples.
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
All containers used in the field or laboratory are acid-soaked with HCI for at least one hour,
rinsed thoroughly with tapwater, then immediately rinsed four times with deionized water. The
containers are then partially refilled with deionized water for storage. The specific conductance of
all containers is checked prior to use. If the value is > 2.0 jiS/cm, the container is either rejected
and put through the entire washing procedure again, or is immediately re-rinsed with deionized
water.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol forAnion Analyses (SO4Z~, NO3~, Cr)
Samples for anion analyses are filtered through Nucleopore 0.4-iim polycarbonate filters,
following LTM protocols in Section 2. Anion samples are refrigerated and analyzed as soon as
possible.
4-1 QA Plan
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2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, A/a, K, and Al)
Samples for cation, SiO2, NH4+, and total AI analyses are filtered through Nucleopore 0.4-
lim polycarbonate filters, following LTM protocols in Section 2. Preservation is by acidification to
pH < 2.0 with HNO3 for cations and Al. Samples for other variables are refrigerated and analyzed
as soon as possible. ;
2.3.4 True Color and DOC
Samples for true color and DOC are filtered through 0.7-nm Whatman GF/F filters.
Preservation of DOC aliquots is by acidification to pH < 2.0 with H2SO4.
2.4 ANALYTICAL METHODS \
See Table 4-1. ;
2.5 CALIBRATION PROCEDURES
2.5.1 pH [
A deionized water blank is used as a standardization check in addition to the suggested
checks in the protocol. The air-equilibrated sample is not stirred in addition to the aeration.
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
3.2.2 Analytical Precision
Two laboratory duplicates (splits) are analyzed each season, with at least 10% analytical
replication.
3.2.3 Sampling and Analysis Precision
Two field collocated samples are analyzed each season.
3.2.4 Accuracy and Bias
Several internal QC samples, and at least one NIST (formerly NBS) or EPA reference mater-
ial sample are analyzed with each batch. One spike sample is also analyzed with each batch.
LRTAP and Watershed Manipulation Project audits are also routinely processed by the laboratory.
3.3 DATA VALIDATION AND REPORTING
i
3.3.1 Cation-Anion Charge Evaluation
A cation-anion ratio is calculated; the range of acceptable ratios is from 0.85 to 1.15
(± 15%). If the ratio exceeds these values, the data for each variable are examined for
possible analytical error. Any suspect variables are then reanalyzed, and the ratio is recalculated.
4-2 QA Plan
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TABLE 4-1. ANALYTICAL METHODS FOR MAINE REGION (through 1989)
Variable
pH, field
pH, lab
Method/Equipment
Closed-cell pH, measured in lab
Aeration with 300 ppm CO2 air,
Reference
Hillman et al.,
Hillman et al.,
1986
1986
ANC
Conductivity
Cr, N03', S042'
Ca, Mg, Na, K
Al, total dissolved
DOC
Si02, NH4+
True color
Both pH measurements made with
Orion Ross™ 81-02 combination
electrodes and Orion EA 920 meters.
Radiometer ARAS™ autotitrators,
Gran plot titrations to pH 3.5
YSI model 35 meter
Dionex 21201, with integrators and
autosamplers.
Perkin-Elmer 703 AAS, Ca and Mg with
N2O-acetylene flame; Na and K with
air-acetylene flame.
Perkin-Elmer 3030B HGA AAS, with
autosampler.
Ol Model 700 Infrared Spectrometer,
with autosampler.
Technicon TRAACS 800, with
autosampler.
Bausch & Lomb Spectronics 70.
Hillman et al., 1986
U.S. EPA, 1983
Hillman et al., 1986
U.S. EPA, 1983
Hillman et al., 1986
Ol standard methods
U.S. EPA, 1983
Hillman et al., 1986
U.S. EPA, 1983
4-3
QA Plan
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Sample's in excess of +10% and -5% receive special scrutiny to ascertain whether a reasonable
explanation exists for the discrepancy.
3.3.2 Specific Conductance Evaluation
A conductance ratio is calculated, and the range of acceptable ratios is from 0.80 to 1.20. If
the ratio exceeds these values, the data for each variable are examined for possible analytical
error. Any suspect variables are then reanalyzed, and the ratio is recalculated. Maine LTM
samples are in the 20 to 30 |iS/cm range.
14.4 QA Plan
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SECTION 5
VERMONT REGION
This section contains information about specific procedures and methods used in the
Vermont region through 1989 and was prepared by Jim Kellogg and Doug Burnham of the
Vermont Department of Environmental Conservation, 103 South Main Street, Waterbury, Vermont,
05676. This information supplements the information in Sections 1 to 3, thus only sections in
which region-specific information is required are listed. These sections include project
organization and responsibilities (1.2), sample containers (2.1), filtration and preservation
protocols (2.3.2 to 2.3.4), analytical methods (2.4), calibration procedures (2.5), procedures for
assessing precision and accuracy (3.2.2 to 3.2.4), and data validation criteria (3.3.1 and 3.3.2).
The following section numbers and titles in italics correspond to numbers in Sections 1 to 3.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
Wallace McLean, Project Officer - Administrative overview and management.
Douglas Burnham, Project Supervisor - Administrative support and supervision of project
manager.
James Kellogg, Project Manager - Manages and conducts program operations: field,
analytical, data management, report writing, QA/QC,
etc.
Gail Center, Project Technician - Assists project manager in all aspects of project
implementations.
Brenda Clarkson, Data Management - Administrative and technical support for data manage-
and Statistician ment, QA/QC, data analysis, statistics, etc.
Water Quality Division Staff - Laboratory services for chemical and biological
analyses, secretarial services, and other project
support as needed.
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
The following information is reported directly from the Vermont Laboratory Glassware
Washing Procedures Manual - June 1987: Acid Deposition Lake Sampling Containers:
A. 1-liter round Nalgene - pH, alkalinity, apparent color, specific conductance.
B. 125-ml rectangular Nalgene - anions (SO42', Cr, NO3") - both bottles are composed of
high-density linear polyethylene, with polypropylene caps.
1. Empty and rinse three times with the highest quality deionized water available.
5-1 QA Plan
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2. Fill with deionized water, cap and allow to stand for 48 hours.
3. After initial cleaning and storage 50% of these bottles are randomly selected for a
conductance check. The bottles are slowly rotated so that water touches all
surfaces and cap. The conductivity is then checked and if found to be greater
than 1 .5 (iS/cm in any of the checked bottles, all are rerinsed, refilled with
deionized water, and retested 48 hours later. This procedure continues until all
bottles pass.
SPECIAL NOTE: No detergent is|ever used on these containers.
C. 60- and 125-ml round Nalgene-metals (Al) and cations (Ca, Mg, Na, K). Both bottles
are composed of high density linear polyethylene, with polypropylene caps.
1 . Empty and rinse three times with the highest grade deionized water available.
2. Rinse three times with 3 N (20%) reagent grade HNO3 followed by six rinses with
deionized water.
3. Fill with deionized and allow to stand for 48 hours.
4. After initial cleaning and storage ' 50% of these bottles are randomly selected for a
conductance check. The bottles are slowly rotated so that the water touches all
surfaces and cap. The conductivity is then checked and if found to be > 1 .5
jiS/cm in any of the checked bottles all are rerinsed and refilled with deionized
water and retested 48 hours later. This procedure continues until all bottles pass.
SPECIAL NOTE: No detergent is ever used and all acid must be rinsed out from
these containers.
A separate notebook is kept with the conductivity meter and is used to record all
results pertaining to the conductivities of washed bottles.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol forAnion Analyses (SO42', NO3~,
Samples are filtered within 12 hours of sample collection with Gelman GA-6 cellular acetate
0.45-|im filters (47 mm). Filters are rinsed by Soaking in Dl water; filtration flask and sample
containers are rinsed once with filtered water. A"'01"1 samples are refrigerated.
2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, Na, K, and Al)
Samples are filtered within 12 hours of sample collection with Gelman GA-6 cellular acetate
0.45-p.m filters (47 mm). Filters are rinsed by shaking in Dl water; filtration flasks and sample
containers are rinsed once with filtered water. Preservation follows LTM protocol in Section 2.
QA Plan
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2.3.4 True Color and DOC
Samples for true color are filtered within 12 hours of sample collection with Gelman GA-6
cellular acetate 0.45-jim filters (47 mm). The filtered samples are refrigerated until analyzed.
2.4 ANALYTICAL METHODS
See Table 5-1.
2.5 CALIBRATION PROCEDURES
2.5.1 pH
A fritted glass diffuser is used for the air-equilibrated pH measurement.
2.5.2 Atomic Absorption Spectrophotometer (AAS): Ca, Mg, A/a, K, and Al
The AAS is calibrated with four reference standards for graphite furnace, and five reference
standards for flame analyses. An EPA QC sample is tested before beginning sample analysis.
The standards are rechecked after every 6-10 samples.
2.5.3 Ion Chromatograph (1C): SO42', Cr, and NO3'
Three 1C reference standards are used to calibrate the instrument. Standards are
rechecked if more than 10 lake samples are analyzed, although batches generally consist of
fewer than 10 samples. One of the three standards are used as a QC check. LRTAP samples are
also saved and used as an additional QC check.
2.5.4 Specific Conductance
Two prepared KCI standards with conductivity < 50 jiS/cm are tested prior to the analysis of
lake samples.
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
3.2.2 Analytical Precision
A minimum of 10% analytical duplicates are analyzed for all variables.
3.2.4 Accuracy and Bias
A minimum of 10% of the samples analyzed for anions and cations are spiked samples.
The Vermont laboratory participates in the LRTAP studies three times per year, and in the EPA
Acid Precipitation Performance Evaluation survey two times per year. Internal checks are
conducted four times per year.
5-3 QA Plan
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TABLE 5-1. ANALYTICAL METHODS FOR VERMONT REGION (through 1989)
Variable
Method/Equipment
Reference
pH, field
pH, lab
(stirred)
(bubbled)
ANC
Conductivity
cr, so42-, N03-
Ca, Mg, Na, K
Al, total dissolved
Color
Beckman 21 metei| with temperature
compensation. Sample placed in 30-ml
plastic beaker and analyzed.
Cole Palmer DigipHase meter, Cole Palmer
KCI combination electrode with calomel
reference
Same meter and electrode but the sample
is air-equilibrated with 300 ppm CO2 (Air-
equilibrated reported separate from lab
PH)
Titration with 0.020 N H2SO4 to pH 3.5,
with about 17 points used for Gran plot
calculation.
YSI model 32 with \ two cells, one for
samples < 20 nmhos, another for samples
> 20 (imhos.
Dionex Ion Chromatograph 2000 with
integrator; manual injection, 3
calibration standards with check
sample (one of the original standards)
run after every 10 samples.
Perkin Elmer 3030B; 5 calibration
standards; acetylene flame, 1 out of
every 10 samples is a duplicate or spike.
Lanthanum added to Ca, Mg.
Perkin Elmer 3030B and HGA 600
furnace with autosampler
True color is filtered through
0.45-nm filter and measured at
420 nm on a spectrophotometer.
Apparent color is unfiltered and
measured on a Taylor color comparator.
U.S. EPA, 1983
U.S. EPA, 1983
U.S. EPA, 1983
Pfeiffer and Festa,
1980
U.S. EPA, 1983
O'Dell, etal., 1984
U.S. EPA, 1979
& 1983
U.S. EPA, 1983
Black & Christman,
1963
U.S. EPA, 1983
5-4
QA Plan
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3.3 DATA VALIDATION AND REPORTING
3.3.1 Cation-Anion Charge Evaluation
Percent ion difference is calculated, and reanalysis criteria are as follows:
Sum of ions fueq/L) % Ion difference
< 50 60
5: 50 < 100 30
> 100 15
If the percent ion difference exceeds these values, the data for each variable are examined for
possible analytical error. Any suspect variables are then reanalyzed, and the percent ion
difference is recalculated.
3.3.2 Specific Conductance Evaluation
Percent conductance difference is calculated, and reanalysis criteria are as follows:
Measured Conductance (uS/cm) % Conductance Difference
< 5 > 50
> 5 < 30 > 30
S: 30 > 20
If the percent conductance difference exceeds these values, the data for each variable are
examined for possible analytical error. Any suspect variables are then reanalyzed, and the
percent conductance difference is recalculated.
5-5 QA Plan
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SECTION 6
ADIRONDACK REGION
This section contains information about specific procedures and methods used in the
Adirondack region through 1989 and was prepared by Charles Driscoll and Rich Van Dreason,
Department of Civil Engineering, Syracuse University, Syracuse, New York, 13244. This
information supplements the information in Sections 1 to 3, thus only sections in which region-
specific information is required are listed. These sections include project organization and
responsibilities (1.2), sample containers (2.1), filtration and preservation protocols (2.3.2 to 2.3.4),
analytical methods (2.4), calibration procedures (2.5), procedures for assessing precision and
accuracy (3.2.2 to 3.2.4), and data validation criteria (3.3.1 and 3.3.2). The following section
numbers and titles in italics correspond to numbers in Sections 1 to 3.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
Researchers at Syracuse University will sample and analyze for chemical constituents in 16
lakes in the Adirondack region of New York. Dr. Charles Driscoll of Syracuse University, principal
investigator of this project, will have overall responsibility for project measurements, sample
custody, and data reporting. A research associate will supervise sample collection, analytical
measurements, and sample transfer and handling, as well as data quality and transfer. Their
quality assurance responsibilities include:
1. Monitoring daily QA/QC activities.
2. Reviewing laboratory notebooks, instrument performance logs, and QA/QC data on a
regular basis.
3. Determining that performance audits, triplicate analyses, and other QA/QC activities are
performed.
4. Examining data summaries and calculations.
5. Assisting in trouble-shooting problems.
6. Preparing quarterly QA summaries and reports.
7. Checking that all project personnel are competent to perform analyses.
8. Ensuring that all QA/QC operations are followed.
A field and laboratory technician will be responsible for:
1. Performing collection, including field blanks and replicated samples.
2. Processing aqueous samples for the analysis of major solutes (Table 6-1).
3. Maintaining appropriate notebooks of field activities and the sample log.
4. Observing and recording events that may affect field data.
5. Understanding their role in the project and laboratory.
6. Maintaining appropriate notebooks, instrument logs, and QA/QC records.
7. Observing and recording events that may affect experimental data.
8. Reporting any problems or concerns to the principal investigator.
9. Performing routine maintenance on instrumentation as required.
6-1 QA Plan
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Regular meetings of project personnel will be hfeld to discuss experimental progress, analytical
problems or concerns, or any other problems wjthin the project.
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
All water samples will be collected in prelabelled, HCI-washed (1.0 N) polyethylene
containers soaked (> 12 hours) and rinsed (> 5 times) with deionized water. Sample containers
will be rinsed three times with sample solution prior to collection.
At least 25% of the cleaned containers wilj have a specific conductance check. Specific
conductance of deionized water in the container will be measured after a 48-hour period. If the
specific conductance is greater than 1.5 |is/cm,; all the containers in that batch will be rerinsed.
