United States
Environmental Protection
Agency
Office ol Acid Deposition, Environmental
Monitoring and Quality Assurance
Washington DC 20460
EPA/600/4-89/029
July 1989
Research and Development
xEPA
Eastern Lake Survey - Phase II
Quality Assurance Report
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EPA/600/4-89/029
July 1989
Eastern Lake Survey - Phase II
Quality Assurance Report
by
I.E. Mitchell-Hall, A.C. Neale, S.G. Paulsen, and J.E. Pollard
A Contribution to the
National Acid Precipitation Assessment Program
U.S. Environmental Protection Agency
Office of Modeling, Monitoring Systems, and Quality Assurance
Office of Ecological Processes and Effects Research
Office of Research and Development
Washington, D.C. 20460
Environmental Monitoring Systems Laboratory, Las Vegas, Nevada 89193
Environmental Research Laboratory, Corvallis, Oregon 97333
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Notice
The information in this document has been funded wholly or in part by the United States
Environmental Protection Agency under Contract Number 68-03-3249 to Lockheed Engineering &
Sciences Company (formerly Lockheed Engineering and Management Services Company) and
Cooperative Agreement Number 812189-03 to the Environmental Research Center of the University
of Nevada at Las Vegas. This document has been subject to the Agency's peer and administrative
review, and it has been approved for publication as an EPA report.
Mention of corporation names, trade names, or commercial products does not constitute
endorsement or recommendation for use.
This document is one volume of a set which fully describes the Eastern Lake Survey - Phase
II. The complete document set includes the major data report1, quality assurance plan, analytical
methods manual, field operations report, processing laboratory operations report, and quality
assurance report. Similar sets are being produced for each Aquatic Effects Research Program
component project. Colored covers, artwork, and the use of the project name in the document title
serve to identify each companion document set.
The correct citation of this document is:
Mitchell-Hall, T. E.2, A C. Neale2, S. G. Paulsen3. and J. E. Pollard2. 1989. Eastern Lake Survey -
Phase II: Quality Assurance Report. EPA/600/4-89/029. U. S. Environmental Protection
Agency, Las Vegas, Nevada.
1 pending.
2 Lockheed Engineering & Sciences Company, Las Vegas, Nevada 89119.
3 Environmental Research Center, University of Nevada, Las Vegas, Nevada 89114.
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Abstract
The Eastern Lake Survey - Phase II was designed primarily to assess seasonal variability
in regional surface water chemistry. This report describes and evaluates the quality assurance
program employed during the survey. The operations component included quality assurance and
quality control procedures to ensure that all samples were collected and analyzed consistently and
to estimate the accuracy and precision of the reported values with a known degree of confidence.
The data management component established a means to store and track data; to identify and
correct entry, reporting, and analytical errors; and to keep a record of such changes. The survey
design identified 24 physical and chemical characteristics of lake water for measurement. Data
quality objectives for detectability, accuracy, precision, representativeness, completeness, and
comparability for ELS-II were based on previous related surveys. During data verification and
validation activities, several issues (concentrated primarily on the data from the chloride, nitrate,
sulfate, and alkalinity analyses) prompted a Special Data Assessment. This process produced a
list of recommendations and justifications for changes to be made to the official verified data base.
Overall, the quality assurance program was successful in identifying and resolving a number
of data quality issues and assuring that the data were of known and documented quality. For ELS-
II as a whole, the data are of acceptable quality and every effort was made to correct any
deficiencies. The accuracy and precision of data for four analytes of primary interest in acidic
deposition research (acid neutralizing capacity, pH, nitrate, and sulfate) were close to or better than
the goals set for intralaboratory performance. The representativeness, completeness, and
comparability of the data meet the project objectives. Special attention should be given to the
data quality objectives for surveys with multiple components, including consideration of specific
objectives for each component.
This report is submitted in partial fulfillment of contract number 68-03-3249 by Lockheed
Engineering and Sciences Company under the sponsorship of the U.S. Environmental Protection
Agency. This report covers a field work period from March 25 to May 3, 1986, for the Spring
Seasonal subsurvey; from July 23 to August 11,1986, for the Summer Seasonal subsurvey; and from
October 8 to November 14,1986, for the Fall Seasonal subsurvey. Data verification was completed
in September 1987. The Special Data Assessment began in February 1988 and ended in March 1989.
in
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Contents
Section Page
Notice ii
Abstract Hi
Figures viii
Tables xi
Abbreviations and Acronyms xiii
Acknowledgments xvi
1. Introduction 1
Overview of the Eastern Lake Survey - Phase I and II 1
Eastern Lake Survey - Phase I 1
Eastern Lake Survey - Phase II 2
Survey Participants 3
Eastern Lake Survey - Phase II Documents 4
2. Conclusions and Recommendations 5
Conclusions 5
Detectability 5
Interlaboratory Bias 5
Accuracy and Precision 5
Other Conclusions 5
Recommendations 6
Field and Processing Laboratory Activities 6
Analytical Laboratory Activities 6
Data Evaluation 7
Design of the Quality Assurance Program 7
3. Design and Operations of the Quality Assurance Program 9
Overview 9
Field Operations 9
Processing Laboratory Activities 9
Analytical Laboratory Activities 11
Types of Quality Assurance and Quality Control Samples 12
Data Quality Objectives 12
Quality Assurance System Audits (On-Site Evaluations) 12
Data Base Management 12
Raw Data Base 14
Official Verified Data Base 14
Other Data Bases (Data Bases 3 and 4) 15
Modified Verified Data Base 15
Data Evaluation and Verification 15
Communications 15
Field and Processing Laboratory Data Review 15
Preliminary Quality Assurance Data Review 18
Data Verification 18
Differences Between the Eastern Lake Survey - Phases I and II 19
4. Results of the Quality Assurance Operations 21
Field Operations 21
Processing Laboratory Operations 21
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Contents (continued)
Section Page
Analytical Laboratory Operations 22
Data Review and Verification 23
Dissolved Organic Carbon 24
Total Aluminum 24
Chloride, Sulfate and Nitrate Issues 24
Ammonium Reporting 26
Anion/Cation and Conductivity Balance 26
Recalculation of ANC and BNC 26
Batch Specific Problems 26
5. Special Data Assessment 28
Chloride, Sulfate and Nitrate Assessments 28
Evaluation of Analysis 2 Data 32
Evaluation of Analysis 1 Data 34
Evaluation of Analysis 3 Data 34
Selection Process for Analysis 1, 2, or 3 34
Acid Neutralizing Capacity 35
Sodium 39
Other Modifications 39
Sample Switches 39
Missing Value Reported as Zero 40
Nonrepresentative Reanalysis Removal 40
Miscellaneous Changes 40
6. Assessment of Data Quality 41
Completeness 41
Sample Collection 41
Analytical Measurement 42
Comparability 42
Comparability of Aluminium Methodology 42
Comparability of Lake Access Procedures 42
Nitrate Data Comparability 42
Representativeness 42
Detectability 43
Calculation of Limits 44
System Level Detectability 44
Summary of Detectability 48
Accuracy and Precision 48
Natural Audit Samples 49
Field Routine-Duplicate Pairs 52
Summary of Accuracy and Precision 53
Interlaboratory Bias 53
Other Methods for Assessing Data Quality 55
Comparison of Measured ANC to Calculated Alkalinity Values 55
Charge Balances 58
Conductivity Check 62
Summary of Other Methods of Assessing Data Quality 64
References 68
Appendices
A Summary of Changes Made During the Special Data Assessment 70
B Results of the Decision Process for Chloride, Sulfate, and Nitrate
Assessments 91
C Summary Statistics for Natural Audit Sample Data 98
vi
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Contents (continued)
Section Pago
0 Confidence Interval and Scatter Plots for Natural Audit Sample Data 124
E Summary Statistics and Plots for Field Routine-Duplicate Sample Data 185
F Split Sample Plots for the Interlaboratory Bias Study 229
Glossary 234
VI!
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Figures
Number Page
1 Component Studies of the National Surface Water Survey 2
2 Major geographical subregions of interest for the Eastern Lake Survey - Phase II ... 3
3 Procedures used for data verification, Eastern Lake Survey - Phase II 16
4 Procedures used during the Special Data Assessment to create the modified
verified data base, ELS-II 29
5 Measured ANC versus carbonate alkalinity calculated from closed system
pH and DIG values for routine samples, Spring Seasonal subsurvey, ELS-II 57
6 Measured ANC versus carbonate alkalinity calculated from analytical laboratory
initial pH and QIC values for routine samples, Spring Seasonal subsurvey,
ELS-II 57
7 Measured ANC versus carbonate alkalinity calculated from closed system pH and
OIC values for epilemnetic routine samples, Summer Seasonal subsurvey, ELS-II.... 57
8 Measured ANC versus carbonate alkalinity calculated from analytical laboratory
initial pH and DIC values, for epilemnetic routine samples, Summer Seasonal
subsurvey, ELS-II 57
9 Measured ANC versus carbonate alkalinity calculated from closed system pH and
DIC values for routine samples, Fall Seasonal subsurvey, ELS-II 58
10 Measured ANC versus carbonate alkalinity calculated from analytical laboratory
initial pH and OIC values for routine samples, Fall Seasonal subsurvey, ELS-II 58
11 Measured ANC verus carbonate alkalinity calculated from closed system pH and DIC
values for routine samples, ELS-1 (ELS-II lakes only) 59
12 Measured ANC versus carbonate alkalinity calculated from analytical laboratory
initial pH and OIC values for routine samples, ELS-I (ELS-II lakes only) 59
13 Sum of anions versus sum of cations for all routine samples, Spring Seasonal
subsurvey, ELS-II 60
14 Sum of anions versus sum of cations for routine samples with chloride concentra-
tions of less than 7.0 mg/l_ Spring Seasonal subsurvey, ELS-II 60
15 Sum of anions versus sum of cations for routine samples with chloride concentra-
tions of greater than or equal to 7.0 mg/L, Spring Seasonal subsurvey, ELS-II 60
16 Sum of anions versus sum of cations for subregion 1A, Spring Seasonal
subsurvey, ELS-II 60
Vltl
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Figures (continued)
Number
17 Sum of anions versus sum of cations for subregion 1B, Spring Seasonal
subsurvey, ELS-II 61
18 Sum of anions versus sum of cations for subregion 1C, Spring Seasonal
subsurvey. ELS-II 61
19 Sum of anions versus sum of cations for subregion 10, Spring Seasonal
subsurvey, ELS-II 61
20 Sum of anions versus sum of cations for subregion 1E, Spring Seasonal
subsurvey, ELS-II 61
21 Sum of anions versus sum of cations for ail epilemnetic routine samples,
Summer Seasonal subsurvey, ELS-II 62
22 Sum of anions versus sum of cations for epilemnetic routine samples with
chloride concentrations of less than 7.0 mg/L, Summer Seasonal subsurvey,
ELS-II 62
23 Sum of anions versus sum of cations for epilemnetic routine samples with
chloride concentrations of greater than or equal to 7.0 mg/L, Summer Seasonal
subsurvey, ELS-II 63
24 Sum of anions versus sum of cations for routine samples Fall Seasonal
subsurvey, ELS-II 63
25 Sum of anions versus sum of cation for routine samples, ELS-II (ELS-II
lakes only) 63
26 Measured versus calculated conductivity for all routine samples, Spring
Seasonal subsurvey, ELS-II 63
27 Measured versus calculated conductivity for routine samples with chloride
concentrations of less than 7.0 mg/L, Spring Seasonal subsurvey, ELS-II 64
28 Measured versus calculated conductivity for routine samples with chloride
concentrations of greater than or equal to 7.0 mg/L. Spring Seasonal
subsurvey, ELS-II 64
29 Measured versus calculated conductivity for subregion 1A, Spring Seasonal
subsurvey, ELS-II 65
30 Measured versus calculated conductivity for subregion 1B, Spring Seasonal
subsurvey, ELS-II 65
31 Measured versus calculated conductivity for subregion 1C, Spring Seasonal
subsurvey, ELS-II 65
32 Measured versus calculated conductivity for subregion 10, Spring Seasonal
subsurvey, ELS-II 65
33 Measured versus calculated conductivity for subregion 1E, Spring Seasonal
subsurvey, ELS-II 66
IX
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Figures (continued)
Number
34 Measured versus calculated conductivity for all epitemnette routine samples,
Summer Seasonal subsurvey, ELS-II 66
35 Measured versus calculated conductivity for epitemnetic routine samples with
chloride concentrations of less than 7.0 mg/L, Summer Seasonal subsurvey,
ELS-II 66
36 Measured versus calculated conductivity for epitemnetic routine samples with
chloride concentrations of greater than or equal to 7.0 mg/L, Summer Seasonal
subsurvey, ELS-II 66
37 Measured versus calculated conductivity for all routine samples, Fall Seasonal
subsurvey, ELS-II 67
38 Measured versus calculated conductivity for all routine samples, ELS-1 (ELS-II
lakes only) 67
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Tables
Number
1 Chemical and Physical Variables Measured During the Eastern Lake Survey -
Phase II [[[ 10
2 Sample Collection Time Frames for the Eastern Lake Survey - Phase II ............ 11
3 Sample Codes Used for the Eastern Lake Survey - Phase II .................... 12
4 Quality Assurance and Quality Control Samples Used in the Eastern Lake
Survey-Phase II [[[ 13
5 Data Base Members for the Eastern Lake Survey - Phase II .................... 14
6 Factors Used to Convert mg/L to ^eq/L ................................... 19
7 Factors to Determine the Conductivity of Ions ............................... 19
8 Differences Between the Eastern Lake Survey - Phases I and II ................ 20
9 Recommended Number of Decimal Places for Reporting Data, Eastern Lake
Survey - Phase II [[[ 22
10 Quality Assurance Samples for the Originally Reported and Reanalyses Dissolved
Organic Carbon Data Results for Batches 3517, 3519, 3525, and 3526; Spring
Seasonal Subsurvey, Eastern Lake Survey - Phase II ......................... 25
11 Quality Assurance Samples for the Originally Reported and Reanalyses Total
Aluminum Results for Batches 3508 and 3714; Spring and Fall Seasonal Subsurveys,
Eastern Lake Survey - Phase II ........................................ 25
12 Summary of Samples Diluted for Chloride Analysis; Spring Seasonal Subsurvey,
Eastern Lake Survey - Phase II ........................................ 33
13 Summarized Results of the Decision Process for Chloride. Sulfate, and Nitrate
Using the Decisions Based on Accuracy and Precision; Spring Seasonal Subsurvey,
Eastern Lake Survey - Phase II ........................................ 36
14 Summary Statistics for the Spring Seasonal Subsurvey Chloride, Sulfate, and
Nitrate Data Based on Natural Audit Samples, Eastern Lake Survey - Phase II ...... 37
15 Acid Neutralizing Capacity Recalculations as a Result of the Special Data
Assessment; Spring Seasonal Subsurvey, Eastern Lake Survey - Phase II ......... 38
16 Summary Statists for the Flame Atomic Absorption Spectroscopy and Inductively
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Tables (continued)
Number Pago
17 Contract-Required Detection Limits (CRDL), System Decision, and System
Detection Limits Calculated from Field Blanks for the Three Seasonal
Subsurveys from ELS-II 46
18 Summary Statistics for Field Blank Data, Eastern Lake Survey - Phase II 47
19 Percentage of Routine Samples Having Values Less Than or Equal to the System
Decision Umit 48
20 Analytical Data Quality Objectives for Detectability, Precision, and Accuracy
for the Eastern Lake Survey - Phase II 50
21 Summary of the Analyses of the Split Data and the FN7 and FN8 Audit Samples for
Interlaboratory Bias 56
XII
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Abbreviations and Acronyms
Acronyms
AAS atomic absorption spectroscopy
AERP Aquatic Effects Research Program
APHA American Public Health Association
AQUARIUS Aquatics Quality Assurance Review, Interactive Users' System
ASTM American Society for Testing and Materials
B field blank sample
CADAVERS Computer Aided Data Verification System
CRDL contract required detection limit
D duplicate lake sample
DQO data quality objective
ELS-I Eastern Lake Survey, Phase I
ELS-II Eastern Lake Survey, Phase II
EMSL-LV U.S. Environmental Protection Agency, Environmental Monitoring Systems
Laboratory, Las Vegas, Nevada
EPA U.S. Environmental Protection Agency
ERL-C U.S. Environmental Protection Agency, Environmental Research Laboratory,
Corvallis, Oregon
FIA flow injection analyzer
PL field low synthetic audit type
FN field natural audit type
1C ion chromatograph
ICPES inductively coupled emission spectroscopy
IDL instrument detection limit
LDR linear dynamic range
LESC Lockheed Engineering & Sciences Company
LL laboratory low synthetic audit type
LN laboratory natural audit type
LS laboratory synthetic
MDL method detection limit
MIBK methyl isobutyl ketone
NAPAP National Acid Precipitation Assessment Program
NLS National Lake Survey
NSS National Stream Survey
NSS-I National Stream Survey, Phase I
NSWS National Surface Water Survey
OMMSQA Office of Modelling, Monitoring Systems, and Quality Assurance
PCV pyrocatechol violet
QA quality assurance
QAMS U.S. Environmental Protection Agency, Quality Assurance Management Staff
QC quality control
QCCS quality control check sample
R routine lake sample
RO reverse osmosis
RSD (%) percent relative standard deviation
RWH rainwater high synthetic audit type
RWL rainwater low synthetic audit type
S split sample
XIII
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Acronym*
SAI
SAS
SOP
WLS
Abbreviations and Acronyms (continued)
Systems Applications, Inc.
Statistical Analysis System
standard operating procedures
Western Lake Survey
Variables and Units
Al-ext aluminum, total extractable
Al-dis aluminum, dissolved monomeric
AJ-org aluminum, nonexchangeable monomeric
Al-total aluminum, total
ANC acid neutralizing capacity
BNC base neutralizing capacity
Ca calcium
Cr chloride
CO;,* carbonate
corid specific conductance
cond-in situ specific conductance, measured in the lake at 1.5 meters
DIG dissolved inorganic carbon
DIC-closed DIG, closed system
DIC-eq DIG, equilibrated
DIC-open DIG, open system
DO dissolved oxygen
DOC dissolved organic carbon
eq/L equilvalents per liter
Fe iron
F fluoride, total dissolved
H+ hydrogen ion
HCO3" bicarbonate
K potassium
m meter
mg/L milligrams per liter
Mg magnesium
Mn manganese
moles/L moles per liter
Na sodium
NH«* ammonium
NO3" nitrate
NTU nephelometric turbidity unit
OH" hydroxyl ion
Pw tenth percentile
Px fiftieth percentile
PSO ninetieth percentile
PK ninety-fifth percentile
Pge ninety-ninth percentile
PCU platinum cobalt unit
pH negative logrithm of the hydrogen ion activity
pH-ANC pH, measured before acid titration for ANC
pH-BNC pH, measured before base titration for BNC
pH-closed pH, closed system
pH-eq pH, equilibrated
pH-in situ pH, measured in the lake at 1.5 meters
XIV
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Abbreviations and Acronyms (continued)
Variables and Units
ppm parts per million
P-total phosphorus, total
SiO, silica
SO/- sulfate
X mean
^eq/L microequivalents per liter
pS/cm microSiernens per centimeter
a standard deviation
xv
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Acknowledgements
The completion of this report would not have been possible without the efforts of many
individuals. In particular, the authors wish to acknowledge the technical assistance, contributions,
and editorial comments that were provided by the following people: R. A. Linthurst, R. D.
Schonbrod, and 0. T. Heggem (U.S. Environmental Protection Agency); E. P. Brantly (Research
Triangle Institute); 0. Coffey and T. Whittier (MSI Technology Services Corporation); L W. Creelman
(Radian Corporation); S. C. Hem (U. S. Environmental Protection Agency); D. J. Chaloud, B. N.
Cordova, M. E. Silverstein. D. V. Peck, D. W. Sutton, J. E. Teberg, K. M. Peres, M. L Faber, C. M.
Monaco, D. C. Hillman, F. X Suarez, L A. Stanley, B. C. Hess, S. K. Drouse, J. K. Bartz, G.
Satterwhite, T. Hody, and G. E. Byers (Lockheed Engineering and Sciences Company).
XVI
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Section 1
Introduction
In 1980 Congress passed the Acid Pre-
cipitation Act, thus establishing the Interagen-
cy Task Force on Acid Precipitation. Given a
10-year mandate, the Task Force implemented
the National Acid Precipitation Assessment
Program (NAPAP) to investigate the causes
and effects of acidic deposition.
The U.S. Environmental Protection Agen-
cy (EPA), as a participant in NAPAP, designed
and implemented the Aquatic Effects Research
Program (AERP). The responsibility of AERP is
to assess the effects of acid deposition on
aquatic ecosystems. A major component of
AERP is the National Surface Water Survey
(NSWS). The primary objectives of the NSWS
were to (1) document chemical and biological
changes in lakes and streams that are poten-
tially susceptible to acid deposition and (2)
select representative regions to quantify future
effects of acid deposition on surface waters.
The NSWS is divided into two components:
the National Lake Survey (NLS) and the Nation-
al Stream Survey (NSS). The NLS consists of
the Eastern Lake Survey (ELS), Phases I and
II, and the Western Lake Survey (WLS).
Figure 1 shows the relationships and time
frames of these various projects.
This report presents the results of the
quality assurance program for the Eastern
Lake Survey - Phase II (ELS-II). The ELS-II
was designed primarily to assess seasonal
variability in regional surface water chemistry.
A quality assurance (QA) program was de-
signed for the ELS-II to ensure consistency in
the collection and analysis of samples, to
verify the reported results, and to inform data
users of the quality and potential limitations of
the data base. In addition to summarizing the
results of the QA program for ELS-II, this
report includes an assessment of analytical
data quality.
The major components of ELS-II that will
be discussed in this report are the Spring
Seasonal, Summer Seasonal, and Fall Season-
al subsurveys. However, the assessment of
analytical data quality discussed in this report
also applies to the data generated by the Fall
Variability study. The Fall Variability study,
conducted during the Fall Seasonal subsurvey,
was designed to estimate the sampling vari-
ability associated with the Eastern Lake Sur-
vey - Phase I (ELS-I) Fall Index Site. Other
components of ELS-II which are not evaluated
in this report include: special studies for trace
metals, zooplankton, chlorophyll, anoxic iron
and anoxic manganese, total nitrogen and total
phosphorous, and the Spring Variability Pilot
Study. The Spring Variability Pilot Study was
designed to obtain data describing the spatial
and temporal variability of lake chemistry and
to provide experience in winter sampling tech-
niques during snowmelt.
Overview of the Eastern Lake
Survey - Phases I and II
The Eastern Lake Survey consists of two
phases: Phase I took place in the Fall of 1984
and Phase II was conducted in the spring,
summer, and fall of 1986.
Eastern Lake Survey - Phase I
The ELS-I, a synoptic survey of the
chemistry of 1,798 lakes in the eastern United
States, was designed to obtain a regional data
base of water quality parameters that are
pertinent to evaluating the effects of acidic
deposition. Lakes that were representative of
the southeastern, northeastern, and upper
midwestern regions of the United States were
sampled to provide characteristic information.
The survey was designed to yield information
that was complete and consistent in terms
of the parameters measured and the sampling
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NATIONAL ACID PRECIPITATION
ASSESSMENT PROGRAM (NAPAP)
AQUATIC EFFECTS RESEARCH
PROGRAM (AERP)
[NATIONAL SURFACE WATER SURVEY (NSWS) |
NATIONAL LAKE SURVEY (NLS) NATIONAL STREAM SURVEY (NSS)
EASTERN LAKE
SURVEY - PHASE I
(ELS-I)
1984
WESTERN LAKE
SURVEY
(WLS)
1985
EASTERN LAKE
SURVEY - PHASE II
(ELS-II)
SPRING VARIABILITY
SPRING SEASONAL
SUMMER SEASONAL
FALL SEASONAL
1986
Figure 1. Component studies of the National Surface Water Survey.
and analytical procedures used. Detailed
sampling procedures (Morris et a!., 1986) and
a rigorous QA program (Drouse et al., 1986)
were implemented. The QA program and
quality of the analytical data were evaluated in
Best et al. (1986). The ELS-I program design
and data interpretations are documented in
Linthurst et al. (1986).
Eastern Lake Survey - Phase II
In the ELS-II. a subset of the lakes
sampled in the ELS-I was resampled to as-
sess chemical variability and biological status.
Lakes chosen to be included in the ELS-II
were restricted to those lakes considered
susceptible to acidification.
To assess this chemical variability, a
subset of 150 lakes that had been sampled in
the northeast region during ELS-I was sam-
pled during each of the ELS-II seasonal sub-
surveys. Figure 2 shows the major geographi-
cal subregions of interest for the ELS-II. To
produce comparable data, procedures for
sample collection, sample analysis, and data
reporting were based on the protocols estab-
lished during ELS-I. Details of the ELS-II
survey design are discussed in an internal
document (Kent Thornton, Joan P. Baker,
Kenneth H. Reckhow, Dixon H. Landers, Parker
J. Wigington, Jr.; February 1986; National
Surface Water Survey: Eastern Lake Survey -
Phase II, Research Plan; U.S. Environmental
Protection Agency, Environmental Research
Laboratory, Corvallis, Office of Research and
Development).
The ELS-II was designed to provide
statistically comparable data that could be
extrapolated, with a known degree of confi-
dence, to a regional scale. The ELS-II also
provides information that can be used to
develop correlative, rather than cause-and-
effect, relationships. The specific objectives of
the ELS-II seasonal subsurveys were as
follows:
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1E: MAINE
&
>a
1A: AOIRONOACKS
lypigiMElg:
r"
1C: CENTRAL
NEW ENGLAND
1D: SOUTHERN
NEW ENGLAND
1B: POCONOS/CATSKILLS
Figure 2. Major geographical aubreglona of Interest for the Eastern Lake Survey-Phase II.
• Provide temporal data from a subset of
lakes that were sampled in ELS-1 and that
are characteristic of the overall population
of lakes within a region.
• Use standardized methods of sample and
data collection.
• Measure a complete set of variables
thought to influence or known to be influ-
enced by surface-water acidification.
• Provide data that can be used to quantify
relationships among chemical variables on
a regional basis.
• Provide reliable estimates of the chemical
status of lakes within a specific subregion.
Survey Participants
Various organizations were involved in
the design and implementation of ELS-II. The
EPA Office of Modeling, Monitoring Systems,
and Quality Assurance (OMMSQA) in Washing-
ton, D.C., funded and administered the ELS-II.
The EPA Environmental Research Laboratory in
Corvallis, Oregon (ERL-C), was responsible for
coordinating the survey activities and for
project design, site selection, data validation,
and lake data interpretation. Northrop Ser-
vices, Inc., provided technical services to ERL-
C. The EPA Environmental Monitoring Systems
Laboratory in Las Vegas, Nevada (EMSL-LV).
was responsible for QA and quality control
(QC) activities, sampling and logistical opera-
tions, communications coordination, analytical
support, and QA data interpretation. Lockheed
Engineering & Sciences Company (LESC)
provided support to EMSL-LV in these areas
and also provided experimental performance
audit samples. The U.S. Soil Conservation
Service and other federal and state agencies
assisted in determining land ownership and in
obtaining access to lake sites. Analytical
services for this survey were provided by two
contractor laboratories that are identified in
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this report as Laboratory 1 and Laboratory Z
Performance audit samples were provided by
Radian Corporation (Austin, Texas). Systems
Applications, Inc. (SAI), in San Rafael, Califor-
nia, was responsible for the development and
the management of the ELS-II data bases and
also assisted ERL-C in data interpretation,
statistical programming, and mapping and
other geographical analyses. The EPA Sample
Management Office in Alexandria, Virginia, was
responsible for sample tracking and assess-
ment of analytical laboratory performance to
determine financial compensation.
Eastern Lake Survey - Phase II
Documents
This QA report is one of a number of
publications that describe the ELS-II. The QA
program for the ELS-II is documented in the
QA Plan (Engels et al., 1988). Field operations
are described in Merritt et al. (1988). The
processing laboratory activities are discussed
by Arent et al. (1988). Kerfoot et al. (1988)
documents the analytical methods used during
the ELS-II at the standard operating procedure
(SOP) level of detail. At the time of this publi-
cation, the plans for an ELS-II final report
containing the details of the survey design and
results have not been completed.
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Section 2
Conclusions and Recommendations
This section provides a summary of
conclusions and recommendations based on
descriptions and evaluations of the ELS-II
quality assurance (QA) program discussed in
Sections 3 through 6. For an explanation or
additional details of the premises on which the
following statements are made, the reader
should refer to the appropriate section.
Conclusions
Overall, the QA program was successful
in identifying and resolving a number of data
quality issues. The program also was effec-
tive in assuring that the data were of known
and documented quality. The majority of the
data are of acceptable quality and every effort
was made to correct any deficiencies. The
following subsections list conclusions about
data quality.
Detectability
• Laboratory 1 had consistently higher sys-
tem decision limits than Laboratory 2 for
8 of 21 analytes (nonexchangeable mpno-
meric aluminum, chloride, conductivity,
equilibrated dissolved inorganic carbon
(DIC-eq), initial dissolved inorganic carbon
(DIC-initial), potassium, magnesium, and
sulfate).
• The issue of detectability should be
viewed in the context of the routine sam-
ple values. For example, when a substan-
tial portion of the routine values are well
above the decision limit, then seasonal
differences between laboratories in their
decision limits become less important.
Interlaboratory Bias
• Nineteen of the thirty analytes in the Sum-
mer Seasonal subsurvey exhibited no
Intel-laboratory bias. Only the values for
conductivity, DIC-eq, DIC-initial, and iron
showed consistent interlaboratory bias.
Additionally, values for acid neutralizing
capacity (ANC), calcium, chloride, total dis-
solved fluoride, potassium, equilibrated pH,
and turbidity showed weak evidence for
slight interlaboratory bias.
Accuracy and Precision
• Data quality objectives for accuracy and
precision were defined in percent differ-
ence and percent variability for all concen-
tration ranges of a given analyte (with the
exception of pH). This procedure was not
effective for low ranges of analyte (with-
in 10 times the detection goals). The quali-
ty of analyte values in low concentration
ranges cannot be evaluated using the
preestablished data quality objectives.