The highest conductivity value for each batch will be recorded.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol for Anion Analyses (SO42', A/O3", Cr)
Samples for anion analyses are transported on ice to the Environmental Engineering Labor-
atory at Syracuse University, then stored at 4°C| until analyzed. These samples are not filtered.
2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, A/a, K, and At)
Samples for cation analyses are transported on ice to the laboratory, stored at 4°C, and not
filtered. Samples for AI fractions are processed shortly after collection, then stored at 4°C.
2.3.4 True Color and DOC
Samples for color are not filtered. Samples for DOC analyses are filtered through baked
GF/F (0.7 urn) filters, then H2SO4 is added. ;
2.4 ANALYTICAL METHODS
See Table 6-1. -
2.5 CALIBRATION PROCEDURES '.
Calibration procedures follow the protocols in Section 2.
i
j
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
i
3.2.2 Analytical Precision |.
Analytical precision is estimated from laboratory triplicate samples analyzed once during
each suite of monthly samples.
6-2 QA Plan
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TABLE 6-1. ANALYTICAL METHODS FOR ADIRONDACK REGION (through 1989)
Variable
pH, field
pH, lab
Method/Equipment
Glass body Ross™ combination
electrode.
Glass body Ross™ combination
Reference
APHA, 1985
APHA, 1985
ANC
Conductivity
cr, so42-; N03-
Ca, Mg, K, Na
Monomeric Al
Nonlabile, organic
monomeric Al
DOC
Dissolved Inorganic
Carbon (DIG)
Dissolved Silica
NH4+
Total F
Apparent Color
electrode, Orion 701 A, aeration
with 300 ppm CO2.
Titration to pH 3.2 with 0.01 N HCI;
Gran plot analysis.
YSI model 32 meter.
Dionex ion chromatograph.
Perkin Elmer 3030B AAS; air-acetylene
flame, Ca and Mg with lanthanum
addition.
Field extraction by 8-hydroxyquinoline
into MIBK, analysis by AAS, graphite
furnace.
Fractionation by ion exchange column,
analysis for monomeric Al.
Dohrman direct injection; UV enhanced
persulfate oxidation, CO2 detection by
IR spectrophotometry.
CO2 detection by infrared (IR)
spectrophotometry.
Heteropoly blue complex colorimetry;
Technicon AutoAnalyzer.
Phenate colorimetry;
Technicon AutoAnalyzer.
Potentiometrically with ion selective
electrode after TISAB addition.
Colorimetric platinum on
unfiltered sample.
Hillman et al., 1986
Gran, 1952
APHA, 1985
Small etal., 1975
Slavin, 1968
Barnes, 1976
Driscoll, 1984
Dohrman, 1984
Dohrman, 1984
U.S. EPA, 1983
U.S. EPA, 1983
Orion, 1976
U.S. EPA, 1983
6-3
QA Plan
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3.2.3 Sampling and Analysis Precision
Sampling and analysis precision is estimated from the analysis of triplicate samples
collected in the field. One field triplicate is collected during each suite of monthly samples.
3.2.4 Accuracy and Bias
QC check samples, along with performance evaluation samples (LRTAP and synthetic Al
samples), are used to estimate accuracy.
3.3 DATA VALIDATION AND REPORTING
3.3.1 Cation-Anion Charge Evaluation
Percent ion difference is calculated, and reanalysis criteria are as follows:
Sum of ions (ueq/U % Ion difference
<50
< 100
3: 100
60
30
15
If the percent ion difference exceeds these values, the data for each variable are examined for
possible analytical error. Any suspect variables are then reanalyzed, and the percent ion
difference is recalculated.
3.3.2 Specific Conductance Evaluation
Percent conductance difference is calculated, and reanalysis criteria are as follows:
Measured Conductance (uS/cm> % Conductance Difference
<5
> 5 < 30
> 30
> 50
> 30
> 20
If the percent conductance difference exceeds these values, the data for each variable are
examined for possible analytical error. Any suspect variables are then reanalyzed, and the
percent conductance difference is recalculated.
6-4
QA Plan
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SECTION 7
UPPER MIDWEST REGION
This section contains information about specific procedures and methods used in the Upper
Midwest region through 1989 and was prepared by Bruce Holdhusen, Department of Civil and
Mineral Engineering, 500 Pillsbury Dr., S.E., Minneapolis, Minnesota, 55455, and Katherine
Webster, Wisconsin Department of Natural Resources, 3911 Fish Hatchery Road, Madison,
Wisconsin, 53711. This information supplements the information in Sections 1 to 3, thus only
sections in which region-specific information is required are listed. These sections include project
organization and responsibilities (1.2), sample containers (2.1), filtration and preservation
protocols (2.3.2 to 2.3.4), analytical methods (2.4), calibration procedures (2.5), procedures for
assessing precision and accuracy (3.2.2 to 3.2.4), and data validation criteria (3.3.1 and 3.3.2).
The following section numbers and titles in italics correspond to numbers in Sections 1 to 3.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
Principal investigator, Patrick L Brezonik, University of Minnesota (UM): project oversight,
including oversight of laboratory methods and procedures, laboratory analyses and laboratory
personnel; review of data before submittal; review of QA/QC procedures; analysis and
interpretation of data set for temporal and spatial trends; preparation and submittal of annual
progress reports and completion reports; oral and poster presentations on project at technical
meetings and LTM review meetings; preparation of manuscripts on results of study for submission
to technical journals; correspondence with co-investigator and with ERL-Corvallis personnel.
Co-investigator, Katherine E. Webster, Wisconsin Department of Natural Resources (WDNR):
management of sampling program, including preparation of materials for sampling trips, partici-
pation in sampling trips, training of other individuals involved in lake sampling, supervision of field
analyses and transfer of samples to analytical laboratory; maintenance of field notes and field
data; maintenance of field equipment; preparation of computerized SAS data base for field and
laboratory data; preparation of sections of annual progress reports on field aspects of project;
review of data for temporal and spatial trends; oral and poster presentations on project at
technical meetings and LTM review meetings; preparation of manuscripts on results of study for
submission to technical journals; correspondence with ERL-Corvallis personnel regarding data
and methodological issues.
7-1 QA Plan
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Laboratory manager, Bruce Holdhusen, University of Minnesota (UM) (since July 1988):
contact person for receipt of lake water samples; supervision of technicians and graduate
students who perform various chemical analyses; analysis of lake samples for variety of chemical
constituents, including major cations and anions; review and evaluation of analytical methods;
direct supervision of QC/QA program and maintenance of records pertaining thereto; review of
chemical data for accuracy and precision before being inserted into laboratory computer data
base; transfer of data to WDNR for addition to 'SAS data file; maintenance and update of labora-
tory manual of procedures; ordering of laboratory supplies; contact person for equipment main-
tenance.
Various technicians (UM and WDNR) and graduate students (UM only): prepare field mater-
ials (e.g., wash sample bottles) and participate in field sampling and field analysis program under
direction of field supervisor; prepare solutions and reagents and perform various chemical anal-
yses under direction of laboratory manager; perform routine calculations and enter data into
computer data files. Note: names are not included here because personnel change periodically
and these individuals report directly to the individuals listed above, who have direct respon-
sibilities for their work. ;
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
Starting with spring 1989, HCI instead of HNO3 has been used to clean cation/metals
containers as recommended in Section 2. Glass bottles for DOC samples are newly purchased
for each sample collection. They are cleaned toy rinsing three times with deionized water.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol for Ariion Analyses (SO42', NO3~, Cr)
Aliquots for anions are filtered through 0.4-|im Nuclepore polycarbonate membrane filters.
Rinses are as described in the protocol, except that filters are pre-rinsed with 250 ml of deionized
water before sample collection begins. Anion; aliquots are frozen prior to shipment. They are
kept frozen in the laboratory until the day of analysis.
2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, A/a, K, and Al)
Aliquots for cations, metals, and silica are filtered through 0.4-|im Nuclepore polycarbonate
membrane filters with rinses as described above. Cation/metals aliquots are preserved with
Ultrex™ HNO3 and kept chilled before and dijring shipment and stored at 4°C until analysis.
Silica aliquots are chilled before shipment and stored at 4°C until analysis.
i 7-2 QA Plan
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2.3.4 True Color and DOC
Aliquots for DOC are filtered as in Section 2.3.2, chilled prior to and during shipment, and
stored at 4°C until analysis. Aliquots for color are not filtered in the field or laboratory but are
taken from the unfiltered "physical parameters" bottle, which is chilled before and during shipment
and stored at 4°C before analysis. Aliquots for color are centrifuged in an International clinical
centrifuge at 3/4 of full speed for 15 minutes and the supernatant is decanted and analyzed for
"true" color.
2.4 ANALYTICAL METHODS
See Table 7-1.
2.5 CALIBRATION PROCEDURES
Procedures follow the LTM protocol as described in Section 2.
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
3.2.2 Analytical Precision
Analytical precision is determined routinely by analyzing 10% of the samples in duplicate,
with a minimum of one per analytical batch, but more typically two or more, depending on size of
analytical batch.
3.2.3 Sampling and Analysis Precision
At least 10% of the field samples are collected in duplicate, including at least one duplicate
sample for each of the three states in the Upper Midwest LTM region in each sampling season.
The field duplicates are analyzed for all chemical variables.
3.2.4 Accuracy and Bias
Samples used to estimate accuracy on a routine basis are from the U.S. EPA (certified
analytical reference samples, hereafter referred to as EPA QC standards). These standards are
prepared as stock solutions according to directions provided with them, and dilutions are
prepared with each analytical run to obtain standards in the approximate range of the lakewater
samples in the Upper Midwest LTM program. The EPA QC standards are analyzed at the begin-
ning of each analytical run and after every 10 samples within an analytical run. In addition,
laboratory standard solutions are analyzed at the beginning of each analytical run.
7-3 QA Plan
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TABLE 7-1. ANALYTICAL METHODS FOR UPPER MIDWEST REGION (through 1989)
Variable
Method/Equipment
Reference8
pH, field
pH, lab
ANC
Conductivity
cr, so42-
N03-
Ca, Mg, Na, K
A!, total dissolved
DOC
SiO,
NH4+
True color
Orion model 501 A, Beckman Futura II Star
Series combination electrode
Beckman model 71 meter, Corning combination
electrode model 476541; air equilibration
with 300 ppm CO2
Titration to pH 3.5 with 0.02 N H2SO4; Gran
plot calculation using all data for pH 4.0
and below to calculate regression line
YSI model 32 conductivity meter
Dionex model 10 ion chromatograph
Automated cadmium reduction method on
Technicon AutoAnalyzer II
Flame atomic absorption spectrophotometry
with Varian model 1475 AAS; air-acetylene
flame for Mg, Na, K; N2O-acetylene flame
for Ca; each calibrateciwith blank plus
five standards
Flameless AAs with Pejrkin Elmer model 4000
and model 400 HGA and autosampler on
filtered samples; calibrated with five
standards plus blank
I
Dohrman DC-80; direct injection with UV
oxidation ;
Manual heteropolyblue method for reactive
silica on Hitachi 100.20 spectrophotometer
Manual indophenol method on Hitachi 100.20
spectrophotometer (similar to manual method
in APHA 1985)
True color on centrifuged sample; absorbance
at 420 nm with 5-cm cells on Beckman model
26 with calibration curve prepared using
standard chloroplatinate solution. Method is
similar to that of EPA (1987) and APHA (1985)
except that a spectrophotometer is used to
quantify absorbance ajt a specific wavelength
rather than estimating icolor by visual
comparison with standards.
U.S. EPA, 1987
U.S. EPA, 1987
U.S. EPA, 1987
U.S. EPA, 1987
U.S. EPA, 1987
APHA, 1981,
1985
U.S. EPA, 1987
U.S. EPA, 1987
U.S. EPA, 1987
APHA, 1981,
1985
Solorzano,
1969
None
a Instrument manuals of the manufacturers of the cited instruments are additional references
DOC. ! \
for cations, anions, and
7-4
QA Plan
-------
The analytical laboratory also participates in the LRTAP Interlaboratory Comparability
Studies three times per year to evaluate laboratory bias, and analyzes synthetic audit samples
when provided by the U.S. EPA-Las Vegas laboratory for aluminum analyses.
3.3 DATA VALIDATION AND REPORTING
3.3.1 Cation-Anion Charge Evaluation
Percent ion difference is calculated, and reanalysis criteria are as follows:
Sum of ions (ueq/L) % Ion difference
< 50 20
> 50 < 100 10
> 100 10
If the percent ion difference exceeds these values, the data are first reviewed to determine
whether there are any transposition recording or calculation errors, then the data for each variable
are examined for possible analytical error. Larger anion deficits than the criteria allow may not
trigger re-analysis for a few lakes with high color, for which such deficits occur consistently and
thus are not indicative of analytical errors. Any suspect variables are reanalyzed, and the percent
ion difference is recalculated.
3.3.2 Specific Conductance Evaluation
Percent conductance difference is calculated, and reanalysis criteria are as follows:
Measured Conductance (uSfcm) % Conductance Difference
< 5 No samples in this category
5: 5 < 30 25
> 30 20
If the percent conductance difference exceeds these values, the data are first reviewed to
determine whether there are any transposition recording or calculation errors, then the data for
each variable are examined for possible analytical error. Larger anion deficits than the criteria
allow may not trigger re-analysis for a few lakes with high color, for which such deficits occur
consistently and thus are not indicative of analytical errors. Any suspect variables are reanalyzed,
and the percent conductance difference is recalculated.
7-5 QA Plan
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-------
SECTION 8
COLORADO REGION
This section contains information about specific procedures and methods used in the
Colorado region through 1989 and was prepared by John Turk and Don Campbell, U.S. Geo-
logical Survey, Bldg. 53, MS 415, Denver Federal Center, Lakewood, Colorado, 80225. This
information supplements the information in Sections 1 to 3, thus only sections in which region-
specific information is required are listed. These sections include project organization and
responsibilities (1.2), sample containers (2.1), filtration and preservation protocols (2.3.2 to 2.3.4),
analytical methods (2.4), calibration procedures (2.5), procedures for assessing precision and
accuracy (3.2.2 to 3.2.4), and data validation criteria (3.3.1 and 3.3.2). The following section
numbers and titles in italics correspond to numbers in Sections 1 to 3.
1.2 PROJECT ORGANIZATION AND RESPONSIBILITIES
This project is a cooperative effort of the U.S. Geological Survey, the U.S. Environmental
Protection Agency, and the Colorado Department of Health. John Turk, USGS principal
investigator, has responsibility for project design, contracting of outfitters, laboratory analysis,
quality assurance, training, safety, and report preparation.
2.1 SAMPLE CONTAINERS: CLEANING AND CONDUCTIVITY CHECKS
All containers are obtained from the USGS-CAL (Central Analytical Laboratory) in Arvada,
Colorado. New lots of sample containers are prepared by the CAL as follows: bottles for DOC
are fired, bottles for cation and metals determination are washed in nitric acid and rinsed with
deionized water, bottles for nutrients and anions are soaked for at least three days in deionized
water.
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol forAnion Anlyses (SO42~, NO3", Cr)
Anions are measured on aliquots filtered through Gelman 0.45-n.m cellulose acetate filters.
2.3.3 Filtration and Preservation Protocol for Cation Anlyses (Ca, Mg, A/a, K, and Al)
Cations are measured on aliquots filtered through Gelman 0.45-jim cellulose acetate filters
and preserved with concentrated nitric acid, provided in ampoules and documented by the CAL.
8-1 QA Plan
-------
2.3.4 True Color and DOC
True color is measured on aliquots filtered through Gelman 0.45-|im cellulose acetate filters.
DOC is measured on aliquots filtered through OJ4-nm Selas sintered silver filters.
2.4 ANALYTICAL METHODS
See Table 8-1.
2.5 CALIBRATION PROCEDURES
Calibration procedures follow the protocols outlined in Section 2.
3.2 PROCEDURES FOR ASSESSING PRECISIOty AND ACCURACY
3.2.2 Analytical Precision
Analytical duplicates are prepared by the CAL, with at least one set of duplicates in each
batch.
3.2.3 Sampling and Analysis Precision
On each sampling trip, a field duplicate is prepared and submitted as a regular sample.
These data are stored as routine samples but keyed with a 5-minute difference in time from the
regular sample.
3.2.4 Accuracy and Bias ;
Laboratory accuracy is addressed by the ICAL with the use of QC check samples and
control charts. Audit samples provided by the EPA are submitted to the laboratory.
3.3 DATA VALIDATION AND REPORTING
3.3.1 Cation-Anion Charge Evaluation
A cation-anion ratio is calculated; the range of acceptable ratios is from 0.85 to 1.15
(± 15%). If the ratio exceeds these values, the data for each variable are examined for
possible analytical error. The most likely anomalous constituents are selected by comparison to
previously validated data, then any suspect variables are reanalyzed, and the ratio is recalculated.
If the rerun value does not meet the check stated above, a new analysis is run from an archived
sample.
8-2
QA Plan
-------
TABLE 8-1. ANALYTICAL METHODS FOR COLORADO REGION (through 1989)
Variable
Method/Equipment
Reference
pH, field
pH, lab
ANC
Conductivity
CT, S042-, N03-, F
Ca, Mg
Na, K
Al, total dissolved
DOC
DIG
Si
NH4+
True Color
Fishman etal., 1985
Glass body Ross™ Combination Electrode Turk, 1986
Glass body Ross™ Combination Electrode Fishman et al., 1985
Titration to pH 3.0 with 0.01639 N H2SO4 Stumm and Morgan,
Gran function endpoint 1981
YSI 32 meter
USGS Arvada Lab, Dionex 1C
USGS Arvada Lab, ICP
USGS Arvada Lab, Low level AAS
USGS Arvada Lab, DC plasma spectrometer
USGS Arvada Lab, UV oxidation, Dohrman
(method used as presented in operating
manual for instrument; being prepared
as USGS method)
USGS Arvada Lab, Dohrman
USGS Arvada Lab, ICP
USGS Arvada Lab, Technicon AutoAnalyzer
USGS Arvada Lab, Comparator
8-3
QA Plan
-------
3.3.2 Specific Conductance Evaluation
If the difference between measured and calculated specific conductance is > 20%, the
analysis is assumed to be in error for at least one major ion or for specific conductance. Specific
conductance is rerun; if this does not correct the imbalance, the ion concentrations are compared
to previously validated data. Suspect variables are reanalyzed as in Section 3.3.1.
8-4 QA Plan
-------
SECTION 9
CATSKILL REGION
This section contains information about specific procedures and methods used in the
Catskill region through 1989 and was prepared by Peter Murdoch, U.S. Geological Survey, Water
Resources Division, Box 1397, Room 348, Albany, New York, 12201. This information supple-
ments the information in Sections 1 to 3, thus only sections in which region-specific information is
required are listed. These sections include project organization and responsibilities (1.2), sample
containers (2.1), filtration and preservation protocols (2.3.2 to 2.3.4), analytical methods (2.4),
calibration procedures (2.5), procedures for assessing precision and accuracy (3.2.2 to 3.2.4),
and data validation criteria (3.3.1 and 3.3.2). The following section numbers and titles in italics
correspond to numbers in Sections 1 to 3.
2.0 PROJECT ORGANIZATION AND RESPONSIBILITIES
Project Leader: Peter S. Murdoch, U.S. Geological Survey, Water Resources Division, Albany,
New York
Field Coordinator: Antony J. Ranalli
Laboratory Coordinator: Debra Horan-Ross
Laboratories
USGS-Albany
NYC-Valhalla
NYC-Grahamsville
USGS-Frost Valley
Analyst
D. Horan-Ross
R. Corradi
S. Schindler
C. Swain
Constituents
Anions, ANC, conductance, pH
Aluminum, silica
DOC, cations
Field pH
2.1 SAMPLE CONTAINERS
Anion, pH, ANC, conductance, and silica aliquot containers are rinsed with deionized water
and soaked for 48 hours at the USGS-CAL (Central Analytical Laboratory) in Arvada, Colorado.
These bottles are used only once and are rinsed three times with filtered sample before filling.
Cation and aluminum aliquot containers are acid rinsed at the USGS-CAL in Arvada,
Colorado. These containers are also used only once and rinsed three times with filtered sample
before filling.
9-1
QA Plan
-------
DOC aliquot containers are rinsed with tap water, then rinsed with a 25% nitric acid solution
before a 24-hour soak with a dilute nitric acid splution. The bottles are then rinsed four times with
delonized water, capped and stored wet. ;
2.3 SAMPLE CUSTODY, PREPARATION, AND PRESERVATION
2.3.2 Filtration and Preservation Protocol lor Anion Analyses (SO4', NO3~, Cr)
Anion aliquots are filtered within 24 hour? of collection through 0.4-^m Nucleopore
polycarbonate filters and refrigerated for analysis within 14 days of collection. Before September
1988, anion aliquots were filtered through 0.1 -^m Nucleopore polycarbonate filters.
I
2.3.3 Filtration and Preservation Protocol for Cation Analyses (Ca, Mg, A/a, K, and Al)
Aliquots for Ca, Mg, Na, and K are filtered within 24 hours of collection through 0.4-^m
Nucleopore polycarbonate filters, acidified with ultra-pure nitric acid, and stored for analysis within
one month of collection. Before September 1988, these aliquots were filtered through 0.1 -^m
Nucleopore polycarbonate filters. Aluminum ajiquots are filtered immediately in the field through
0.1 -/mi Nucleopore polycarbonate filters and acidified with ultra-pure nitric acid for analysis within
one month.
2.3.4 True Color and DOC
DOC aliquots are filtered through 0.4-nrrj Nucleopore polycarbonate filters and chilled for
analysis within two weeks.
2.4 ANALYTICAL METHODS ;
See Table 9-1 . ,
2.5 CALIBRATION PROCEDURES
Calibration procedures follow the protocols described in Section 2. A low conductance
standard (approximately 15 (iS/cm) is used in| addition to a 50 jiS/cm standard.
i
i
3.2 PROCEDURES FOR ASSESSING PRECISION AND ACCURACY
3.2.2 Analytical Precision |
The Catskill LTM program utilizes laborajtory split samples from the same aliquot bottle (10%
of samples), QC samples provided by the EPA, and QC samples provided by the USGS to assess
analytical precision. ;
9-2
QA Plan
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TABLE 9-1. ANALYTICAL METHODS FOR CATSKILL REGION (through 1989)
Variable
Method/Equipment
Reference
pH, field
pH, lab
ANC
Conductivity
cr, so42-, N03-
Ca, Mg, Na, K
Al, total dissolved
DOC
SiO,
Glass body Ross™ combination electrode
Beckman Model 071 meter; Ross™ combination
epoxy body electrode; do not aerate
because have found no difference between
aerated and nonaerated pH measurement for
streams.
Radiometer ABU93 autotitrator with SAC8O
sample changer; titrations to pH 3.6, use
at least 4 points under pH 5.0 for Gran
analysis.
Altex (Beckman) meter and probe. Use USGS
standards.
Dionex 2000 with autosampler. Filtered
with 0.4-nm filter.
NYCDEP Grahamsville lab, Perkin Elmer 3030.
Atomic absorption spectrophometer (AAS),
multiple standard calibrations. Before Oct.
1988, analyzed at USGS Central Analytical
Lab (CAL) - Arvada.
NYCDEP Valhalla lab with furnace atomic
absorption spectrophotometer (AAS).
Filtered with 0.1 -\im filter.
NYCDEP Grahamsville lab; filtered with
0.4-nm polycarbonate filter; Dohrman direct
injection.
NYCDEP Valhalla laboratory. Colorimetric,
silico-molybdate, spectrophotometer. Before
Oct. 1988, analyzed at USGS-CAL
APHA, 1985
APHA, 1985
Gran, 1952
APHA, 1985
Small etal., 1975
Slavin, 1968
Driscoll, 1984
Dohrman, 1984
U.S. EPA, 1979
9-3
QA Plan
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3.2.4 Accuracy and Bias \
The Catskill LTM program uses QC check solutions, standard reference materials, performance
evaluation samples (both the USGS and LRTAP audit samples), synthetic audit samples, and spike
samples for cations and aluminum.
3.3 DATA VALIDATION AND REPORTING
3.3.1 Cation-Anton Charge Evaluation
A cation-anion ratio is calculated, and reana|ysis criteria are as follows:
Sum of ions fueq/L) Range of acceptable ion ratios
< 50 0.7 - 0.30
& 50 < 100 0.85 - 1.30
& 100 i 0.80 - 1.10
If the ratio exceeds these values, the data for each variable are examined for possible analytical error.
Any suspect variables are then reanalyzed, and the ratio is recalculated. Data entries are reviewed by
a project person other than the one who entered the data to ensure data have been properly entered.
3.3.2 Specific Conductance Evaluation ••
A conductivity ratio is calculated, and reanalysis criteria are as follows:
Measured Conductance (uS/cm) Range of acceptable conductivity ratios
<5 0.7-1.30
> 5 < 30 0.85 - 1.15
> 30 No samples in this category
If the ratio exceeds these values, the data for each variable are examined for possible analytical error.
Any suspect variables are then reanalyzed, and trje ratio is recalculated.
9-4
QA Plan
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SECTION 10
REFERENCES
APHA. 1981, 1985. Standard Methods for the Examination of Water and Wastewater, 15th and 16th
eds. (respectively). American Public Health Association, American Water Works Association,
Water Pollution Control Federation, Washington, D.C.
Aspila, K.I. 1989. A Manual for Effective Interlaboratory Quality Assurance. NWRI contribution 89-99.
National Water Research Institute, Canada Centre for Inland Waters, P.O. Box 5050, Burlington,
Ontario, Canada L7R 4A6.
Barnes, R.B. 1976. The determination of specific forms of aluminum in natural water. Chemical
, Geology 15:177-191.
Black, A.P., and R.F. Christman. 1963. Characteristics of colored surface waters. Jour. A.W.W.A.
55:753-770.
Dohrmann. 1984. Automated Laboratory Total Organic Carbon Analyzer. Xertex Corporation. Santa
Clara, CA.
Driscoll, C.T. Personal communication. Department of Civil Engineering, 237 Hinds Hall, Syracuse
University, Syracuse, NY 13244, U.S.A.
Driscoll, C.T. 1984. A procedure for the fractionation of aqueous aluminum in dilute acidic waters.
Intemat. J. Environ. Analyt. Chem. 16:267-284.
Drouse, S.K., D.C. Hillman, L.W. Creelman, J.F. Potter, and S.J. Simon. 1985. National Surface Water
Survey, Eastern Lake Survey (Phase IA) Quality Assurance Plan. Internal Report. U.S.
Environmental Protection Agency, Las Vegas, Nevada. 213 pp.
Fishman, M.J., and L.C. Friedman. 1985. Methods for Determination of Inorganic Substances in Water
and Fluvial Sediments. Open-File Report 85-495. U.S. Geological Survey, Denver, CO. 709 pp.
Glass, G., pers. comm., U.S. EPA Environmental Research Laboratory, 6201 Congdon Boulevard,
Duluth, MN 55804, U.S.A.
Gran, G. 1950. Determination of the equivalence point in potentiometric titrations. Acta Chem. Scan.
4:559-577.
Gran, G. 1952. Determination of the equivalence point in potentiometric titrations. Part 2. Analyst
77:661-671.
Hillman, D.C., J.F. Potter, and S.J. Simon. 1986. National Surface Water Survey, Eastern Lake Survey -
Phase I, Analytical Methods Manual. EPA/600/4-86/009. U.S. Environmental Protection Agency,
Las Vegas, NV.
Newell, A.D., C.F. Powers, and S.J. Christie. 1987. Analysis of Data from Long-Term Monitoring of
Lakes. EPA/600/4-87/014. U.S. Environmental Protection Agency, Washington, D.C. 150pp.
O'Dell, J.W., J.D. Pfaff, M.E. Gales, and G.D. McKee. 1984. Technical Addition to Methods for the
Chemical Analysis of Water and Wastes, Method 300.0, The Determination of Inorganic Anions in
10-1 QAPIan
-------
Water by Ion Chromatography. EPA-600/4-85-017. U.S. Environmental Protection Agency,
Cincinnati, Ohio. i
OI Corporation. 1984. Ol Model 700 total organic
-------
APPENDIX A1
WORKING PROTOCOL FOR SAMPLING, SAMPLE ANALYSIS, AND QA/QC
FOR THE USEPA LONG-TERM SURFACE WATER MONITORING PROGRAM
May 1985
INTRODUCTION
An EPA program for long-term monitoring of lakes and streams was initiated in 1982 within the
NAPAP organizational framework. An ad hoc committee, with representation from USEPA, USGS,
TVA, USD-FS, USFWS, USNPS, and Brookhaven National Laboratory, developed a draft sampling and
analysis protocol to standardize monitoring efforts among the member Task Group E agencies. This
document, with periodic reviews and updates, has served as the standard protocol for the EPA
surface water monitoring program since its inception.
In 1984, EPA initiated the National Surface Water survey (NSWS). This three-phase program is
scheduled to culminate in the selection of geographically representative lakes for long-term monitoring
in the east, upper midwest, and mountainous west. This third phase of NSWS is expected to sub-
sume the existing sites are probably compatible with Phase II owing to their location in low alkalinity
regions and their positioning with respect to minimization of extraneous effects that could compromise
interpretations of observed changes or trends.
The methods manual developed for NSWS (Hiliman et al.( 1986) has been used, together with
the Task Group E sampling and analysis protocol document, to produce the present "working pro-
tocol" for the Long-Term Monitoring Project. Laboratory analytical methodology, detection limits, and
QA/QC procedures are more adequately and precisely specified; site selection criteria are not
included. The objective has been to align the long-term monitoring methodology with that of NSWS,
without undue disruption of existing monitoring procedures. The present document replaces the Task
Group E protocol (Aquatic Effects Task Group, March 1984 revised) as the procedural document for
the EPA monitoring program. Participating agencies and institutions must be able to demonstrate
their use of these or equivalent sampling, analysis, and QA/QC procedures. Audits will be conducted
to determine compliance with these procedures.