• The accuracy and precision of data for
four analytes of primary interest in acidic
deposition research (ANC, pH, nitrate, and
sulfate) were close to or better than the
goals for intralaboratory performance
during the National Surface Water Survey
(NSWS).
• Extractable aluminum data from natural
audits were highly variable. The accuracy
and precision data quality objectives set
for this analyte appear to be too stringent
for current methodological capabilities.
Other Conclusions
• No amount of quality assurance, quality
control, or software will replace a meticu-
lous laboratory.
• At the time of this publication, the only
data qualifiers that remain appropriate in
the modified verified data base are the X
flags (indicating invalid data) which were
the only flags evaluated during the Special
Data Assessment (Section 5). Plans to
update these qualifiers have not been
finalized.
-------
• The representativeness, completeness, and
comparability of the data are adequate for
the project objectives.
• The ELS-II QA program was successful in
identifying and resolving several significant
data quality issues (e.g., acid neutralizing
capacity (ANC) in the Spring Seasonal
subsurvey, sodium data in the Fall Sea-
sonal subsurvey, dissolved organic carbon
in the Spring Seasonal subsurvey) and in
documenting data quality.
• In a few cases, data interpretation may
be limited by considerations of data quali-
ty in terms of precision, accuracy, and
detectability. This may be especially true
for Laboratory 1 analyses of chloride.
• Internal consistency checks of the analyti-
cal results for each sample provided sup-
port to the conclusions based on the other
procedures used to assess the data quali-
ty.
• Both the plots of the sum of anions ver-
sus the sum of cations and of the mea-
sured versus calculated conductivities
illustrate that when Laboratory 1 chloride
values of greater than 7.0 mg/L are elimi-
nated in the plots, almost all data points
fall in close proximity to the 1-to-1 line, for
all seasonal subsurveys.
• When both the plots of the sum of anions
versus the sum of cations and of the
measured versus calculated conductivities
are plotted by subregions for the Spring
Seasonal subsurvey, it is evident that all
data points for subregions 1A (Adiron-
dacks) and 1E (Maine) fall in close proxim-
ity to the 1-to-1 line.
• The results from the Phase I of the East-
ern Lake Survey (ELS-1) and the ELS-II Fall
Seasonal subsurvey appear to be compa-
rable based on internal consistency
checks. ELS-1 and the Fall Seasonal sub-
survey plots for measured ANC versus
calculated carbonate alkalinity, sum of
anions versus sum of cations, and mea-
sured versus calculated conductivity dis-
play similar patterns.
• The plots of measured ANC versus car-
bonate alkalinity calculated by using the
processing laboratory closed-system pH
and DIC measurements show that Labora-
tory 1 (Spring Seasonal subsurvey) mea-
sured ANC results may be slightly lowei
than Laboratory 2 (Fall Seasonal subsur
vey) measured ANC results.
• Analyses of interlaboratory bias and com
parison of the measured ANC versus the
calculated alkalinity indicate a problem
with the analytical laboratory OIC mea-
surements. This problem is not evident ir
alkalinity balance checks when the pro-
cessing laboratory DIC and pH are used.
Closed-system pH and DIC measurements
should be used in data interpretation.
These measurements are less subject to
changes in the dissolved carbon dioxide
concentration between the time of collec-
tion and analysis. Therefore, the closed-
system measurements provide the best
estimates of the in-situ conditions at the
time of sampling.
Recommendations
The experience gained and conclusions
drawn from the ELS-II provided insight for the
following recommendations.
Field and Processing Laboratory
Activities
• The FIA-aluminum method provides more
specific information concerning particular
aluminum species. Because a reliable
FIA-aluminum method has been developed,
eliminating the extractable aluminum meth-
od (MIBK) should be considered. Sole use
of the FIA methods provides specific
aluminum species data, reduces laboratory
cost, and substantially reduces the vol-
ume, handling, and disposal of hazardous
waste in the laboratory.
• Designate an assistant to the field base
coordinator for the purpose of reviewing
and correcting all field data forms before
shipment to the QA staff.
• The simultaneous use of two pH meters
for batches with more than 20 samples
was successful in ELS-II and should be
continued for future surveys. This method
helps ensure that the maximum allowable
holding times are achieved.
Analytical Laboratory Activities
• Analytical laboratories should be required
not only to perform the charge balance
-------
and the conductivity balance checks, but
also to report these results.
• At least two on-site evaluations should
be conducted at the analytical laboratories
during early sample analyses. Follow-up
evaluations may then be performed, when
necessary.
• Dilutions should either be limited to a
certain dilution factor (e.g., 1 to 5) or
dilutions should be required to be per-
formed in a stepwise manner (e.g., 2X, 5X,
10X) with a step after each dilution to
determine if the result is within the range
of the instrument calibration.
• The laboratories should be required to
titrate a QCCS for ANC measurement to
assess the titration capabilities of the
laboratories as well as the ability to mea-
sure pH.
Data Evaluation
• Closed-system pH and DIG measurements
should be used in data interpretation.
These measurements are less subject to
changes in the dissolved carbon dioxide
concentration between the time of collec-
tion and analysis. Therefore, the closed-
system measurements provide the best
estimates of the in-situ conditions at the
time of sampling.
• Perform as much data verification as
possible while the survey is in progress so
that any analytical problems can be identi-
fied early in the process and so that any
necessary reanalysis can be performed
within sample holding times. Early verifi-
cation should include statistical analysis
of the QA data to assess overall data
quality and detect any problems not identi-
fied through sample-by-sample verification.
Early verification should also include inter-
nal consistency data. For example, plot-
ting measured ANC versus calculated
carbonate alkalinity, the sum of anions
versus the sum of cations, and measured
versus calculated conductivities early in
the survey would also help identify prob-
lems.
• Use the system detection limits to deter-
mine the lowest concentrations desirable
for both natural and synthetic audit mate-
rials.
• Name sample types to reflect accurately
the purpose and use of the samples for
future surveys. The use of the term "field
natural audit" is misleading as these audit
samples were not taken into the field.
Field natural audit samples were intro-
duced into the system at the processing
laboratory and can therefore only be used
to assess the components of processing
and analytical laboratory variability. The
term "field natural" was introduced in
previous NSWS surveys when sample
processing was conducted in mobile
processing laboratories (trailers) stationed
at the field base sites. The use of the
term "field natural" is used in this report to
keep terminology consistent with previous
NSWS reports and with the ELS-II QA
Plan.
• View interlaboratory bias in the context of
the magnitude of the seasonal differences
which are of interest.
• Use Laboratory 1 chloride data with cau-
tion, especially at concentrations greater
than 7.0 mg/L
Design of the Quality Assurance
Program
• If the goal of the QA program is quality
management rather than quality documen-
tation, design the QA program for this
goal.
• Develop data quality objectives (DQOs) for
both the total and analytical systems.
Also, define DQOs at low analyte concen-
trations by absolute difference rather than
percent relative standard deviation or
percent difference (Hunt and Wilson).
• Establish data quality objectives for inter-
laboratory bias whenever more than one
laboratory is involved in sample analyses.
Establish within-laboratory control limits
in order to achieve the desired among-
laboratory goal. Consider the use of
analyses of audit samples or collocated
samples (replicate splits of routine sam-
ples) representing a wide range of concen-
trations in order to monitor, assess, and
possibly correct for any biases that occur.
• Design future surveys that have multiple
components (e.g., the spring, summer, and
fall seasonal subsurveys of ELS-II) to
be as consistent as possible across
-------
components. Analytical laboratories
across components should be consistent
or the differences between the laborato-
ries must be quantifiable.
• Evaluate methods designed to improve the
precision and accuracy of the aluminum
speciation and recommend improved
methods for future research.
• Include only double-blind audit samples in
the first two batches in future surveys to
ensure the analytical capabilities of the
laboratory. (The laboratory should have
some type of electronic data transfer to
make rapid data evaluations possible.)
When it is determined that the laboratory
is performing acceptably, the number of
double-blind audit samples should be
optimized during the course of the study.
• Include the laboratory managers in rele-
vant aspects of the planning process in
order to ensure their full understanding
of the scope and needs of the project.
• Design contingency plans (including a
back-up laboratory designation) to accom-
modate possible emergencies in cases of
an analytical laboratory shutdown or if
laboratory performance is unresolvably
inadequate to complete the sample analy-
ses.
• Provide the analytical laboratories with, or
require them to obtain, known perform-
ance standards from a single source so
that all laboratories can calibrate their
measurement systems to a given target
value to reduce interlaboratory bias.
• Provide well-characterized natural audit
samples for use in a QA program. This
characterization is best accomplished by
large-scale round-robin analyses. From a
QA program design perspective, each
laboratory should receive the same num-
ber and type of audit samples.
• Consider any programmatic need to identi-
fy error components within the system
when designing the QA sample distribu-
tion. Well-characterized natural audit
samples can be used to identify sources
of error by inserting them into various
steps of collection, processing, and analy-
sis.
• Specify clearly in the contract the number
of points to be used in the calibration
curve.
• Specify clearly in the contract the required
number of decimal places or significant
figures to which the analytical results
must be reported.
• Use identical software at each laboratory
to calculate the ANC and BNC.
• Include all titration data used to calculate
ANC and BNC part of the raw data base
or require that it at least be available
electronically. If an electronic form of the
titration data is available, then electronic
QC checks of the titration data points can
be performed. These checks are espe-
cially important if the titration was not
performed on an automatic titrator or
whenever the titration data are entered
manually. If a problem is found with the
ANC determinations, the data would be
recoverable if these titration files are
readily available.
• Design verification software to be com-
pletely automated and include in this
software all the data verification checks
so a rapid verification process can be
implemented. This recommendation could
significantly decrease transcription errors
and increase efficiency for both the QA
staff and the analytical laboratory.
-------
Section 3
Design and Operations of the Quality
Assurance Program
Overview
The quality assurance (QA) and quality
control (QC) program is a multifaceted pro-
gram designed to reduce uncertainty in the
data quality. Numerous approaches were
implemented to perform this task. Monitoring
techniques were incorporated at each stage of
collecting, processing, and analyzing the lake
sample. For the purpose of this report, these
stages are referred to collectively as the
system.
One method of monitoring the system
was to introduce QA and QC samples at each
stage. On-site evaluations also were per-
formed to monitor the system. As a reference,
data quality objectives (DQOs) were estab-
lished to determine the relative (compared to
the OQOs) quality of the data. Involvement in
the data base management system was also
part of the QA program.
Other ELS-II companion documents
provide more details for specific subjects. The
QA plan (Engels et al., 1988) describes the
design of the QA program for ELS-II. Kerfoot
et al. (1988) documents the analytical methods
used during the survey at the standard operat-
ing procedure level of detail. The major as-
pects of the operational QA program are
summarized in the following subsections; the
results of the QA program operations are
presented in Section 4.
Field Operations
Field operations, under the supervision of
base coordinators, were conducted from field
base sites. Both helicopter and ground crews
(direct access by boat) performed the sam-
pling activities. A helicopter crew consisted of
a pilot, an observer, and a sampler; a ground
crew consisted of a crew leader and a sam-
pler.
Lake samples were collected for each
seasonal subsurvey from each lake at approxi-
mately the same location and depth (1.5 me-
ters below the water surface), and by the
same sampling protocols as used during ELS-
I. Lake site measurements are summarized in
Table 1. All measurements were recorded on
the lake field data form. The QC protocols for
the field measurements are documented in
Engels et al. (1988). The timeframe of the
sampling activities is given in Table 2.
Processing Laboratory Activities
During earlier NSWS surveys, sample
processing was conducted in mobile process-
ing laboratories (trailers) stationed at field
base sites near the sampling areas. For ELS-
II and NSS-I, the processing facilities were
centrally located in Las Vegas. The Spring
Seasonal subsurvey samples were processed
during the same period of time as the NSS-I
samples. The processing laboratory consisted
of four stations for filtration and aliquot
preparation; two stations for pH, dissolved
inorganic carbon (DIG), true color, and specif-
ic conductivity determinations; one station
for flow-injection analysis (FIA); and one
station for methyl isobutyl-ketone (MIBK)
extraction. Additional stations were also
available for filtration and aliquot preparation.
Analytical measurements that were performed
in the processing laboratory are summarized in
Table 1.
The main function of the processing
laboratory was to process (preserve and
organize into batches) water samples received
from the field and, on the same day, ship the
prepared aliquots to analytical laboratories
for subsequent analyses. Each ELS-II batch
-------
Table 1. Chemical and Physical Variables Measured during the Eastern Lake Survey-Phase II
Variable (units)
Abbreviation*
Instrument
or analytical method"
Field Site
Conductivity (pS/cm)
Depth (m)
Dissolved oxygen (mg/L)
pH. field (pH units)
Temperature (°C)
cond-in situ
DO
pH-field
Hydrolab Surveyor II
Weighted Secchi disk Hydrolab Surveyor H calibrated
cable; Digital Sonar
Hydrolab Surveyor II
Hydrolab Surveyor II
Hydrolab Surveyor II
Processing Laboratory
Aluminum (mg/L)
Total monomeric Al-dis
Nonexchangeable monomeric Al-org
pH. closed system (pH units) pH-closed
Dissolved inorganic carbon, QIC-closed
closed system (mg/L)
True color (PCU)
Turbidity (NTU)
Colorimetry (complexation with pyrocatechol violet,
automated flow injection analyzer); La Chat Quick
Chem System IV Colorimeter
Same as total monomeric
pH/millivolt meter (Ross Model 611), and glass
combination electrode (Orion-Ross Model 8104)
Infrared spectrophotometry (carbon analyzer)
(Dohrmann Model DC-80)
Comparator (Hach Model CO-1)
Nephelometer (Monitek Model 21)
Analytical Laboratory
Acid-neutralizing capacity (peq/L) ANC
Aluminum, (mg/L)
Total extractable Al-ext
Total Al-total
Ammonium (mg/L) NH/
Base-neutralizing capacity (peq/L) BNC
Calcium (mg/L) Ca
Chloride Cl"
Conductivity (pS/cm) Cond
Dissolved inorganic carbon (mg/L)
Initial DIC-initial
Equilibrated DIC-eq
Dissolved organic carbon (mg/L) DOC
Fluoride, total dissolved (mg/L) F'-total
Iron (mg/L) Fe
Acidimetric titration, modified Gran analysis
Atomic absorption spectroscopy (furnace) on
methylisobutyl ketone extract
Atomic absorption spectroscopy (furnace)
Colorimetry (phenate, automated)
Alkalimetric titration, modified Gran analysis
Atomic absorption spectroscopy (flame) or inductive
coupled plasma emission spectroscopy (ICPES)
Ion chromatography
Conductivity cell and meter
Infrared spectrophotometry
Infrared spectrophotometry
Infrared spectrophotometry
Ion-specific electrode
Atomic absorption spectroscopy (flame) or inductively
coupled plasma emission spectroscopy (ICPES)
(Continued)
10
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Table 1. Continued.
Variable (units)
Abbreviation'
Instrument
or analytical method"
Analytical laboratory (continued)
Magnesium (mg/L) Mg
Manganese (mg/L) Mn
Nitrate (mg/L) N
-------
Table 3. Sample Code* Used for the Eastern Lake
Survey - Phase II
Sample type
Code
Description
Routine lake sample
Duplicate lake sample
Field blank sample
Processing laboratory
blank
Processing laboratory
duplicate
Triplicate lake sample
Audit
R
D
B
TB
TD
S
FL1
•—Concei
ntrate lot* number
I—Concentration level/audit
type
L - low
N - natural
S - synthetic
—Type of audit sample
F = field audit sample
L - laboratory audit
sample
RW - simulated rainwa-
ter audit sample
* A lot is a quantity of bulk material of similar composi-
tion whose properties are under study (Taylor, 1987).
analytical laboratories used to measure each
analyte also are listed in Table 1. A detailed
description of these procedures is given in the
methods manual (Kerfoot et al.. 1988). The
ELS-II QA plan and the contract provide a
detailed discussion of laboratory requirements
such as holding times and internal QC proce-
dures. Table 4 includes the description, func-
tion, and frequency of use of the various QC
samples.
Types of Quality Assurance and
Quality Control Samples
The QA program used a variety of QA
and QC samples, the characteristics of which
are summarized in Table 4. QA samples were
double-blind to the analyst (i.e., the analyst did
not know the sample type or the chemical
composition). QC samples were control sam-
ples for which the theoretical or true analyte
concentrations were known by the analyst.
The uses and limitations of the various QA and
QC sample types are discussed in detail in
Section 6 of this report. QA and QC samples
were used by the EMSL-LV QA staff to monitor
activities during the survey, to assist in verifi-
cation, and finally to assess data quality.
Data Quality Objectives
Data quality objectives (DQOs) for ELS-
II were based on previous NSWS DQOs.
DQOs were established for completeness,
comparability.representativeness.detectability,
accuracy, and precision. These terms are
defined and discussed in Section 6, Assess-
ment of Data Quality. Table 20 in Section 6
provides the specific goals for detectability,
accuracy, and precision.
Quality Assurance System
Audits (On-Site Evaluations)
A system audit is a qualitative evaluation
of the field, processing laboratory, and analyti-
cal laboratory facilities, equipment, and opera-
tions such as record keeping, data reporting,
sample analysis, and QC procedures. The
results of the system audits were documented
and submitted to the EPA technical monitor at
EMSL-LV. The questionnaires that were used
by evaluators during system audits can be
found in the ELS-II QA plan (Engels et al.,
1988). The results of the on-site evaluations
are summarized in Section 4.
Data Base Management
The data management system was
designed to assemble, modify, and store data
collected during the NSWS surveys. An inde-
pendent data management company, Systems
Applications, Inc. (SAI), was contracted to
manage the data base generated during ELS-
II. As was the case in each of the previous
NSWS surveys, SAS (Statistical Analysis
System) software was used to manipulate the
data. The ELS-II data bases are SAS data set
libraries or groups of SAS data set libraries.
Each SAS data set library is composed of files
(members) which contain specific types of
survey data (Table 5).
The ELS-II data bases are: the raw
data base (DS1), the official verified data base
(DS2), the validated data base (DS3A), the
reduced validated data base (DS3B), the final
data base (DS4A), and the personal computer
(PC) final data base (DS4B). An additional
data base, the modified verified data base,
was created as a result of the Special Data
Assessment (Section 5).
12
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Table 4. Quality Assurance and Quality Control Sample* Used In the Eastern Lake Survey-Phase II
Sample type
Description
Function
Frequency of use*
Field blank
Processing
laboratory blank
Field duplicate
Quality Aeeurance
Type I reagent-grade deionized
water* subjected to sample col-
lection, processing, and analysis
Type I Reagent-grade deionized
water* subjected to sample pro-
cessing and analysis
Duplicate sample collected im-
mediately after the routine lake
sample
To assess system detectability
and identify possible sample
contamination resulting from
collection, processing and analy-
sis
To verify the quality of the re-
verse osmosis (RO) water purifi-
cation system; to identify possi-
ble processing and analytical
laboratory contamination
To estimate overall withicvbatch
system precision at various con-
centrations ranges; estimate
processing precision.
One per batch
As scheduled
One per batch*
Field performance
audit*
Laboratory
performance audit*
Field triplicate'
Calibration blank
Reagent blank
Quality control check
sample (QCCS)
Detection limit
QCCS
Synthetic or natural lake sam-
ple; prepared at support labora-
tory and processed at process-
ing laboratory
Synthetic or natural lake sam-
ple; prepared and processed at
support laboratory
Triplicate routine sample
Type I reagent-grade deionized
water"
Reagent-grade deionized water*
plus reagents for total aluminum
and silica analyses
Standard solution from source
other than instrument calibration
standard
Standard solution at 2 to 3
times the required detection lim-
To estimate analytical precision
of processing and analytical
laboratory measurements; to
estimate relative accuracy
To estimate analytical precision
of analytical laboratory meas-
urements; to estimate relative
accuracy
To estimate relative intertabor-
atory bias
Quality Control
To identify signal drift
Processing laborato- Split of lake sample
ry duplicate
Analytical laboratory Split of sample aliquot
duplicate
To identify contamination due to
reagents
To determine accuracy and con-
sistency of instrument calibra-
tion; to check statistical con-
trol of measurement process; to
evaluate batch-to-batch pre-
cision and within-batch pre-
cision.
To determine precision and ac-
curacy at tower end of linear
dynamic range of measurement
method; to verify instrument
detection limits
To monitor analytical precision
of processing laboratory mea-
surements
To monitor analytical precision
of analytical laboratory mea-
surements
As scheduled*
As scheduled*
1 or 2 per batch
One per batch for
applicable variables
One per batch for
total aluminum and
silica
Before the first mea-
surement, after the
last, and at speci-
fied intervals in
between for each
batch
One per batch for
applicable variables
One per batch
One per batch
* Planned frequency for use of QA samples was not always possible due to logistical constraints
0 As specified in ASTM (1984).
c The Summer Seasonal subsurvey included a duplicate sample from both the epilimnetic and hypolimmetic layers
of the lake.
d Performance audit samples were provided by a support laboratory.
* The schedule is documented in the ELS-II QA Plan (Engels et al.. 1988).
' Summer Seasonal only.
13
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Table 5. Data Base Members for the Eastern
Survey - Phase II
Member Form number
name within member
Description
M01
F18
F19
F20
F22
10
02
11
18
19
20
22
Lake data
Batch/QC processing labora-
tory data
Summary of sample results
Required and reported instru-
ment detection limits
Sample date of analysis and
holding time Summary
Calibration/reagent blanks
and QCCS results
Internal duplicate results
Raw Data Base
The EMSL-LV QA staff sent copies of the
field forms, the processing laboratory form,
and the analytical laboratory data package to
SAI for entry of the data into the raw data
base. Analytical data packages consisted of
either hard copy or electronic copy of forms 11,
18,19, 20, and 22. These forms are described
in Table 5. In some cases, corrections were
made to the forms after they were received by
SAI but before the data were entered into the
raw data base. Such corrections were made
at the direction of the EMSL-LV QA staff. For
the Spring Seasonal subsurvey, data packages
were submitted on hard copy. During the
Summer Seasonal and Fall Seasonal sub-
surveys, the analytical laboratories used the
Aquatics Data Entry System, which was devel-
oped at EMSL-LV. The analytical laboratory
data packages for the Summer and Fall Sea-
sonal subsurveys were submitted on floppy
diskette. The analytical laboratories sent a
copy of each diskette directly to SAI.
SAI used a PC-based, double-entry
system to ensure correct transcription of data
submitted on paper. Two data entry operators
independently entered data into separate
dBASE III data base files. The two text files
were then compared using software written
by SAI specifically for that purpose. The
program generated a report identifying the
inconsistencies between the two data bases.
Any inconsistencies between the two data
bases were corrected.
SAI used the dBASE III data base files
to write ASCII text files containing the data.
Two different procedures were used to pro-
duce SAS data sets from those text files. PC
SAS was used to create SAS data bases for
the Spring Seasonal subsurvey. That data
base was later uploaded to the SAI mainframe
computer. The text files for the Summer
Seasonal subsurvey and the Fall Seasonal
subsurvey were uploaded to the mainframe.
Mainframe SAS was used to create the Sum-
mer and Fall Seasonal subsurvey SAS data
bases.
Official Verified Data Base
Although SAI was responsible for pro-
ducing the official verified data base, the
EMSL-LV QA staff had primary responsibility
for verifying the data. The Computer-Aided
Data Verification System (CADAVERS) was
developed by the EMSL-LV QA staff to facili-
tate data analysis and data modification.
CADAVERS is a driver system which provides
an environment for data processing and analy-
sis. CADAVERS was referred to as AQUARIUS
III in the ELS-II QA Plan.
SAI sent the raw data base for the
Spring Seasonal subsurvey on floppy diskettes
to Las Vegas where the EMSL-LV QA staff
uploaded the data to the IBM 3090 computer
at the National Computer Center (NCC). SAI
sent the raw data bases for the Summer
Seasonal subsurvey and the Fall Seasonal
subsurvey to NCC on magnetic tape. The
EMSL-LV QA staff then copied them. CADAV-
ERS created an initial working data base for
each subsurvey by copying the raw data base.
All changes to the working data base
were made via transactions. A transaction is
a record which reflects changes made to a
data base. An auditor would generate the
transaction and check it before the working
data base was updated with it. Only the
CADAVERS data base administrator (DBA)
updated the working data base with the trans-
action. Updating the working data base did
two things; it modified the working data base
by incorporating the changes contained in the
transaction and it recorded the transaction in
the history files. The CADAVERS data bases
were protected from unauthorized modification
by internal as well as external security sys-
tems. CADAVERS allowed only the DBA to
modify the working data base and history data
base. Only the auditor or the DBA could
modify the auditor's transaction data base.
CADAVERS provided no facility whatsoever for
any modification of the raw data base. RACF
(Resource Access Control Facility) protected
14
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the data bases from modification by someone
outside the CADAVERS system.
When data verification had been com-
pleted, the EMSL-LV QA staff sent both the
final working data base and the history data
base to SAI via magnetic tape prepared at
NCC. SAI read the tape and produced the
official verified data base by updating a copy
of the raw data base with the history data
base. The official verified data base was then
compared with the working data base sent by
the EMSL-LV QA staff to ensure the integrity of
the data.
Other Data Bases (Data Bases 3
and 4)
Other data bases, i.e., the validated data
base, the reduced validated data base, the
final data base, and the PC final data base,
were produced by SAI under the direction of
ERL-C. The ERL-C staff had primary responsi-
bility for validating the data and directed all
value changes made by SAI during the valida-
tion process. These other data bases do not
reflect the changes made as a result of the
Special Data Assessment (Section 5). At the
time of this publication, plans have not been
finalized to create updated versions of these
data bases.
Modified Verified Data Base
The modified verified data base is the
product of the Special Data Assessment
(Section 5) which occurred after verification
and validation were completed. This data
base was created by much the same process
as discussed in the previous subsection titled
Official Verified Data Base. Through CADAV-
ERS, a working data base was created for
each seasonal subsurvey by copying the
verified data base. All changes were made via
transactions, which were updated at the
working data base by the DBA. All changes
were recorded in the history files. At the time
of this printing, the modified verified data base
exists as a working data base at EMSL-LV. All
changes made between the official verified and
the modified verified data bases are contained
in Appendix A.
Data Evaluation and Verification
The objectives of data evaluation and
verification were to identify and then correct or
qualify suspect data and to maintain continu-
ous control over the data acquisition process.
These objectives were accomplished following
an organized process that included: (1) com-
municating with the field base coordinators
and the laboratories, (2) reviewing field and
processing laboratory data forms, (3) evaluat-
ing preliminary QA data from each analytical
laboratory, (4) checking on the completeness
of each data package and on the consistency
of sample data, (5) confirming or correcting
suspect data, (6) requesting reanalysis of
samples for which data remained suspect
after confirmation or correction, (7) assigning
of data qualifier tags and flags to data when
necessary, and (8) entering the corrected
values and data qualifiers into a copy of the
raw data base for the creation of the verified
data set. Figure 3 shows the processes used
during data evaluation and verification.
Communications
Telephone calls were made to the field
base coordinators as needed to discuss field
sampling QC. Daily telephone calls were made
to the processing laboratory and to each
analytical laboratory to check that QA/QC
guidelines were being followed and that sam-
ples were being handled and analyzed proper-
ly, to obtain preliminary sample data, and to
discuss problems that may have occurred
during analyses. The intent of frequent calls
was to identify and resolve issues before they
affected data quality or interfered with the
completion of the sample analyses.
During data review and verification,
communication with the analytical laboratories
continued to obtain documentation such as
copies of data forms that were incomplete
when originally submitted, corrections of
previously reported data, confirmation of
previously reported results, and results of
reanalysis for samples that previously did not
meet QA and QC criteria. The QA staff often
requested that raw data be submitted to
support the confirmation of data for a particu-
lar sample or batch. Each analytical laborato-
ry was required to submit confirmation and
reanalysis data on a standard form, and the
QA staff tracked requests for these data.
Field and Processing Laboratory
Data Review
All forms received from the field crews
and processing laboratory were reviewed by
the QA staff. This review included the follow-
ing: (1) lake IDs, (2) Hydrolab calibration and
15
-------
o>
PERFORM PRELIMINARY
RECEIVE FIELD,
PROCESSING LAB,ANO
ANALYTICAL LAB DATA
EMSL-LY
ARE
DISCREPANCIES
IDENTIFIED
IS
NEW
VALUE
STILL
SUSPECT
ABE
EDITED
CHANGES
CORRECT
ARE
ORIGINAL
DATA
CONFIRMED
COMPLETE ELS-ll
f ISST PASS VERIFICATION
EPORT WORKSHEET AND
EXCEPTION RECORD
EMSL-l
IS
ORIGINAL
VALUE
STILi.
SUSPECT
REANALYSIS REQUEST
IS
REANALYZE
VALUE OF
BETTER QUALITY
HAN ORIGINA
OR SEW
VALUE
IS
New
VALUE OF
BETTER QUALITY
THAN ORIGINAL
VALUE
-------
RERUN EXCEPTION
GENERATING PROGRAMS
WITH DATA FROM INITIAL
VERIFICATION
EMSL-LV
REVIEW EXCEPTION
RECORDS (FLAGS)
FOR REASONABILITY
EMSL-LV
EDIT A COPY
OF THE RAW BASE
TO INCORPORATE
APPROPRIATE FLAGS
EMSL-LV
INCLUDE IN
HISTORY FILE
EMSL-LV
APPLY HISTORY FILE
OF CHANGES TO COPY
OF THE RAW BASE
AND COMPARE WITH
CHANGED DATA BASE
EMSL-LV
DO
TWO DATA
BASES MATCH
COMPLETE ELS-II
FINAL VERIFICATION
EMSL-LV
WRITE TAPE CONTAINING
WORKING VERIFIED DATA BASE
AND COMPLETE HISTORY FILE
OF CHANGES
EMSL-LV
fSEND VERIFIED^
DATA BASE
TAPE TO SAI
Vy
n
^
CREATE OFFICIAL .
VERIFIED DATA BASE
SAI
Figure 3. Continued.
17
-------
QCCS data, (3) comparison of pH values from
the Hydrolab and processing laboratory, (4)
processing laboratory QC data, (5) data quali-
fiers, and (6) comments made by the sampling
crew and processing laboratory staff. Data
anomalies were reported to the field base
coordinator or the processing laboratory coor-
dinator for review, and data reporting errors
were corrected before entry into the raw data
base.