This document recognizes that U.S. Geological Survey protocols used in their stream research
and monitoring program are not necessarily identical with those set forth here. By prior agreement
with the EPA project officer, USGS protocols are acceptable in the existing cooperative EPA-USGS
stream studies. Differences are few, and are noted where appropriate in this document. The USGS
laboratory at Denver, where samples from the cooperative studies are analyzed, is a participant in the
NSWS. Therefore, there should be no differences in laboratory analytical methodology.
1 This appendix contains the QA plan and sampling methods that were used by LTM cooperators
from May 1985 until the current QA Plan (this document) was completed in February 1989.
A-1 QA Plan
-------
1.0 COLLECTION OF SAMPLES IN THE FIELD
1 .1 Lakes
Lakes should be sampled near their deepest points (af least 20 m from shore if possible). If the
water column Is not thermally stratified, one sample should be collected approximately one-half meter
beneath the water surface. If the water body is stijatified, one sample should be collected approxi-
mately one-half meter beneath the water surface ahd a second sample one or two meters above the
bottom. These two samples should not be mixed or composited. A plastic closing sampling device of
the Van Dom type should be used to obtain samples at depth; do not use a metal sampler. Samples
should be collected from the sampling device in plastic bottles that have been treated as described in
3.0. (See 6.1 regarding replicate samples.)
1.2 Streams
Samples are obtained by hand as near mid-stream as possible, using a properly cleaned and
rinsed plastic container of appropriate size. (See 6.1 regarding replicate samples.) Keep hands away
from the mouth of the container,- and minimize the number of people handling the samples.
1.3 Carefully record any observed conditions that might affect analysis or interpretation of samples in
field notes or sampling log. ;
1.4 Key project personnel who are responsible for sample integrity must be identified.
2.0 MEASUREMENTS
A set of "core" measurements are specified for the EPA monitoring program. These measure-
ments, which are considered to provide sufficient |characterization of stream or lake water quality for
assessment of sensitivity and changes related to acidification, are:
i
pH (field and laboratory air equilibrated) '
total alkalinity
specific conductance ,
temperature
Secchi disk transparency (lakes) :
true color
major cations (Ca, Mg, Na, K) ;
A-2 QA Plan
-------
major anions (SO4, NO3, Cl)
total aluminum (filtered)
Additional measurements, including titrated acidity, DIG, DOC, F", Fe, Mn, NH4, SiO2, and total P, are
being made by the NSWS; some of these analyses, while not required, are also being made by some
cooperators in the monitoring program.
Care must be taken to assure that the highest quality deionized water is used throughout all
stages of sampling and analysis. Specific conductance of such water should not exceed 1.0 S/cm.
3.0 SAMPLE CONTAINERS
3.1 Type
Containers should be composed of high-density, linear polyethylene, with polypropylene caps
(do not use polyseal caps).
3.2 Cleaning of Plastic Containers
3.2.1 Containers to be used for pH, acidity, alkalinity, and anion determinations will be rinsed three
times with deionized water, filled with deionized water, and allowed to stand for 48 hours, then
emptied and sealed in clean plastic bags until used in the field.
3.2.2 Sample containers for cations and metals will be rinsed three times with deionized water, rinsed
three times with 3N HNO3 (prepared from Baker Instra-Analyzed HNO3 or equivalent), then rinsed six
times with deionized water. They will then be filled with deionized water and allowed to stand for 48
hours. They are then emptied, capped, and placed in clean plastic bags.
3.2.3 After the initial cleaning, 5% of the containers will be checked by filling with deionized water,
capping, and slowly rotating the container so water touches all surfaces. Check conductivity; if
greater than 1 |iS/cm in any of the checked containers, rerinse all containers and retest 5%.
4.0 SAMPLE FILTRATION
4.1 For anion analysis (including SO4, NO3, Cl): Rinse a cleaned 250-mL bottle three times with
sample water which has been filtered directly into the sample bottle (discarding each rinse). Then fill
A-3 QA Plan
-------
I
to 250 mL with filtered sample. Use a 0.45-nm pop size membrane filter (e.g., Nucleopore polycar-
bonate or cellulose acetate). Ice or refrigerate. Ajgood portable unit for filtering samples at field sites
Is described by Kennedy et al., 1976. >
i'
4.2 For metals and cation analyses (including Caj Mg, Na, K): Filter 100 mL of sample into an acid-
washed bottle (see 3.2.2) after rinsing three times :by passing 100 mL of sample through a 0.45-|im
filter and discarding each rinse. Add a 1-mL ampoule of concentrated ultrapure nitric acid (Baker
Ultrex or equivalent) to the sample. Ice or refrigerate.
4.2.1 U.S. Geological Survey presently uses 0.1 -
(They are conducting comparisons of various pore
filters for Al, Fe, and Mn in their stream work.
sizes.)
4.3 Samples for pH, alkalinity, specific conductance, and true color are not filtered.
5.0 SAMPLE PRESERVATION AND MAXIMUM HOLDING TIMES
5.1 Refrigeration at 4°C is the only recommended method of preservation for the following constitu-
ents. (Maximum allowable holding times appear ih parentheses.) For present purposes, icing must
be considered equivalent to 4°C refrigeration.
specific conductance (14 days) !
color (48 hours) !
pH (no approved holding time; field sample should be analyzed immediately, and air-
equilibrated laboratory samples as soon as possible)
alkalinity (14 days, according to NSWS protocol
sulfate (28 days)
chloride (28 days) ;
silica (28 days)
nitrate-nitrogen (7 days)
5.2 Refrigeration at 4°C plus acidification with nitric acid to pH < 2.0 is recommended for the
following constituents: ;
calcium (6 months)
magnesium (6 months) ;
sodium (6 months)
A-4
QA Plan
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potassium (6 months)
aluminum (6 months)
5.3 Labels on all containers should include sufficient information to permit tracing the sample back to
point and time of collection.
6.0 QA/QC SAMPLES: LAKES
Replicate samples, filtration blanks, and container blanks (total of four additional samples) are to be
obtained once for approximately every 10 lakes sampled, as described in 6.1, 6.2, and 6.3. These are
minimum requirements. For each project, this results in the following:
No. of Rep/Blank Sets
Project
University of Minnesota
University of Maine
Vermont
Syracuse University
TVA
USGS Colorado
Per Sampling Interval*
3 (1 /state)
1
2
2
1
2
Per Year
9
3
8
8
4
6
* Sampling intervals are: Minnesota, Maine - spring, summer, fall; Vermont, TVA - spring, summer,
fall, winter; Syracuse - quarterly (17 lakes are sampled monthly, 2 rep/blank sets per quarter);
Colorado - monthly, summer only.
6.1 Replicate Samples
Obtain a replicate sample by repeating step 1.1 or 1.2. These replicate samples are analyzed to
determine the adequacy of the sampling process in obtaining a representative sample of the lake or
stream at a particular point in time.
6.2 Filtration Blanks
Prepare two filtration blanks by filtration of deionized water into properly cleaned (1) anion
container (3.2.1) and (2) cation container (3.2.2). Analysis of the filtrate for the appropriate ions
determines the adequacy of the filtration process and the cleanliness of the sample containers.
A-5 QA Plan
-------
6.3 Container Blanks ;
Prepare one unfiltered container blank by filling a properly cleaned container (see 3.2.1) with
deionized water. Analysis of this sample for pH, alkalinity, specific conductance, and strong/weak
acidity (if applicable) provides a check on the adequacy of the container.
[
6.4 EPA-USGS Cooperative Stream Monitoring Projects
Replicate samples, filtration blanks, and container blanks will be taken at the primary (intensive)
stream site each time that site is sampled. In addition, replicates will be obtained on two satellite
streams three times yearly under low, intermediate, and high flow conditions.
7.0 MEASUREMENT METHODS ;
7.1 pH
7.1.1 Field Measurement
Measure as soon after collection as possible. pH should be measured to ± 0.02 units
using a high-quality pH meter with an expanded or digital scale. A good electrode is the Corning No.
i
476182 glass combination or the Ross Model 81-02. The electrode should be calibrated in the field in
pH 4.0 and 7.0 buffer solutions and checked with a sulfuric acid solution with a theoretical pH of 4.0
(5 x 10"5 molar H^O^. Rinse probe copiously with sample or deionized water and immerse in the
sample. Do not stir. The electrode should remain Jin the sample until there is no discernible drift in
the pH reading, but no longer than 15 minutes. At least 10% of the samples must be measured in
replicate. Upon completion of measurement of a sample batch, recheck the pH of the acid solution.
7.1.2 Laboratory (Air Equilibrated) Measurement
For normalization of pH values obtained by various participating investigators, air-equilibrated pH
measurements should be obtained in the laboratory. Equilibration is achieved by bubbling samples
i
with standard air containing 300 ppm CO2 for 20 rpinutes while stirring on a magnetic stirrer. Use an
acid-washed (see 3.2.2) fritted glass diffuser for dispersal of air in the sample. Measure pH
immediately following equilibration, following the procedure in 7.1.1. At least 10% of the samples
must be measured in replicate (Hillman et al., 1986).
7.2 Specific Conductance (nS/cm at 25°C)
Measured in the laboratory using a wheatstone bridge type conductivity meter. See 8.2 for cali-
bration and QA/QC instructions (Hillman et al., 1986).
4-6
QA Plan
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7.3 True Color
Comparison of centrifuged sample with platinum-cobalt color standards (U.S. EPA, 1979).
7.4 Total Alkalinity
Titration with 0.020 NH2SO4 using Gran plot calculations. Fixed endpoint titration is not accep-
table (Gran, 1950, 1952; Golterman and Clymo, 1969; Zimmerman and Harvey, 1978-1979; Hillman et
al., 1986).
7.5 Calcium, Magnesium, Sodium, and Potassium
Atomic absorption spectrometry, direct aspiration (U.S. EPA, 1979).
7.6 Sulfate, Chloride, Nitrate
Ion chromatography (Hillman et al., 1986).
7.7 Aluminum, Total Filtered
Graphite furnace atomic absorption (EPA Method 202.2) (Hillman et al., 1986; U.S. EPA, 1979).
7.8 Phosphorus, Total
Colorimetric, automated, block digestor AAII (U.S. EPA, 1979), or USGS colorimetric,
phosphomolybdate, automated (Hillman et al., 1986).
7.9 Ammonium
Colorimetric, automated phenate (U.S. EPA, 1979).
7.10 Kieldahl Nitrogen
Colorimetric, automated phenate (U.S. EPA, 1979).
7.11 Table 1 states desired minimum analytical detection limits and within-laboratory relative precision
goals.
8.0 QUALITY CONTROL PROCEDURES
Procedures normally followed by participants in the Long-Term Monitoring Project should be
continued. The intent of this section is to ensure the common use of standardized quality control
A-7 QA Plan
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Table 1. Required Minimum Analytical Detection Limits, Within-Laboratory Relative Precision, and
Bias Limits3
Parameter13
Acidity
Alkalinity
Al, total
Ca
cr
Color
DIG
DOC
F
Fe
K
Mg
Mn
Na
NK,
• "' *A
N03
pH, field
pH, lab
SiO
SO4
Specific
conductance
Total P
Units
fieq/L
fteqlL
mg/L
fieq/L
/ueq/L
ALPH units
mg/L
mg/L
fteq/L
mg/L
/ieq/L
mg/L
ueq/L
ueq/L
/weq/L
pH units
pH units
mg/L
jueq/L
fiS/cm
mg/L
* Some listed measurements may not apply
Dissolved Ions and
1 Inlncc rMhnrwTcA ni
metals are determined,
rrtnd. this is the relative
Required
Detection
Limit
5
5
0.005
0.5
0.3
0
0.05
0.1
0.3
0.01
0.3
0.8
0.01
0.4
0.6
0.1
—
~
0.05
1.0
6
0.002
i
to the existing Long-Term
except where noted.
orecision at concentration
Intralab
Relative
Precision
Goal (%)°
10
10
10 (Al>0.01)
20 (Al<0.01)
5
5
i-d
± 5
10
5 (DOC>5)
10 (DOC<5)
5
10
5
5
10
5
5
10
± 0.1d
± 0.05d
5
5
1
10 (P>0.01)
20 (P<0.01)
Monitoring Project (see 2.0).
s above about 10 times instrumental
Bias
Upper
Limit (%)
10
10
10/20
10
10
~~
10
10
10
10
10
10
10
10
10
10
—
10
10
10/20
detection limit:
Absoluts precision goal in terms of applicable units.
Blank must be < 1.0 ftS/om.
A-8
QA Plan
-------
procedures for comparability of results. Any of the procedures given here that are not now being
followed by cooperating agencies or institutions should be added to their QA/QC programs.
8.1 Precision and Accuracy
8.1.1 Precision
8.1.1.1 Definition
Precision is a measure of agreement among individual measurements of the same property,
under prescribed similar conditions. In this project, we recognize (1) intralaboratory precision and
(2) sampling and analysis precision.
8.1.1.2 Intralaboratory Precision
Intralaboratory precision is determined by analyzing an individual sample in replicate. This
should be done for at least one sample per batch for each variable being measured. The difference
between the two resultant values is multiplied by 0.89 to approximate the standard deviation. The
standard deviation divided by the mean of the duplicate values and multiplied by 100 yields the
relative standard deviation (RSD) in percent. The RSD is an operational statistic (also called the
coefficient of variation) indicating the dispersion of a set of replicate measurements as a percentage of
the mean value. In reporting precision for a given variable, show the number of replicate analyses,
range of RSD values, and average RSD.
8.1.1.3 Sampling and Analysis Precision
Sampling precision cannot be estimated directly. However, the precision in the combined
sampling and analysis procedure can be estimated from the analysis of the duplicate samples taken in
the field (see 6.1). Then the sampling variance can be estimated by subtracting the analytical
variance obtained in 8.1.1.2. The precision in the combined sampling and analysis operation is
estimated by applying the same methodology described for intralaboratory precision (8.1.1.2).
8.1.2 Accuracy
8.1.2.1 Definition
Accuracy is a measure of the closeness of an individual measurement or an average of a
number of measurements to the true values. Accuracy includes both precision and recovery and can
be expressed as a percent recovery or percent bias interval.
8.1.2.2 Evaluation of Accuracy
Two approaches are specified:
A-9 QA Plan
-------
8.1.2.2.1 Fortify an actual sample with a known amount of material, analyze the fortified (spiked)
sample, and calculate the percent recovery. This should be done for at least one sample per batch
for each variable being measured. In reporting accuracy for a given variable, show the number of
spiked analyses, concentration of spike, range of bias (+ and - percent), and average bias (+ or -
percent).
8.1.2.2.2 Audit samples are provided three times each year by an independent contractor. Analysis
results are compared with the known concentrations to determine (1) intralaboratory bias and (2)
comparability of measurements among the various monitoring projects.
8.2 Cautions Regarding Specific Conductance and Alkalinity
8.2.1 Specific Conductance
After calibration and before measuring the first sample, measure the conductance of a QC
standard. The standard should have a theoretical or certified conductance of about 50 /iS/cm
(0.00050000 M KCl has a conductance of 73.90 pS/cm at 25°C). It must be prepared from a stock
solution that is different from that from which the calibration standard is prepared. If the measured
conductivity is not within ± 1% of the certified value, then restandardize the meter and cell and
repeat the measurement.