Preliminary Quality Assurance
Data Review
Preliminary QA data were obtained from
each analytical laboratory via telephone or
computer modem. These data were examined
by QA staff so that analytical problems might
be identified immediately after the samples
had been analyzed and before the data pack-
ages were submitted. Each data package
contained the sample results for one batch of
routine, QA, and QC samples and was due 35
days after the batch had been received by the
laboratory.
Data Verification
After each data package was received,
the ELS-II verification report (Engels et al.,
1988) systematically guided the QA staff
through the verification process. The report is
a worksheet that tracks data resubmissions,
tracks confirmation and reanalysis requests,
lists the steps to explain exceptions, explains
how to flag data, and summarizes modifica-
tions to the raw data base. The report en-
sures consistent review of all data packages.
Each data package was reviewed for
completeness, internal QC compliance, and
appropriate use of data qualifiers. Any dis-
crepancies related to analytical data were
reported to the appropriate analytical laborato-
ry manager for corrective action. Comments
provided in the cover letter also were reviewed
to determine their impact on data quality and
the need for any follow-up action by the QA
staff or the laboratory. The remainder of the
verification report was completed with the use
of data packages, field forms, and printed
output from the exceptions programs. These
computer programs identified samples that did
not meet QA or QC criteria and generated the
appropriate data qualifier flags. A listing and
description of these programs is documented
in Engels et al. (1988).
The verification process was completed
in three steps which were termed first pass,
second pass, and final pass. The exceptions
programs were used in each pass. The first
pass was performed to detect data that did
not meet QA and QC criteria and then to
confirm or correct suspect data or to request
reanalysis of the affected samples. The
second pass was primarily to flag data.
During the final pass, flag changes as a result
of the recalculation of all ANC and BNC data
and reanalysis results not previously available
were incorporated in the data base. For
ELS-II, CADAVERS automated much of the
verification process and provided a mechanism
for editing the data set and applying data
qualifiers. CADAVERS included the exceptions
programs and also provided for updating the
data base and for creating the history file,
which contained all changes made to the data
set.
Exceptions programs were used to verify
all analytical data by individual sample and by
batch. Each sample was checked for both the
anion-cation percent ion balance difference and
the percent conductance difference criteria.
The synthetic audits used for the Fall Seasonal
subsurvey were not designed to meet these
criteria. The percent ion balance difference
was calculated by converting measured values
in milligrams per liter to microequivalents per
liter (see Table 6 for conversion factors) and
then comparing the sum of the measured
anions to the sum of the measured cations.
A calculated conductance value was deter-
mined by multiplying the concentration of each
ion in milligrams per liter by the corresponding
conversion factor (Table 7) and then summing
the individual calculated conductance values.
This total in microSiemens per centimeter then
was compared to the conductivity measure-
ment. If any sample data fell outside the
criteria, the reason for the imbalance was
investigated. Imbalances could indicate ana-
lytical errors, data reporting errors, or the
presence of unmeasured organic species or of
ions measured but not in the algorithm (e.g.,
iron, total aluminum). Any samples for which
the data remained outside the criteria after
confirmation, correction, or reanalysis were
flagged appropriately.
Values for a given analyte were flagged
on a batch basis when the field blanks, field
duplicates, or audits did not meet the QA
criteria. Data also were flagged if inter-
nal duplicate precision, instrumental detec-
tion limits, QCCS results, calibration blanks,
18
-------
Table 6. Factors' Used to Convert mg/L to jieq/L
Ion
Factor
Ion
Factor
Ca"
cr
Mg"
NOj-
F'-total
49.9
28.2
82.3
16.1
52.6
NH/
K*
Na*
so,-3
55.4
25.6
43.5
20.8
* Engels et al. (1988).
Table 7. Factors to Determine the Conductivity of
Ions'*
Ion
Ca"
cr
co,-J
H*
HCO,-
Mg"
Factor
per mg/L
2.60
2.14
2.82
3.5 x 105<:
0.715
3.82
Ion
Na*
NH/
so<-3
NO,-
K*
OH-
Factor
per mg/L
2.13
4.13
1.54
1.15
1.84
1.92 x 105C
' Specific conductance is expressed in /jS/cm at 25 °C.
* Modified from American Public Health Association et al.
(1985) and Weast (1972).
c H* and OH' are converted by factors in moles/L.
reagent blanks, or required holding times did
not meet specifications. An auditor evaluated
all flags generated by the exceptions programs
and decided whether or not each flag was
appropriate. Subsequently, these flags were
reevaluated by a second auditor before they
were entered into the verified data base. In
addition, flags were generated by the protolyte
analysis program. This program performed a
rigorous evaluation of all pH, DIG, DOC, ANC,
and BNC data (Engels et al., 1988). Based on
this rigorous evaluation, the program gener-
ated flags for any suspect results. The list of
all flags that were applied to the data are
given in the ELS-II QA Plan (Engels et al.,
1988). Data users should be aware that
although many of the flags would remain the
same, the flags apply to the official verified
data base and not to the modified verified
data base.
Reanalysis data and corrections sub-
mitted by each laboratory, as well as flags,
were entered into the working data base by
QA staff. All entries were reviewed manually
to check for entry errors. Entry was accom-
plished by using CADAVERS to generate trans-
action files that were updated to the working
data base. This system also generated a
history file which was sent to the data base
manager. The official verified data base (DS2)
was created by applying the history file to the
raw data set.
Differences Between the
Eastern Lake Survey - Phases I
and II
Many concepts developed during ELS-I
were implemented in ELS-II. In addition,
further modifications based on the experience
gained from previous NSWS surveys were
made to improve the ELS-II program. Table 8
summarizes the changes that were imple-
mented in ELS-II.
19
-------
Tcbto 8. Difference* Between the Eastern Lake Survey Phasee I and II
ELS-I
ELS-II
QA program
The matrix spike analysis was required. These
results were used to determine possible matrix
interference in lake chemistry data.
During the preliminary data review, the majority of
data were retreived from the analytical laboratory
over the telephone.
• Synthetic audit samples were available at or two
concentration levels.
The Gran analysis program used a manual system
of outlier elimination.
The protolyte program considered all possible
ANC, BNC pH, and OIC measurements in determin-
ing the calculated ANC.
An Interiaboratory Bias Study was not conducted
during ELS-I.
The "tuple* system of data management was used.
This system was limited in producing the documen-
tation of all changes that were made to a data
set.
Field site
e Helicopters were used to access lakes for sample
collection.
• DO was not measured as an in-situ measurement.
Processing laboratory
e The ELS-I samples were processed in the field
laboratories located near or at the field base site.
• Dissolved and nonexchangeable monomeric
aluminums were not measured in the processing
laboratory.
e Only one pH meter was used per batch of sam-
ples.
Analytical laboratory
• All final data packages were submitted to the QA
staff in the form of a hard copy.
e The precision (% RSD) requirement for conductivity
was 1%.
QA program
e The matrix spike analysis requirement was elimi-
nated from the contract because no matrix interfer-
ence was apparent in the ELS-I (Best et al., 1987)
or WLS (Silverstein et al., 1987) routine lake sam-
ples. Eliminating the spike also resulted in the
reduction of sample analysis costs.
e Daily direct data transfers via electronic media were
implemented (at the point of the sample analysis)
between the analytical laboratories and the QA
staff.
e An improved synthetic audit sample system was
designed that enables precision and accuracy to be
estimated at five concentration levels.
• The Gran analysis program was modified to elimi-
nate outliers electronically.
• In the Phase II. the protolye program was modified
to run faster and to calculate ANC taking into
consideration the partial pressure of carbon dioxide.
e An Interiaboratory Bias Study was conducted in the
Summer Seasonal subsurvey of ELS-II.
• A transaction editing system was developed for the
mainframe computer. This system allowed the QA
staff to edit (change) data more efficiently. The
system also included a history file that contains the
historical record of all value and flag changes.
Field site
• Helicopters and boats were used for lake access.
A comparability study conducted during WLS-I
(Landers et al., 1986) demonstrated there was no
significant difference between the two sampling
methods that would affect data interpretation.
• DO was measured as an in-situ measurement.
Processing laboratory
• The ELS-II samples were processed in one central-
ized processing laboratory.
• The processing laboratory performed dissolved and
nonexchangeable monomeric aluminum measure-
ments.
• Two pH meters were used for one batch when the
batch size was >20 samples. Field routine-dupli-
cate pairs were always analyzed on the same
meter.
Analytical laboratory
• Final data packages from the analytical labora-
tories were submitted electronically (on computer
disks).
• The precision (% RSO) requirement for conductivity
was 2%.
20
-------
Section 4
Results of the Quality Assurance Operations
This section describes field, processing
laboratory, and analytical laboratory operations
including the results of on-site evaluations.
Data review and verification activities will also
be discussed in this section.
Field Operations
Field operations for ELS-II successfully
completed collection and shipping of samples
according to protocol. Details of the field
operations are documented in Merritt and
Sheppe (1988).
On-site evaluations were used to monitor
all field operations. The EMSL-LV QA staff
conducted on-site evaluations of the field
sampling operations during both Summer and
Fall Seasonal subsurveys. The Summer Sea-
sonal subsurvey on-site evaluation was con-
ducted July 27-28, 1986, at two base sites.
The Fall Seasonal subsurvey on-site evaluation
was conducted November 4-5, 1986, at one
base site.
On-site evaluations included inspections
of Hydrolab calibrations, QC procedures,
sample collection activities, in-situ measure-
ment techniques, and sample shipping proce-
dures. Both evaluations concluded that a
checklist for field equipment should be used by
field crews prior to departure from the base
site. During the Fall Seasonal subsurvey the
evaluators observed a problem related to
completing the forms and stressed the neces-
sity of correct and concise completion in
discussion with the base coordinator. The
results of both of the on-site evaluations
concluded that the field sampling personnel,
most of whom had previous experience in
other NSWS projects, were adhering to QA/QC
protocols. None of the findings of the field on-
site evaluations had an adverse effect on data
quality.
Processing Laboratory
Operations
The processing laboratory successfully
processed and analyzed the ELS-II samples.
Samples were prepared for shipment to the
analytical laboratories within the specified
holding times in all cases. The processing
laboratory operations are documented in Arent
et al. (1988).
Two processing laboratory on-site evalu-
ations were performed by the EMSL-LV QA
staff to assure that laboratory procedures and
QA and QC protocols were being performed
properly and to identify and resolve any exist-
ing problems. One evaluation during the
Spring Seasonal subsurvey was conducted on
April 3,1986; a follow-up evaluation prior to the
Summer Seasonal subsurvey (during the sam-
ple processing of NSS-I samples) was con-
ducted on May 13, 1986. The significant re-
sults of the on-site evaluations are as follows.
At the sample filtration station, the non-
acid-washed filtration apparatus was placed in
the middle of a series of acid-washed filtration
apparatus in order to expedite sample pro-
cessing and to allow two technicians to filter
samples simultaneously. The QA auditors
were concerned that there was a potential
nitrate contamination in aliquot 3 when the
squeeze bottle filled with dilute nitric acid
solution was used to rinse the surrounding
apparatus.
During the follow-up on-site evaluation,
the two acid-washed filtration apparatus were
now separated by a larger space from the
non-acid-washed apparatus. However, the
non-acid-washed filtration apparatus was still
placed between the two acid-washed appa-
ratus. Plexiglas shields were installed be-
tween the filtration apparatus to reduce the
21
-------
possibility of contamination before the Summer
Seasonal subsurvey began.
Although the measurement error intro-
duced by Laboratory 1 at low concentrations
(Section 5, Chloride, Sulfate, and Nitrate
Assessments, and Section 6, Detectability)
makes it difficult to evaluate whether any
nitrate contamination occurred, the results of
the on-site evaluations indicated that process-
ing laboratory operations were satisfactory
and that laboratory personnel performed their
duties well.
Analytical Laboratory
Operations
Standard EPA contract laboratory pro-
curement procedures were used to secure the
services of two analytical laboratories to per-
form sample analyses for the ELS-II. Labora-
tory 1 performed the sample analyses for the
Spring Seasonal subsurvey. Laboratory 2
performed the sample analyses for the Fall
Seasonal subsurvey. Both Laboratory 1 and
Laboratory 2 analyzed samples for the Sum-
mer Seasonal subsurvey. During the Summer
Seasonal subsurvey, samples designed to
evaluate interlaboratory bias were analyzed by
both laboratories (Section 6, Interlaboratory
Bias).
Laboratory 1 reported analyses results to
the number of decimal places recommended
under the original ELS-II contract (Table 9) for
the Spring Seasonal subsurvey. However,
early in the subsurvey the laboratory was
asked to comply with the reporting recommen-
dations of a new contract which had not yet
gone into effect. The new contract recom-
mended that the values be reported to the
instrument detection limit (IDL), plus one deci-
mal place, or to a maximum of four significant
figures (Table 9).
The decimal places reported by the labo-
ratory for the Spring Seasonal subsurvey were
as follows:
• Batches 3500 through 3503: Al-ext,
AJ-total, Ca, DIC-eq, QIC-initial, DOC, Fe,
F'-total, K, Mg, Mn, Na, NH4+, P-total, and
SiO2 were reported in accordance with the
original contract for these batches.
• Batches 3504 through 3508: P-total, P-
total, and SiO2 were reported in accord-
ance with the original contract for these
batches.
Table 9. Recommended Number of Decimal Placet
for Reporting Data, Eat Urn Lake Survey-
Phase II
Original New
Analyte Units contract* contract*
Al-ext
Al-total
ANC
BNC
Ca
cr
Cond
DIC-eq
DIC-initial
DOC
F'-total
Fe
K
Mg
Mn
Na
NH4*
NOr
P-total
pH-ANC
pH-BNC
pH-eq
SiO,
so4a-
mg/L
mg/L
/jeqA-
peq/L
mg/L
mg/L
pS/cm
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
pH units
pH units
pH units
mg/L
mg/L
3
3
1
1
2
2
1
2
2
1
3
2
2
2
2
2
2
3
3
2
2
2
2
2
4
4
1
1
3
3
1
3
3
2
4
3
3
3
3
3
3
4
4
2
2
2
3
3
* Laboratory 1 was operating under the original contract
during the Spring Seasonal subsurvey and the new
contract during the Summer Seasonal subsurvey.
However, a request was made for Laboratory 1 to
report data for the Spring Seasonal subsurvey in
accordance with the new contract which would be in
place for the Summer Seasonal subsurvey.
6 Laboratory 2 was operating under the new contract
during the Summer and Fall Seasonal subsurveys.
• Batches 3509 through 3528: P-total and
SiO? were reported in accordance with the
original contract for these batches.
All other analytes not listed above were
reported in accordance with the new contract.
For the Summer and Fall Seasonal subsurveys,
both laboratories reported to the numbers of
decimal places recommended under the new
contract.
An on-site evaluation of Laboratory 1
was conducted on April 7, 1986, by the EMSL-
LV QA staff. At this time. Laboratory 1 was
completing analyses of samples collected
during the Spring Variability Pilot Study
and had begun to analyze Spring Seasonal
samples for some analytes. The laboratory
evaluation resulted in several findings:
22
-------
• Sample receipt, storage, and labelling
procedures were adequate.
• Ion chromatograph (1C): Laboratory 1 had
purchased a Dionex 2000I 1C complete
with autosampler and integrator/recorder.
However, the system was not fully
automated with respect to controlling the
pump and detector. The primary
disadvantage was that it was necessary
to conduct two analyses per sample, i.e.,
one for NO3" and one for CI" and SO^, be-
cause the detector range could not be
adjusted during the analysis. A second
problem was that the integrator was not
fully utilized. The detector was being used
to drive the strip-chart recorder instead of
the integrator. Peak height measurements
and data reduction were performed manu-
ally. Using the integrator as a data
system to perform all measurements and
data reduction would have hastened
analyses and would have reduced the
chance of human error.
• The instrument detection limit for CI' (0.02
mg/L) exceeded the contract-required
detection limit of 0.01 mg/L This problem
was discussed with laboratory personnel
during the audit. The laboratory was
advised to increase the loop size on the
instrument (i.e., the amount of sample
injected into the 1C column). This
recommendation was implemented and
did resolve this particular problem.
• Laboratory 1 was well equipped to analyze
metals. However, the laboratory had
experienced problems with aluminum
contamination in the total aluminum
digestion. The laboratory was advised of
various techniques used by other
laboratories to resolve the problem. These
techniques include the isolation of the
digestion from the atmosphere by
constructing a Plexiglas cover to minimize
dust contaminants or cleaning the hood
where the digestion was being performed.
• Laboratory 1 personnel had not been
preparing QCCS control charts, but instead
had been preparing percent relative
standard deviation (%RSD) control charts
using the internal duplicate data. QCCS
control charts monitor both accuracy and
precision, whereas %RSD control charts
only monitor precision. The laboratory
personnel were advised that QCCS control
charts were contractually required. At this
point in the survey, the laboratory was
brought into compliance with the contract
by initiating the use of QCCS control
charts.
In summary, Laboratory 1 had some
deficiencies at the time of the on-site evalua-
tion but was performing adequately. However,
the purchase of a system controller for the
pump and detector and full utilization of the
integrator for the ion chromatograph would
have improved laboratory efficiency for deter-
mining Cr, SO,2", and NO3\
The EMSL-LV QA staff conducted an on-
site evaluation of Laboratory 2 on August 8,
1986. During this evaluation, analyses of the
samples from the Summer Seasonal subsurvey
were being performed. The evaluation of
Laboratory 2 resulted in several findings:
• Sample receipt, storage, and labelling
procedures were adequate.
• Data for pH-ANC and pH-BNC were
reported incorrectly on Form 11 (Sample
Results). Laboratory 2 had been reporting
a calculated pH value instead of the
measured pH value. The evaluation team
requested that the laboratory report the
measured pH value. All instances prior to
the on-site evaluation, for which the pH
values were reported incorrectly, were
corrected during data verification.
• Laboratory 2 was not warming samples in
a temperature-controlled water bath to 25
°C before performing specific conductance
measurements as contractually required.
Laboratory 2 did purchase a water bath,
but, for samples analyzed before this
purchase, a temperature-compensated
result was reported. There should be no
significant impact on the data quality as
the result of this deviation in protocol.
Other minor problems detected during
the on-site evaluation were discussed with the
laboratory personnel. The evaluation team
concluded that the overall performance of
Laboratory 2 was acceptable and that the
laboratory was operating within the
contractual framework.
Data Review and Verification
The data review and verification process
implemented by the EMSL-LV QA staff for the
ELS-II is described in Section 3. The following
23
-------
discussions address the reanalysis request
process, specific anomolies found during data
verification, and the resolution of problems. In
addition to this process, the ELS-II data were
also subjected to an intensive investigation
which is discussed in detail in Section 5.
Sample reanalysis was only requested as
a final corrective action when there was no
other alternative for correcting data problems
identified by the data review and verification
process. Reanalyses were requested at two
different times. A nominal number of
reanalyses were requested in 1986 as a result
of analytical problems detected during actual
sample analyses. Some of these reanalyses
were performed at the laboratory's own
volition and some were requested by the
EMSL-LV QA staff as a result of analytical
problems identified using the QA data. These
reanalyses were performed either within or just
outside the maximum required holding time.
The majority of the reanalyses were
requested by the EMSL-LV QA staff during
data verification in April and May of 1987.
Because of contractual complications, the
verification process for ELS-II differed from
previous NSWS surveys in that a longer time
period elapsed between the end of the
analytical activities and the verification of the
data. The number of these reanalyses was
kept to a minimum because the delayed
verification process caused the sample holding
time to be exceeded and because it was
necessary to finalize the data bases.
Dissolved Organic Carbon
During the Spring Seasonal subsurvey
data verification process, four batches were
identified with problems in the dissolved
organic carbon (DOC) results. Reanalyses
were requested for all samples in batches
3517 (10 samples), 3519 (10 samples), 3525 (9
samples), and 3526 (10 samples). Reanalyses
were perfomed on all samples requested
except for one sample (sample ID # 26) in
batch 3526 for which there was an insufficient
volume of sample to perform the reanalysis.
The contract-required holding time for DOC
was 14 days; sample reanalyses were
performed one year after the holding time had
expired. Although carbonaceous leachate was
a holding time concern, the data for the field
blanks associated with these four batches
(Table 10) did not exhibit any evidence of
leaching of organic material from the
polyethylene containers in which the samples
were stored.
Table 10 presents the QA sample data
originally reported and reanalyses results for
the above batches. All four batches show an
improvement in accuracy based on the field
natural audit data. In three of the four
batches, precision improved based on the
routine/duplicate paired data. The fourth batch
(3526), showed a slight decrease in precision.
Because the reanalysis results improved the
quality of the DOC data in these four batches,
all reanalysis results were incorporated in the
official verified data base.
Total Aluminum
*,
Reanalyses of samples for total
aluminum were requested for all samples in
batch 3508 (8 samples) for the Spring
Seasonal subsurvey and also for batch 3714 (9
of the 13 samples) for the Fall Seasonal
subsurvey. Table 11 presents the QA sample
data for batches 3508 and 3714 for the
originally reported results and the reanalysis
results. The QA data in the table show a
substantial improvement in both accuracy and
precision. For this reason, the reanalysis data
were included in the official verified data base.
Chloride, Sulfate, and Nitrate
Issues
During the actual sample analyses
(1986), preliminary QA and QC data were
obtained for review of the Spring Seasonal
data. This review by the EMSL-LV QA staff
identified problems with the chloride,
sulfate, and nitrate results. Laboratory 1
personnel determined that calibration
standards were diluted incorrectly. To correct
for this error, the laboratory submitted new
values for batches 3500 through 3522 (201 of
247 samples). Laboratory 1 reported that
these new values were the results of recalcula-
tions. Further investigation by EMSL-LV during
the Special Data Assessment (Section 5,
Chloride, Sulfate, and Nitrate Assessments)
determined that these data resulted from
reanalyses rather than recalculations.
During data verification (1987), the QA
data indicated that there were still problems
with chloride, sulfate, and nitrate analyses
performed by Laboratory 1. The EMSL-LV QA
staff requested reanalysis of several samples
for these anions. As a result of reanalysis, 48
chloride, 26 nitrate, and 28 sulfate reanalysis
24
-------
Table 10. Quality Assurance Samples for the Originally Reported and Reanalyses Dissolved Organic Carbon
Data Results for Batches 3517, 3519, 3525, and 3526; Spring Seasonal Subsurvey, Eastern Lake
Survey - Phase II
Batch ID
3517
3519
3525
3526
QA sample Reference value"
type' (mg/L)
FN7
8
R. Drf
FN8
B
R.D*
FN7
B
R.D*
LN7
FN7
B
R.D*
3.56
NA
NA
3.55
NA
NA
3.56
NA
NA
3.56
3.56
NA
NA
Originally reported
result (mg/L)
4.15
0.73
3.37, 2.68
3.45
0.55
3.16. 3.99
6.17
1.54
1.58, 2.29
7.18
10.77
2.03
16.83, 18.03
%RSDC
of routine-
duplicate pair
NA
NA
16.13
NA
NA
16.42
NA
NA
25.95
NA
NA
NA
4.87
Reanalysis result
(mg/L)
3.77
0.161
3.09. 3.00
3.64
0.34
3.25, 3.07
3.53
1.57
1.55. 1.46
•
3.65
0.22
11.40. 10.44
%RSDC
of routine-
duplicate pair
NA
NA
2.09
NA
NA
4.03
NA
NA
4.23
NA
NA
NA
6.21
' Only QA samples are shown to indicate the improvement in accuracy, precision, and detectability.
6 The reference values were generated from all the ELS-II data (including Spring Variability Pilot study) for the Seventh
Lake natural audit data reference value. Big Moose Lake audit data were determined from ELS-II and NSS-I data.
NA - not applicable to this sample type.
c The percent relative standard deviation (%RSO) is calculated by dividing the standard deviation of the routine-duplicate
pair by the mean of that same pair. NA - not applicable to this sample type.
d The concentrations of the samples in the routine-duplicate pair are indicated by: R for the routine and D for the
duplicate sample.
* Insufficient sample value to perform reanalysis.
Table 11. Quality Assurance Samples for the Originally Reported and Reanalyses Total Aluminum Results
for Batches 3508 and 3714; Spring and Fall Seasonal Subsurveys, Eastern Lake Survey - Phase II
QA sample Reference value*
Batch ID
3508*
3714'
type*
FN8
B
R.D'
LS1
B
R. D'
(mg/L)
0.265
NA
NA
0.020
NA
NA
Originally reported
result (mg/L)
0.1615
0.0094
0.0770, 0.0370
0.2358
0.0829
0.0124. 0.0791
%RSDC
of routine-
duplicate pair
NA
NA
49.62
NA
NA
103.12
Reanalysis result
(mg/L)
0.3010
0.0100
0.2871. 0.3328
0.0220
a
0.0290. 0.0270
* RSDC
of routine-
duplicate pair
NA
NA
10.43
NA
NA
5.05
* Only QA samples are shown to indicate the improvement in accuracy, precision, and detectability.
" The reference values were generated from all the ELS-II data (including Spring Variability Pilot study) for the Seventh
Lake natural audit data reference value. Big Moose Lake audit data were determined from ELS-II and NSS-I data.
NA - not applicable to this sample type.
c The percent relative standard deviation (%RSD) is calculated by dividing the standard deviation of the routine-duplicate
pair divided by the mean of that same pair. NA - not applicable to this sample type.
" The samples in this batch were analyzed by Laboratory 1 during the Spring Seasonal subsurvey.
* The concentrations of the samples in the routine-duplicate pair are indicated by. R for the routine and D for the
duplicate sample.
' The samples in this batch were analyzed by Laboratory 2 during the Fall Seasonal subsurvey.
a This sample was not reanalyzed.
results were incorporated into the official
verified data base. These reanalyses repre-
sent 19.4 percent of all the chloride values, 10.5
percent of the nitrate values, and 11.3 percent
of the sulfate values in the Spring Seasonal
subsurvey official verified data base. However,
further modifications to chloride, sulfate, and
nitrate data were made in 1988 as the result of
the Special Data Assessment (Section 5,
Chloride, Nitrate, and Sulfate Assessments).
25
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During analyses of samples from the
Summer Seasonal subsurvey, Laboratory 1
was not always able to meet the contract-
required instrument detection limit (IDL) for
chloride. This limit is defined as three times
the standard deviation of ten nonconsecutive
measurements of calibration blanks (Engels et
al., 1988) or 0.01 mg/L For batches 3603,
3605, 3663, and 3665, the laboratory reported
and confirmed an IDL of 0.1176 mg/L for
chloride, which is much higher than the 0.01
mg/L required by the contract. When the
detection limit criteria was not met, the excep-
tion generating program flagged values of less
than ten times the reported IDL Twenty-six
values from these four batches are less than
ten times the reported IDL and therefore
should be used with caution.
Ammonium Reporting
During the verification of the Summer
Seasonal subsurvey data, the exception gener-
ating programs detected problems with the
Laboratory 1 reported ammonium (NH4+) data.
The suspect NH4+ data were consistently
confirmed by Laboratory 1 at 1.28 times the
originally reported results. All Laboratory 1
Summer Seasonal NH4+ data then became
suspect. The laboratory confirmed that all
NH4+ data had been reported incorrectly as
nitrogen. After all the analytical laboratory raw
data for NH44 were obtained and reviewed, the
results were corrected by the EMSL-LV QA
staff. The corrected data were incorporated
into the official verified data base.
Anion/Cation and Conductivity
Balances
Once all the analyte values were deter-
mined for each sample, the laboratories were
contractually required to check the anion/cation
balance and the conductivity balance. If there
were imbalances outside specified criteria and
if there was no obvious reason for these
imbalances, such as unmeasured organic
protolytes, then the laboratories were sup-
posed to reanalyze the samples for the ana-
lytes suspected of causing the imbalance or
contact the QA manager for consultation. The
QA plan (Engels et al., 1988) documents the
criteria to be used by the laboratories in re-
gard to sample reanalysis.
Laboratory 2 calculated these balances
for every sample and performed the necessary
reanalyses. During data verification Laboratory
1 stated that the anion/cation and conductivity
balances were only spot checked. Unfortu-
nately, the laboratory could not quantify the
frequency of the spot checks.
Although the laboratories were required
to perform anion/cation and conductivity bal-
ances, they were not required to submit the
results with the rest of the data. The submis-
sion of balance results should be a recommen-
dation for future surveys.
Recalculation of the ANC and BNC
ANC and BNC were originally calculated
by the analytical laboratories using software
provided by EMSL-LV. During data verification
all the titratton data were obtained by EMSL-
LV. Based on the submitted data, the ANC
and BNC were recalculated using an improved
calculation procedure. To ensure consistency
among surveys, recalculation was performed
for all NSWS surveys. The values originally
submitted were replaced with the recalculated
values in the official verified data base for
ELS-II.
Batch-Specific Problems
Due to an error in preparing standard
solutions, Laboratory 1 had incorrectly reported
all sulfate and nitrate values for batches 3602
(29 samples) and 3603 (24 samples). To
correct the data, the analytical laboratory
manager confirmed that all the sulfate and
nitrate values for these batches must be
multiplied by a factor of 2. This correction
was implemented by the EMSL-LV QA staff
during the data verification. The problems with
these batches were identified by the anion/
cation balances and the conductivity balances.
It is probable that these errors would have
been detected by Laboratory 1 at the time of
the sample analysis if the contractually re-
quired anion/cation and conductivity balances
had been calculated more frequently.
In addition to the sulfate and nitrate
errors, batch 3602 data contained gross re-
cording errors. During the sample log-in
procedure at Laboratory 1, sample 19 was
overlooked and was added to the end of the
work order. A data entry clerk failed to notice
that the sample numbers were out of se-
quence. Consequently, the results for total
fluoride, equilibrated pH, equilibrated dissolved
inorganic carbon, initial dissolved inorganic
carbon, and total aluminum for samples 19
through 29 were entered incorrectly. The
majority of the errors associated with these
26
-------
batches were corrected during data verifica-
tion. The remainder of the errors were cor-
rected during the Special Data Assessment
(see the next section) after aH the analytical
raw data were received.
27
-------
Section 5
Special Data Assessment
During the ELS-II data verification and
validation activities, several issues regarding
data quality were of concern. This concern,
which concentrated primarily on the data from
the chloride, nitrate, sulfate, and alkalinity
analyses, prompted a Special Data Assess-
ment. This Special Data Assessment took
place after the completion of the official veri-
fied data base and during the final phases of
data validation. This assessment included an
extensive examination of the raw data from
both analytical laboratories for many parame-
ters. The analytical raw data include the
sample login sheets, recorded results from the
analytical instruments, and data entry forms.
The procedures used in the Special Data
Assessment are described in detail in the
flowchart shown in Figure 4. The following
discussion summarizes the procedures shown
in the figure. All modifications were applied to
the official verified data base. Changes that
were initially agreed to by EMSL-LV and ERL-
C were made to the official verified data base
to create a working data base. Various com-
puter programs were developed and run using
data from QA and QC samples (e.g., method
and system blanks, routine-duplicate pairs,
internal duplicates, splits, and audits). Each
sample that was identified as an outlier was
placed in a separate file based on the respec-
tive program algorithm. The exception generat-
ing programs for aniorvcation and conductivity
balances were also run and the sample values
targeted as outliers were also placed in the
outlier data base. In addition, samples that
were flagged by the ERL-C validation process
were included in the outlier data base. The
collective set of files of sample values targeted
as outliers was referred to as the outlier data
base. In summary, the outlier data base con-
sisted of outliers targeted from three primary
sources: the QA and QC sample programs,
the verification programs, and the validation
outliers. This method of identifying problem
samples provided a mechanism for the QA
auditor to determine if an analyte had multiple
problems. Historical data were also used as
a separate indication of outliers. These re-
sults, however, were not placed in the outlier
data base, but were provided to each QA
auditor to aid in detecting trends in the data.
All outliers in the outlier data base were
prioritized by analyte. Chloride, sulfate, nitrate,
and alkalinity were of the highest priority. If a
sample was considered an outlier, then the
entire batch was reviewed using the analytical
raw data. A list was generated of recommen-
dations and justifications for changes.
As a result of the Special Data Assess-
ment, changes were made to a copy of the
official verified data base. The resulting data
base is referred to as the modified verified
data base. The list of changes made is given
in Appendix A. Changes were made only to
the M01 member due to time and cost con-
straints. Therefore, data in other members do
not reflect results of the Special Data Assess-
ment. The only flags or tags that remain
appropriate in the modified verified data base
are the X flags which were the only flags
evaluated during the Special Data Assessment.
The samples and analytes for which X flags
changed as a result of the Speciaj Data As-
sessment are presented in Appendix A, Table
A-3. The following discussion, organized by
the type of modification made to the data
base, describes the findings and corrective
actions taken as a result of the Special Data
Assessment.
Chloride, Sulfate, and Nitrate
Assessments
At the onset of the Special Data Assess-
ment, EMSL-LV and ERL-C personnel agreed
that all samples analyzed by Laboratory 1
should be reanalyzed for chloride, sulfate, and
28
-------
REVIEW STATISTICS
TO OETEBMIHF WHICI
ANALYSIS YIELDS
THE BEST BESULTS
GENERATE LISI '»
RECOMMENDATIONS
FNO JUSTIFICATIONS
:OR APPROPRIATE
CHANGES
Figure 4. Procedurea uaed during the Special Data Assessment to create the modified verified data base, ELS-II.
29
-------
METHOD AND SYSTEM BLANKS
REVIEW AHO MODIFY
ELS-II METHOD AHO
SYSTEM BLANK WINDOWS
TO ISOLATE AND IDCHTIfV
OUTLIERS. AH ESTIMATE OF
THE WINDOW ABOUT THE
POPULATION DISTRIBUTION
(CLUSTER OF VALUES)
IS DETERMINED (NOT
INCLUDING NEGATIVE BIAS)
ROUTINE-DUPLICATE PAIRS
SPLITS
Flgur* 4. (Continued).
30
-------
HISTORICAL DATA
ASCOON
QA EXPEDIENCE.
IS THERE A VISUAL
DIFFERENCE (EXCEPTION)
BETWEEN SEASON
OR YEAR
INTERNAL DUPLICATES
FOR INTERNAL DUPLICATE
COMPARE TRENDS TO ASSESS
1.) POSSIBLE SYSTEMATIC
(WITHIN OR AMONG BATCH)
2.) NATURAL VARIABILITY
DOES
VALUE DEVIATE
FROM I TO 1 LINE
GREATER
THAN DOfl
WINDOWS. NEW WINDOWS
IS
VALUE
OUTSIDE
SELECTED
CRITERIA
Figure 4. (Continued).
31
-------
nitrate. However, the laboratory had inadver-
tently disposed of the samples, thereby elimi-
nating any possibility of further analyses. An
alternative data restoration plan was imple-
mented.
All the raw data documentation gener-
ated by the analytical laboratory was obtained
and examined by EMSL-LV QA staff. It was
during this raw data examination that it be-
came apparent that Laboratory 1 had not
recalculated the first set of results as reported
to EMSL-LV during data verification, but had
actually reanalyzed the samples (Section 4,
Chloride, Sulfate, and Nitrate Issues).
The first analysis, which took place in
March and April of 1986, consisted of chemical
analyses of all Spring Seasonal subsurvey
samples. This analysis is referred to in this
discussion as "Analysis 1.* The second analy-
sis (or reanalysis), which took place in May
and June of 1986 and also consisted of analy-
ses for chloride, nitrate, and sulfate of all
Spring Seasonal subsurvey samples, is re-
ferred to as "Analysis 2." A third set of analy-
ses also exists arid is referred to as "Analysis
3"; it consists of reanalyses requested by
EMSL-LV QA staff during data verification.
These reanalyses, which occurred in May of
1987, were only performed on selected sam-
ples (Section 4, Chloride, Sulfate, and Nitrate
Issues). The objectives of the chloride, sul-
fate, and nitrate Special Data Assessment
were not only to investigate and correct, where
possible, the problems related to Laboratory 1
analyses, but also to make a choice, on a
batch-by-batch basis, of which analytical result
to include in the modified verified data base.
To make a reasonable choice between analy-
sis 1, 2, or 3 data, a thorough examination of
the result of each analysis was necessary.
The following discussion begins with the
evaluation of the Analysis 2 data because the
Analysis 2 data, included in the official verified
data base, prompted the Special Data Assess-
ment.
Evaluation of Analysis 2 Data
Several different types of problems
contributed to the poor quality of chloride,
nitrate, and sulfate data. These problems
include, but were not limited to, dilution errors,
QCCS values exceeding the contract-required
limits, and reporting errors.
Dilution-
The linear dynamic range (LOR) for the
chloride analysis was 0.02 to 2.0 mg/L. Ap-
proximately 47 percent of the routine samples
contained concentrations outside this range
and therefore required dilution. Dilution, which
is a normal laboratory practice, may have been
a major source of variability in Laboratory 1
chloride analyses. In contrast, approximately
97 percent of the sulfate samples were within
the LDR of 0.1 to 10 mg/L and a similar per-
centage of the nitrate samples were also
within the LDR of 0.01 to 2.0 mg/L Thus,
dilution was not a major factor in the variabili-
ty of sulfate and nitrate.
Table 12 presents the dilutions used for
chloride analysis. Unfortunately, there were
not any systematic procedures for determining
the dilution factor to be used; thus samples
with concentrations just above the LDR may
have been diluted by a factor of 5, 10, or even
higher. Dilution alone does not necessarily
lead to increased variability; however, the
apparently random approach of selecting the
dilution factor and the large percentage of
samples diluted by a factor of 10 or more (17
percent) increases the probability that dilution
may have introduced a high degree of variabili-
ty into chloride data.
QCCS Data-
The contract specified that quality control
check samples (QCCS) must be analyzed at
the beginning and at the end of each batch, as
well as after every ten samples within a batch,
and that these concentrations must be re-
ported. The contract also required that the
difference between the target value and the
measured value must be within a specified
percentage (±5 percent for chloride and sul-
fate; ±10 percent for nitrate). If an unaccept-
able value for the QCCS was obtained, then
the instrument must be recalibrated and all
samples that were analyzed after the last
acceptable QCCS must be reanalyzed.
On examination of the raw data, it be-
came apparent that many samples were
associated with analyses for which the QCCS
values were outside the contractual limits. The
contractually required QCCS values that were
reported indicated general laboratory control.
However, the analytical laboratory raw data
contained many QCCS results that were
outside the contractual limits; however, the
32
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Table 12. Summary of Samplea Diluted for Chloride Analysis; Spring Seasonal Subaurvey, Eastsm Lake Survey-
Phase II
Sample type
Routine
Duplicate
Dilution ratio
1:2
1:5
1:10
1:20
Total diluted
1:10
1:20
1:40
Total diluted
Number of
samples diluted
5
14
6
20
45
2
6
1
9
Total number
of samples
146
146
146
146
146
29
28
29
28
Percentage of
samples diluted'
3.42
9.59
4.11
13.70
30.82
6.90
20.69
3.45
31.03
Seventh Lake
field natural
audit (FN7)*
Seventh Lake
laboratory
natural audit
(LN7)*
1:2
1:5
1:10
Total diluted
1:5
Total diluted
1
4
1
6
2
2
11
11
11
11
5
5
9.09
36.36
9.09
54.55
40.00
40.00
' Number of samples diluted divided by total number of samples x 100.
* These were the only audit samples with concentrations outside of linear dynamic range.
laboratory did not follow the correct procedure
as explained in the preceding paragraph in
response to these results. These poor QCCS
results were not originally reported with the
rest of the data. Such an unreported QCCS
measurement that was outside contractual
limits does not necessarily indicate poor
quality analyses. There were many instances
in which the QCCS in the raw data were poor
but in which the QA data for double-blind
samples indicated adequate data quality.
Therefore, although QCCS measurements that
were out of specified control limits showed
that a problem did exist, these measurements
alone do not provide adequate information to
evaluate the laboratory performance.
Calibration Standards Used to Define
the Calibration Curve--
In most cases, seven instrument calibra-
tion standards were prepared and analyzed for
use in defining the calibration curve for each of
the three analytes. For all three analytes,
some standards were eliminated by the labora-
tory when calculating the calibration regres-
sion. Although this is not considered unusual
laboratory practice, in some instances as few
as three points were used to calculate the
calibration regression. Problems with the
calibration curves and linear dynamic ranges
were most prevalent for the nitrate analyses.
The CRDL for nitrate was 0.005 mg/L All the
Analysis 2 calibration blank results were below
-0.005 mg/L. Calibration standards below 0.1
mg/L were eliminated for five of the twelve
calibration curves. Therefore, the low-level
concentrations were calculated on an extrapo-
lated curve. Twenty-six percent of the routine
samples were below the lower limit of the
actual LOR. Twenty of the twenty-nine field
blanks analyzed had the same value as the
calibration blanks. For seven of the ten curves
where low-level standards were eliminated, the
instrument readings for calibration blanks were
zero. Consequently, any routine sample with
a value less than the lowest standard used in
the calibration curve would be reported at the
value of the calibration blank.
Reporting Errors-
There were various other errors made
during the sample analysis of chloride, nitrate,
and sulfate in Analysis 2. These included
transcription errors, values reported that
were outside the LDP when a value that was
within the LDR was available, and reporting
of average values of laboratory duplicate
33
-------
analyses rather than the original sample result.
All reporting errors were corrected.
Evaluation of Analysis 1 Data
Although a thorough evaluation of the
Analysis 1 data was performed, the review of
the dilutions, QCCSs, and standards used to
define the calibration curve could not be as-
sessed as extensively as was done for the
Analysis 2 data evaluation. The strip charts,
which are direct printouts from the ion chro-
matograph, were the only form of raw data
provided (by Laboratory 1) for the Analysis 1
data evaluation and did not provide the same
degree of detail as the Analysis 2 analytical
laboratory raw data.
Based on information provided by Labo-
ratory 1 and based on double-blind audit data,
it was evident that, for certain dates of analy-
ses, the instrument was incorrectly calibrated
to read the concentration of a 2 mg/L calibra-
tion standard as 1 mg/L After the evaluation
of the audit sample data and comparison of
ELS-I and ELS-II routine lake data, the appro-
priate ELS-II routine and QA sample results
were multiplied by 2. This adjustment was
made based on the date of analysis (i.e.,
either all values or no values were multiplied
by 2 for any given date). However, there was
one date of analysis (April 21, 1986) for which
the sulfate data were multiplied by a factor of
20 based on the QA and historical data pat-
terns. In addition, analytical laboratory field
blank results recalculated by the EMSL-LV QA
staff were not the same as the Laboratory 1
results; therefore, all field blank values were
set to zero for Analysis 1.
After all of the modifications were com-
pleted, comparisons were made to determine
how consistent and reasonable the data were.
The sample concentrations that were modified
by multiplication were statistically compared to
the samples that were not modified. The
mean, median, and standard deviation of each
audit type for the modified data and the un-
modified data were compared to a reference
value for that audit type (see Appendix C for
reference values). As a secondary compari-
son, the routine data (for a given lake) were
reviewed for the ELS-I and the Summer and
Fall Seasonal data. These routine data were
compared to both the modified Analysis 1 data
and the unmodified Analysis 1 data for consis-
tency. The results of the comparisons indi-
cated that the modifications made to the data
were consistent and reasonable.
Evaluation of Analysis 3 Data
For Analysis 3, all of the QCCSs were
within the contractual limits, all of the calibra-
tion standards were used in the calibration
regression, and sample dilution was performed
effectively. Based on this information, the
laboratory was operating in control.
Selection Process for Analysis 1,
2, or 3
At this point of the Special Data Assess-
ment, three distinct data sets, each containing
one of the three analyses, had been created
(i.e., one data set contained Analysis 1 data,
one data set contained Analysis 2 data, and
one contained Analysis 3 data). A stepwise
approach was developed to make the appro-
priate selection between analyses 1, 2, or 3.
For each analysis, the percent difference
relative to the reference value was calculated
for each natural audit sample and percent
difference of the routine sample relative to its
duplicate sample for the routine-duplicate data.
The audit percent differences (accuracy esti-
mates) were calculated using the following
equation:
% Relative
Difference
(reference - measured)
reference
X100
The reference concentration for a given
audit sample is the median value of all the
Laboratory 2 data and, where possible, the
National Stream Survey (NSS-I) data. The
measured value refers to the measured con-
centration of a given audit sample.
The percent differences for the routine-
duplicate pairs (precision estimates) were
calculated using the following equation:
% Relative
Difference
(routine - duplicate)
routine
X100
Thus, accuracy and precision estimates
were used to select the data from analyses 1,
2, or 3. Chloride, nitrate, and sulfate selec-
tions were made independently of each other
(e.g., for a given batch chloride could be se-
lected from Analysis 1, nitrate could be se-
lected from Analysis 2, and sulfate could be
selected from Analysis 3). There were cases
where the choice was clear. However, there
were also cases where the decision was not
straightforward. Appendix B illustrates the
34
-------
precision and accuracy estimates (for each
analysis) used in the selection process.
The following is a description of the
process used to select the chloride, nitrate,
and sulfate data for inclusion in the modified
verified data base:
1. Accuracy estimates based on audit
sample results and precision estimates
based on pairs of routine and duplicate
samples were compared for analyses 1,
2, and 3 for a given batch. If the accura-
cy and precision estimates indicated the
selection of the same analysis (e.g., for
chloride, both the accuracy and precision
estimates selected Analysis 1 as the
best data), then that analysis was se-
lected for that given batch.
2. For the accuracy estimates, a number of
batches had multiple audits for a batch.
In a few cases (e.g., chloride data for
batch 3507, Appendix B, Table B-1). the
two audit sample results were contradic-
tory. In these cases, accuracy for that
batch was considered indecisive.
3. If the choice for precision was not in
agreement with the choice for accuracy,
then the accuracy estimate determined
the selection. For most cases this
selection was justifiable because the
precision estimate may be misleading.
Low concentrations in the routine-dupli-
cate pair samples resulted in very small
absolute differences between routine and
duplicate values, even though the percent
relative difference may have been large.
4. If the choice for either accuracy or
precision was not clear (e.g., Analysis 1
precision estimate is not significantly
different from the Analysis 2 precision
estimate), then the results based on the
decisive statistic (whether accuracy or
precision) were used to select the analy-
sis.
5. If both accuracy and precision were
indecisive, and there was no other evi-
dence that any selection would improve
the data quality, then the default value
(Analysis 2) remained in the data base.
Table 13 summarizes the results of the
decision process based on accuracy and
precision as presented in Appendix B. Summa-
ry statistics for analyses 1, 2, 3, and the
selected chloride, sulfate, and nitrate field
natural audit data are provided in Table 14.
This table demonstrates that the selected
combination of the three analyses yielded
results that were generally of better quality
than any one analysis. The changes made as
the result of the analyses 1, 2, and 3 selection
process reflect the best possible data that are
documentable and defensible. All changes
made in the modified verified data base as a
result of these chloride, sulfate, and nitrate
assessments are contained in Appendix A
(Table A-1, footnote a).
Acid Neutralizing Capacity
There were two batches in the Spring
Seasonal subsurvey (batches 3515 and 3517)
that contained acid neutralizing capacity (ANC)
values that were much too high. The average
calculated alkalinity is derived from the alkalini-
ty values calculated from the processing and
analytical laboratory pH and DIG results. This
value, calculated by the protolyte analysis
program, indicated that the reported ANC
results were two to three times what they
should have been. Results for audit samples
were also two to three times too high (see
Table 15 for reported and expected results).
The titrations for these two batches, along
with batches 3514 and 3516, were performed
by a different analyst than for the rest of the
Spring Seasonal subsurvey batches. Commu-
nications with Laboratory 1 indicated that the
alternate analyst was less familiar with the
instrument than the regular analyst.
A detailed investigation suggested that
the concentration of the acid titrant reported
by the laboratory and used in calculating ANC
values for batches 3515 and 3517 was less
than one-half the value that Laboratory 1 had
reported (Table 15). To support this theory,
the Gran-analysis program (used to calculate
ANC and BNC from the titration data) was
modified to calculate the actual concentration
of the acid titrant used in the titration. The
program then calculated an ANC value using
the calculated concentration of acid titrant
instead of that reported by Laboratory 1. The
results of this investigation are presented in
Table 15. Batch 3516 is included in the table
to demonstrate how this approach would
affect a batch with correctly reported acid
titrant concentrations. The newly calculated
ANC values are much closer to the expected
result for QA samples and much closer to the
average calculated alkalinity. It should be
noted that the measured pH values were used
35
-------
Table 13. Summarized Result* of the Decision Process for Chloride, Sulfate, and Nitrate Using the Decisions Based on Accuracy and Precision; Spring
Seasonal Subsurvey, Eastern Lake Survey - Phase II
Batch ID
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
Decision based
on accuracy
Indecisive
Indecisive
Analysis 2
Indecisive
Indecisive
Analysis 3
Analysis 3
Indecisive
Indecisive
Indecisive
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 2
Analysis 2
Analysis 2
Chloride
Decision based
on precision
Analysis 1
Analysis 1
Analysis 2
Analysis 3
Analysis 1
Analysis 3
Analysis 3
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Analysis 2
Analysis 1
Indecisive
Indecisive
Analysis 2
Analysis 1
Indecisive
Analysis 1
Analysis 1
Analysis 2
Indecisive
Indecisive
Analysis 2
Analysis 1
Analysis 1
Indecisive
Final
decision'
Analysis 1
Analysis 1
Analysis 2
Analysis 3
Analysis 1
Analysis 3
Analysis 3
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Decision based
on accuracy
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Indecisive
Indecisive
Analysis 1
Analysis 3
Indecisive
Analysis 2
Analysis 2
Indecisive
Analysis 2
Indecisive
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 2
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Sulfate
Decision based
on precision
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Indecisive
Analysis 3
Analysis 2
Analysis 2
Analysis 2
Indecisive
Indecisive
Analysis 1
Indecisive
Analysis 1
Indecisive
Indecisive
Analysis 3
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Indecisive
Indecisive
Indecisive
Final
decision'
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 3
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 2
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Decision based
on accuracy
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Indecisive
Analysis 2
Indecisive
Indecisive
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Indecisive
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Indecisive
Analysis 2
Indecisive
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Nitrate
Decision based
on precision
Indecisive
Analysis 2
Analysis 2
Analysis 1
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 1
Indecisive
Final
decision'
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
• Data from this analyses were included in the modified verified data base.
-------
Table 14. Summary Statlatlca' for Chloride, Sulfate, and Nitrate Data Baaed on Natural Audit Samplea; Spring Seaaonal Subeurvey, Eaatem Lake Survey
Phase -II
Audit
type*
FN7 (2.950)
LN7 (2.950)
FN8 (0.412)
LN8 (0.412)
FN7 (6.850)
LN7 (6.850)
FN8 (6.335)
LN8 (6.335)
FN7 (1.264)
LN7 (1.264)
FN8 (1.200)
LN8 (1.200)
Analysis 1
Number
11
5
10
6
11
5
10
6
11
5
10
6
Mean
2.897
2.421
0.352
0.284
6.258
5.893
5.666
5.207
1.241
1.252
1.029
0.933
Median
2.916
£464
0.341
0.241
6.269
6.602
5.878
5.646
1.159
1.238
1.071
1.041
Standard
deviation
0.936
1.210
0.156
0.114
0.710
2.125
0.761
1.341
0.199
0.151
0.134
0.285
Number
11
5
10
6
11
5
10
6
11
5
10
6
Analysis 2
Mean
3.961
3.935
0.355
0.378
7.051
7.709
6.176
5.645
1.558
1.458
1.181
1.305
Median
3.020
3.101
0.369
0.348
7.169
7.450
6.269
5.692
1.373
1.370
1.228
1.268
Standard
deviation
Chloride
1.823
1.391
0.160
0.191
Sulfate
0.676
1.243
0.810
1.388
Nitrate
0.627
0.204
0.190
0.399
Number
1
0
3
2
0
0
2
2
1
1
1
1
Analysis 3
Mean
4.251
0.463
0.423
—
6.828
6.774
1.125
1.008
1.381
1.305
Standard
Median deviation
4.251 -
0.431 0.069
0.423 0.011
mmm mm —
6.828 0.037
6.774 0.113
1.125 —
1.008 -
1.381 —
1.305 —
Number
11
5
10
s"
11
5
10
6
11
5
10
6
Selected datac
Mean
3.175
£196
0.408
0.390
6.798
6.777
6.082
6.115
1.256
1.224
1.083
1.097
Median
2.940
2.464
0.434
0.395
6.849
6.602
6.097
6.227
1.272
1.238
1.066
1.056
Standard
deviation
1.103
0.938
0.077
0.047
0.321
0.527
0.570
0.649
0.108
0.119
0.139
0.144
• All mean and median concentrations are in mg/L.
Reference values for each sample type are given in parentheses and are in mg/L Reference value la the median concentration of all the Laboratory 2 data and.
where possible, the NSS-I data. Medians were derived from pooled FN7 and LN7 audit data and pooled FN8 and LN8 data.
e These are tha data that were selected for inclusion in the modified verified data baaa.
" One LN8 chloride value waa flagged with an XO flag in tha modified verified data baaa and la therefore not Included in these statistics.
-------
Table 15. Acid Neutralizing Capacity Recalculation* a* a Reault of the Special Data Assessment; Spring Seasonal
Subsurvey, Eastern Lake Survey-Phase II
Batch Sample
ID ID
3515
3515
3515
3515
3515
3515
3515
3515
3S1&
3516/
3516/
35167
3S16>
ssie7
3516^
3516/
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
1
2
3,
4'
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
9
10
Sample
type
R
R*
D*
B
FL15
R
R
R
R*
FL15
D*
LL15
R
R
R
B
R
R
FN7
D*
n"
R
R
R
R
B
Mean'
0»q/L)
NA
NA
NA
NA
113
NA
NA
NA
NA
113
NA
113
NA
NA
NA
NA
NA
NA
154
NA
NA
NA
NA
NA
NA
NA
Reported
ANC6
(^eq/L)
82.0
-15.1
3.3
7.6
265.5
-1Z4
136.4
268.9
15.9
130.6
17.5
120.9
47.0
318.1
371.8
15.3
157.7
182.8
392.0
96.9
107.4
213.1
422.6
802
215.6
28.3
Average
calculated
alkalinity*
(W/L)
33
-4
-5
-1
97
-5
48
98
16
87
11
108
45
351
405
2
52
89
169
46
43
82
178
23
72
9
Recalculated
ANC"
0*q/L)
28.7
-3.1
1.9
10.1
100.2
-10.3
50.6
992
16.1
127.6
12.6
112.9
42.9
306.2
339.3
17.0
59.3
70.2
1532
38.2
41.6
84.8
166.9
29.3
82.8
10.4
Difference*
(peq/L)
53.3
•tt.0
1.4
-2.5
165.3
-Z1
85.8
168.7
•02
3.0
4.9
8.0
4.1
11.9
32.5
-1.7
98.4
112.6
238.8
58.7
65.8
128.3
255.7
50.9
132.8
17.9
Reported
acid titrant
cone/
(eq/L)
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
Calculated
acid titrant
cone*
(eq/U
0.0038
0.0038
0.0038
0.0089
0.0038
0.0038
0.0038
0.0038
0.0088
0.0092
0.0068
0.0089
0.0088
0.0092
0.0067
0.0089
0.0039
0.0039
0.0039
0.0040
0.0039
0.0040
0.0039
0.0039
0.0039
0.0039
' Reference value for the respective audit type. Mean calculated using all ELS-I1 ANC data for the specific audit type.
Grubbs list was used to eliminate outliers. NA means not applicable for these sample types.
* ANC value in the official verified data base.
c Average alkalinity value calculated by the protolyte analysis program.
" ANC value calculated by Gran analysis using calculated acid titrant concentration instead of reported acid titrant
concentration.
' Difference between reported and recalculated ANC.
' Concentration of acid titrant standardized and reported by Laboratory 1 and used to calculate ANC values in the
official verified data base.
a Acid titrant concentration calculated from the titratton data.
* Indicates field duplicate pair contained within each batch.
' This sample from batch 3515 was analyzed with samples from batch 3516.
1 Batch 3516 is included to demonstrate how this approach would affect a batch with correctly reported acid titrant
concentrations.
in the recalculations to obtain both the acid
titrant concentration and the ANC values. In
contrast, the ELS-II ANC values in the official
verified data base were derived from the
calculated pH values.
To perform the above calculations of
ANC, it was assumed that the measured pH
values and the volume of titrant added, both
recorded by a Fischer computer-aided titrator,
were accurate. There was no evidence to
suggest a problem with the pH values record-
ed by the titrator. There did not appear to be
any problems with the pH QCCSs for these
two batches. There is also no evidence to
suggest that the volume of titrant added was
incorrect. It is unlikely that the Laboratory 1
Fischer titrator would add one volume but
record another, especially without the laborato-
ry being aware of such an occurrence. As a
result of this investigation, the appropriate
changes were made in the modified verified
data base (Appendix A, Table A-1, footnote e).
These changes substantially improved the
quality of the ANC data for batches 3515 (7
samples) and 3517 (10 samples).
38
-------
Sodium
The analytical laboratory contract speci-
fies that sodium must be measured by either
atomic absorption spectroscopy (AAS) or
inductively coupled plasma emission spectros-
copy (ICPES). Both methods should yield the
same results. Laboratory 2 used the Perkin
Elmer 703 AAS to measure sodium in the Fall
Seasonal subsurvey samples. Laboratory 2
also used the Gerald Ash 1150 ICPES for a
sodium measurement. Although the ICPES
was not calibrated for the measurement of
sodium, the double-blind quality assurance
samples indicated that the ICPES results had
less variability than the AAS results (Table 16).
However, the ICPES results for one day of
analyses exhibited more variability and higher
bias than the AAS results. For this one day
during which batches 3720, 3721, and 3723
were analyzed, the sodium data using the AAS
method remain 'm the modified verified data
base. For the other Fall Seasonal batches, the
sodium ICPES results were substituted for the
AAS results in the modified verified data base.
The result of the comparison between
the methods indicates that the precision
improved when using the ICPES data for each
of the specified audit types with the exception
of the laboratory synthetic lots 2 and 3.
Although there is a slight increase in the
variation from AAS to ICPES for these two
lots, there is no statistical difference between
the AAS and ICPES sodium results for the LS2
or the LS3. Also, the number of samples in
the range of the Lot 2 and Lot 3 synthetics is
relatively small. The reference concentration of
an LS2 is 10 mg/L Seven percent (17 sam-
ples) of the total 239 routine samples are at a
concentration of 9.582 or above. The LS3
reference concentration is 0.03 mg/L Less
than 1 percent (2 samples) of the routine
samples are in the range of 0.058 mg/L or
less. In summary, substituting the ICPES
sodium results for the AAS results improved
the quality of the sodium data substantially as
evidenced in Table 16. As a result of this
investigation, the appropriate changes were
made in the modified verified data base (Ap-
pendix A, Table A-1. footnote m).
Table 16. Summary Statistics for the Flam* Atomic Absorption Spectroscopy and Inductively Coupled Plaama
Emission Spectroscopy Methods of Sodium Analysis; Fell Seasonal subsurvey, Eastern Lake Survey-
Phase II
Audit
type
FN7
FN8
LS1
LS2
LS3
LS4
LS5
Reference
value*
2.176
0.606
0.800
10.000
0.030
3.500
1.500
Support lab Support lab
measured value' standard
(mg/L)
NA
NA
0.826
10.295
0.043
3.665
1.622
deviation*
NA
NA
0.019
0.055
0.002
0.021
0.030
Flame Atomic Absorption
Mean
Number
11
11
8
7
6
6
7
(mg/L)
2.197
0.612
0.799
10.596
0.032
3.765
1.665
Standard
deviation
0.229
0.158
0.144
0.352
0.008
0.379
0.136
Inductively Couol
Mean
Number
n
n
8
6C
5e
5C
7
(mg/L)
2.203
0.616
0.837
10.701
0.052
3.717
1.633
ed Plasma
Standard
deviation
0.066
0.042
0.053
0.510
0.052
0.063
0.130
' Reference value for FN7 and FN8 is a median of all the available Seventh Lake and Big Moose Lake field natural audit
sample data, respectively. Reference values for LS audit types are the theoretical concentrations.
* The mean and standard deviation of five experimental laboratory synthetic audit sample values measured by the
support laboratory that prepared them. NA means not applicable for these audit types.
c One day of analyses (batches 3520, 3521, and 3523) was not included because of analytical error.