Remeasure the conductance of the QC standard at least once every 10 samples. One sample
per batch must be measured in duplicate.
8.2.2 Alkalinity
At least 10% of alkalinity titrations must be run in replicate. Agreement must be ± 10%
or less. If not, run a third determination.
8.3 Further Procedural Checks
Once each variable in a sample has been determined, there are several procedures which must
be followed to check the correctness of the analyses. These are outlined below.
8.3.1 Cation-Anlon Balance
Theoretically, the sum of equivalents of anions equals the sum of equivalents of cations in a
sample. In practice, this rarely occurs due to analytical variability and ions which are present but not
measured. For each sample, the sums of the measured anion and cation equivalents, total ion
strength, and ion percent difference are calculated as follows:
2 anions = [CP] + [F] + [NO3'] + [SO42'] + [HCO31 + [CO32']
A-1 o QA Plan
-------
2 cations = [Na+] + [K+] + [Ca2+] + [Mg2+] + [NH4+] + [H+]
2 anions - 2 cations
% ion difference = x 100
2 anions + 2 cations
Total ion strength = 2 anions + 2 cations
Omission of F", CO32", and NH4+ will not significantly affect results. Alkalinity plus H+ (calculated from
pH) may be used for HCO3".
All concentrations are expressed as microequivalents/liter (ueq/L). Table 2 lists factors for
converting mg/L to neq/L for each of the parameters.
Samples that have-a poor ion balance may have to be reanalyzed. Table 3 lists the reanalysis
criteria.
8.3.2 Specific Conductance Balance
An estimate of the specific conductance of a sample can be calculated by summing the
equivalent conductance values for each measured ion at infinite dilution.
The calculated conductance is determined by multiplying the concentration for each ion (in
by the appropriate factor (F) in Table 4.
The calculated conductance for the entire sample is obtained from the relationship,
2 (F x Cone, in /*eq/L)
Calculated conductance = : x 100
1000
The percent difference between measured conductance and calculated conductance if given by:
Calculated - Measured
% conductance difference = x 100
Measured
Samples that have percent conductance differences exceeding the limits listed in Table 3 may
have to be reanalyzed.
A-11 QAPIan
-------
Table 2. Factors to Convert mg/L to peq/L
Ion
Ca2+
cr
co32-
F~
K+
Mg2+
Na+
NH4+
N03-
so42-
Alkalinity
(as CaCO3
Factor
(«eq/L per mg/L)
49.9
28.2
33.3
52.6
25.6
82.3
43.5
55.4
16.1
20.8
20.0
A-12 QAPIan
-------
Table 3. Chemical Reanalysis Criteria
B.
Cation-Anion Balance
Total Ion Strength (aeq/L)
< 50
;» 50 < 100
> 100
Calculated vs. Measured Conductance
Measured Conductance (aS/cm)
< 5
S 5 <30
> 30
% Ion Difference8
> ± 60
> ± 30
> ± 15
% Conductance Difference3
>50
> 30
> 20
If the percent difference exceeds these values, the sample is reanalyzed. When reanalysis is indicated, the data for each
parameter are examined for possible analytical error. Any suspect parameters are then reanalyzed and the above percent
differences recalculated.
Table 4. Conductance Factors (F) of Ions
Ion8
Conductance
(fiS/cm at 25°C)
Ion8
a LJ +
H+ and OH calculated as: [H+] = 10-pH x 106 jieq/L.
Conductance
(aS/cm at 25°C)
per fteq/L
Ca2+
Mg2+
Na+
K+
H+
NH4+
0.052
0.047
0.049
0.072
0.350
0.075
N03-
cr
so42~
Hco3-
OH~
0.071
0.076
0.074
0.044
0.198
A-13
QA Plan
-------
REFERENCES
Aquatic Effects Task Group. March 1984 (revised). Sampling and analysis protocol for long-term
monitoring of lakes and streams relative to effects of acidic deposition. Draft document. 18 pp.
Golterman, J.L., and R.S. Clymo. 1969. Methods for chemical analysis of fresh waters. IBP
Handbook No. 8, International Biological Program, Blackwell Scientific Publishers, Oxford and
Edinburgh.
Gran, G. 1950. Determination of the equivalence point in potentiometric titrations. Acta Chem. Scan.
4:559-577.
1952. Determination of the equivalence point in potentiometric titrations. Part 2. Analyst
77:661-671.
Hillman, D.C., J.F. Potter, and S.J. Simon. 1986. National Surface Water Survey, Eastern Lake Survey -
Phase I, Analytical Methods Manual. EPA/600/4-86/009. U.S. Environmental Protection Agency,
Las Vegas, NV. 157 pp.
Kennedy, V.C., E.A. Jenne, and J.M. Burchard. 1976. Backflushing filters for field processing of water
samples prior to trace element analysis. U.S. Geological Survey, Water Resources
Investigations, Open File Report 76-126. 12 pp.
Maclnnes, D.A. 1961. The principles of electrochemistry. .Dover Publications, Inc., New York.
U.S. EPA. 1979. Methods for chemical analysis of water and wastes. Environmental Monitoring and
Support Laboratory, Office of Research and Development, U.S. Environmental Protection
Agency, Cincinnati. EPA/600/4-79/020.
Zimmerman, A.P., and H.H. Harvey. 1978-1979. Final report on sensitivity to acidification of waters of
Ontario and neighboring states. University of Toronto. 136pp.
A-14
QA Plan
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APPENDIX B
LABORATORY NOTEBOOK GUIDELINES
ENVIRONMENTAL RESEARCH LABORATORY - CORVALLIS
RESEARCH NOTEBOOK GUIDANCE
1. ERL-C staff should use bound pre-numbered laboratory and field notebooks. If it is necessary to use
loose data sheets, the sheets should be consecutively numbered and bound before storage in the
archive.
2. Notebooks with carbon-copy pages are recommended. It is recognized that carbon-copy notebooks are
not suitable for use by all laboratory staff. A notebook system that is appropriate for staff use should be
determined in consultation with quality assurance (QA) staff. Non-carbon notebooks are to be
photocopied and stored in a location different from the storage area of the orginial notebook. Carbon
copies should be bound and stored in a location different from the original notebook.
3. Archive storage procedures for laboratory notebooks should be determined by the project officer at the
beginning of the project and managed within each project. The length of time archive records should be
retained and the location of archive storage should be defined in the project's quality assurance project
plan (QAPP).
4. Notebook entries should be made in ink and each entry dated. Mistakes should be crossed out with a
single line and initialed. Exceptions to this rule will be determined by the project officer and will be
made with the agreement of the QA staff.
5. Spaces and pages left blank should be crossed out to prevent entries from being made at a later time.
Dates of entry should be provided on each page.
6. Project staff working in a shared notebook should initial and date each entry. The full name and initials
of each person sharing the notebook should appear at the beginning of the notebook. Persons with the
same initials should determine a convention to differentiate between entries.
7. Pages should not be removed from any notebook.
8. Supporting records can be included in the laboratory notebook. These records should be attached with
glue, staples, or tape. Attached records should be signed and dated to overlay both the page and the
record so that removed records can be identified.
9. Supporting results and conclusions (e.g., computer printouts, data sheets, calibration records) should be
referenced in sufficient detail to allow retrieval of the record.
B-1 QA Plan
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-------
PART II:
LONG-TERM MONITORING PROJECT
DATA DICTIONARY
December 1991
By
Avis D. Newell
Randy Hjort
ManTech Environmental Technology, Inc.
c/o U.S. EPA Environmental Research Laboratory
200 S.W. 35th Street
Corvallis, OR 97333
-------
-------
SECTION 1
INTRODUCTION
1.1 INTRODUCTION
This document is a guide for data users of the United States Environmental Protection
Agency's (EPA) Long-Term Monitoring (LTM) Project data base. This dictionary describes the
data base, lists and defines the variables included in the data base, and briefly describes the LTM
Project.
The LTM Project was intended to measure chemical trends in surface waters of the United
States expected to be susceptible to acidification from acidic deposition (see Section 2 of this
document, Newell et al., 1987; Newell, in press). The lakes and streams were chosen in clusters,
across sulfate and hydrogen ion depositional gradients, in different geographic regions of the
United States. Sites for which data already existed were chosen preferentially, in an effort to
extend the period of record for the program. Water chemistry and limited hydrologic data are col-
lected at the LTM sites. These data comprise the data base described here.
1.2 DATA DICTIONARY DESCRIPTION
This document is organized to suit two purposes: (1) to provide background information
about the project and the data set for the user, and (2) to concentrate specific information about
the variables in the data set in easily accessed sections for quick reference. Changes in the
methods used to collect the data, and the resulting data substitutions, may affect interpretation of
the data. Thus the data user is strongly urged to read this report carefully before using the data.
Section 2 provides background information about the LTM Project, including information
about the cooperators who participate in the project and the numbers of lakes and streams
included in the data set. The remainder of the document describes the structure of the LTM data
base and provides information pertaining to the use of the data. The project guidelines for data
quality are outlined in Section 3. In order to identify as many erroneous data as possible, both
the individual cooperators and the EPA have validated the data. The procedures followed in this"
process are described in Section 3, followed by a description of the data substitutions made and
the tags assigned to the data as a result of the validation process. Section 4 contains information
about the data base itself, including a description of the structure of the data set, the variables
1-1 Data Dictionary
-------
Included, and definitions of the variables. Appendix A is a list of the sites included in the data set;
Appendix B lists the period of record for each variable in each region. The QA Plan, describing
the procedures used in the LTM Project and the requirements for data quality, is included as Part
I of this publication.
1-2
Data Dictionary
-------
SECTION 2
PROJECT DESCRIPTION
The LTM Project was initiated by the EPA in 1983, under the National Acid Precipitation
Assessment Program (NAPAP). A committee of representatives from several federal agencies
produced a protocol for the project. Sampling was initiated in the fall of 1983, with personnel
from state and federal agencies and universities cooperating to complete the sampling and
chemical analyses (Table 2-1). Although the data are now available to the public, the LTM
cooperators have requested that researchers using this data base contact the cooperator who
collected the data of interest. A contact person and respective address for each region are
included in Table 2-1. In addition to the professional courtesy extended, contact with the investi-
gators most familiar with the sites and data will yield valuable information to any one interested in
the LTM data.
Surface waters in several regions of the country are included in the LTM project (Figure
2-1):
• 5 Tunk Mountain watershed Lakes in Maine
• 24 lakes throughout the state of Vermont
• 16 lakes in the Adirondack region of New York
• 28 lakes in the Upper Midwest (UMW), including northeastern Minnesota (4 lakes),
northcentral Wisconsin (13 lakes), and the Upper Peninsula of Michigan (11 lakes)
• 10 lakes in the Mt. Zirkel (4 lakes) and Weminuche (6 lakes) Wilderness areas of
Colorado
• 7 streams in the Catskill region of New York
In four other regions, sampling was conducted briefly and then discontinued-lakes in
Montana and the Southern Blue Ridge region of the southeast, and streams in Pennsylvania and
the Sand Hills of North Carolina. The period of record is too short, however, for data from these
regions to be included in the data base. More details about the lakes monitored and the
methods used can be found in Newell et al. (1987) and in the QA plan included in this volume.
Although LTM funding was not available until the fall of 1983, prior data were available for
many of the lakes monitored in the LTM Project, from monitoring programs already established in
2-1 Data Dictionary
-------
ts
LU
3
tr
a
I
z
E
g
z
o
S
S
cc
ui
I
3
O)
S
Ul
C^l
a
m
§
III
I
£ E
II il
•P Q)
o> a.
§>
o> o
|8 |!
CO i- S •
*ft 30
± 2 juS/cm if cond.
5% if cond. > 25
± 5 Pt-Co units
5
± 2 (teq/L if NO3 <
10% if NO3 S: 15
5
5
5
5
5
20 if Al < 50 ,«g/L
10 if Al > 50^g/L
; 30
< 25
-
10
15 10
10
10
10
10
10
20 if Al <
10 if Al >
10
5
50 ftg/L
50 fiQlL
Additional measurements
DIG
DOC
F
NH4+
SiO2
mg/L
mg/L
fieq/L
/ieq/L
mg/L
0.05
0.1
0.3
0.6
0.05
10
10 if DOC < 5 mg/L
5 if DOC > 5 mg/L
5
5
5
10
10
10
10
10
Expressed as percent relative standard deviation (standard deviation divided by the mean) when concentrations
measure at least 10 times above instrumental detection limits, unless concentration range is noted, or if ±
units appear, as plus or minus the specified number of units.
Expressed as percent difference from a reference value.
Blank must be < 2.0 /
-------
plans for approval. Since then, an overall QA plan has been adopted that allows for procedural
flexibility among regions. Each region has specified criteria for data validation checks, such as
ion balances and conductance ratios, which are listed in the LTM QA plan. Each cooperator is
responsible for ensuring that these criteria are met, with annual EPA review of the required QA
data.
3.2 DATA VALIDATION
Spurious contamination, analytical errors, and reporting errors can lead to incorrect data
values that do not reflect the natural variation of the surface water represented and that can affect
statistical analyses and interpretation. Many of these errors can be identified through careful
examination of the data. This examination, termed data validation, is a process of checking for
internal consistency among the data values. Ion balances, intervariable relationships, and
comparison to other data collected at the same sites leads to identification of questionable data
values (Section 3.2.1). In this data base, data values clearly in error have been removed. Each
analytical variable in the data set has an affiliated tag variable, and the value of the tag variable
indicates whether the datum has been removed as a result of the validation process (Section
3.2.2) or replaced.
3.2.1 Outlier Identification
Strong relationships among variables can be examined to identify data points that are in
error. Table 3-2 lists the several types of relationships that are inspected. Scatter diagrams can
be used to identify outliers, despite the lack of a linear relationship, by identifying points that are
far away from the majority of points. Linear regression can be used to quantitatively identify
outlying data points of linear relationships by examining those points that lie more than 2.5 times
the studentized residuals from the predicted values. Outliers on the histograms of ion ratios and
ion differences are those that occur at values beyond the acceptable values for those variables,
as defined in the LTM QA plan.
Univariate distributions of the data from each lake over time are also inspected for outliers.
Many of the chemical data do not follow a normal distribution, thus not all "outliers" identified in
comparing the data to normality are in error. However, inspection of these distributions can help
to identify the variable in error in a bivariate relationship, and can suggest further investigation of
data, such as SiO2 or Al, for which there are not good binomial relationships.
3-3 Data Dictionary
-------
TABLE 3-2. RELATIONSHIPS USED IN DATA VALIDATION PROCEDURES
Scatter diagrams
pH vs. ANC
Anion deficit vs. DOC
Anion deficit vs. organic ion
Potentially linear relationships3
Mg vs. Cr
Mg vs. S042'
Na vs. Of
Na vs. SO/' "
Ca vs. Cl"
Cavs. SO42"
Generally linear relationships
ANC vs. Ca
ANC vs. Ca + Mg
Mg vs. Ca
Calculated (Kanciruk, 1985) vs. measured conductance
Sum of cations vs. organic ion + sum of anions
Histograms for inspection
Ratio of sum of anions : sum of cations
Ion difference (sum of anions — sum of cations)
Box and whisker diagrams
Each chemical variable across all lakes, by each sampling event
Each chemical variable across all sampling events for each lake
These are often not linear when one variable has a small range of concentrations across all lakes within a region, as
commonly occurs for Cl" at sites distant from the coast.