Other Modifications
There were other modifications that were
made during the Special Data Assessment.
These changes included sample switches,
missing values reported as zero, and nonrepre-
sentative reanalysis removal. These are all
discussed below.
Sample Switches
Sample switches are defined as samples
that were inadvertantly switched when the
analyst was performing the sample analysis.
These can be analyte specific or aliquot
specific, or can involve the entire sample.
These anomalies were discovered by using the
39
-------
double-blind QA samples to monitor each
batch. When the QA sample concentration
was substantially different from the expected
value, investigative procedures were initiated.
One of the first steps was to check the sam-
ples in the batch that are directly before and
after the QA sample to determine if that con-
centration is in the expected range of the QA
sample. If so, further investigation was war-
ranted to determine if the concentrations of
the samples in the batch were shifted by one
or more samples. To assist in identifying
sample switches, the other historical lake data
were used as a comparison to estimate the
approximate concentration of the routine
samples.
Analyte-specific sample switches were
identified in both the Spring Seasonal and
Summer Seasonal subsurvey data. In the Fall
Seasonal subsurvey, a lake sample switch
was identified by comparing the data for the
lakes to historical data. This lake sample
switch was also detected during data vali-
dation. The data for batch 3713, sample 3
(Lake ID 1C1-068) were switched with batch
3713, sample 14 (Lake ID 1C 1-066). The sam-
ple container was mislabeled in the field. All
appropriate corrections were applied in the
modified verified data base and are illustrated
in Appendix A (Table A-1).
Missing Values Reported as Zero
The Aquatics Data Entry System record-
ed missing values as zeros. Most of these
zeros were replaced with a character denoting
missing values during the data verification.
The few remaining zeros were replaced during
the Special Data Assessment (Appendix A,
Table A-1, footnotes j and n).
Nonrepresentative Reanalysis
Removal
Results of reanalysis, for a given analyte,
that were not representative of the analysis of
at least 50% of the samples in a given batch
were removed. The majority of reanalysis
values that were removed were for QA sam-
ples that had not been reanalyzed with the
routine samples in the batch. The rationale for
removal of these data was that QA samples
did not represent the analysis of the routine
samples. In addition, the reanalysis value was
removed for several routine samples which
were reanalyzed without the reanalysis of
associated QA samples. The originally re-
ported result or, if possible, a corrected value
obtained from scrutiny of the analytical labora-
tory raw data was included in the modified
verified data base (Appendix A, Table A-1,
footnote b).
Miscellaneous Changes
There are several other types of modifi-
cations included in Appendix A (Table A-1).
Most of these modifications are self-explanato-
ry. The category "transcription error* includes
such errors as misplaced decimal points or
misrecorded values introduced at any of vari-
ous stages of sample analysis.
40
-------
Section 6
Assessment of Data Quality
The quality of the ELS-II data base is
described in terms of six characteristics:
completeness, comparability, representative-
ness, detectability, precision, and accuracy.
Completeness, representativeness, and compa-
rability apply to the sampling design and its
implementation. Detectability, accuracy, and
precision quantify the performance of one or
several components of the collection and
measurement system. Interlaboratory bias is
also discussed as an estimate of the system-
atic and random differences in performance
between Laboratory 1 and Laboratory 2. The
following subsections also discuss other
methods used to assess the quality of the
ELS-II data; these methods include measured
ANC to calculated carbonate alkalinity compari-
sons, ion charge balances, and conductivity
balances.
All tables and plots presented in the
following discussions and in the appendices
relative to these discussions were derived from
the modified verified data base. Values that
were assigned an X flag (invalid data) are not
included in the plots or tables.
Completeness
Completeness refers to the amount of
data that was successfully collected with
respect to the amount intended in the survey
design. A certain percentage of the intended
amount of data must be successfully collected
in order for conclusions based on these data
to be valid. For example, missing data may
reduce the precision estimates, introduce bias,
and thus reduce the level of confidence in the
conclusions. In this regard, the goal of the
ELS-II was to obtain data bases that were at
least 90 percent complete. Completeness of
the data base met program goals, based on
the ratio of lake samples collected to the lake
samples targeted for collection and on the
percentage of acceptable data generated from
those samples.
Sample Collection
All sampling periods yielded results that
were between 99 and 100 percent complete
even though there were some unsampled
lakes. In the original ELS-II lake selection
design, 150 lakes were selected to be sampled
during each season (i.e., spring, summer, and
fall). There were two lakes, however, that
were removed from the list of lakes to be
sampled either because of pollution or be-
cause of other lake treatment that could
interfere with the natural lake chemistry.
During the Spring Seasonal subsurvey, 146 of
the 148 targeted lakes were sampled. One
lake (1B3-025) was not sampled because it
was a bog. A second lake (1E2-069) was not
sampled because permission for access was
not granted.
The number of lakes targeted for sampl-
ing during the Summer Seasonal subsurvey
was reduced to 146 to match the number of
lakes sampled in the Spring Seasonal sub-
survey. All 146 of the Summer Seasonal lakes
were sampled. In addition to the targeted
lakes, the lake (1B3-025) identified as a bog
during the Spring Seasonal subsurvey was
accidentally sampled.
The total number of lakes selected for
sampling in the Fall Seasonal subsurvey was
146. One of the lakes (1A1-059) sampled
during the Fall Seasonal subsurvey was not
sampled during the Spring or Summer Season-
al subsurveys. Lake 1A2-058, which was
sampled during the Spring Seasonal and
Summer Seasonal subsurveys, was not sam-
pled during the Fall Seasonal subsurvey due to
inclement weather. Details of the ELS-II lake
sampling design are discussed in Merritt and
Sheppe (1988).
41
-------
Analytical Measurements
Completeness with respect to ELS-II
analytical measurements was evaluated for
each parameter for both the processing and
analytical laboratories. The percent completion
for analyses for each parameter ranged from
98 to 100 percent. Data that were missing or
qualified with an X flag (invalid data) in the
data base were considered incomplete. The X
qualifier recommends to the data user that
these data should not be used in any statisti-
cal analyses. For further definition of the
different types of X flags, refer to the ELS-II
QA plan (Engels et al., 1988). The percent
completion for any given parameter was calcu-
lated by subtracting the total number of incom-
plete data points from the total number of
data points, and then dividing the result by the
total number of data points. This value was
multiplied by 100 to yield units of percent.
Comparability
The ability to compare the ELS-II data
base with the other data bases generated
from the AERP projects was a critical objective
of the ELS-II. Although several differences
exist between the ELS-II and its precursor,
ELS-I (Table 8), the use and documentation of
standardized sampling and analytical proce-
dures allow for a quantitative evaluation of the
data between the ELS-I and the ELS-II as well
as allowing ELS-II data to be compared to
data from other past and future studies.
Some of the primary comparability issues are
discussed below.
Comparability of Aluminum
Methodology
The ELS-I, and the ELS-II both used the
8-hydroxyquinoline method was used for the
determination of monomeric aluminum. This
method assumes that only monomeric alumi-
num is complexed with 8-hydroxyquinoline at a
pH level of 8.3 and extracted into methyl
isobutyl ketone (MIBK). The ELS-II also incor-
porated an additional method for monomeric
aluminum determination. This method is
based on a colorimetric reaction of monomeric
aluminum with pyrocatechol violet (PCV) at a
pH of 6.1. This method was adapted to be
performed using flow injection analysis (FIA).
Although the 8-hydroxyquinoline method per-
formed satisfactorily, a more efficient and less
hazardous method (MIBK is a hazardous
material) of monomeric aluminum measure-
ment was desirable. The PCV-FIA method
fulfilled these specifications. The results of
the comparability study indicated that the
results from the PCV-FIA and the 8-hydroxy-
quinoline methods correlate well, despite the
apparent differences in affinity for monomeric
aluminum compounds (Henshaw et al., 1988).
Comparability of Lake Access
Procedures
In the ELS-I, all samples were collected
by helicopter crews. In the ELS-II, lakes were
sampled by both helicopter and ground crews.
Although there were two methods of lake
access in the ELS-II, a comparability study
conducted during the Western Lake Survey
indicated that there were no significant differ-
ences between the samples collected by
helicopter crews and samples collected by
ground crews that would affect data quality or
interpretation (Landers et al., 1987; Silverstein
et al., 1987). The ELS-II was designed on the
premise that there would be no significant
difference between the ELS-I and the ELS-II
samples because of differences in lake access
procedures.
Nitrate Data Comparability
For both the ELS-I and the ELS-II, nitrate
was analyzed using Aliquot 3, a filtered, unpre-
served 250-mL sample. During the ELS-I,
nitrate contamination was evident in the field
blanks. Because the contamination was
apparent in Aliquot 3 for all samples pro-
cessed on or before November 1,1984, reanaly-
sis was performed using Aliquot 5, an unfil-
tered, unpreserved 500-mL sample. Thus, in
the ELS-I, all nitrate data processed on or
before November 1, 1984, were replaced with
the Aliquot 5 nitrate reanalysis results. Details
of the ELS-I nitrate contamination are dis-
cussed in Best et al. (1986). For the ELS-II,
there was no evidence of any significant
nitrate contamination. The final nitrate data
used in the ELS-I should be comparable to the
ELS-II nitrate data.
Representativeness
Representativeness refers to the degree
to which the data collected accurately reflect
the population, group, or medium being sam-
pled. Standardized protocols defined the
appropriate weather conditions for sampling
activities and the criteria for selecting the
appropriate sampling sites. These protocols
42
-------
helped to ensure that each of the samples
collected was representative of the chemical
condition of the lake at the time of sampling.
From the QA perspective, representative-
ness can be defined by how well the audit
samples reflected the matrices and the con-
centration ranges of the routine samples.
Field natural audit samples were used during
the ELS-II. These audit samples were com-
posed of characterized, stabilized lake water.
Two lakes, Seventh and Big Moose, were
chosen as the lakes from which the reference
materials would be collected. Seventh Lake
was representative of a dilute lake that has
moderate ANC, and Big Moose Lake was
representative of an acidic system.
Experimental laboratory synthetic audit
samples used during the Fall Seasonal sub-
survey were designed to represent the ex-
pected concentration ranges of the ELS-II
routine samples. These laboratory audit sam-
ples were prepared at five concentrations. As
a product of a continually evolving QA pro-
gram, this approach was developed in time for
implementation into the Fall Seasonal sub-
survey on an experimental basis.
Detectability
Detectability can be addressed at two
levels. The first level concerns the detectability
associated with a particular instrument or
analytical method (i.e., instrument detection
limits [IDLs] and method detection limits
[MDLs]). These limits represent the lowest
value of an analyte that can be detected under
ideal laboratory conditions and are generally
determined by using calibration blanks or
reagent blanks. Method-level limits represent
the optimum detectability that can be expected
for a given methodology and that can be used
by program and laboratory managers to estab-
lish and implement QC measures. These limits
can be compared with DQOs to determine if
the analytical laboratories are meeting contract
specifications. It is important to note that the
samples used to determine method level limits
are not blind to the analyst.
The second level evaluates the system
detectability. System-level limits represent the
lowest value of an analyte that can be de-
tected when the entire process, from sample
collection through laboratory analysis, is taken
into consideration. Field blank samples (re-
agent grade distilled water poured through the
sampling device and sent through the entire
processing and analytical procedure) were
used to determine the system-level limits.
These limits differ from the method-level limits
in that they consider the background contam-
ination that occurs from the time the sample
is taken until it is analyzed in the analytical
laboratory. System-level limits give the data
user a more realistic value for the lowest level
of analyte detectable.
Within each level (method and system)
two specific limits can be distinguished: the
decision limit and the detection limit. It is
extremely important to understand clearly the
definition of each of these limits and their
application in data interpretation. The use of
these limits has historically caused some
misunderstanding among user groups, and the
problem is largely due to semantics. As used
in this report the terms are defined as follows:
Decision limit: The decision limit repre-
sents the lowest measured sample value
that can be distinguished from a blank
sample or from background noise.
Detection limit: The detection limit
represents the lowest true or theoretical
concentration, above the decision limit,
that can be measured with a specified
level of reliability.
Two types of statistical errors are impor-
tant in evaluating the question of detectability:
Type I and Type II errors. A Type I error is
the error of concluding that an analyte is
present when, in fact, it is not. The second
error, Type II, is the error of concluding that
an analyte is not present when, in fact, it is.
The decision limit and detection limit
differ in two important respects. The first is
the specification of Type II error allowable,
and the second is the actual function of each
limit.
The decision limit is selected as a point
along the distribution of blank samples, wheth-
er it is the distribution of field blanks or cali-
bration blanks, such that a specified portion of
the blank measurements fall below that point.
This decision limit which specifically addresses
the Type I error, has in past NSWS QA reports
been set at the 95th percentile (P95) of the
blank measurements. There is a 0.05 probabil-
ity of making a Type I error, i.e., concluding
that an analyte is present when in fact it is
not. The specification of allowable Type II
errors is not of concern when calculating the
43
-------
decision limit. The decision limit is the point to
which routine measurements are compared
when trying to determine if they are different
from blank measurements or from background
noise. If the measurements are above the
decision limit, they can be considered to con-
tain analyte above the background concen-
trations. If the measurements are below the
decision limit, they fall within the range of
blank measurements and cannot be reliably
distinguished from the blanks.
The detection limit answers a very differ-
ent question. In calculating this limit, one
wants to avoid concluding that an analyte is
absent when in fact it is present (Type II
error). A value is selected such that a small
portion of the distribution about that true
concentration is below the decision limit. Past
NSWS QA reports (Best et al., 1986, Cougan et
al., 1988, Silverstein et al., 1987) set this true
concentration such that only 5 percent of the
distribution fell below the decision limit (Type
II error of 0.05 probability). Thus, in repeated-
ly analyzing a sample with a concentration at
the detection limit, at least 95 percent of the
measurements on that sample should yield
values that are above the decision limit, i.e.,
distinguishable from blank samples or back-
ground noise. In preparing a synthetic audit
sample or selecting a natural audit material
with tow analyte concentration, the detection
limit represents the lowest true concentration
that can be measured reliably and thus is the
lowest concentration that should be used for
the audit material.
Note that once measurements on routine
samples are obtained, measurement values
that fall between the decision limit and detec-
tion limit should not be considered less reliable
than values above the detection limit. Both
measurements fall above the range of 95
percent of the blank measurements and thus
are reliably (0.05) different from blanks. How-
ever, if a measurement has not yet been taken,
and the purpose is to measure a sample that
has a concentration between the decision limit
and detection limit, the probability, and there-
fore confidence, of detecting the concentration
(Type II error) would be between 50 percent
and 95 percent.
For most data users, the system deci-
sion limit will be of primary interest. Only this
limit provides the data user with a number that
can be compared to the values for the routine
samples, in order to determine if the samples
are distinguishable from blanks or background.
This may appear confusing, because many
chemists and biologists call this limit the
detection limit or limit of detection. Because
much of the chemical and biological literature
refers to this limit as the "limit of detection," it
is important for the data user to be aware of
the semantic difference and to understand
that, conceptually, the "decision limit" cited in
NSWS literature is often the same as the "limit
of detection" cited in the general chemical and
biological literature.
Calculation of Limits
The limits of detectability for ELS-II are
based on the mean and standard deviation of
the blank measurements. These limits differ
from the calculations of detectability for the
ELS- 1 and WLS which were based on nonpara-
metric statistics (Permutt and Pollack 1985;
Best et al., 1986). Unfortunately, the number
of blank samples in any one subsurvey of ELS-
II is too small to permit the use of the same
nonparametric calculations. The traditional
calculation for detection limit has been:
The conceptual limit to which this actually
refers is the same conceptual limit referred to
in NSWS as the decision limit. The J? + 3o is
comparable to a decision limit at P^, although
PK was used as the decision limit in the ELS-
I and WLS reports. A comparable measure-
ment for Pw would be 5? + 1.65a. Based on
the logic used in the ELS- 1 QA report (Best et
al., 1986), the detection limit should be twice
the distance from the mean of the blanks, as
is the decision limit. This would make the
detection limit X + 3.3o. For the purposes of
this report, the following computations were
used for the indicated limits:
System
decision limit = 5? + 1.65o (field blanks)
System
detection limit = X + 3.3a (field blanks)
System-Level Detectability
For the purposes of routine data interpre-
tation, system-level detectability is of most
interest. This assessment of detectability will
therefore deal exclusively with system-level
detectability.
System-level detectability was calculated
by using field blank samples. These samples
44
-------
consisted of reagent-grade deionized water
which was taken into the field, poured through
the sampling device, and processed as a
routine sample. These samples were designed
to evaluate the amount of analyte introduced
into samples during collection, processing, and
analysis. Additionally, they can be used to
evaluate relative precision of the measurement
process at low or zero analyte levels. One
field blank was placed in each field batch
analyzed by the analytical laboratories.
Table 17 presents the calculated system
decision and detection limits for samples
collected each of the seasons studied for ELS-
II. Of interest is the comparability between
the data for the different seasons in the level
of detectability. If significant differences exist
between seasons, the ability to interpret the
seasonal data will vary and thus interpretation
of the overall program will be more difficult.
The primary concern, because of ELS-II objec-
tives, is a comparison of spring and fall de-
tectability. Given the method of calculating
decision and detection limits, it is only neces-
sary to examine the results of the decision
limit calculations, because the detection limit
is a straightforward multiple. Differences in
the decision limits may result from differences
in the mean values for the blank measure-
ments, variability of the measurements, or
both. Table 18 provides summary statistics for
the field blank data for ELS-II. A difference in
mean values would most likely be an indication
of potential contamination, and a difference in
standard deviations would likely result from
greater variability in the measurement process,
indicating poorer laboratory performance.
The decision limits for 13 of the analytes
in Table 17 are greater in the spring than in the
fall. For nine of these (Al-org. Ca, Cl~. DIC-eq,
QIC-initial, K, Mg, P-total, and SiOJ the greater
decision limit resulted from both a higher mean
value and a larger standard deviation (Table
18). Two of these thirteen analytes (DOC, and
SO*) had larger decision limits as a result of
poor precision for the blank measurements.
The larger decision limits for Cond and Na
resulted primarily from larger mean values.
For three analytes (Al-ext, Al-total, NH4+) there
were larger decision limits in fall than in
spring.
The summer sampling period presents an
interesting analysis because overall detectabili-
ty during that season results from work per-
formed by two different laboratories. There
are 11 analytes for which the decision limits
resulting from Laboratory 1's performance
exceed those of Laboratory 2 (Al-total, Al-org,
Cr, Cond, DIC-eq, DIC-initial. Fe, K, Mg, NO3',
SO42'). For four analytes, the decision limit for
Laboratory 2 exceeded Laboratory 1 (Al-ext,
DOC, NH4+, and SiOJ.
The system decision limits calculated
from the field blanks should be used to identi-
fy measurements on routine field samples that
cannot be distinguished from background.
Routine sample measurements with values
below the system decision limits cannot confi-
dently be said to contain analyte or to contain
analyte above the background contamination
levels. The data should be interpreted with
extreme caution because, although analyte
may be present, it is uncertain whether the
analyte was contained in the lake sample or
was introduced in the handling and processing
procedure. Routine sample measurements
that have values above the system decision
limits can reliably be said to contain analyte
above background contamination.
The system decision limits also provide
a means of evaluating the routine data. If the
routine data are consistently greater than the
system decision limits, detectability is not a
critical issue. If the routine data are consis-
tently at or below the system decision limit,
then the data must be evaluated cautiously. It
is not a matter of poor quality or high quality
data, but rather a matter of how the routine
values compare with expected background
values. Table 19 presents the percentage of
routine samples with values below the system
decision limit for each analyte in each season-
al sampling. This table can be used to evalu-
ate the degree of caution needed when inter-
preting the values obtained for each analyte.
The routine samples contain very low
concentrations of ammonium; 70 to 80 percent
of the routine ammonium values are below the
system decision limits, and 64 percent of sum-
mer nitrate values are below the decision limit.
All species of aluminum are found in relatively
low concentrations in the routine samples:
anywhere from 2 percent to 94 percent of the
routine values are below the system decision
limit. Routine values for DIC-eq are also quite
low: 37 percent to 50 percent of the values
are below the system decision limit. Eighteen
to ninety-three percent of the total phosphorus
values measured for routine samples are less
than the system decision limit.
45
-------
Table 17. Contract-Required Detection Umlts (CRDL), System Decision, and System Detection Limits Calculated From Field Blanks for the Three Seasonal
Surveys From ELS-1I
System decision limit*
Analyte
Al-ext
Al-total
Al-dis"
Al-org*
Ca
ci-
Cond
DIC-eq
DIC-initial
DOC
F'-total
Fe
K
Mg
Mn
Na
NH/
NO,-
P-total
SiO,
so,-'
(units)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(pS/cm)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
CRDL
0.005
0.005
NA
NA
0.01
0.01
<0.9
0.05
0.05
0.10
0.005
0.01
0.01
0.01
0.01
0.01
0.01
0.005
0.002
0.05
0.05
Spring
Labi
(28)
0.001
0.013
0.016
0.029
0.033
0.284
2.403
0.730
0.544
0.610
0.011
0.005
0.020
0.007
0.001
0.071
0.024
0.030
0.023
0.046
0.067
Ub1
(6)
•0.001
0.056
NA
NA
0.025
0.138
£475
0.940
0.670
0.218
0.007
0.015
0.011
0.021
C
0.009
0.018
0.086
0.005
0.016
0.059
Summer
Lab 2
(11)
0.005
0.011
NA
NA
0.029
0.018
1.570
0.153
0.328
0.505
c
0.003
0.005
0.003
-0.003
0.010
0.026
0.015
0.004
0.053
0.031
Pooled
(17)
0.012
0.034
0.016
0.019
0.029
0.079
2.114
0.659
0.546
0.444
0.007
0.012
0.007
0.013
0.001
0.009
0.024
0.057
0.005
0.045
0.046
Fall
Lab 2
(26)
0.008
0.054
0.017
0.007
0.014
0.028
2.071
0.194
0.268
0.347
c
0.004
0.008
0.002
0.001
0.064
0.033
0.033
0.004
0.020
0.039
Spring
Labi
(29)
0.011
0.020
0.022
0.042
0.058
0.531
2.929
1.085
0.793
1.087
0.014
0.009
0.035
0.011
0.001
0.135
0.048
0.070
0.042
0.090
0.135
System
Labi
(6)
0.016
0.094
0.020
0.044
0.037
0.242
3.086
1.368
0.681
0.322
0.008
0.021
0.019
0.033
C
0.015
0.030
0.129
0.007
0.025
0.083
detection limit*
Summer
Lab 2
(11)
0.009
0.016
0.023
0.021
0.051
0.028
1.866
0.204
0.430
0.747
C
0.006
0.008
0.005
•0.001
0.017
0.054
0.022
0.007
0.104
0.040
Pooled
(17)
0.029
0.058
0.022
0.031
0.048
0.142
2.741
1.071
0.783
0.676
0.013
0.022
0.013
0.023
0.004
0.016
0.048
0.094
0.008
0.086
0.064
Fall
Lab 2
(26)
0.012
0.094
0.025
0.011
0.027
0.045
2.867
0.270
0.372
0.495
c
0.010
0.018
0.004
0.003
0.132
0.061
0.054
0.006
0.052
0.056
* Numbers in parentheses represent the number of samples.
* This analyte was measured only in the processing laboratory. The decision limit for this analyte is for the processing laboratory for the indicated season.
c Standard deviation not calculated; only a single value was reported by the laboratory.
NA - Not applicable.
-------
Table 18. Summary Statistics for Field Blank Data, Eastern Lake Survey - Phase II*
Sorina-1
Analytes (units)
Al-ext (mg/L)
Al-total (mg/L)
Al-dis* (mg/L)
Al-org* (mg/L)
ANC (ueq/L)
BNC (ueq/L)
Ca (mg/L)
Cl- (mg/L)
Cond (uS/cm)
DIC-eq (mg/L)
DIC-initial (mg/L)
DOC (mg/L)
F'-total (mg/L)
Fe (mg/L)
K(mg/L)
Mg(mg/L)
Mn(mg/L)
Na (mg/L)
NH/ (mg/L)
NO/ (mg/L)
P-total (mg/L)
pH-BNC (pH units)
pH-ANC (pH units)
pH-eq (pH units)
SiO. (mg/L)
S0> (mg/L)
N
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
Mean
•0.0085
0.0064
0.0109
0.0151
2.9207
32.0379
0.0090
0.0369
1.8759
0.3762
0.2947
0.1336
0.0077
0.0005
0.0048
0.0026
•0.0002
0.0069
-0.0002
-0.0103
0.0042
5.7331
5.7169
5.6924
0.0010
-0.0018
STD
0.0060
0.0042
0.0034
0.0083
5.7421
12.6142
0.0147
0.1497
0.3192
0.2147
0.1509
0.2888
0.0019
0.0026
0.0091
0.0026
0.0004
0.0389
0.0147
0.0244
0.0115
0.1542
0.1501
0.3211
0.0270
0.0416
N
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
Summer-1
Mean
•0.0169
0.0182
0.0108
0.0112
3.6500
27.9500
0.0133
0.0339
1.8633
0.5115
0.4592
0.1148
0.0063
0.0095
0.0032
0.0088
0.0000
0.0030
0.0051
0.0421
0.0030
5.4953
5.4993
5.5902
0.0072
0.0361
STD
0.0100
0.0230
0.0027
0.0099
4.3693
11.7740
0.0072
0.0631
0.3706
0.2595
0.1278
0.0627
0.0004
0.0034
0.0048
0.0074
0.0000
0.0036
0.0075
0.0263
0.0011
0.1930
0.1902
0.1557
0.0055
0.0141
N
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
Summer-2
Mean
0.0005
0.0061
0.0106
0.0052
2.1273
21.0727
0.0077
0.0076
1.2810
0.1016
0.2265
0.2636
0.0000
•0.0006
0.0010
0.0008
-0.0039
0.0018
•0.0019
0.0082
0.0008
5.6636
5.6364
6.1082
0.0014
0.0218
STD
0.0025
0.0031
0.0039
0.0047
6.1693
6.3032
0.0132
M062
0.1754
0.0310
0.0617
0.1464
0.0000
0.0025
0.0021
0.0014
0.0008
0.0047
0.0169
0.0041
0.0017
0.1826
0.2203
0.5118
0.0312
0.0054
N
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
Summer-1+2
Mean
•0.0056
0.0104
0.0107
0.0073
2.6647
23.5000
0.0097
0.0169
1.4865
0.2463
0.3086
0.2111
0.0022
0.0030
0.0018
0.0036
-0.0026
0.0022
0.0006
0.0201
0.0016
5.6042
5.5880
5.9254
0.0035
0.0269
STD
0.0104
0.0144
0.0034
0.0073
5.5060
8.9235
0.0116
0.0379
0.3800
0.2498
0.1436
0.1414
0.0031
0.0057
0.0033
0.0058
0.0020
0.0043
0.0144
0.0225
0.0018
0.1984
0.2150
0.4862
0.0250
0.0114
N
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
Fall-2
Mean
0.0036
0.0132
0.0103
0.0018
1.6000
19.8538
0.0018
0.0110
15749
0.1186
0.1646
0.1981
0.0000
-0.0023
•0.0022
-0.0001
0.0000
•0.0035
0.0053
0.0113
0.0015
5.8835
5.7231
5.6281
•0.0115
0.0233
STD
0.0025
0.0245
0.0043
0.0029
2.7527
4.5423
0.0075
0.0102
0.4824
0.0460
0.0629
0.0900
0.0000
0.0036
0.0062
0.0013
0.0008
0.0412
0.0170
0.0129
0.0013
0.1929
0.0678
0.1416
0.0192
0.0098
* This analyte was measured only in the processing laboratory. The decision limit for this analyte is for the processing laboratory for trie Indicated season.
" The number following the season indicated the laboratory; where the 1 represents Laboratory 1. the 2 represents Laboratory 2, and the 1+2 represent the combined
laboratory 1 and 2 data.
-------
Table 19. Percentage of Routine Sample* Having
Valuee Lea* Than or Equal to the System
Decision Umlt*
Analyte
Spring
Summer
Fall
Al-ext
AHotal
Al-dis
Al-org
Ca
ci-
Cond
OlC-eq
DIC-initial
DOC
F'-total
Fe
K
Mg
Mn
Na
NH4*
NO,'
P-totat
Sia
so4*-
True Color
Turbidity
23
2
17
52
0
7
0
50
11
2
2
5
0
0
3
0
75
22
93
3
0
25
10
58
43
32
94
0
2
0
46
17
2
0
18
3
0
26
0
81
64
23
2
0
11
b
36
52
16
20
0
0
0
37
2
1
1
8
0
0
3
1
71
34
18
2
0
8
1
* Routine values for each season are compared to the
system decision limit for that season.
* There was no system decision limit for summer be-
cause all the values were reported as the same
number.
Summary of Detectability
The greatest concern in the field blank
data is the apparent difference in the results
of Laboratory 1 and Laboratory 2. For 8 of 23
analytes measured. Laboratory 1 consistently
had a larger system decision limit as a result
of either larger values, poorer precision, or
both. This was especially consistent for Al-
org, Cf, Cond, DIC-eq, DIC-initial, K, Mg, and
SO/2. The importance of this apparent prob-
lem depends entirely on the relative levels of
these analytes in the routine samples. As
indicated in Table 19, this presents a potential
problem for Al, DIG, NH4+, N03-, and P-total
which are present in relatively low concentra-
tion in the lakes surveyed. This is not surpris-
ing for ammonium, nitrate, and phosphorus be-
cause the lakes under consideration are, in
general, dilute, oligotrpphic systems. These
systems are often nutrient-limited with respect
to phytoplankton primary productivity and, al-
though phytoplankton btomass is tow, dis-
solved nutrients will be assimilated quickly.
The systems usually have low buffering capac-
ity, which corresponds to low DIG values.
With this background in mind, it is not
unexpected to find a high percentage of sam-
ples below the decision limit. It does not
necessarily indicate poor data quality but
rather reflects the systems being studied. It
does, however, mean that analysis and inter-
pretation of trends in these data will require
more attention to the issue of detectability,
and differing levels of performance from par-
ticipating laboratories can become a problem.
The analysis of concentrations found in
audit samples with respect to system detec-
tion limits indicates that greater care must be
taken in creating or selecting audit samples.
The quantity of DIG, CT, NH/. and P-total
present in the natural audit samples was tow
relative to the system detection limits. The
NSWS has provided a great deal of back-
ground information on expected system detec-
tion limits; this information should be applied
in creating or selecting future audit samples.
The lowest concentrations in these audits
should not be less than the expected system
detection limits.
Accuracy and Precision
Accuracy or bias can be defined as the
closeness of a measurement to a true or
known value, and precision is the dispersion of
repeated measurements about a central ten-
dency. The EPA has evaluated accuracy by
calculating a percent difference from a known
value, and has evaluated precision by calculat-
ing the percent variability of repeated measure-
ments (coefficient of variation or percent
relative standard deviation). Although this ap-
proach to documentation of data quality has
proven effective for studies of a single analyte
over a limited range of concentrations, it is not
as effective when evaluating multianalyte, wide
concentration range, survey data. This report
presents a different approach for assessing
precision and accuracy than was used in previ-
ous QA reports for the NSWS. Summary
statistics from QA data for each analyte are
presented by season in appendices C and E.
The procedures and calculations that can be
used to interpret these statistics are presented
within the text. The data user is then provided
with the tools for evaluation of the level of
error associated with survey data without any
reference to preset DQOs.
Precision and accuracy measurements
of ELS-II data indicate good quality data
when compared with the DQOs for a majority
of analytes. There were a few exceptions,
which are discussed in detail in the following
48
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subsections. Predetermined DQOs for the
ELS-II were based upon previous NSWS OQOs
for intralaboratory performance. These DQOs
can be used as a relative standard to assess
the quality of ELS-II data. Accuracy and
precision DQOs (Table 20) were expressed in
percent difference and percent relative stan-
dard deviation (variability), respectively, for all
analytes except pH, which was expressed in
pH units. Calculation of percent difference is
based upon comparison of median target
values to median measured values for a given
audit/laboratory combination. Medians are
considered the best statistic for this purpose
because they are not influenced by outlier data
points, whereas a mean value would be. The
performance ranges (DQOs) in Table 20 only
apply to data values that were at least 10
times greater than the detection limit for a
given analyte, because percent difference and
percent variability can be highly inflated at very
low concentrations due to use of a percent
scale. For example, a percent relative stan-
dard deviation of 100 at the extreme limits of
measurement capability for an analyte repre-
sents a very small absolute error in the mea-
surement.
The DQOs for ELS-II were established to
provide a minimum acceptable performance
range for each laboratory. The laboratories
used numerous QC samples to monitor their
own performance. These samples included
laboratory blanks, QC check samples, and
laboratory duplicates. Assessment of these
data would provide a biased estimate of
measurement error, because the laboratories
knew the content of the samples and were
required by contract to achieve prescribed
quality standards for measurement of these
samples. Therefore, the internal QC sample
data are not used to describe measurement
error in this report; they were used by the
EMSL-LV QA staff to identify and correct, if
possible, laboratory errors. Assessment of
double-blind QA sample data (audits and field
duplicates) does provide an unbiased estimate
of laboratory performance, because the labora-
tories could not distinguish these performance
samples from routine samples. A summary of
the results of assessment of audit and dupli-
cate data is presented below.
Natural Audit Samples
Natural audit samples from Seventh Lake
and Big Moose Lake were the only double-blind
QA performance audit samples that were used
by both laboratories throughout all seasonal
subsurveys. Field natural (FN) audit data
provide the most conservative estimate of
measurement precision and bias, because
these samples were subject to all processing
and analytical procedures except the field
collection step. Laboratory natural (LN) audits
were subject to analytical laboratory sources
of error but were not exposed to shipping and
handling procedures before the samples were
preserved and divided into aliquots. Therefore,
they may underestimate the magnitude of
measurement error. Target values for analyte
concentrations in natural audit samples were
generated by taking the median of all available
data for that sample generated during the
NSWS by analytical laboratories. These values
included data from four laboratories, two of
which were operating during the NSS-I and
two of which were operating during the ELS-
II surveys. All four laboratories received
natural audit samples from the same source
and used the same protocols for preparation
and analysis. Statistical characterization of
both FN and LN audit material are presented
in Appendix C along with estimates of the
target values for each audit type. Descriptive
statistics presented in Appendix C include
sample size, median, mean, standard devia-
tion, and percentile data for 10, 25, 75, and 90
percent of the data. In addition, the scatter
and confidence interval plots given in Appendix
D for the field natural audits can be used in
conjunction with the summary statistics.
Several approaches can be used to define
and assess the measurement error of survey
data. The approach presented in this report
is based upon the design of the QA program.
This approach probably provides a conserva-
tive (worst case) estimate of measurement
error, because the sample size for any given
audit type is generally small (Appendix C).
The statistics and plots presented in
appendices C and D can be used to estimate
measurement error in the following manner:
• Confidence intervals can be calculated
about the mean values using standard
techniques (Sokal and Rohlf, 1969). Sam-
ple sizes and standard deviations are
provided for this purpose in the tables.
• Percent relative standard deviation ([stan-
dard deviation/mean] x 100) can be calcu-
lated to determine the percent variability of
audit sample data, or of grouped duplicate
pair data. Standard deviations and means
49
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Tabla 20. Analytical Data Quality Ob|actlvaa for Datactablllty. Precision, and Accuracy for tha Eastern
Lake Survey-Phase II'
Analyta (Units)
Detection
limit
objective
(units)
Within-
laboratory
precision
(%RSD)*
Within-
laboratory
accuracy (%)
Field Sita
pH, field (pH units)
Conductivity (uS/em)
Dissolved oxygen (mgA.)
Depth (m)
—
—
—
—
—
—
—
-
±0.20*
±20C
—
—
Processing Laboratory
Aluminum (mgA.)
Total monomark:
Nonexchangeabto monomerie
pH, closed system (pH units)
Dissolved inorganic carbon closed
system (mgA)
Tnje color (PCU)
Turbidity (NTU)
0.01
0.01
—
0.05
0
2
10 (>0.01 mgA)
20 (£0.01 mgA)
10 (>0.01 mgA)
20 (£0.01 mgA)
0.1*
10
5C
10
10 (>0.01 mgA)
20 (£0.01 mgA)
10 (>0.01 mgA)
20 (£0.01 mgA)
±0.1C
10
—
10
Analytical Laboratory
Acid-neutralizing capacity (peqA)
Aluminum (mgA.)
Total
Extractabte
Ammonium (mgA)
Base-neutralizing capacity (peqA)
Calcium (mgA)
Chloride (mgA)
Conductivity (pS/cm)
Dissolved inorganic carbon (mgA.)
Initial
Equilibrated
Dissolved organic carbon (mgA)
Fluoride. Total dissolved (mgA)
Iron (mgA)
Magnesium (mgA)
d
0.005
0.005
0.01
d
0.01
0.01
0
0.05
0.05
0.1
0.005
0.01
0.01
10
10 (>0.01 mgA)
20 (£0.01 mgA)
10 (>0.01 mgA.)
20 (^0.01 mgA)
5
10
5
5
2
10
10
5 (>5.0 mgA)
10 (£5.0 mgA)
5
10
5
10
10 (>0.01 mgA)
20 (£0.01 mgA)
10 (>0.01 mgA)
20 (£0.0 1 mgA.)
10
10
10
10
5
10
10
10
10
10
10
(Continued)
50
-------
Table 20. Continued.
Analyte (Units)
Detection
limit
objective
(units)
Within-
laboratory
precision
(%RSD)*
Within-
laboratory
accuracy (%)
Analytical Laboratory (continued)
Manganese (mg/L)
Nitrate (mg/L)
pH (pH units)
Initial ANC
Initial BNC
Equilibrated
0.01 10
0.005 10
— 0.05C
- 0.05C
— 0.05C
10
10
i0.1c
±0.1C
±0.1C
Phosphorus, total dissolved (mg/L)
Potassium (mg/L)
Silica (mg/L)
Sodium (mg/L)
Sulfate (mg/L)
0.002
0.01
0.05
0.01
0.05
10(>0.010 mg/L)
20(50.010 mg/L)
5
5
5
5
10(>0.010mg/L)
20(s0.010mg/L)
10
10
10
10
' These data quality objective are applicable for all of the Eastern Lake Survey-Phase II subsurveys.
* %RSO - percent relative standard deviation. Unless otherwise noted, this is the precision goal at concentrations
greater than or equal to 10 time* the required detection limit.
c Precision or accuracy goal in terms of applicable units.
" The absolute value of each laboratory calibration blank measurement was required to be less than or equal to 10
jieq/L
• The mean of six nonconsecutive blank measurements must not exceed 0.9 fjS/cm.
calculated for duplicate pair data
pooled estimates of these statistics.
are
• The plots include both scatter diagrams by
survey and means and confidence intervals
for each survey. The plots can be used to
screen analytes to determine if the error is
acceptable for the intended use of the
data. These plots provide a visual sum-
mary of the data that were designed to
estimate measurement precision and
accuracy.
• Interquartile ranges can be used as a
nonconservative estimate of the expected
spread of data about a central tendency
(the median for nonparametric statistics).
• The range between the 10th and 90th per-
centiles can be used as a conservative
estimate of the expected spread about a
central tendency.
• Medians or means can be compared to
target values to determine the accuracy of
the data.
• Audit data represent the low and high
range of data to be expected within the
population of ELS-II lakes for most ana-
lytes measured. Midrange data should
have error bounds somewhere between
the high value and the low value data.
Ideally, a midrange audit would provide
this information. However, for ELS-II only
two natural audit sources were available
for use.
Comparison of natural audit data to the
precision DQOs can be accomplished by com-
paring the mean and standard deviation col-
umns in the tables in Appendix C. Because
most of the precision DQOs were 5 percent or
10 percent, it is a simple matter to determine
if the standard deviation was less than 5
percent or 10 percent of the mean. (The %RSD
is the standard deviation divided by the mean).
For example, if the mean value for an analyte
51
-------
was 0.100, then the standard deviation should
be 0.01 or less. Conductivity and pH are
exceptions to the 5 percent and 10 percent
DQOs and need to be compared somewhat
differently. The DQO for pH is an absolute
value and can be compared directly to the
standard deviation column in Appendix C
tables. Conductivity has a 2 percent precision
DQO and needs to be calculated accordingly.
Comparison of natural audit data to the
accuracy DQOs is complex, but because al-
most all the accuracy DQOs were 10 percent
(pH and conductivity are again exceptions), the
calculation is not unmanageable. In Appendix
C tables, the Field Target and Lab Target
columns should be compared to the Median
column. These values should be within 10
percent of each other to meet the DQOs. For
example, if a target value is 0.100, the median
value should fall between 0.09 and 0.11. Con-
ductivity values should be within 5 percent,
and pH values should be within 0.1 pH units.
It will be difficult to interpret this type of com-
parison in some cases because there are
substantial differences between the field and
laboratory target values. Extractable alumi-
num, ammonium, phosphorus, and laboratory
DIC fall within this category for at least one of
the natural audits. In addition, fluoride, iron,
manganese, ammonium, and phosphorus are
in very low concentration ranges in the natural
audits, thus limiting comparability of these
data to the DQOs.
Data quality for a majority of analytes
was within or very close to the quality goals
set prior to the survey (DQOs). Exceptions to
this pattern were extractable aluminum in all
seasons at low analyte concentrations and in
fall at high concentrations, chloride in spring
and summer for Laboratory 1, laboratory DIC-
eq measurements, and summer sodium mea-
surements for Laboratory 2. Extractable
aluminum and DIC-eq appeared to be inaccu-
rate and relatively imprecise. This pattern was
confounded, however, by the discrepancies be-
tween the LN and FN target values, which may
have been a function of method capability,
laboratory capability, or a characteristic of the
audit sample. Chloride for Laboratory 1 was
imprecise in the spring (12.1 to 42.7 percent)
and in the summer (3.4 to 88.5 percent). Sum-
mer sodium values appeared to be highly
imprecise for Laboratory 2; however, the plots
of these data presented in Appendix D indicate
this imprecision was due to a single outlying
value; the central tendency of the data ap-
pears comparable to other seasons and labo-
ratories. Plots of Cl data indicate general
imprecision rather than imprecision generated
by a single outlier as shown for Na.
A series of analytes (ANC for Big Moose
Lake, F'- total for spring, Fe, Mn, NH4+, and P-
total) had higher %RSD values than the DQOs,
thus giving the appearance of poor quality
data. Concentrations of analytes in the audits
for these chemical species were less than 10
times the detection limit goals (Table 20).
Therefore, comparison of the precision of
these analytes in not relevant as explained
above. The accuracy of these data appears
very good, even at low analyte concentrations,
with the exception of ammonium which did
appear to exceed the accuracy DQOs. The
quality of nitrate values for the Spring Season-
al subsurvey is very close to the DQOs, but is
much more variable than the summer or fail
nitrate values and shows no consistent trend
for high or low bias.
Field Routine-Duplicate Pairs
Previous NSWS survey data emphasized
field routine-duplicate pair data for documenta-
tion of overall within-batch precision. This was
reasonably effective because large numbers
of duplicate pairs were collected in these
surveys. The sample size for duplicate pairs
for each seasonal subsurvey in ELS-II ranged
from 17 to 29. The number of duplicate pairs
within any concentration range was variable,
both between analytes and between surveys
(Appendix E). In addition, analysis of these
data would provide estimates of precision
which only include the error associated with
samples from one batch. Although duplicate-
pair data theoretically include the field compo-
nent of error, audit samples include among-
batch error. This error is generally much larger
than the within-batch error (see Cougan et al.,
1988) because it includes batch to batch
analytical sources of error (e.g., calibration
error, dilution of standards). For example, the
standard deviation of ANC values in the range
of 100 to 150 /jeq/L ranged from 2.2 to 5.5 for
duplicate pairs "and 6.2 to 27.2 for natural
audits. The audits provide a much higher
estimate of sample variance than do the
duplicate pair data for a given concentration
range. Therefore, it is not recommended that
duplicate-pair data be used to assess preci-
sion. Statistical summaries for duplicate pairs
are presented in Appendix E, which includes
the sample size, grand mean, pooled standard
deviation, and %RSD for data grouped into
analyte ranges of interest. Appendix E also
52
-------
contains plots of the mean concentration of
the field routine-duplicate pair versus the
%RSD. The ranges of interest for analytes
follow those defined in Cougan et al. (1988) by
data users interested in acidic deposition
effects in streams and the range of concentra-
tion of ELS-II data. These data can be used
as estimates of precision outside the range of
audit sample concentrations. The data user
needs to be aware that these values will
underestimate the variance of samples be-
cause the among-batch error is not accounted
for.
Duplicate pair data (Appendix E, %RSD
column) can be compared to the predeter-
mined DQOs for within-laboratory precision by
comparing the precision column in Table 20
with the %RSD column in Appendix E. Addi-
tionally, the grand mean column in Appendix E
tables can be compared to the detection limit
column in Table 20 to evaluate the relevancy of
the precision estimate for a given concentra-
tion range. Comparison of duplicate pair data
to the DQOs is not relevant for aluminum,
fluoride, manganese, ammonium, nitrate,
phosphorus, and silica because of the low
concentration of a majority of the samples
collected. In addition, ANC data presented in
Appendix E indicate high variation in the low
concentration range. This does not indicate
poor quality data, however, because high
variability in this concentration range reflects
a small (<10 ^eq/L) absolute error in the data.
With the exception of the Spring Seasonal
and Summer Seasonal subsurvey chloride
data from Laboratory 1, data for analytical
laboratory DIG, and iron measurements, all
analytes compared favorably with the preset
DQOs.
Summary of Accuracy and
Precision
Based upon the data from both audit
and duplicate pair double-blind QA samples,
the ELS-II data base reflects high quality data
for most analytes of interest. For example,
ANC, pH, NO,', and SO4* had accuracy and
precision well below or near to the DQOs.
Nitrate values were very close to the upper
bounds of the DQOs for Spring Seasonal
subsurvey data and need to be evaluated
carefully by the data user to determine if the
quality is adequate to answer the questions
being asked. Analytes that did not meet pre-
established DQOs included Laboratory 1 chlo-
ride data, and all analytical laboratory mea-
surements of extractabte Al, DIC, and DOC
data. Processing laboratory measurements for
DIC should be used rather than the DIC-eq
values because the processing laboratory data
met the DQOs. Extractable aluminum data are
comparable in quality to those produced in
other NSWS surveys (Best, et al., 1986, Silvers-
tein et al., 1987, Cougan et al., 1988). High
levels of total aluminum imprecision are a
function of one or two outlying data points for
all seasonal subsurveys (Appendix D). The
data in this report should be closely examined
to determine the impact of variability and bias
on specific uses of the data. Ultimately, the
data user must decide if the quality of the
data described in this report is sufficient to
satisfy the goals of a particular study.
Interlaboratory Bias
The issue of interlaboratory bias is an
especially critical one in ELS-II. The survey
design used two laboratories over the course
of the three seasonal samplings. Both labora-
tories performed analyses for the Summer
Seasonal subsurvey; Laboratory 1 was used
exclusively during the Spring Seasonal sub-
survey and Laboratory 2 was used exclusively
during the Fall Seasonal subsurvey. If signifi-
cant analytical bias exists between the labora-
tories, then apparent seasonal differences
between routine samples could simply be the
result of interlaboratory bias rather than actual
differences. In analyzing the QA and QC data
for interlaboratory bias, three sets of samples
can be used. The first set is referred to as
the summer split samples. During the summer
a triplicate routine sample was taken. Then,
equal portions of the split were sent to each
laboratory for analysis. The second and third
sets are the Seventh Lake and Big Moose Lake
natural audit samples (Lots FN7 and FN8,
respectively.)
Interlaboratory bias can only be directly
assessed for the Summer Seasonal subsurvey,
when both laboratories operated simultaneous-
ly. In order to apply the information to spring
and fall data, it is necessary to assume that
the interlaboratory bias that exists for the
summer analyses is the same as the bias for
spring and fall. Such bias should be apparent
in data for audit material distributed during
each season. Unstable audit material or
variable seasonal performance for either of the
laboratories invalidates this assumption.
Additionally, the split data indicate only relative
bias because they are based on samples for
which a true or expected value is unknown.
On the other hand, well-characterized natural
53
-------
audit material (i.e., an established expected
value) provides some basis for estimating
absolute bias. However, the natural audit
samples provide an indication of interlabora-
tory bias at only one specific concentration.
In order to evaluate the impact of the inter-
laboratory bias on routine data, it is necessary
to establish the interlaboratory bias over the
range of concentrations seen in the routine
data. In the following analysis, the split
samples and the two natural audit materials
are used to examine the issue of inter-
laboratory bias.
Analyses of the summer split samples of
routine lake triplicates can be used to examine
interlaboratory bias over the range of concen-
trations found in the routine samples. The
expectation for the analyses of splits is that
the data for the laboratories will fall on a line
with a slope of one and an intercept of zero,
the identity line. If a regression line is fit to
the data, a 95 percent confidence interval can
be generated for the estimate of the slope of
the predicted regression line. If the slope of
the identity line (i.e., slope* 1) falls within the
95 percent confidence interval, then the best fit
line for the data cannot be said to differ
significantly from the identity line. The pre-
dicted line can have a slope of one and still
indicate bias if the Y-intercept is significantly
different from zero. This would result in a line
parallel to the identity line with a non-zero
intercept.
Results of split sample data analyses
are presented in plots in Appendix F. The
figures show the data for each analyte with
the identity line plotted. In only two instances
is there a visually apparent bias, DIC-initial
and DIC-eq. In each of these instances, the
bias appears to be a linear function of concen-
tration with a slope of 1.5 for DIC-eq and 1.3
for QIC-initial, both of which are significantly
different from a slope of one. Chloride exhib-
its a slight constant bias (slope 0.836) in
which Laboratory 1 measures low relative to
Laboratory 2. Conductivity shows a small (4.5
juS/cm) but constant bias, with Laboratory 1
measuring high relative to Laboratory Z The
bias shown for iron is created primarily by two
samples at high concentrations with high
measurements for Laboratory 1 relative to
Laboratory 2. Given the lack of intermediate
points, it would be difficult to consider this as
strong evidence of overall bias in the iron
measurements. For three analytes, Al-total,
NO3~, and NH4+, the split plots show extremely
poor relationships between measurements
from Laboratory 1 and Laboratory 2. In all
three instances, greater than 50 percent of the
samples are below the system decision limit
and fit the situation where Laboratory 1 exhib-
ited poorer precision for field blank measure-
ments than did Laboratory 2.
Analysis of the FN7 and FN8 audit sam-
ples for spring and fall provides information
about interlaboratory bias at two specific
concentrations, one for each audit type.
Additionally, these data can be used to devel-
op some idea of absolute bias because prior
information is available on the expected values
for these materials (see target values in Ap-
pendix C) and an assessment about which
measurements are closer to the expected
values can be made. Statistical summaries
for FN7 and FN8 in each of the three seasons
are presented in Appendix C. The figures in
Appendix 0 present the same information in
graphical form. Each figure consists of an
upper and lower component. The upper com-
ponent on each figure presents the mean and
95 percent confidence interval for each of the
three seasons, and the lower component
shows the individual sample points. There are
two figures for each analyte, one representing
the results for FN7 and a second the results
for FN8. Analyses of the FN7 and FN8 data
for interlaboratoy bias represent several inter-
esting cases. For those analytes (AJ-dis, Al-
org, DIC-closed, pH-closed, True color, Turbidi-
ty) measured at the processing laboratory,
which was located in Las Vegas during both
the spring and fall subsurveys, the inter-
laborary bias analysis actually represents bias
in the processing laboratory over time. For the
remainder of the analytes that were measured
by the analytical laboratories, the interlabora-
tory bias analysis represents bias between
two different laboratories that made measure-
ments during two different time periods.
Because a primary interest of the survey is to
evaluate spring and fall differences, both of
the cases of interlaboratory bias are of inter-
est and will be addressed.
In examining the data from FN7 and FN8,
14 of 60 (30 analytes for each audit type)
analyses show statistically significant differ-
ences between spring and fall mean values.
This represents 9 of 30 possible analytes.
These analytes are: Al-ext, Al-org, Ca, Cond,
DIC-eq, DIC-initial, F'-total, Fe, and pH-eq. Of
these, only Cond, DIC-eq, F'-total, Fe, and
pH-eq showed consistent results for both
FN7 and FN8. The information obtained for
conductivity, DIC-eq, and Fe appears to be
54
-------
consistent with the results of the split plot
analysis. For DIC-eq, FN7 and FIM8 behave
similarly, with Laboratory 1 producing results
greater than Laboratory 2. The results of this
analysis are consistent with the slope of 1.5
derived from the split analysis. Laboratory 1
also produces greater values for Fe than does
Laboratory 2, again consistent with the split
analysis. The results from the conductivity
analysis also agree with the split plot analysis,
with Laboratory 1 producing values greater
than Laboratory 2 when the concentration is
above 20 to 30 jjS/cm.
In evaluating the results of all of these
analyses, it is helpful to consider the relative
magnitude of the apparent bias and the mag-
nitude of seasonal differences. For example,
if a bias of 4 pS/cm for conductivity is within
the range of seasonal differences expected,
then this statistically significant bias may also
be important to data interpretation. However,
if this bias is much smaller than the seasonal
difference that is of interest, then statistically
significant bias may be insignificant in a
practical sense relative to the data interpreta-
tion.
The results of the split samples, FN7
audit, and FN8 audit analyses for interlabor-
atory bias are summarized in Table 21. The
table is intended to serve as a tool to alert the
data user to potential interlaboratory bias that
may affect the interpretation of routine data.
For the split samples, interlaboratory bias is
indicated when the predicted line is significant-
ly different from the identity line. For the FN7
and FN8 audit materials, interlaboratory bias is
indicated if the means from spring and fall
were statistically different for the analyte and
audit material of concern. Additionally, ana-
lytes were targeted for potential interlaboratory
bias if, upon visual examination of the data
tables and scatter, plots (appendices C and D,
respectively), there appeared to be interlabora-
tory bias, which was masked by poor precision
for either laboratory. An analyte was flagged
for interlaboratory bias in the summary column
when at least two of the analyses indicated
potential problems with the data.
The results of these analyses support
the conclusion that the clearest instances of
interlaboratory bias are for measurements of
Cond, DIC-eq, DIC-initial, and Fe. Additionally,
there is somewhat weaker evidence to indicate
potential problems with interlaboratory bias for
ANC, Ca, Cr, F'-total, K, pH-eq, and Turbidity.
As discussed above, there are other instances
of interlaboratory bias which appear to be
supported by either the split analyses or the
natural audit analyses, but not both. The
scientific implications of these biases depend
almost entirely on the magnitude of differences
that the data user is trying to detect between
the data for different seasons.
Other Methods for Assessing
Data Quality
In addition to the analyte-by-analyte
evaluations, three checks of overall sample
data quality are provided in the following
discussions: comparison of measured ANC
values and calculated carbonate alkalinity;
comparison of the total ionic charge of anions
and cations; and comparison of measured and
calculated specific conductance values. These
three checks, which were also used by the
analytical laboratories during actual sample
analysis and by the EMSL-LV QA staff during
data verification, are useful because each
sample can be evaluated on an individual
basis.
Comparison of Measured ANC and
Calculated Alkalinity Values
Comparison of measured ANC values
and calculated carbonate alkalinity values
provides (1) an indication of the reliability of
pH, DIG, and ANC measurements, and (2) an
indication of the presence of unmeasured
noncarbonate protolytes.
The calculated carbonate alkalinity
(ALKAJ represents the contribution of bicar-
bonate, carbonate, hydroxide, and hydrogen
ions to the ANC. The measured ANC repre-
sents the total acid-combining capacity of a
water sample determined by titration with a
strong acid. The ANC includes alkalinity (car-
bonate species) as well as other basic species
(e.g.. borates, dissociated organic acids, and
aluminohydroxy complexes). Carbonate alka-
linity values were calculated by using the
following equation (Hillman et al., 1986):
ALKA,.
[DIG]
12,011 I [H+]a + [H+1K, +
Kw
x 10e
55
-------
Table 21. Summary of the Analyses of the Split Data and the FN7 and FN8 Audit Samples for Interlaboratory
Blaa.
Analyte
Al-ext
AMotal
Al-dis
Al-org
ANC
BNC
Ca
ci-
Cond
DIC-closed
DlC^q
QIC-initial
DOC
F'-total
Fe
K
Mg
Mn
Na
NH/
NCy
P-total
pH-closed
pH-ANC
pH-BNC
pH*q
SiO,
so/-
Cotor
Turbidity
Splits
a
N
b
b
Y
N
Y(slight)
Y
Y(sllght)
b
Y
Y
N
N
Y(slight)
Y(slight)
Y(slight)
Y(slight)
N
N(poor correlation)
N(poor correlation)
N
b
N
N
N
N
Y(s«ght)
b
b
FN7
Y
N
N
Y
Y(slight)
N
Y(slight)
N
Y(alight)
N
Y
Y
N
Y
Y(slight)
Y(5light)
N
N
N
N
N
N
N
N
N
Y(slight)
N
N
N
Y(slight)
FN8
N
Y
N
N
Y(slight)
N
N
Y(slignt)
Y(slight)
N
Y
N
N
Y(slight)
Y
N
N
N
N
Y(slight)
Y(s«ght)
N
N
N
N
Y(slight)
N
N
Y(slight)
Y(sltght)
Potential
interlaboratory
bias
No
No
No
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
* Al-ext was not a required measurement for the interlaboratory bias split sample.
* These analytee were measured by the processing laboratory and were not part of the interlaboratory bias study.
where:
DIG
PH
calculated carbonate alkalinity
DIC-initial value or the closed
system DIG measurement in units
of mg/L (12,011 converts mg/L to
motos/L).
1Q-(PH)
value from the mean of the analyti-
cal laboratory initial pH measure-
ments or the closed-system pH
measurement, which-ever is appli-
cable.
4.4463 x 10'7 at 25 degrees C
4.6881 x 10'" at 25 degrees C
1.0023 x 1Q-" at 25 degrees C
Calculated carbonate alkalinity values
were obtained by using both the processing
laboratory closed system pH and DIG mea-
surements (Calculated Alkalinity 1) and by
using the analytical laboratory pH (mean of
pH-ANC and pH-BNC) and DIC-initial measure-
ments (Calculated Alkalinity 2).
Figures 5, 7, and 9 show the relation-
ships between the measured ANC and Calcu-
lated Alkalinity 1 for each season of the ELS-
II. Figures 6, 8, and 10 show the relationship
between the measured ANC and Calculated
Alkalinity 2 for each season. The diagonal line
drawn on the plots represents the 1:1 corre-
spondence line, where measured ANC equals
calculated carbonate alkalinity. The measured
ANC should be greater than or equal to the
carbonate alkalinity because the carbonate
alkalinity is a portion of the titrated ANC. If
calculated carbonate alkalinity is greater than
measured ANC. then pH, DIG, or ANC analyti-
cal errors are indicated. Observed differences
between the measured ANC and the calculated
carbonated alkalinity that are above the 1:1 line
can be explained by the presence of noncar-
bonate protolytes such as DOC, aluminum,
dissolved metal complexes, or paniculate
metal oxides or inorganic acid species such as
silicic acid.
For the Spring Seasonal subsurvey, Figure
5 demonstrates that for all routine samples,
56
-------
o
in
o
500
300
100
-100
o
TJ
£
m
o
500
300
100
-100
' A A
-100 100 300 500
Calculated Alkalinity 1 (/teq/L)
Figure S. Measured ANC versus carbonate afcalinfty
calculated from closed system pH and DIG
values for routine samples, Spring Seasonal
subsurvey, ELS-IL n - 145.
-100 100 300 500
Calculated Alkalinity 2 (/teq/L)
Figure 6. Measured ANC versus carbonate alkalinity
calculated from analytical laboratory initial pH
and DIG values for routine samples. Spring
Seasonal subsurvey, ELS-IL n - 145.
cr
jj,
CJ
1
in
I
500
300
100
-100 H
500
300
o
TJ
£
in
O
u
2
-100 100 300 500
Calculated Alkalinity 1 0/eq/l)
-100 100 300 500
Calculated Alkalinity 2 G/eq/L)
Figure 7. Measured ANC versus carbonate alkalinity cateu- Figure 8.
lated from closed system pH and DIC values for
epilemnetic routine samples. Summer Seasonal
subsurvey, ELS-II. A - Laboratory 1.O-Labora-
tory 2. n - 147.