3-4 Data Dictionary
-------
The procedure developed for identifying questionable data values was as follows. Plots of
the. relationships listed in Table 3-2 were inspected. For linear relationships, data values outside
of 2.5 studentized residuals from the regression were listed in a data file. Outliers for histograms
and nonlinear relationships were identified by eye, and also included in the outlier data file.
Comment fields in this data file indicated which relationship led to the identification of the
questionable data point. Sorting the list by observation number allowed data points that were
outliers in more than one relationship to be identified. Each of these observations was examined
to determine which if any of the variables should be tagged. Grouping the data as described
facilitated this effort. The list of outliers was then compared to plots of the data over time, to see
if all suspicious data had been identified. The final list of suspicious data was sent to each
cooperator for inspection with resultant confirmation or correction of erroneous data.
Several of the suspicious points were caused by analytical problems, and these were
tagged and excluded from trend analysis (see Section 4). Others resulted from sampling
conditions, such as sampling under ice or just after a storm, and they were maintained in the data
set. The distinction between analytical and sampling influences was made by the cooperator who
collected the data. Thus, it is imperative to have cooperator input in assigning tags. Deleting
correct data from analyses can have as large an impact on interpretation as including poor data,
because the deletion can greatly affect the variance estimates.
3.2.2 Tag Assignments
Tag variables have been created for each of the variables that require measurement in the
data sets. Tags are assigned to each variable as appropriate, resulting from validation,
substitution, or analysis. The values assigned to the tag variables, called tags, provide
information about the data sources. A list of possible tag values and their meanings appears in
Table 3-3. Three tag values appear in the final data set: 'X,' 'S,' and 'Z. Values that were clearly
in error, but for which no corrective action could be taken, were replaced with missing values in
the final data set, and tagged with an 'X', to indicate that original data existed but were not
acceptable.
The 'Z' tag is used to indicate concentrations that are below the detection limit for a
particular variable. These observations have a value of zero in the data set, in order to
standardize data that are below the detection limit in the LTM data base. The values reported to
the U.S. EPA Environmental Research Laboratory in Corvallis (ERL-C) for these variables ranged
3-5 Data Dictionary
-------
TABLE 3-3. TAG VALUES FOR CHEMICAL VARIABLES IN THE LTM DATA BASE
S This is a substituted value (see Sections 3.2.2, 4)
X This missing value resulted from exclusion of poor data
Z This zero value resulted from a below detection limit response
from zero to the detection limit value, but were censored data; the analytical reading was reported
as either the detection limit value or as zero before the data were submitted to the EPA. The
standard "below detection limit" value chosen for this LTM data base was zero. As a result of
reporting low-level data in this way, the only negative values in the data set are for AMC, a
variable that commonly has negative values in acidic lakes.
A complete list, including definitions, for the data base variables is included in Section 5.
However, in an attempt to reconcile method changes occuring throughout the period of record,
some data values have been replaced with values collected by methods that differ from those
described in the variable definitions. These substitutions may or may not have required a
calibration process; they are described in Section 4 for each region.
The tag value of'S' identifies data where substitutions were made. Substitutions made in
each region (described in Section 4.1) were based on studies where both methods were com-
pared for a single set of samples for each region (Newell and Morrison, in press). By far, the
most common substitution was to use unfiltered data as estimates of the filtered value, in the
manner indicated by the appropriate overlap study. For most of these observations, no values
were changed; the 'S1 is present to indicate that the data were collected under different protocols.
Other method changes included the anion analysis method and the sample collection procedure
used in Vermont. These more commonly required calibration of the data during the substitution
process. These substitutions are briefly described in Section 4.1, and are described in greater
detail in Newell and Morrison (in press).
,3-6
Deita Dictionary
-------
SECTION 4
REGIONAL DATA CHARACTERISTICS
This section provides information on some of the individual characteristics of each regional
data set. Not all chemical variables are measured by each cooperator. Other differences, such
as sample collection method and sampling schedules, occur among cooperators. This section
briefly summarizes the method changes that have occurred in each region and how these
changes were dealt with in the data base, which should be considered in interpretation of the
data set.
4.1 MAINE DATA CHARACTERISTICS
Maine lakes have been monitored since 1982. Samples are taken in spring, summer, and
fall for these lakes. Variables not measured in the Maine lakes include monomeric and organic
Al, DIG, F, NH4+, and Si.
Samples were unfiltered prior to the spring of 1983. Anion data have always been analyzed
by ion chromatography (1C), which has an associated prefilter. However, from spring 1983 on, all
anion samples were first filtered through a 0.4-^m polycarbonate filter. We presumed that due to
the 1C prefilter, there would not be significant differences between filtered and unfiltered anions,
although we do not have data from an overlap study to confirm this. Overlap data do exist for
filtered and unfiltered cation data in these Maine lakes; they indicate that filtration has not had a
significant effect on the measurement of major cation concentrations: Ca, Mg, Na, and K. Thus
for the anions NO3', SO42', and CI", and the cations Ca, Mg, Na, and K, unfiltered data prior to
spring 1983 have been included in the data set under filtered variable names and accompanied
with a substitution tag. There was a significant filtration effect on total Al concentration; therefore,
Al data prior to the spring of 1983 were not included in this data set.
The meter used to measure specific conductance from the project inception through the fall
of 1983 was found to be faulty, and was replaced prior to spring 1984 sampling. These early
data were considered to be unreliable, and are not included in the data set. The missing
conductance values are accompanied by an 'X' tag.
4-1 Data Dictionary
-------
In the summer of 1985, the method of measuring pH in these Maine lakes was changed to the
closed cell method used during the National Surface Water Surveys (NSWS; U.S. EPA, 1989).
Thus pH values prior to this date were not included in the data set.
4.2 VERMONT DATA CHARACTERISTICS
Data have been collected from Vermont lakes since 1980. Samples are taken seasonally,
winter, spring, summer and fall. At the project! inception, some lakes were monitored in alternate
years, thus there is not a complete annual record for every lake. Variables not measured in the
Vermont sampling program are monomeric or organic Al, DIG, DOC, F", NH4+, Si, or SiO2.
Early ANC values were not measured using the Gran titration method, so ANC values are
commonly missing in 1980 and 1981; only Gran ANC values are included in the data, base. Not
all cations and anions were measured during the first two years. Samples from the inception of
the project through the summer of 1984 were not filtered. Studies on filtered and unfiltered split
samples indicated that filtration affected only the analyses for Ca and Na. The Ca and Na
unfiltered values were calibrated (Newell and Morrison, in press), whereas the remaining unfiltered
cation values, Mg and K, were substituted into the data set without applying a calibration
equation.
Anion methods were changed from colorimetric to ion chromatography after the spring
sample in 1985. Overlap studies for SO4a' and Cf provided calibration curves to account for both
the analytical method change and the filtration change concurrently (Newell and Morrison, in
press). These anion value tags have been assigned an 'S' value, indicating that the value in the
data base is a substitute value. Nitrate was not calibrated, nor were early colorimetric values
included in the data base, as the colorimetric data were total NO3' + NO2', and the 1C data
reflected dissolved NO3" concentrations only.
Sample collection methods were also changed in Vermont in early 1985. Early in the
project, a hose sampling method was used to collect integrated-column samples. A 6-m hose
was lowered into the lake, collecting a vertical column of water. In unstratified lakes, hose
sampling probably mimics epilimnetic sampling reasonably well. However, the full 6 m of hose
was filled with sample, regardless of the stratification status of the lake. Thus, for the deeper
lakes, summer samples occasionally contained hypolimnetic water. This sampling method was
changed to use of a Kemmerer sampler during early 1985. In this method, a discrete epilimnetic
4_2 Data Dictionary
-------
sample was taken at 1 m depth, and an additional Kemmerer sample was taken at 1 m above the
lake bottom only when the lake was stratified. Overlapping samples, using both methods at the
same site at the same time, indicated that a bias existed between the two methods, regardless of
stratification status (Newell and Morrison, in press). The bias was found to be significant for ANC,
pH, Ca, and SO42". The hose values for these variables were calibrated according to the equa-
tions described in Newell and Morrison (in press). As all data were calibrated to reflect Kemmerer
epilimnetic samples, the SAMSTRAT variable was given a value of 'EPI.'
4.3 ADIRONDACK DATA CHARACTERISTICS
This data set includes data funded by the EPA (INVEST='LTM') and data funded by EPRI as
part of RILWAS (INVEST='RILWAS'). No method changes occurred in the Adirondack data set,
so tag values reflect either spurious analytical problems ('X') or data below detection limit (Z1).
Samples are taken monthly, although some lakes have been sampled biweekly during spring
runoff. Most lakes are sampled at the lake outlet, with the exception of Little Echo Pond (LAKEID
1A1-107), a seepage lake with no outlet present. Variables not measured in the Adirondack
program are total filtered Al and true color.
4.4 UPPER MIDWEST DATA CHARACTERISTICS
Data have been collected seasonally from many of the Upper Midwest lakes since 1978.
From 1978 until 1983, the data were collected by Gary Glass at the EPA Environmental Research
Laboratory in Duluth, Minnesota (pers. comm.). Analytical and filtration procedures changed
significantly between this and the LTM project, without comparative overlap studies to quantify the
effect of the changes. Thus, the earlier data have not been included in this data base.
Twenty-eight lakes have been monitored seasonally, except in winter, in three Upper
Midwest states: Minnesota, Wisconsin, and Michigan's Upper Peninsula. Samples are taken from
the deepest part of the lake, at a depth of 1 m, with an additional hypolimnetic sample collected
at 1 m above the bottom when a lake is stratified. Monomeric and organic Al, DIG, and Si are not
measured in this LTM project.
Beginning in fall of 1983, aliquots for anions and cations were prepared separately, and
filtered through different filters. Anions were filtered through 0.45-^m cellulose triacetate filters for
the fall 1983 sample. In the spring of 1984, these filters were changed to 0.4-^am polycarbonate
4-3 Data Dictionary
-------
filters. This filter change appeared to have an effect on low concentrations of CI" and NO3". Thus
fall 1983 Cl" and NO3" values have been removed from the data base and tagged with an 'X'.
Cation aliquots were filtered through glass fiber prefilters and 0.1-^m polycarbonate filters
until summer of 1985 when the two-filter sequence was replaced with a single filtration through
0.4-^m polycarbonate filters. Overlap studies, and indeed mere data inspection, showed a
significant filtration effect of the two-filter system on Na and K concentrations. This effect was too
extreme and too variable for the data to be calibrated, thus these values have been removed from
the data base, and tagged with an 'X'. The overlap studies indicated no filtration effects on Ca or
Mg data.
The DOC analytical procedure used in the fall of 1983 was found to be unreliable. It was
modified for the spring 1984 sample analyses. The fall 1983 values are also excluded from the
data set and tagged accordingly.
4.5 COLORADO DATA CHARACTERISTICS
Ten lakes in two wilderness areas of Colorado have been monitored since the summer of
1985. The sampling schedule of these high-elevation lakes (> 3,000 m) differs from the other
regions due to the very long winter season of the mountainous location. Samples are taken
shortly after ice-out in July, during midsummer in August, and before ice cover in September.
Epilimnetic and hypolimnetic Kemmerer sampling is used in four of the Colorado lakes: Elbert,
Seven, Eldorado, and Little Eldorado. Outlet sampling is performed at the remaining sites. The
sampling method is indicated in the value of SAMSTRAT, where EPI reflects epilimnetic sampling,
and OUT refers to outlet sampling. Variables not measured in the Colorado lakes are monomeric
and organic Al and SiO2. The LTM data base contains only the routine samples.
4.6 CATSKILL STREAM DATA CHARACTERISTICS
Funding for Catskill stream monitoring was initiated in 1983. By 1986, another EPA project,
the Episodic Response Project, also funded episodic sampling for three of the same sites. The
Episodic Response Project data base (Wigington, pers. comm.) will contain the episodic data for
two LTM streams, the East Branch of the Neversink, at both the headwater and mid-length
reaches, and High Falls Brook. Variables not measured in the Catskill Stream data set include
4.4 Data Dictionary
-------
monomeric and organic Al, color, DIG, F, NH4+, and Si. Streams are sampled nine times per
year, independent of flow conditions.
Filtration changes have occurred during the period of record for Catskill streams. The available
overlap data do not indicate significant filtration effects in these streams, but not all overlap data
sets are large enough to conclude that no differences exist due to filter changes. Polycarbonate
0.1 Tarn filters were used from the project initiation until August 1988; thereafter aliquots for all
major cations and anions were filtered through 0.4-jum polycarbonate filters (Newell and Morrison,
in press). These filters were used until August 1989, when they were replaced with OA5-jum cellu-
lose ester filters (Newell and Morrison, in press). Polycarbonate (0.1 fim) filters were used to filter
the Al aliquots until August 1989. After this time, 0.2-^m cellulose ester filters were used.
4-5 Data Dictionary
-------
-------
SECTION 5
DATA BASE DESCRIPTION
5.1 SAS DATA BASE
The LTM data are contained in a SAS (SAS, 1985) data base, often referred to as a library,
with separate files for each LTM region. The data base, with member data sets, is described in
Table 5-1. The SAS system easily lends itself to statistical analyses, and has extensive graphics
capabilities. The LTM data base was created from data sets in various formats. SAS data sets
were provided by the Wisconsin Department of Natural Resources (WDNR). Data from other
regions were provided in either ASCII (Maine, Vermont and Colorado lake data) or LOTUS files
(Adirondack data).
The available variables differ somewhat among regions, but the variable lists for all lake data
sets are similar. The variables included in the stream data set differ somewhat from those for the
lakes. Each observation in the data set has a unique identifier (MERGEID), built from the lake or
stream ID, to facilitate correct merges.
Each record in the data set represents a separate analysis of water. Multiple observations
per day at each site may result from samples collected at various depths, or at various times
during a storm in the Catskill streams. Collocated duplicate samples have been averaged for this
data set. The variables and their SAS attributes are listed in Table 5-2 for the lake and stream
files. These variables are defined in detail in alphabetical order in Table 5-3. A list of LTM sites
and locations is presented in Appendix A, and the period of record for each variable for lakes and
streams in each region is listed in Appendix B.
5.2 VARIABLE DEFINITIONS
Table 5-3 contains detailed definitions for each variable. A short description of the analytical
methods or the source of the data is included in the definition. Analytical methods are described
in greater detail in the QA plan, Part I of this publication.
Physical variables, such as watershed and lake area and elevation, were obtained from the
National Surface Water Survey data base (Kanciruk et. al., 1986, 1987). Most of the LTM lakes
were sampled as part of these surveys (Linthurst et al., 1986; Landers et al., 1987). Some of the
Catskill stream sites were also included in the National Stream Survey (Kaufmann et al., 1988).
Data for sites not included in these surveys were obtained from individual cooperators.