Measured ANC versus carbonate alkalinity
calculated from analytical laboratory initial pH
and DIC values for epilemnetic routine sam-
ples, Summer Seasonal subsurvey, ELS-II. A
* Laboratory 1. Q - Laboratory 2. n =• 147.
57
-------
o
10
o
500'
300
100
-100 100 300 500
Calculated Alkalinity 1 (/teq/L)
Figure 9. Measured ANC versus carbonate alkalinity
calculated from closed system pH and DIG
values for routine samples. Fall Seasonal
subsurvey. ELS-II. n - 238
o
T3
in
a
a
500
300
100
-100 ^
-100 100 300 500
Calculated Alkalinity 2 (/ieq/L)
Figure 10. Measured ANC versus carbonate alkalinity
calculated from analytical laboratory initial pH
and DIG values for routine samples, Fall
Seasonal subsurvey. ELS-II. n - 238
data points fall just below, on, or above the
line when measured ANC was plotted against
Calculated Alkalinity 1. In contrast, Figure 6
(measured ANC versus calculated alkalinity 2)
shows the majority of the points falling below
the 1:1 line. An analytical problem not with
ANC but with either pH-initial or DIC initial is
indicated by Figure 6 because a problem with
measured ANC was not indicated when mea-
sured ANC was plotted against alkalinity
calculated from processing laboratory pH and
QIC values. The interlaboratory bias discus-
sion notes that analytical Laboratory 1 DIC-
initial results were high relative to Laborato-
ry 2. Summary statistics for estimates of
accuracy (Appendix C) also indicate that
Laboratory 1 DIC measurements were high
relative to the target values. The relationship
shown in Figure 6 is probably due to an over-
estimation of the DIC content of the samples.
The majority of the points fell above the
1:1 line for the Fall Seasonal subsurvey as
shown in Figures 9 and 10, and for the Sum-
mer Seasonal as shown in Figure 7. Figure 8,
a plot of measured ANC versus Calculated
Alkalinity 2 for the Summer Seasonal sub-
survey, also shows several data points falling
below the 1:1 line. As was the case for the
Spring Seasonal subsurvey samples analyzed
by Laboratory 1, the Figure 8 results are prob-
ably due to an overestimation of the DIC-initial
content of the samples by Laboratory 1.
A comparison of Figures 5 and 9 shows
that when measured ANC values are compared
to calculated alkalinities (processing lab pH
and DIC values) the Spring Seasonal sub-
survey measured ANC values may be slightly
lower than the Fall Seasonal subsurvey ANC
values, assuming that the processing labora-
tory pH and DIC were measured consistently.
Table 21 indicates that there was no intersea-
sonal bias between the Spring Seasonal and
Fall Seasonal subsurvey measurements of
closed system pH and DIG.
Figures 11 and 12 show ELS-1 relation-
ships between measured ANC and calculated
alkalinities. These plots show data points
from the ELS-I verified data base and only
include the lakes that were sampled in ELS-II.
From a visual inspection of the plots, there
does not appear to be a significant difference
between the measured ANC and calculated
alkalinity relationships between the Fall Sea-
sonal subsurvey and ELS-I.
Charge Balances
The anion and cation balance, another
method of assessing data quality, provides an
estimate of the internal consistency of the
samples. An ion balance was calculated for
all the routine lake samples in the ELS-II.
The sum value for anions was the total of
chloride, sulfate, nitrate, fluoride, bicarbonate,
58
-------
O"
1)
O
in
o
500'
300
100
-100 "If
-100 100 300
Calculated Alkalinity 1
500
Figure 11. Measured ANC versus carbonate alkalinity
calculated from closed system pH and DIG
values for routine samples, ELS-I (ELS-II
lakes only), n - 149
o
TJ
at
o
-------
-------
1500
1500
0 300 600 900 1200 1500
Sum of Cations (/xeq/L)
Figure 17. Sum of anions versus sum of cations for
subregion 1B, Spring Seasonal subsurvey,
ELS-II. n - 17.
Figure 18.
0 300 600 900 1200 1500
Sum of Cations (/teq/L)
Sum of anions versus sum of cations for
subregion 1C, Spring Seasonal subsurvey,
ELS-II. n-32.
CT
-------
variability associated with samples containing
greater than 7.0 mg/L is greater than for those
containing less than 7.0 mg/L, it is not as
extreme as for the Spring Seasonal subsurvey.
Most of the variability in the Summer Seasonal
subsurvey plot can probably be attributed to
the Laboratory 1 analysis for chloride. Figures
21, 22, and 23 also show that, with the excep-
tion of Laboratory 1 results containing high
(>7.0 mg/L) chloride values, the majority of the
data points fall below the 1:1 line for the
Summer Seasonal subsurvey. For the Fall
Seasonal subsurvey, Figure 24 shows the
majority of the data falling below the line.
This relationship could be due to slight oyeres-
timation of cations, slight underestimation of
anfons, the presence of unmeasured anions, or
a combination of all three.
Figure 25 is a plot of the sum of anions
versus the sum of cations of the ELS-I results
for the lakes sampled in ELS-II. This plot was
generated by using values in the ELS-I verified
data base. The relationship between the
anions and cations based on a visual inspec-
tion of the plots does not appear to be signifi-
cantly different from the Fall Seasonal sub-
survey (Figure 24).
Conductivity Balances
Comparison of the measured and the
calculated values for specific conductivity
provides an additional check on analytical
errors in the measurements or on the presence
of unmeasured ionic species. The calculated
conductivity was estimated by summing the
equivalent conductivity of ions for calcium,
magnesium, potassium, sodium, ammonium,
chloride, sulfate, nitrate, carbonate, bicarbon-
ate, hydronium, and hydoxkJe. The conversion
factors used to derive conductivities of ions
are given in Table 7.
Plots illustrating the relationships be-
tween measured and calculated conductivity
for routine samples are provided in Figures 26
through 38. The diagonal line on the plot
represents the 1:1 correspondence, where the
measured conductivity equals the calculated
conductivity. The same relationships are
illustrated for the Spring Seasonal subsurvey
measured versus calculated conductivity (Fig-
ures 26 through 28) as are illustrated for the
sum of anions versus the sum of cations for
the Spring Seasonal subsurvey (Figures 13
through 15). When chloride values of greater
I
w
o
o
£
CO
1500'
1200
900
600
300
0
1500
300 600 900 1200
Sum of Cations (/xeq/L)
1500
Figure 21. Sum of anions versus sum of cations for all
epitemnetic routine samples. Summer Season-
al subsurvey, ELS-II. A » Laboratory 1. O -
Laboratory 2. n - 146.
1200 1500
300 600 900
Sum of Cations (/i
Figure 22. Sum of anions versus sum of cations for
epilemnetic routine samples with chloride
concentrations of less than 7.0 mg/L, Sum-
mer Seasonal subsurvey, ELS-II. A = Labo-
ratory 1. O = Laboratory 2. n = 113.
62
-------
1500
1500
300 600 900 1200
Sum of Cations (/teq/L)
1500
Figure 23. Sum of anions versus sum of cations for
epitemnetic routine samples with chloride
concentrations of greater than or equal to 7.0
mg/L, Summer Seasonal subsurvey. ELS-II.
A - Laboratory 1. O » Laboratory 2. n * 33.
0 300 600 900 1200 1500
Sum of Cations (/ieq/L)
Figure 24. Sum of anions versus sum of cations for
routine samples Fall Seasonal subsurvey.
ELS-II. n - 239.
1500'
160'
300 600 900 1200 1500
Sum of Cations (/ieq/L)
Figure 25. Sum of anions versus sum of cations for
routine samples, ELS-I (ELS-II lakes only).
n - 149.
0 40 80 120 160
Calculated Conductivity (/^S/cm)
Figure 26. Measured versus calculated conductivity for
all routine samples, Spring Seasonal sub-
survey, ELS-II. n =• 143.
63
-------
160
160
0 40 80 120
Calculated Conductivity
160
Figure 27. Measured versus calculated conductivity for
routine samples with chloride concentrations
of less than 7.0 mg/L, Spring Seasonal sub-
survey. ELS-II. n - 111.
0 40 80 120 160
Calculated Conductivity (/iS/cm)
Figure 28. Measured versus calculated conductivity for
routine samples with chloride concentrations
of greater than or equal to 7.0 mg/L, Spring
Seasonal subsurvey, ELS-II. n - 32.
than 7.0 mg/L are excluded, the data points
fall close to the 1:1 line (Figures 27). When
measured versus calculated conductivities are
plotted by subregion (Figures 29-33), plots for
subregions 1A (Adirondacks) and 1E (Maine)
demonstrate that most data points fall in
close proximity to the 1:1 line. For subregion
1B (Poconos/Catskills), all data points fall
close to the 1:1 line with two exceptions. Data
points from subregions 1C (Central New En-
gland) and 10 (Southern New England) show
more variability than the other subregions due
to high chloride content of samples from those
two subregions.
The plots of the Summer Seasonal
subsurvey measured versus calculated conduc-
tivities (Figures 34 through 36) once again
show less variability when chloride values of
less than 7.0 mg/L are excluded (Figure 35).
Two outliers also appear on the plot (Figures
34 and 35) but are probably due to analytical
error other than for chloride.
The plot of the Fall Seasonal subsurvey
measured versus calculated conductivity (Fig-
ure 37) shows most data points falling in
close proximity to the 1:1 line. One data point
from the Fall Seasonal subsurveys was not
plotted because it fell outside the axis range.
Figure 38 is a plot of the measured
versus calculated conductivity for ELS-I results
for those lakes sampled in ELS-II. This plot
was constructed by using values from the
ELS-I verified data base. The relationship
between measured versus calculated conduc-
tivity does not appear to be different between
the Fall Seasonal subsurvey (Figure 37) and
ELS-I.
Summary of Other Methods of
Assessing Data Quality
The plots of measured ANC versus
Calculated Alkalinity 1 (Figures 5 and 9) show
that Laboratory 1 (Spring Seasonal subsurvey)
measured ANC results may be slightly lower
than Laboratory 2 (Fall Seasonal subsurvey)
measured ANC results. The plots also show
that the DIC-initial measurements performed
by Laboratory 1 have a high bias. As a recom-
mendation to the data user, the QIC data from
the processing laboratory would provide a
better estimate of the DIC content of the
sample.
The plots of the sum of anions versus
cations (Figures 13 through 23) and of the
measured versus calculated conductivities
(Figures 26 through 36) both illustrate the
problems at Laboratory 1 related to chloride
analysis. When only those chloride values of
less than 7.0 mg/L are included in the plots,
almost all data points fall in close proximity to
the 1:1 line.
64
-------
160'
0 40 80 120 160
Calculated Conductivity (/zS/cm)
Figure 29. Measured versus calculated conductivity for
subregion 1A, Spring Seasonal subsurvey,
ELS-II. n - 36.
E
o
CO
"0
o
O
en
o
0 40 80 120 160
Calculated Conductivity (/iS/cm)
Figure 30. Measured versus calculated conductivity for
subregion 1B, Spring Seasonal subsurvey,
ELS-II. n = 17
160
120
"D
c
o
O
U]
o
-------
160'
0 40 80 120 160
Calculated Conductivity (/zS/cm)
Figure 33. Measured versus calculated conductivity for
subregion 1E. Spring Seasonal subsurvey.
ELS-II. n - 34.
0 40 80 120
Calculated Conductivity (fjS/
160
Figure 34. Measured versus calculated conductivity for
all epilemnetic routine samples, Summer
Seasonal subsurvey. ELS-II. A - Laboratory
1.O- Laboratory 2. n - 145.
160'
120
160'
E
o
>>
.*•»
'>
o
o
X)
4)
o
0)
2
0 40 80 120 160
Calculated Conductivity (/iS/cm)
Figure 35. Measured versus calculated conductivity for
epilemnetic routine samples with chloride
concentrations of less than 7.0 mg/L. Sum-
mer Seasonal subsurvey, ELS-II. A - Labora-
tory 1.O- Laboratory, n - 112
40 80 120
Calculated Conductivity
160
Figure 36.
Measured versus calculated conductivity for
epilemnetic routine samples with chloride
concentrations of greater than or equal to
7.0 mg/L, Summer Seasonal subsurvey, ELS-
II. A - Laboratory 1. D - Laboratory 2. n
- 33.
66
-------
160"
120
160"
£
o
_>
*5
U
o
o
1
en
o
0. 40 80 120 160
Calculated Conductivity (/LtS/cm)
Figure 37. Measured versus calculated conductivity for
all routine sample*. Fall Seasonal subsurvey.
ELS-II. n - 239
I
CO
9
•w
u
3
•o
5
U
I
0)
a
-------
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Sokal, Robert R., and F. James Rohlf. 1969. Biometry: The Principles and Practices of Statistics
in Biological Research. W. H. Freeman and Company, San Francisco.
Taylor, J. K. 1987. Quality Assurance of Chemical Measurements. Lewis Publishers, Inc., Chelsea
Michigan.
U.S. Environmental Protection Agency. 1987. Handbook of Methods for Acid Deposition Studies,
Laboratory Analyses of Surface Water Chemistry. EPA-600/4-87/026. U.S. Environmental
Protection Agency, Washington. D.C.
Weast, R. C. (ed.). 1972. CRC Handbook of Chemistry and Physics, 53rd Ed. CRC Press
Cleveland, OH.
69
-------
Appendix A
Summary of Changes Made During
the Special Data Assessment
Changes applied to the official verified data base to create the modified verified data base
as a result of the ELS-II Special Data Assessment are presented in Tables A-1 through A-3. Table
A-1 contains all numeric data changes and Table A-2 contains predominately character data
changes. Table A-3 consists of X flag changes. An X flag is applied when a value is deemed
invalid. It is recommended that this value should not be used in statistical analyses.
All changes apply to member M01 only. The member M01 consists of data from Form 1D
(Lake Data), Form 02 (Processing Laboratory Data), and Form 11 (Analytical Laboratory Summary
of Sample Results). Identifications in the three tables are provided by batch ID, sample ID, and
data base variable name. Variable (data base variable name) refers to the analytical laboratory
derived concentration for the particular analyte. The units for the official verified value and the
modified verified value are the same.
Sample codes used in these tables are:
R » routine lake sample
D • field duplicate lake sample
B = field blank sample
FN.FL.LN.LL = audit types; numeric notation is the audit lot (e.g., FN7 - field natural audit,
lot?)
The official verified value refers to the reported concentration in the verified data base before
the Special Data Assessment. The modified verified value refers to the best concentration for that
sample as determined by the Special Data Assessment.
70
-------
Table A-1. Numeric Changes Applied to the Official Verified Data Base to Create the Modified Verified Data
Base (for Member M01 Only), Eastern Lake Survey - Phase II
Batch ID
3500
3500
3500
3500
3500
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3501
3502
3503
3503
3503
3503
3503
3503
3503
3503
3503
3503
3503
3503
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
3504
Sample ID
1
2
3
4
5
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
5
1
1
2
2
3
3
4
4
5
5
6
6
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
Variable
CL11
CL11
CL11
CL11
CL11
CL11
NO311
S0411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
N0311
S0411
CL11
N0311
S0411
CL11
NO311
SO411
CL11
NO311
CL11
N0311
SO411
N0311
SO411
N0311
SO411
N0311
S0411
N0311
SO411
N0311
S0411
CL11
N0311
SO411
CL11
NO311
S0411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NO311
S0411
CL11
N0311
SO411
CL11
N0311
S0411
CL11
N0311
Sample
type
R
FN7
D
R
B
R
R
R
R
R
R
LN7
LN7
LN7
D
D
D
R
R
R
B
B
B
FN7
FN7
R
R
R
R
R
FN7
FN7
B
B
D
D
R
R
R
R
R
D
0
D
R
R
R
FN7
FN7
FN7
R
R
R
R
R
R
LN7
LN7
LN7
R
R
Official
verified
value
38.218
4.120
16.604
24.244
-0.044
8.122
0.8223
6.521
12.604
-0.0432
7.129
5.469
1.0080
6.393
8.658
0.8223
6.383
7.816
-0.0432
7.048
0.017
-0.0432
-0.014
3.004
1.1251
13.810
-0.0419
-0.057
•0.0011
11.948
1.3659
7.216
0.0086
-0.014
-0.0419
13.442
0.7293
8.842
10.657
-0.0208
2.773
15.295
-0.0208
2.535
19.786
-0.0208
3.589
3.815
1.4076
7.617
23.966
-0.0208
6.912
4.163
0.0862
6.157
5.437
1.7013
7.895
11.204
0.2098
Modified
verified
value
11.439
2.050
^637
12.748
0.000
13.368
0.9745
7.129
10.184
0.0005
6.393
0.951
1.3700
7.450
8.868
0.9509
7.048
12.702
0.0309
8.140
0.000
-0.0413
0.001
4.363
1.3456
21.352
0.4040
7.046
0.0056
11.774
1.2720
6.912
0.0000
0.000
0.0056
12.730
0.4040
9.156
5.778
0.0000
5.898
5.662
0.0000
5.968
7.406
0.0000
8.278
1.451
1.2170
6.932
7.392
0.0100
6.708
1.632
0.3990
5.578
1.530
1.2380
7.144
3.942
0.1850
Comment
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
(Continued)
71
-------
Table Ar1. Continued
Batch ID
3504
3504
3504
3504
3504
3504
3504
3505
3505
3505
3505
3505
3505
3505
3505
3505
3505
3505
3506
3506
3506
3506
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3508
3508
3508
3508
3508
3508
3508
3508
3508
3508
3508
3508
Sample ID
8
9
9
9
10
10
10
1
2
2
3
4
5
5
6
7
8
9
1
1
3
5
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9
9
9
9
1
1
2
2
3
3
4
4
5
5
6
6
Variable
SO411
CL11
NO311
SO411
CL11
NO311
SO411
SO411
NO311
S0411
SO411
S0411
NO311
SO411
SO411
S0411
SO411
SO411
PTL11
SO411
PTL11
PTL11
CL11
NO311
SO411
CL11
N0311
S0411
CL11
N0311
S0411
CL11
N0311
SO411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NO311
S0411
CL11
NO311
SO411
ALKA11
CL11
NO311
S0411
NO311
SO411
NO311
SO411
NO311
S0411
N0311
SO411
NO311
SO411
NO311
SO411
Sample
type
R
B
B
B
R
R
R
R
FN8
FN8
R
R
B
B
R
D
R
R
B
B
LN8
FN8
B
B
B
D
D
D
R
R
R
R
R
R
FN7
FN7
FN7
R
R
R
R
R
R
LN7
LN7
LN7
R
R
R
R
R
R
R
R
FN8
FN8
R
R
R
R
D
D
Official
verified
value
7.549
0.026
-0.0320
0.025
1.751
-0.0208
11.272
5.682
1.1923
6.534
5.774
6.270
0.0362
-0.026
4.795
4.868
2.672
3.972
0.0017
-0.013
0.0006
0.0012
-0.013
-0.0267
-0.026
24.774
1.2255
10.299
1.386
0.4764
5.542
19.900
0.6597
6.822
2.454
0.9994
5.444
1.694
-0.0267
5.576
31.376
1.5288
12.266
6.114
1.2660
6.876
146.3
0.598
0.6661
7.005
0.2485
8.795
0.0187
6.768
1.1069
5.753
0.4626
8.039
0.9845
9.297
0.0134
5.486
Modified
verified
value Comment
6.966
0.000
0.0000
0.000
0.998
0.0000
13.602
6.221
0.8808
5.578
6.272
6.890
-0.0524
0.000
5.866
6.406
3.912
5.268
0.0600 b
-0.
129 a
0.0290 b
0.0270 b
0.000 a
0.0000 a
0.000 a
18.742 * a
1.3904 a
12.803 a
1.354 a
0.5162 a
6.290 a
13.796 a
0.9836 a
7.943 a
2.582 a
1.1036 a
6.269 a
1.314 a
0.0000 a
4.589 a
21.024 a
1.3634 a
12.495 a
2.464 a
1.0772 a
6.103 a
155.1 C
0.126 a
0.5444 a
6.060 a
0
1954 a
8.446 a
0.0432 a
6.017 a
1.0134 a
5.624 a
0.3930 a
8
014 a
0.9060 a
9.743 a
0
6
0399 a
074 a
(Continued)
72
-------
Table A-1. Continued
Batch ID
3508
3508
3508
3508
3509
3509
3509
3509
3509
3509
3509
3509
3509
3510
3510
3510
3510
3510
3510
3510
3510
3511
3511
3512
3512
3512
3512
3512
3512
3513
3513
3513
3514
3514
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3515
3516
3516
3516
3516
3516
3516
3516
3516
3516
Sample ID
7
7
8
8
1
1
2
2
3
3
4
4
4
1
2
3
4
5
6
7
7
4
8
2
3
4
4
4
8
3
4
9
1
5
1
1
2
2
3
3
4
5
5
6
6
7
7
8
8
1
2
3
4
4
5
5
6
7
Variable
NO311
SO411
NO311
SO411
NO311
SO411
NO311
SO411
NO311
SO411
ALKA11
N0311
SO411
NO311
NO311
N0311
N0311
NO311
N0311
CL11
NO311
SO411
CL11
CL11
CL11
CL11
NA11
N0311
CL11
CL11
CL11
CL11
CL11
CL11
ALKA11
NO311
ALKA11
NO311
ALKA11
N0311
NO311
ALKA11
NO311
ALKA11
N0311
ALKA11
NO311
ALKA11
NO311
SO411
SO411
SO411
CL11
SO411
CL11
S0411
SO411
S0411
Sample
type
B
B
LN8
LN8
D
D
R
R
FN7
FN7
B
B
B
R
LN8
R
R
FN8
D
B
B
R
R
R
R
R
R
R
D
D
R
R
R
R
R
R
R
R
D
D
B
FL15
FL15
R
R
R
R
R
R
R
FL15
D
LL15
LL15
R
R
R
R
Official
verified
value
-0.0267
-0.022
0.9325
4.843
0.8814
11.811
0.8692
11.838
1.4376
7.463
-15.8
-0.0267
•0.023
0.3480
1.4503
0.0692
0.3555
1.4822
0.2800
-0.049
-0.0267
3.956
14.584
4.829
4.874
4.833
10.656
0.7376
4.889
4.775
4.767
4.810
4.786
4.745
82.0
1.2497
-15.1
0.1802
3.3
0.1895
-0.0211
265.5
0.2371
-12.4
0.4371
136.4
0.2383
268.9
0.1079
3.152
2.071
2.910
0.288
2.020
4.925
2.741
6.186
6.477
Modified
verified
value Comment
0.0000
0.000
1.0316
5.823
0.6014
10.071
0.6692
10.140
1.1586
6.441
2.5
0.0000
0.000
0.1774
1.0208
0.0614
0.1954
1.0050
0.1082
-0.036
0.0000
3.959
17.370
12.336
37.324
25.728
10.769 b
0.7169 a
14.534 a
10.698 a
15.694 a
15.910 a
14.566 a
16.256 a
28.7 e
1.1022 a
-3.1 9
0.1062 a
1.9 e
0.1540 a
0.0000 a
100.2 9
0.3680 a
-10.3 9
0.3320 a
50.6 9
0.1890 a
99.2 9
0.0902 a
2.674 a
1.924 a
2.738 a
0.188 a
2.120 a
6.652 a
2.636 a
6.086 a
6.564 a
(Continued)
73
-------
Table A-1. Continued
Batch ID
3516
3516
3516
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3517
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3518
3519
3519
3519
3519
Sample ID
8
8
8
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9
9
9
9
10
10
10
10
10
11
11
11
1
1
1
2
Variable
CL11
NA11
SO411
ALKA11
N0311
ALKA11
NO311
ALKA11
NO311
ALKA11
NO311
ALKA11
N0311
ALKA11
N0311
ALKA11
N0311
ALKA11
NO311
ALKA11
NO311
ALKA11
NO311
CL11
NO311
SO411
CL11
N0311
SO411
CL11
NO311
S0411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NO311
S0411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NH411
N0311
S0411
CA11
CL11
NA11
N0311
S0411
CL11
N0311
SO411
CL11
NO311
SO411
CL11
Sample
type
B
B
B
R
R
R
R
FN7
FN7
0
D
R
R
R
R
R
R
R
R
R
R
B
B
R
R
R
FN7
FM7
FN7
D
D
D
B
B
B
R
R
R
R
R
R
LN7
LN7
LN7
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
n
R
Official
verified
value
-0.001
-0.004
-0.003
157.7
0.0453
182.8
0.2391
392.0
3.3706
96.9
1.2084
107.4
1.2003
213.1
0.4500
422.6
0.3332
80.2
0.0122
215.6
•0.0046
28.3
0.0080
0.335
0.5761
5.422
4.475
1.7795
8.087
0.469
0.8662
6.837
0.011
-0.0237
-0.037
4.751
0.0211
5.135
2.874
-0.0237
6.461
6.986
1.6528
9.740
6.685
1.5505
8.060
9.632
-0.013
0.1345
15.211
6.350
22.125
5.717
-0.0237
6.422
3.403
0.1234
6.308
0.207
0.5719
4.503
0.214
Modified
verified
value Comment
-0.004 a
0.188 b
0.000 a
59.3 8
0.
1226
70.2
0.
1866
153.2
1.1308
38.2
0.8626
41.