5-1 Data Dictionary
-------
TABLE 5-1. NAMES AND ATTRIBUTES OF THE DATA SETS INCLUDED IN THE LTM DATA
BASE
Region
Data Set Name
#Obs
# Variables
Maine
Vermont
Adirondacks
Catskills
Upper Midwest
Colorado
MEDS4
VTDS4
ADDS4
CATDS4
UMWDS4
CODS4
122
843
1,567
431
526
165
62
63
62
60
62
62
# Bytes
68,154
430,817
794,989
228,972
271,366
89,783
5-2
Detta Dictionary
-------
TABLE 5-2. VARIABLES INCLUDED IN THE LAKE AND STREAM DATA SETS8
Variable
ALFIL
ALFILT
ALMON
ALMONT
ALORG
ALORGT
ANC
ANCT
CAFIL
CAFILT
CLFIL
CLFILT
COLTRU
COLTRUT
COND
CONDT
DATSMP
DIG
DICT
DOC
DOCT
ELEV
FFIL
FFILT
FLOW*
FLOWT*
HYDROTYP*
etc.)
INVEST
Type
N
C
N
C
N
C
N
C
N
C
N
C
N
C
N
C
N
N
C
N
C
N
N
C
N
C
C
C
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Format
F8.1
F8.1
F8.1
F8.1
F8.1
F8.1
F8.0
DATE7.
F8.2
F8.1
F8.1
F8.1
F8.5
Label
Total filtered Al, ftg/L
Total filtered Al tag
Monomeric Al, figlL
Monomeric Al tag
Organic Al, fig/L
Organic Al tag
ANC, Acid Neutralizing Capacity, ^aeq/L
ANC tag
Calcium, filtered, fieq/L
Calcium, filtered, tag
Chloride, filtered, fieq/L
Chloride, filtered, tag
True color (PCU)
True color (PCU) tag
Conductance, pS/cm
Conductance tag
Date sampled, ddmmmyy (i.e., 07NOV80)
Dissolved inorganic carbon, mg/L
Dissolved inorganic carbon tag
Dissolved organic carbon, mg/L
Dissolved organic carbon tag
Lake/stream elevation
Fluoride, filtered, ,«eq/L
Fluoride, filtered, tag
Stream discharge, m3/s
Flow tag
Lake hydrologic type (drainage, seepage,
Investigator
This table includes the names of all variables in alphabetical order. The variable name Is under VARIABLE and a short
definition of the variable appears under LABEL TYPE refers to character (C) or numeric (N) values The length of the
variable appears under LENGTH and the SAS format is in the FORMAT column. * = stream data set onlv * = lake
data set only. "
(Continued)
5-3
Data Dictionary
-------
TABLE 5-2. VARIABLES INCLUDED IN THE LAKE AND STREAM DATA SETS3 (Continued)
Variable
KFIL
KFILT
LAKEID*
LAKENAME*
LAKESIZE*
LATDD
LONGDD
MERGEID
MGF1L
MGFILT
MONTH
NAFIL
NAFILT
NH4FIL
NH4FILT
NO3FIL
N03FILT
ORGION
PH
PHT
RT*
SAMDP*
SAMSTRAT*
SAMTYP*
SEASON
SECCHI*
SIFIL
SIFILT
Type
N
C
C
C
N
N
N
C
N
C
N
N
C
N
C
N
C
N
N
C
N
N
C
C
C
N
N
C
Length Format
8 F8.1
8
10
30
8 F8.1
8 F8.4
8 F8.4
15
8 F8.1 :
8
2 F2.0
8 F8.1
8
8 F8.1
8
8 F8.1
8
8
8
8
8
8
4
4
1
8
8
8
Label
Potassium, filtered, fieq/L
Potassium, filtered, tag
NSWS lake identification
Lake name
Lake surface area, ha
Lake latitude, decimal degrees
Lake longitude, decimal degrees
Unique ID number for each observation
Magnesium, filtered, ^eq/L
Magnesium, filtered, tag
Month sampled, 1-12
Sodium, filtered, fieq/L
Sodium, filtered, tag
Ammonium, filtered, ^aeq/L
Ammonium, filtered, tag
Nitrate, filtered, /teq/L
Nitrate, filtered, tag
Estimated organic ion, (Oliver model)
PH
pH tag
Retention time in years
Sample depth, m
Stratum sampled (epi, hypo, etc.)
Sample type, baseflow (B), or episodic (E)
Season sample taken (W, P, U, F)
Secchi depth, m
Silicon, filtered mg/L
Silicon tag
a This table includes the names of all variables in alphabetical order. The variable name is under VARIABLE and a short
definition of the variable appears under LABEL TYPE refers to character (C) or numer.c (N) values. The length of the
v"31""" ' . . _TT _.~, * • i •_ 11 ir/~ion«AT n^inmr> * — rfroam Hata set on v :* = lake
variable appears under LENGTH and the SAS format is in the FORMAT column.
data set only.
•• stream data set only; * = lake
(Continued)
5-4
Data Dictionary
-------
TABLE 5-2. VARIABLES INCLUDED IN THE LAKE AND STREAM FILES3 (Continued)
Variable
SIO2
SIO2T
SO4FIL
SO4FILT
STAGE*
STATE
STREAM*
STREAMID*
TIMSMP*
WSHED
WTEMP
YEAR
Type
N
C
N
C
N
C
C
C
N
N
N
N
Length Format
8 F8.2
8
8 F8.1
8
8
2
8
10
8 HHMM5.;
8 F8.0
8 F8.1
4
Label
Silica, filtered mg/L
Silica tag
Sulfate, filtered, jueq/L
Sulfate, filtered, tag
Stage height in outlet stream, m
State of site location
Stream name
Stream Id number
Time sampled (2400 clock)
Watershed area, ha
Water temperature, deg C
Year sampled
This table includes the names of all variables in alphabetical order. The variable name is under VARIABLE and a short
definition of the variable appears under LABEL TYPE refers to character (C) or numeric (N) values. The length of the
variable appears under LENGTH and the SAS format is in the FORMAT column. * = stream data set only; * = lake
data set only.
5-5
Data Dictionary
-------
TABLE 5-3. VARIABLE DEFINITIONS AND REPORTING UNITS3
Variables
ALFIL
ALF1LT
ALMON
ALMONT
ALORG
ANC
ANCT
CAFIL
CAFILT
CLFIL
CLFILT
COLTRU
COLTRUT
DATSMP
DIG
DICT
DOC
DOCT
ELEV
FFIL
Units
Definition
/cteq/L
fieq/L
fieq/L
PCU
Date?.
mg/L
mg/L
meters
Total filtered aluminum. Graphite furnace atomic absorption (AAS).
Total filtered aluminum tag.
Monomeric aluminum. Hydroxyquinoline extraction into MIBK,
graphite furnace AAS.
Monomeric aluminum tag.
Organic aluminum. Ion exchange column fractionation,
hydroxyquinoline MIBK extraction, graphite furnace AAS.
Acid neutralizing capacity is a measure of the amount of acid
necessary to neutralize the bicarbonate, carbonate, alurninohydroxy
complexes, and other bases in a sample. Gran titration,
ANC tag.
Filtered calcium. Atomic absorption spectrophotometry, with N2O
-flame or La addition.
Filtered calcium tag.
Filtered chloride. Ion chromatography.
Filtered chloride tag.
True color. Visual comparator, filtered or centrifuged sample.
True color tag.
Date sampled (DDMMMYY, i.e., 07NOV80).
Dissolved inorganic carbon. Gas chromatography.
Dissolved inorganic carbon tag.
Dissolved organic carbon. Methods vary among cooperators.
Dissolved organic carbon tag.
Elevation at which sampling site is situated.
Filtered fluoride. Ion selective electrode, or ion chromatography.
* = variable appears in lake data set; * = variable appears in stream data set only.
(Continued)
5-6
Data Dictionary
-------
TABLE 5-3. VARIABLE DEFINITIONS AND REPORTING UNITS3 (Continued)
Variables
Units
Definition
FFILT Filtered fluoride tag.
FLOW* cms Stream discharge, measured in ungauged streams, calculated from
stage:discharge relationships in gauged streams.
FLOWT* Flow tag.
HYDROTYP* Lake hydrologic type.
DRAIN = Drainage: inlets and outlets present, or just outlets
present.
CLOSE = Closed lake: inlets but no outlets.
SEEP = Seepage lake: no inlets and no outlets.
RES = Reservoir: a lake with controlled flow.
INVEST Principal investigator.
KFIL jueq/L Filtered potassium. Air-acetylene flame atomic absorption.
KFILT Filtered potassium tag.
LAKEID* A 7-character lake identification number from the National Surface
Water Survey (Linthurst et al., 1986; Landers et al., 1987). The first
character represents the region, the second character the
subregion, the third character the alkalinity map class, and the last
three digits the assigned lake number.
Lake name as identified by LTM cooperators.
Lake surface area, data from NSWS database.
Latitude expressed as decimal degrees in xx.xxxx format. From
data base.
Longitude expressed as decimal degrees in xxx.xxxx format. From
NSWS data base.
Unique observation number for each observation. Assigned
consecutively to file sorted by lake (LAKEID), date sampled
(DATSMP), depth sampled (SAMDP), and sample type.
MGFIL peq/L Filtered magnesium. Flame atomic absorption with N2O flame or La
addition.
MGFILT Filtered magnesium tag.
LAKENAME
LAKESIZE*
LATDD
NSWS
LONGDD
MERGEID
ha
decimal
degrees
decimal
degrees
= variable appears in lake data set; * = variable appears in stream data set only.
5-7
(Continued)
Data Dictionary
-------
TABLE 5-3. VARIABLE DEFINITIONS AND REPORTING UNITS3 (Continued)
Variables
MONTH
NAF1L
NAFILT
NH4FIL
NH4FILT
N03FIL
N03FILT
ORG10N
Units Definition
Month sampled (1-12)
fteqfL Filtered sodium. Air-acetylene flame atomic absorption.
Filtered sodium tag.
fieq/L Filtered ammonium.
Filtered ammonium tag.
fieq/L Filtered nitrate. Ion chromatography.
Filtered nitrate tag.
Estimated organic ion concentration, from Oliver et al., 1983.
Calculations: ORGION = (konst x DOC x 10)/(konst + 10"pH),
Where: konst = ID''0-96 + °'9 x PH ' a°39 x pH2).
RT*
SAMDP* meters
SAMSTRAT*
SAMTYP*
SEASON
SECCHI
SIFIL
Years Retention time of water in lake. From NSWS data base. Calculated
from lake and watershed area, site depth, and average precipitation
and runoff.
Depth at which sample was taken.
Water stratum sampled:
EPI = Epilimnion.
OUTLET = Sampled in outlet of lake.
HOSE = Epilimnetic sample taken with 6 m hose.
KEMM = Epilimnetic sample taken at 1 m depth with Kemmerer
sampler.
Type of stream sample.
B = baseflow conditions
Season when sample was taken:
W = Winter.
P = sPring.
U = sUmmer.
F = Fall.
meters Secchi depth.
rhg/L Dissolved silicon. Measured by ICP on filtered samples.
* = variable appears In lake data set; * = variable appears in stream data set only.
(Continued)
5-8
Data Dictionary
-------
TABLE 5-3. VARIABLE DEFINITIONS AND REPORTING UNITS3 (Continued)
Variables
Units
Definition
SIFILT
SI 02
SIO2T
SO4FIL
S04F1LT
STAGE*
STATE
STREAM*
STREAMID*
TIMSMP
WSHED
WTEMP
YEAR
mg/L
mg/L
meters
ha
°C
Dissolved silicon tag.
Silica. Measured on filtered sample colorimetrically using molybdate
or heteropoly blue.
Silica tag.
Filtered sulfate. Ion chromatography.
Filtered sulfate tag.
Stage height from staff gauges in outlets.
State where site is located. (Two letter abbreviation).
Stream name, as used by LTM cooperator.
WATSTORE ID code also used as stream ID in this data set.
Time sampled, 2400 hour clock.
Watershed area of lake, from NSWS database.
Water temperature at sample depth.
Year of the date sampled.
= variable appears in lake data set; * = variable appears in stream data set only.
5-9
Data Dictionary
-------
-------
SECTION 6
REFERENCES
Aspila, K.I., ed. 1989. A Manual for Effective Interlaboratory Quality Assurance. NWRI
Contribution 89-99. National Water Research Institute, Canada Centre for Inland Waters,
P.O. Box 5050, Burlington, Ontario L7R 4A6, Canada.
Driscoll, C. Personal communication. Department of Civil and Environmental Engineering,
Syracuse University, Syracuse, NY 13244.
Glass, G. Personal communication. U.S. EPA Environmental Research Laboratory, 6201
Congdon Boulevard, Duluth, MN 55804.
Kanciruk, P., M. Gentry, R. McCord, L. Hook, J. Eilers, and M. Best. 1986. National Surface Water
Survey: Eastern Lake Survey - Phase I Data Base Dictionary. ORNL/TM-10153. Oak Ridge
National Laboratory, Oak Ridge, TN. 85 pp.
Kanciruk, P., M. Gentry, R. McCord, L. Hook, J. Eilers, and M. Best. 1987. National Surface Water
Survey: Western Lake Survey - Phase I Data Base Dictionary. ORNL/TM-10307. Oak Ridge
National Laboratory, Oak Ridge, TN. 77 pp.
Kaufmann, P.R., A.T. Herlihy, J.W. Elwood, M.E. Mitch, W.S. Overton, MJ.Sale, J.J. Messer, K.A.
Cougan, D.V.Peck, K.H. Reckhow, A.J. Kinney, S.J. Christie, D.D. Brown, C.A. Hagley, and
H.I. Jager. 1988. Chemical Characteristics of Streams in the Mid-Atlantic and Southeastern
United States. Volume I: Population Descriptions and Physico-Chemicat Relationships.
EPA/600/3-88/021 a. U.S. Environmental Protection Agency, Washington, D.C. 397 pp.
Landers, D.H., J.M. Eilers, D.F. Brakke, W.S. Overton, R.D. Schonbrod, R.E. Crowe, R.A. Linthurst,
J.M. Omernik, S.A. Teague, and E.P. Meier. 1987. Characteristics of Lakes in the Western
United States. Volume I: Population Descriptions and Physico-Chemical Relationships.
EPA/600/3-86/054A. U.S. Environmental Protection Agency, Washington, D.C. 136 pp.
Linthurst, R.A., D.H. Landers, J.M. Eilers, D.F. Brakke, W.S. Overton, E.P. Meier, and R.E. Crowe.
1986. Characteristics of Lakes in the Eastern United States. Volume I: Population
Descriptions and Physico-Chemical Relationships. EPA/600/4-86/007A. U.S. Environmental
Protection Agency, Washington, D.C. 275 pp.
Newell, A.D. In press. Inter-regional comparison of patterns and trends in surface water acidifi-
cation across the United States. Water Air Soil Pollut. (expected 1992)
Newell, A.D., and M.L. Morrison. In press. Use of overlap studies to evaluate method changes in
water chemistry protocols. Water Air Soil Pollut. (expected 1992)
Newell, A.D., C.F. Powers, and S.J. Christie. 1987. Analysis of Data from Long-Term Monitoring
of Lakes. EPA/600/4-87/014. U.S. Environmental Protection Agency, Washington, D.C. 150
PP-
Oliver, E.G., E.M. Thurman, and R.L. Malcolm. 1983. The contribution of humic substances to
the acidity of colored natural waters. Geochim. Cosmochim. Acta 47:2031-2035.
6-1 Data Dictionary
-------
SAS. 1985. SAS User's Guide: Basics. Version 5 Edition. SAS Institute, Inc., Box 8000, Gary,
NC 27511-8000. 1290pp.
Taylor, J.K. 1987. Quality Assurance of Chemical Measurements. Lewis Publishers, Chelsea, Ml.
328 pp.
U.S. EPA. 1989. Handbook of Methods for Acid Deposition Studies Field Operations for Surface
Water Chemistry. EPA/600/4-89/020. U.S. Environmental Protection Agency, Washington,
D.C.
Watson C R. and A.R. Olsen. 1984. Acid Deposition System (ADS) for Statistical Reporting:
System Design and User's Code Manual. EPA/600/8-84/023. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Wigington, P J. Personal communication. U.S. EPA Environmental Research Laboratory, 200 SW
35th Street, Cotvallis OR 97333.
6-2 Data Dictionary
-------
APPENDIX A
SITES INCLUDED IN THE LTM DATA BASE
This appendix lists the sites in each LTM region. The table includes the site name, the LTM-
NSWS site identification number, the latitude and longitude of the site in decimal degrees, and the
first and last dates of the sampling record for each site.
A-1 Data Dictionary
-------
Site Name
LTM ID
State where
site is
located
Site Site
latitude, longitude,
decimal decimal
degrees degrees
First date
sampled
Last date
sampled
MAINEtLTM LAKES
ANDERSON POND
UTTLE LONG POND
MUD POND
SALMON POND
TILDEN POND
BIG MUD
BIG MUDDY
BOURN
BRANCH
COW MOUNTAIN
FORESTER
GRIFFITH
GROUT
HARDWOOD
HAYSTACK
HOWE
KETTLE
LILY
LITTLE, WINHALL CO.
LITTLE, WOODFORD CO.
UTTLE ROCK
OSMORE
PIGEON
SOUTH. MARLBORO CO.
STAMFORD
STRATTON
SUCKER
SUNSET
UNKNOWN
1E1-131E
1E1-132E
1E1-134E
1E1-135E
1E1-133E
1C1-100E
1C1-108E
1C1-089E
1C1-101E
1C2-075E
1C3-076E
1C1-109E
1C1-090E
1C1-091E
1C1-110E
1C1-112E
1C3-064E
1C1-092E
1C1-094E
1C1-093E
1C1-104E
1C2-073E
1C2-071E
1C3-075E
1C1-095E
1C1-096E
1C1-106E
1C1-097E
1C1-098E
ME
ME
ME
ME
ME
VERMONT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT ,
VT
VT
44.6478
44.6375
44.6330
44.6314
44.6347
LTM LAKES
43.3139
44.7556
43.1055
43.081 1
44.5611
43.0817
43.3022
43.0455
44.4680
42.9167
42.7856
44.2944
43.2342
43.1236
42.9250
43.400
44.3083
44.2458
42.8439
42.8222
43.1042
42.8250
42.9194
44.9097
68.0597
68.0780
68.0908
68.0861
68.0722
72.9305
72.6000
73.0028
73.0186
71 .7028
72.8680
72.9597
72.9458
72.5000
72.9167
72.9875
72.3189
72.7514
72.9417
73.0653
72.9556
72.2792
72.3292
72.7125
73.0653
72.9694
73.1292
72.6833
71.8444
21 NOV82
01 MAY82
01 MAY82
01 MAY82
01 MAY82
10FEB82
18FEB81
17AUG82
30JAN81
23FEB81
05MAR81
21JAN82
20FEB80
28JAN82
20FEB80
20F3B80
05FEB80
19FEB80
21JAN82
20FEB80
01JUL82
05FEB80
07FEB80
19FEB80
27JAN82
20FEB80
05MAR81
19FEB80
21JUN82
19NOV88
19NOV88
19NOV88
19NOV88
19NOV88
24OCT89
230CT89
25OCT89
25OCT89
03OCT89
10OCT89
26OCT89
11OCT89
19OCT89
07NOV89
01 NOV89
18OCT89
10OCT89
08NOV89
02NOV89
24OCT89
13OCT89
17OCT89
310CT89
01 NOV89
08NOV89
02NOV89
31OCT89
04OCT89
ADIRONDACK LTM LAKES
ARBUTUS
BIG MOOSE LAKE
BLACK
1A1-052O
1A1-103O
1A1-071O
NY
NY
NY
43.9875
43.8292
44.4391
74.2417
74.8500
74.2939
19FEB83
30JUN82
30JUN82
26NOV89
25NOV89
25NOV89
A-2
(Continued)
Data Dictionary
-------
Site Name
BUBB LAKE
CASCADE LAKE
CLEAR POND
CONSTABLE
DART LAKE
HEART LAKE
LAKE RONDAXE
LITTLE ECHO POND
MOSS LAKE
OTTER LAKE
SQUASH POND
WEST POND
WINDFALL LAKE
LTM ID
1A1-113O
1A1-105O
1A2-077O
1A1-017O
1A1-106O
1A1-102O
1A1-110O
1A1-107E
1A1-109O
1A2-078O
1A1-111O
1A1-112O
1A1-087O
State where
site is
located
ADIRONDACK
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
Site
latitude,
decimal
degrees
LTM LAKES
43.7708
43.791 1
44.000
43.8333
43.7972
44.1822
43.7639
44.3055
43.7861
43.1880
43.8264
43.81 1 1
43.8110
UPPER MIDWEST LTM
ANDRUS
BASS
BUCKEYE
CAMP TWELVE
CLEAR
CRUISER
CUSINO
GREATER BASS
JOHNSON
KELLY
LAKE CLARA
LOCATOR
LOITEN
LONG (Wl)
LUNA
MCGRATH
MCNEARNEY
MONOCLE
MORGAN
MURRAY
NEVINS
2B3-082E
2B2-043E
2B2-102E
2C1-075E
2C1-074E
2A2-063E
2B2-105E
2C1-065E
2B1-047E
2B3-083E
2C2-058E
2A2-067E
2A2-066E
2C1-073E
2C2-062E
2C1-029E
2B1-048E
2B3-081E
2D3-071E
2B2-101E
2B2-106E
Ml
Ml
Ml
Wl
Wl
MN
Ml
Wl
Wl
Ml
Wl
MN
MN
Wl
Wl
Wl
Ml
Ml
Wl
Ml
Ml
46.7000
46.4639
46.4658
45.9482
45.3667
48.4983
46.4544
45.3569
46.4250
46.4400
45.5122
48.5405
48.5258
45.7167
45.9053
45.7917
46.4264
46.4750
45.7742
46.4708
46.5167
Site
longitude,
decimal
degrees
(Continued)
74.8542
74.8041
73.8222
74.7958
74.8583
73.9694
74.9055
74.3975
74.8500
74.5000
74.8897
74.8792
74.8500
LAKES
85.0403
85.7167
85.7386
89.3706
89.2306
92.8053
86.2583
89.1917
85.0439
85.6458
89.5708
93.0036
92.9233
89.6042
88.9597
89.6444
84.9583
84.6458
88.5430
85.7014
86.2430
First date
sampled
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
30JUN82
12DEC82
30JUN82
30JUN82
05NOV83
04NOV83
03NOV83
14NOV83
08NOV83
01NOV83
03NOV83
08NOV83
05NOV83
05NOV83
08NOV83
01 NOV83
01 NOV83
02MAY84
01NOV83
07NOV83
04NOV83
04NOV83
06NOV83
03NOV83
03NOV83
Last date
sampled
25NOV89
25NOV89
26NOV89
25NOV89
25NOV89
25NOV89
25NOV89
25NOV89
25NOV89
26NOV89
25NOV89
25NOV89
25NOV89
25OCT89
23OCT89
24OCT89
17OCT89
19OCT89
31OCT89
24OCT89
19OCT89
25OCT89
25OCT89
16OCT89
31OCT89
31OCT89
17OCT89
18OCT89
16OCT89
230CT89
23OCT89
18OCT89
23OCT89
24OCT89
A-3
(Continued)
Data Dictionary
-------
Site Name
State where
site is
LTM ID located
Site Site
latitude, longitude,
decimal decimal
degrees degrees
First date
sampled
Last date
sampled
UPPER MIDWEST LTM LAKES (Continued)
NICHOLS
SAND
SHOEPACK
STUART
SUGAR CAMP
SUNSET
VANDERCOOK
BIG ELDORADO LAKE
LAKE ELBERT
LITTLE ELDORADO LAKE
LONG LAKE RESERVOIR
LOWER SUNLIGHT LAKE
SEVEN LAKES
SUMMIT LAKE
UPPER GRIZZLY LAKE
UPPER SUNLIGHT LAKE
WHITE DOME LAKE
2C1069E
2C1-068E
2A2-065E
2B2-103E
2C2-063E
2C1-063E
2C1-064E
4E2-066E
4E1-063E
4E2-067E
4E2-068O
4E2-069O
4E2-009E
4E2-0600
4E3-0650
4E2-070O
4E2-0710
Wl
Wl
MN
Ml
Wl
Wl -
Wl
COLORADO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
46.1039
45.7244
48.5036
46.5900
45.8000
45.9264
45.9819
LTM LAKES
37.7133
40.6341
37.7133
40.4758
37.6344
40.8955
40.5458
37.6219
37.6278
37.7089
89.6875
89.6514
92.8833
85.5097
89.3042
89.3375
89.6869
107.543
106.707
107.546
106.690
107.579
106.681
106.680
107.385
107.580
107.553
07NOV83
07NOV83
01 NOV83
04NOV83
08NOV83
08NOV83
07NOV83
31JUL85
18JUL85
31JUL85
16JUL85
31JUL85
15JUL85
16JUL85
31JUL85
31JUL85
31JUL85
17OCT89
I6OCT89
31OCT89
23OCT89
19OCT89
I9OCT89
I7OCT89
11SEP89
06SEP89
12SEP89
11OCT89
26AUG88
08SEP89
11OCT89
28JUL88
26AUG88
12SEP89
CATSKILL LTM STREAMS
BEAVERKILL
EAST BRANCH NEVERSINK,
HEADWATER
EAST BRANCH NEVERSINK,
MIDREACH
HIGH FALLS BROOK
HOLLOW TREE BROOK
ROUNDOUT CREEK
WOODLAND CREEK
01417820
0143400690
01434010
0143410505
01362342
01364959
01362285
NY
NY
NY
NY
NY
NY
NY
42.0172
41.9725
41 .9633
41 .9758
41.1422
41.9367
42.0394
74.5819
74.4485
74.4553
74.5219
74.2653
74.3764
74.3336
03NOV83
13JUN84
15AUG83
15AUG83
21JAN85
16AUG83
15AUG83
07DEC89
26JUN89
08DEC89
23JUN89
05DEC89
02JAN90
05DEC89
A-4
Data Dictionary
-------
APPENDIX B
PERIOD OF RECORD FOR THE VARIABLES IN EACH LTM REGION
The period of record for each variable is identified for each region in this appendix. The
table was constructed by obtaining the first and last dates of the period of record for all sites
within each region, thus each site within the region does not necessarily have data for the entire
period identified. For definitions of the variables listed here, see Table 5-3.
B-1 Data Dictionary
-------
LISTING OF THE CHEMICAL VARIABLES IN THE LAKE AND STREAM FILES AND THEIR
PERIODS OF RECORD IN EACH LTM REGION
Variable
ALFIL
ALMON
ALORG
ANC
CAFIL
CLFIL
COLTRU
COND
DIG
DOC
FFIL
FLOW
KFIL
MGFIL
NAFIL
NH4FIL
NO3FIL
ORGIQN
Maine
02APR83
14NOV89
01MAY82
14NOV89
01MAY82
14NOV89
01MAY82
14NOV89
07JUL83
14NOV89
27APR84
14NOV89
08NOV85
14NOV89
01MAY82
14NOV89
01MAY82
14NOV89
01MAY82
14NOV89
01MAY82
14NOV89
08NOV85
14NOV89
Adirondacks
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
Vermont
25JAN83
08NOV89
;
27JAN81
08NOV89
05FEB80
08NOV89
12JUN80
08NOV89
05FEB80 ;
08NOV89
12JUN80
08NOV89
13JUL82
08NOV89
05FEB80
08NOV89
13JUL82
08NOV89
04JUN85
08NOV89
Upper Midwest
01 NOV83
31OCT89
01NOV83
310CT89
01 NOV83
31OCT89
01 MAY84
31OCT89
01 NOV83
31OCT89
01 NOV83
31OCT89
01 MAY84
31OCT89
01 NOV83
31OCT89
15JUL85
31OCT89
01NOV83
31OCT89
15JUL85
31 OCT89
01 NOV83
31 OCT89
01MAY84
31OCT89
01NOV83
31OCT89
Colorado
15JUL85
12SEP89
15JUL85
11OCT89
15JUL85
11OCT89
15JUL85
11OCT89
15JUL86
12SEP89
15JUL85
11OCT89
15JUL85
12SEP89
15JUL85
12SEP89
15JUL85
11OCT89
15JUL85
11OCT89
15JUL85
11OCT89
15JUL85
11OCT89
15JUL85
12SEP89
15JUL85
11OCT89
15JUL85
12SEP89
Catskills
15AUG83
13SEP89
15AUG83
02JAN90
15AUG83
08DEC89
15AUG83
02JAN90
15AUG83
08DEC89
15AUG83
29OCT89
15AUG83
05DEC89
15AUG83
08DEC89
15AUG83
08DEC89
15AUG83
08DEC89
15AUG83
02JAN90
15AUG83
29OCT89
(Continued)
B-2
Data Dictionary
-------
LISTING OF THE CHEMICAL VARIABLES IN THE LAKE AND STREAM FILES AND THEIR
PERIODS OF RECORD IN EACH LTM REGION (Continued)
Variable
PH
SECCHI
SIFIL
SIO2
SO4FIL
STAGE
WTEMP
Maine
28APR86
14NOV89
01 MAY82
14NOV89
27APR84
14NOV89
01 MAY82
14NOV89
01MAY82
14NOV89
Adirondacks
30JUN82
26NOV89
30JUN82
26NOV89
30JUN82
26NOV89
29JUL82
26NOV89
30JUN82
26NOV89
Vermont
05FEB80
08NOV89
06MAY82
08NOV89
12JUN80
08NOV89
05MAR81
08NOV89
Upper Midwest Colorado
01NOV83 15JUL85
31OCT89 11OCT89
15JUL85
11OCT89
01 NOV83
31 OCT89
01NOV83 15JUL85
31OCT89 11OCT89
15JUL85
11OCT89
Catskills
15AUG83
08DEC89
15AUG83
08DEC89
15AUG83
02JAN90
B-3
Data Dictionary
-------
-------
-------
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