6
0.8612
84.8
0.3368
166.9
0.2796
29.3
0.0000
82.8
0.0000
10.4
0.0000 a
0.452 a
0.4320 a
5.144 «. a
3.310 a
1.1490 a
6.494 a
0.442 a
0.4202 a
5.122 a
0.000 a
0.0000 a
0.000 a
3.372 a
0.1626 a
3.526 a
2.614 a
0.0000 a
4.372 a
3.252 a
1.1354 a
6.602 a
0.242 a
0.7510 a
5.302 a
5.840 a
-0.003 f
0.0766 a
13.886 a
6.191 b
7.634 a
5.541 b
0.0000 a
5.638 a
3.328 a
0
1240 a
5.882 a
0.308 a
0.4912 a
4.612 a
0.280 a
(Continued)
74
-------
Table Arl. Continued
Batch ID
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3519
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3520
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
Sample ID
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9
9
9
10
10
10
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
Variable
N0311
S0411
CL11
N0311
SO411
CL11
NO311
SO411
CL11
N0311
SO411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NO311
SO411
CL11
NO311
S0411
CL11
N0311
SO411
CL11
NO311
CL11
NO311
CL11
N0311
CL11
NO311
CL11
N0311
CL11
NO311
CL11
NO311
CL11
N0311
CL11
NO311
CL11
NO311
CL11
N0311
SO411
CL11
N0311
SO411
CL11
NO311
SO411
CL11
N0311
S0411
CL11
N0311
S0411
CL11
NO311
S0411
Sample
type
R
R
R
R
R
B
B
B
D
D
D
R
R
R
FN8
FN8
FN8
R
R
R
R
R
R
R
R
R
R
R
R
R
FL15
FL15
R
R
B
B
R
R
R
R
R
R
D
D
R
R
D
D
D
B
B
B
R
R
R
R
R
R
R
R
R
R
R
R
Official
verified
value
2.9748
5.520
0.965
1.8948
6.099
0.064
0.0586
•0.038
1.008
1.8770
6.042
3.503
1.6638
6.969
0.366
1.3631
6.791
4.211
-0.0237
3.711
2.859
0.0731
3.435
1.249
0.0908
3.983
0.478
2.2823
0.289
1.6427
0.674
0.1924
0.338
0.2301
-0.032
-0.0237
0.264
0.3601
0.386
1.9059
0.332
0.5633
0.214
1.6009
4.277
-0.0237
0.352
0.9267
6.428
-0.044
-0.0116
-0.026
0.241
1.4565
5.254
1.127
0.9267
6.641
0.257
0.9267
6.321
0.289
0.3970
5.894
Modified
verified
value
Z4074
5.330
1.136
1.7046
5.752
0.000
0.0000
0.000
1.140
1.6926
5.722
3.540
1.4146
6.448
0.448
1.0936
6.148
3.326
0.0000
3.304
3.346
0.1060
3.018
1.326
0.1044
3.482
0.336
1.9728
0.336
1.4478
0.386
0.4150
0.440
0.2660
0.000
0.0000
0.370
0.2818
0.308
1.6840
0.438
0.4650
0.320
1.4342
4.384
0.0448
0.340
0.8722
5.770
0.000
0.0000
0.000
0.288
1.2220
4.700
2.928
0.7912
6.064
0.360
0.8592
5.584
0.344
0.3712
5.310
Comment
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
(Continued)
75
-------
Table Ar1. CentlniMd
Batch ID
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3521
3523
3523
3523
3523
3523
3523
3523
3523
3525
3526
3526
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
3600
Sample ID
7
7
7
8
8
8
9
9
9
10
10
10
11
11
11
12
12
12
12
13
13
13
13
14
14
14
14
15
15
15
15
16
16
16
16
1
2
3
4
5
6
7
8
6
2
3
1
3
5
6
7
7
8
8
12
12
15
19
19
19
21
22
Variable
CL11
N0311
SO411
CL11
N0311
SO411
CL11
N0311
SO411
CL11
N0311
80411
CL11
N0311
S0411
CL11
COND11
NO311
SO411
CL11
CON011
NO311
S0411
CL11
COND11
N0311
SO411
CL11
COND11
NO311
S0411
CL11
CON011
N0311
SO411
S0411
S0411
SO411
S0411
SO411
SO411
S0411
SO411
CL11
CL11
CL11
DOC11
DICI11
N0311
MN11
DICE11
DICI11
DICE11
DICI11
DICI11
NO311
DICI11
DICE11
DICI11
DOC11
NH411
CL11
Sample
type
R
R
R
R
R
R
FN8
FN8
FN8
R
R
R
R
R
R
R
R
R
R
LN8
LN8
LN8
LN8
R
R
R
R
R
R
R
R
R
R
R
R
R
B
R
LL15
R
FL15
D
R
B
R
D
R
R
R
R
D
D
FN8
FN8
S
S
R
R
R
R
R
D
Official
verified
value
0.289
1.2673
5.574
0289
1.9862
5.574
0.542
1.3808
6.854
0.273
1.7970
6.108
0.368
3.0129
6.001
0.431
25.1
1.6457
5.681
0.415
27.4
1.3051
6.694
5.953
28.8
0.1700
4.454
2.642
21.2
0.0565
Z108
5.888
54.7
-0.0570
6.428
3.515
0.056
3.885
2.074
4.121
1.996
3.946
3.132
0.062
0.214
0.225
2.15
0.418
0.5826
5.066
0.332
0.387
0.097
0.488
0.761
0.5528
1.899
0.382
0.101
1.18
0.018
12.465
Modified
verified
value Comment
0.328
1.0250
4.952
0.332
1.7400
4.942
0.436
1.0868
6.046
0.286
1.4960
5.452
0.320
2.5572
5.392
0.434
25.3
1.4216
5.040
0.428
29.0
1.0814
6.082
4.542
21.8
0.1356
3.916
2.516
24.5 * b
0.1256 a
1.956 a
4.664 a
51.3 b
0.4794
5.688
3.334
0.000
3.734
2.126
3.966
2.108
3.954 a
3.182 a
0.362 a
0.950 a
0.275 a
2.51 f
0.337 b
0.0352 g
0.507 f
0.168 b
0.748 b
0.332 b
1.524 b
0.571 b
0.0553 /
1.945 b
0.255 b
0.390 b
1.81 f
0.043 d
14.695 f
(Continued)
76
-------
Table A-1. Continued
Batch ID
3600
3600
3600
3600
3600
3601
3601
3601
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3603
3603
3603
3603
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3604
3605
3605
3605
Sample ID
22
23
24
25
26
1
13
23
19
19
19
19
20
20
21
21
21
22
22
23
23
23
24
24
24
25
25
26
26
27
27
27
28
28
29
29
29
29
3
19
21
24
3
7
10
11
12
12
13
14
15
16
17
18
19
20
21
25
10
12
19
Variable
NH411
NH411
NH411
NH411
NH411
NO311
DICE11
DOC11
FTL11
N0311
PHEQ11
S0411
FTL11
PHEQ11
FTL11
N0311
PHEQ11
FTL11
PHEQ11
FTL11
NO311
PHEQ11
ALTL11
FTL11
PHEQ11
FTL11
PHEQ11
FTL11
PHEQ11
ALEX11
FTL11
PHEQ11
FTL11
PHEQ11
S1O211
ALEX11
FTL11
PHEQ11
CL11
CL11
DIC02
FE11
CL11
NO311
CL11
CL11
CL11
S0411
SO411
SO411
SO411
SO411
S0411
S0411
S0411
S0411
SO411
N0311
CL11
CL11
N0311
Sample
type
D
LN8
R
TB
S
R
R
R
LN8
LN8
LN8
LN8
R
R
R
R
R
R
R
R
R
R
R
R
R
FL16
FL16
R
R
S
S
S
D
D
S
S
S
S
R
B
R
S
LNS
S
B
R
TB
TB
R
LN7
R
R
R
S
LL16
R
R
R
LL16
B
R
Official
verified
value
0.044
0.005
0.063
0.033
0.012
0.0382
0.108
6.33
0.5370
0.1446
6.94
6.744
0.4430
4.56
0.0348
0.0411
4.58
0.0702
6.45
0.0212
0.0439
5.84
0.1928
0.0419
7.03
0.0816
6.91
0.0859
6.93
0.0000
0.0711
6.47
0.0777
6.08
0.463
0.0000
0.0714
5.15
2.830
0.275
-1.090
0.685
0.399
0.0516
0.020
0.114
0.032
6.232
6.511
9.137
5.081
5.756
4.473
2.033
2.631
5.405
5.549
0.0636
0.338
0.008
0.4524
Modified
verified
value
0.005
0.063
0.033
0.012
0.023
0.0361
4.108
8.10
0.0714
1.2609
5.15
6.654
0.0537
6.94
0.0443
0.0421
4.56
0.0348
4.58
0.0702
0.0301
6.45
0.0114
0.0212
5.84
0.0419
7.03
0.0616
6.91
,
0.0854
6.93
0.0711
6.47
1.852
0!0777
6.08
0.279
0.023
1.090
0.168
0.790
0.0596
0.104
0.273
0.061
_
6.232
6.511
9.137
5.081
5.756
4.473
2.033
5.176
5.405
0.0634
0.554
0.079
0.0452
Comment
h
h
h
h
h
g
f
f
i
i
i
i
i
I
i
i
I
1
i
i
i
1
i
i
i
» /
/
/
/
/
/
/
/
/
f
j
i
k
f
f
f
b
f
f
f
f
1
1
1
1
1
1
1
b.l
1
1
f
b
f
f
(Continued)
77
-------
Table A-1. ContlniMd
Batch ID
3610
3659
3661
3662
3662
3662
3662
3662
3662
3662
3662
3662
3664
3665
3665
3666
lAfifi
oooo
4£|*O
wvvO
3666
3666
3700
3700
3700
3700
3700
3700
3700
3700
3700
3700
3700
3701
3701
3701
3701
3701
3701
3701
3701
3701
3701
3702
3702
3702
3702
3702
3702
3702
3702
3703
3703
3703
3703
3703
3703
3703
Sample ID
23
2
3
1
1
1
2
2
2
3
3
3
2
3
3
1
1
2
2
2
1
2
2
3
4
5
6
7
8
9
10
1
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
1
2
2
3
4
5
6
Variable
NO311
CL11
NH411
CL11
N0311
SO411
CL11
N0311
S0411
CL11
N0311
S0411
DOC11
CA11
MG11
FE11
S0411
CA11
FE11
MG11
NA11
ALTL11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
K11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
K11
NA11
NA11
NA11
NA11
K11
Sample
type
D
LN7
S
LN8
LN8
LN8
S
S
S
S
S
S
S
S
S
S
S
LN8
LN8
LN8
D
LS4
LS4
R
R
LN8
TB
R
B
R
R
FN10
FN10
R
R
FN7
D
B
LS2
R
R
LS5
FN8
R
R
LS3
0
RWHA
B
R
LS1
LS1
FN9
D
R
LS1
Official
verified
value
4.1300
3.873
0.006
1.463
0.0205
14.138
0.371
1.2586
5.895
0.345
0.0150
4.328
4.73
1.780
0.313
0.080
7.171
5.820
0.585
1.145
0.326
0.1170
3.110
0.529
0.372
0.589
0.003
0.663
0.004
0.564
0.494
0.329
0.561
0.400
2.610
2.220
0.417
0.002
10.000
1.450
3.780
1.560
0.589
0.483
0.256
0.042
0.250
0.303
-0.001
0.884
0.256
0.884
1.563
1.450
1.563
0.256
Modified
verified
value
0.7031
2.873
0.001
0.371
1.2586
5.895
0.345
0.0150
4.328
1.463
0.0205
14.138
9.456
5.820
1.145
0.585
7.576
1.780
0.080
0.313
0.396
0.2183
3.702
0.718
0.387
0.569
-0.056
3.828
-0.047
0.713
0.694
0.250
0.536
0.359
2.587
2.172
0.368
-0.051
10.028
1.408
3.789
1.552
0.554
0.476
0.219
-0.023
0.219
0.389
-0.070
0.860
0.531
0.773
2.629
1.477
1.445
0.287
Comment
/
/
/
/
/
/
/
1
1
1
1
1
k
1
1
1
f
1
1
1
, m
b
m
m
m
m
m
m
m
m
m
b
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
b
m
m
m
m
b
(Continued)
78
-------
Table A-1. CentbiiMd
Batch ID
3703
3703
3703
3703
3703
3703
3703
3703
3703
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3704
3705
3705
3705
3705
3705
3705
3705
3705
3705
3705
3705
3705
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3706
3707
3707
3707
3707
3707
3707
3707
Sample ID
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1
2
3
4
5
6
7
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
Sample
type
LS1
R
FN8
LS5
B
R
R
R
R
R
R
R
FN7
R
R
R
B
D
RWHB
LS2
R
R
TB
R
R
FN7
R
R
FN8
R
LS4
R
R
B
D
R
R
R
R
B
R
FN7
O
FN10
FN8
R
R
R
R
R
R
R
R
R
R
R
R
R
LN7
R
R
R
Official
verified
value
0.884
0.356
0.615
1.790
-0.002
0.771
1.111
1.111
0.997
0.884
1.903
0.884
2.242
0.347
0.365
0.884
-0.000
0.365
0.260
10.960
0.884
0.884
-0.000
0.884
1.286
2.386
1.530
1.530
0.675
1.286
4.218
1.652
1.164
-0.003
1.408
1.041
2.695
0.048
0.997
-0.003
2.356
2.242
0.048
0.374
0.405
1.450
2.016
2.695
2.922
1.676
5.864
1.676
2.695
3.148
1.450
5.196
0.553
0.798
2.263
0.453
2.263
2.630
Modified
verified
value
0.778
0.526
0.567
1.541
-0.004
0.700
0.476
0.970
0.892
0.871
1.873
0.790
2.214
0.554
0.611
0.872
-0.029
0.612
0.424
10.670
0.872
0.900
-0.038
0.842
1.262
2.277
1.406
1.402
0.597
1.165
3.795
1.541
0.939
-0.048
1.257
0.939
2.494
0.058
0.925
-0.019
2.191
2.176
0.058
0.544
0.587
1.377
1.959
2.576
2.705
1.647
5.786
1.598
2.657
3.028
1.463
5.246
0.545
0.942
2.190
0.455
2.172
2.672
Comment
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
, m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
(Continued)
79
-------
Table A-1. Continued
Batch ID
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3707
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3708
3709
3709
3709
3709
3709
3709
3709
3709
3709
3709
3709
3709
3710
3710
3710
3710
3710
3710
3710
3710
3710
3710
Sample ID
8
9
10
11
12
13
14
15
16
17
18
19
20
20
20
20
21
22
23
24
25
26
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
CL11
NA11
N0311
SO411
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NAM
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
Sample
typ*
D
R
R
R
LS1
R
FN7
R
R
R
R
R
RWLA
RWLA
RWLA
RWLA
R
R
B
R
R
R
FN9
R
R
R
LS2
R
R
LS3
LS4
R
R
R
R
0
R
R
R
B
R
FN10
R
R
LS5
D
LS5
R
R
LS2
RWHO
B
B
RWHC
FN10
R
FN8
LS4
0
LN8
R
TB
Official
verified
value
2.019
0.445
0.438
£874
1.041
2.996
2.386
0.553
3.119
2.019
£263
0.438
0.000
0.250
0.0000
0.000
3241
0.508
-0.002
0.677
3.730
0.919
£514
1.988
0.058
£198
10.393
£724
3.565
0.025
3.985
1.778
1.673
0.728
1.253
0.728
3.667
1.778
0.728
-0.001
0.094
0.728
2.304
1.043
1.568
0.048
1.463
3.565
0.004
11.024
0.728
-0.004
-0.004
0.056
0.728
5.666
0.728
3.774
6.716
0.079
1.778
0.000
Modified
verified
value
£095
0.482
1.532
£929
0.906
£866
£285
0.617
£740
£064
£226
0.473
,
0227
.
.
3.182
£564
0.058
0.919
3.821
0.865
2.842
£260
0.157
£057
10.792
£603
3.770
0.103
3.762
1.770
IQQft
-«M7V
0.734
1.347
0.715
3.789
1.822
0.764
0.058
0.474
0.609
2.208
0.932
1.546
0.387
1.522
3.666
0.387
10.333
0.454
0.024
0.044
0.464
0.614
6.900
0.662
3.696
6.997
0.633
1.745
0.010
Comment
at
m
m
m
m
m
m
m
IT,
m
m
m
n
m
n
n
m
m
m
m
m
m
m
m
m
m
» m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
(Continued)
80
-------
Table A-1. CentlmMd
Batch ID
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3712
3712
3712
3712
3712
3712
3712
3712
3712
3712
3712
3712
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
Sample ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
5
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
ACC011
AL002
ALEX11
ALKA11
AL002
ALTL11
CA11
CL11
COLOR02
COND11
DIC02
DICE11
DICI11
DOC11
FE11
FTL11
K11
MG11
MN11
NA11
NH411
N0311
PH02
PHAC11
PHAL11
PHEQ11
PTL11
SI0211
SO411
TUR02
NA11
NA11
Sample
typ*
LS5
R
R
FN7
FN8
D
RWLC
R
R
R
R
R
R
R
B
R
R
R
R
R
LS2
FN8
D
R
B
RWLB
R
R
R
LN8
LS3
FN7
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
Official
verified
VttlllB
1.801
0.864
1.883
2.197
0.836
1.437
0.049
1.271
1.780
0.624
1.179
1.536
0.991
3.124
-0.000
2.991
7.094
1.801
1.404
3.918
10.666
0.466
1.272
6.962
-0.010
0.364
2.462
5.242
4.315
0.719
0.031
2.330
44.4
0.0360
0.0099
95.4
0.0302
0.1861
3.285
8.020
95
52.9
1.535
0.796
1.405
7.48
0.616
0.0634
0.660
0.739
0.018
5.638
0.035
0.3172
6.51
6.66
6.59
6.99
0.0125
5.054
4.970
1.40
0.478
4.050
Modified
verified
value
1.643
0.503
0.7G8
2£08
0.667
0.507
0.237
1.015
0.903
0.469
0.609
1.493
0.686
3.179
0.039
2.864
7.243
1.662
1.241
3.931
10.469
0.652
1.241
7.106
0.034
0.256
2.376
5.293
4.252
0.647
0.041
2.140
174.5
0.0992
0.0390
-54.3
0.0750
0.1250
0.630
0.842
200
23.1
0.658
0.058
0.539
15.30
0.261
0.0350
0.152
0.189
0.025
1.195
0.010
0.0090
4.55
4.57
4.50
4.42
0.0190
0.935
1.970
1.80
0.497
3.855
Comment
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
* m
m
m
m
m
m
o
o
o
0
o
o
o
0
0
o
0
o
o
o
o
o
o
o
o
m
o
o
o
o
o
o
o
o
0
o
m
m
(Continued)
81
-------
Table A-1. Continued
Batch ID
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3713
3714
3714
3714
3714
3714
3714
3714
3714
3714
3714
3714
3714
3714
3715
3715
3715
3715
3715
3715
3715
3715
Sample ID
6
7
8
9
10
11
12
13
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
15
16
17
18
19
1
2
3
4
5
6
7
8
9
10
11
12
13
1
2
3
4
5
5
6
7
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
ACCO11
AL002
ALEX11
ALKA11
AL002
ALTL11
CA11
CL11
COLOR02
CON011
DIC02
DICE11
DICI11
DOC11
FE11
FTL11
K11
MG11
MN11
NA11
NH411
NO311
PH02
PHAC11
PHAL11
PHEQ11
FTL11
SI0211
SO411
TUR02
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
C0151D
NA11
NA11
NA11
Sample
type
R
B
LN7
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
0
R
R
R
R
LS3
R
FN9
R
R
R
LS1
D
LN8
R
R
B
R
R
D
R
R
R
FN7
LN7
Official
verified
value
3.388
-0.009
2.198
7.358
0.633
6.300
1.404
1.393
174.5
0.0992
0.0390
-54.3
0.0750
0.1249
0.630
0.842
200
23.1
0.658
0.058
0.539
15.30
0.261
0.0352
0.152
0.189
0.025
1.404
0.010
0.0087
4.55
4.57
4.50
4.42
0.0186
0.935
1.970
1.80
0.033
1.404
11.726
6.829
5.242
0.892
0.039
10.540
2.628
5.266
7.317
5.119
0.702
4.680
0.062
5.852
11.713
0.007
7.317
1.016
7.024
0.559
860
7.170
1.602
1.602
Modified
verified
value
3.231
-0.017
2.118
7.645
0.739
6.446
1.154
0.957
44.4
0.0360
0.0100
95.4
0.0302
0.1860
3.285
8.020
95
52.9
1.535
0.796
1.405
7.48
0.616
0.0630
0.660
0.739
0.018
5.661
0.035
0.3170
6.51
6.66
6.59
6.99
0.0120
5.054
4.970
1.40
0.349
1.162
12.669
7.173
5.296
0.953
0.041
10.839
2.734
5.132
7.243
5.144
0.813
5.177
0.587
5.916
12.505
-0.012
7.422
1.030
6.884
0.727
86
7.190
Z102
2.180
Comment
m
m
m
m
m
m
m
m
0
o
o
o
o
0
o
o
o
0
o
o
o
0
0
o
o
o
0
m
* 0
o
o
o
o
o
o
o
0
o
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
f
m
m
m
(Continued)
82
-------
Table A-1. Continued
Batch ID
3715
3715
3715
3715
3715
3715
3715
3715
3715
3715
3715
3715
3715
3715
3718
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3716
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3717
3718
3718
3718
3718
3718
3718
3718
3718
3718
3718
3718
Sample ID
8
8
8
9
10
11
12
13
14
15
16
17
18
19
1
2
3
4
5
6
7
8
9
10
11
12
13
14
14
15
16
17
18
19
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
2
3
4
5
6
7
8
9
10
11
Variable
CA11
CL11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
MN11
NA11
NA11
NA11
NA11
NA11
NA11
CL11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
Sample
type
LS1
LS1
LS1
R
R
B
R
R
R
R
R
R
R
R
R
R
R
R
LS1
R
B
R
R
R
R
FN8
R
R
R
R
0
R
R
R
LS1
LS1
R
R
R
R
LS3
RWHE
B
FN9
R
R
R
R
0
R
R
R
R
FN7
R
LS4
R
B
R
R
R
R
D
Official
verified
value
0.033
26.400
0.753
1.456
0.600
0.007
1.163
2.335
1.158
4.826
0.425
2.335
1.096
1.456
1.309
1.096
0.856
7.170
0.866
0.231
•0.007
3.507
1.456
4.972
6.877
0.764
1.163
0.036
0.074
7.610
7.317
14.204
2.481
0.591
24.880
0.639
1.276
5.235
2.000
5.822
0.023
0.373
-0.006
2.596
5.528
12.566
5.151
1.231
5.404
7.048
1.863
1.863
10.660
2.009
0.569
3.622
6.554
-0.002
0.275
2.009
0.499
2.009
2.302
Modified
verified
value
0.030
19.150
0.813
1.404
0.525
0.004
1.154
2£62
0.944
5.038
0.546
6.915
0.998
1.659
1.443
0.916
0.848
7.526
0.843
0.485
0.000
4.026
1.763
5.755
7.164
0.668
1.332
0.035
0.358
7.931
7.805
14.720
2.524
0.795
30.000
0.855
1.315
5.630
2.006
5.990
0.098
0.437
0.033
2.754
5.513
13.182
5.147
0.807
5.578
7.163
1.914
1.702
10.870
2.185
0.964
3.631
6.878
0.016
0.434
2.350
0.672
2.102
2.327
Comment
b
b
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
* m
f
m
m
m
m
m
m
b
m
m
m
m
m
m
m
m
m
m-
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
(Continued)
83
-------
Table A-1. Continued
Batch ID
3718
3718
3718
3718
3718
3718
3718
3718
3718
3718
3718
3719
3719
3719
3719
3719
3719
3719
3719
3719
3719
3719
3719 *
3719
3720
3721
3721
3721
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3722
3723
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
3724
Sample ID
12
13
14
15
16
17
18
19
20
21
22
1
2
3
4
5
6
7
8
6
9
10
1 10
11
1
2
8
8
1
2
3
4
5
6
7
7
7
8
9
10
11
12
13
3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
K11
NA11
NA11
K11
NA11
NA11
SI0211
NA11
NA11
SO411
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NH411
S0411
NA11
NA11
NA11
NA11
NA11
NA11
NH411
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
Sample
type
R
R
R
R
R
R
R
R
R
R
R
R
TB
FN7
0
FN8
R
R
LN8
LN8
B
FN10
FN10
R
R
FN9
LN7
LN7
R
RWLE
R
LS1
FN7
R
LN7
LN7
LN7
D
B
R
R
R
R
LN8
R
LS2
B
FN8
R
LS5
RWHL
R
R
R
R
R
R
R
D
Official
verified
value
9.487
7.874
1.716
6.994
0275
6.848
0247
0.457
0.044
0.331
0.471
0.331
0.011
2.156
0.983
0.415
6.701
9.194
0.426
0.429
•0.001
0.316
0.457
0.695
9280
2.650
£079
6.725
7.522
0.173
7.668
0.624
2.394
9.426
2.121
0.071
6.645
7.962
0.197
3.420
11.770
5.471
7.668
0.076
5.982
10.675
•0.002
0.789
7280
1.688
0.337
1.788
6.980
0.396
7.180
9.476
14.869
5.182
6.880
Modified
verified
value
9.552
7.959
1.732
7205
0.498
6.915
0.429
0.659
0.124
0.920
0.724
0.485
0.003
2.158
0.924
0.632
6.728
9.167
0288
0.594
0.025
0206
0.559
0.924
7280
2.980
2.541
8.025
7.413
0.255
6.185
0.913
2.320
9.582
2243
0.131
7.425
9.632
0.032
3243
11.797
5.276
7.385
0.167
6.610
11.662
-0.075
0.627
8.564
1.819
0.423
1.910
8.208
0.464
8.062
10.695
17.944
5.824
7.977
Comment
m
m
m
m
m
m
m
m
n
m
m
m
m
m
m
m
m
m
b
m
m
b
m
m
f
* b
b
b
m
m
m
m
m
m
m
b
b
m
m
m
m
m
m
b
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
(Continued)
84
-------
Table A-1. Continued
Batch 10
3725
3725
3725
3725
3725
3725
3725
3725
3725
3725
3725
3725
3725
Sample ID
1
2
3
4
5
6
7
8
9
10
11
12
13
Variable
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
NA11
Sample
type
LS5
TB
R
R
B
R
R
FN8
D
FN9
R
FN10
R
Official
verified
value
1.788
0.002
0.177
4.983
-0.002
1.388
4.983
0.454
1.088
2.886
2486
0.512
5.182
Modified
verified
value
1.811
•0.082
10.584
5.852
-0.092
1221
5.831
0.636
1211
3.094
2601
0.570
5.655
Comment
m
m
m
m
m
m
m
m
m
m
m
m
m
* This change was made as the result of the extensive investigations of chloride, sulfate, and nitrate.
* Reanalysis result was removed because the reanalysis was not representative of the batch sample analyses.
c Laboratory entered wrong trtratfon data for this sample. Titratton data were re-entered and recalculated.
a Laboratory reported incorrect acid titrant concentration. Result was recalculated.
* This change was made as the result of errors found in the reported acid titrant concentrations. The alkalinity value
included in the modified verified data set was calculated from the titration data using a calculated acid titrant
concentration instead of the reported acid titrant concentration.
' Transcription error.
a No raw data to support official verified value. Value changed to match raw data.
" This correction of a sample switch was made during verification and was reflected in the LESC working data base
and the validated data base but did not appear in the official verified data set. Therefore, the change had to be
made in the modified verified data base since it was created from the official verified data base.
' This change was made as the result of an extensive investigation of batch 3602. This batch contains various types
of errors including sample switches, transcription errors, calibration standards dilution errors, or a combination of
these three.
' Extractable aluminum analysis was not required for split sample analysis. Zero set to missing.
* Sample result miscalculated by laboratory.
' Sample switch.
mThe original AAS result was unacceptable. The ICPES result was substituted because it was of better quality.
" Analysis not required for rainwater audits. 0.0 set to missing.
0 Lake switch.
85
-------
Table A-2. Character and Numeric Change* Applied to the Official Verified Data Base to Create the Modified
Verified Data Base (for Forms 1D and 02 Only)
Batch ID
3523
3523
3600
3600
3600
3600
3600
3600
3600
3600
3601
3601
3601
3601
3601
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3602
3603
3603
3603
3603
3604
3604
3604
3604
3604
3604
3604
3604
3604
3605
3605
3605
3605
3605
3605
3605
3605
3605
3605
3605
3605
3605
3605
3606
3606
3606
3606
Sample ID
2
3
1
2
3
7
11
12
13
16
9
12
27
28
29
1
5
7
10
12
22
23
24
26
27
28
29
5
12
19
20
1
2
2
4
17
20
21
25
25
3
4
5
11
12
13
15
15
19
19
20
25
26
28
1
4
6
6
Variable
WDIR1D
WDIR10
DP B1D
PRECO1D
PREC01D
PREC01D
PREC01D
PRECO1D
PRECO1D
PRECO1D
PRECO1D
PRECO1D
WSPD1D
WSPD1D
PRECO1D
PRECO1D
PRECQ1D
DP BID
DP~B1D
ACCES1D
ACCES1D
DP B1D
PRECO1D
PREC01D
PREC01D
DP B1D
DP_B1D
PRECO1D
PREC01D
PREC01D
PREC01D
PREC01D
PREC1D
PREC01D
PRECO1D
PREC1D
PRECO1D
PREC1D
PREC1D
PREC01D
PRECO1D
PRECO1D
PREC01D
PRECO1D
PRECO1D
PREC1D
ACCES1D
PREC1D
ACCES1D
PREC1D
PREC01D
PREC01D
PREC01D
PREC1D
PREC1D
ACCES1D
ACCES1D
RPREC1D
Sample
type
B
R
R
R
R
D
R
S
B
R
R
R
R
R
S
R
R
R
D
R
R
R
R
R
S
D
S
R
R
B
R
R
R
R
R
R
R
R
R
R
R
R
S
D
B
R
R
R
R
R
D
R
R
R
R
R
R
R
Official
verified
value
V
V
6.0
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
NO WIND
NO WIND
CURRENT
CURRENT
CURRENT
10.9
10.9
—
—
10.9
CURRENT
CURRENT
CURRENT
10.9
10.9
PREV
—
_
—
CURRENT
NONE
CURRENT
CURRENT
—
CURRENT
—
NONE
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
_
—
—
__
—
CURRENT
CURRENT
CURRENT
—
^^,
—
_
—
Modified
verified
value
—
6.8
__
_
_
_
_
__
—
^mm
_
LIGHT
LIGHT
—
±
_
11.1
11.1
H
H
11.1
_
—
__
11.1
11.1
CURRENT
CURRENT
CURRENT
CURRENT
mi_^
RAIN
PREV
_
RAIN
—
RAIN
RAIN
PREV
_
_
—
_
_
RAIN
D
RAIN
D
RAIN
_
—
—
RAIN
RAIN
D
D
LIGHT
Comment
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
(Continued)
86
-------
Table A-2. Continued
Batch ID
3606
3606
3606
3606
3606
3607
3607
3607
3607
3607
3607
3607
3607
3607
3607
3607
3608
3608
3608
3608
3608
3608
3608
3608
3608
3610
3610
3610
3610
3610
3610
3610
3611
3611
3611
3611
3611
3611
3611
3611
3612
3612
3613
3613
3613
3613
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
Sample ID
9
11
15
17
22
2
7
9
12
21
21
23
24
26
28
28
1
2
4
9
11
12
19
24
25
1
6
12
14
16
18
19
1
2
9
15
17
20
22
30
4
17
7
12
15
22
1
3
4
5
6
8
9
10
11
14
15
16
17
Variable
PREC1D
PREC1D
PREC1D
ACCES1D
PREC1D
PRECO1D
PREC010
PREC1D
PREC10
PRECO1D
RPREC1D
PREC1D
PREC1D
PRECO1D
PREC01D
RPREC1D
PRECO1D
PRECO1D
PREC1D
PREC1D
PREC1D
PREC1D
PREC1D
PRECO1D
PREC1D
PRECO1D
PRECO1D
PRECO1D
PREC01D
PREC01D
PREC01D
PRECO1D
PREC01D
PRECO1D
PRECO1D
PREC01D
PREC01D
PRECO1D
PREC01D
PREC01D
PRECO1D
PRECO1D
PREC10
PRECO1D
PREC1D
PRECO1D
PRECO1D
PRECO1D
PREC1D
WSPD1D
PREC01D
WSPD1D
WSPD1D
WSPD1D
WSPD1D
PREC1D
PREC1D
WSPD1D
PRECO1D
Sample
type
R
S
R
R
R
R
S
R
R
R
R
R
R
R
R
R
R
R
B
R
R
R
S
D
R
R
R
B
R
R
R
R
R
R
R
R
R
R
R
S
R
R
R
R
R
R
R
R
R
R
R
D
R
B
D
S
R
S
R
Official
verified
value
—
—
—
—
^
—
—
—
_
—
—
—
_
—
—
CURRENT
CURRENT
_
—
—
—
_
CURRENT
—
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
.
CURRENT
_
CURRENT
CURRENT
PREV
— -
NO WIND
PREV
NO WIND
NO WIND
NO WIND
NO WIND
_
—
NO WIND
CURRENT
Modified
verified
value Comment
RAIN
RAIN
RAIN
D
RAIN
CURRENT
CURRENT
RAIN
RAIN
CURRENT
LIGHT
RAIN
RAIN
CURRENT
CURRENT
LIGHT
_
—
RAIN
RAIN
RAIN
RAIN
RAIN
—
RAIN
»
—
—
_
—
—
—
«.
—
—
_
_
—
—
—
mmm
—
RAIN a
mwm g
RAIN a
— a
___
CURRENT
RAIN
LIGHT
CURRENT
LIGHT
LIGHT
LIGHT
LIGHT
RAIN
RAIN
LIGHT a
— a
(Continued)
87
-------
Table A-2. Continued
Batch ID
3616
3616
3616
3616
3616
3616
3650
3650
3651
3651
3652
3654
3655
3656
3056
3657
3657
3658
3661
3662
3664
3664
oooo
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3711
3717
3718
3719
3720
3720
3720
Sample 10
1
2
4
5
6
7
2
3
3
3
3
1
2
3
3
2
2
1
3
3
2
3
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
99
13
14
6
9
12
19
Variable
PRECO1D
PREC1D
PRECO1D
PRECO1D
PREC1D
PRECO1D
PREC010
ACCES1D
ACCES1D
PRECO1D
PRECO1D
PREC01D
PRECO1D
ACCES10
PREC1D
ACCES1D
PREC01D
PREC1D
PREC01D
ACCES1D
WSP01D
PREC1D
PREC01D
DATSH02
DATSH02
DATSH02
DATSH02
OATSH02
DATSH02
OATSH02
DATSH02
DATSH02
DATSH02
DATSH02
DATSH02
DATSH02
DATSH02
DATSH02
DATSH02
OA1SH02
DATSH02
DATSH02
PRECO1D
PREC01D
PRECO1D
ACCES1D
PRECO1D
PREC01D
Sample
type
R
R
0
B
R
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
LS5
R
R
FN7
FN8
D
RWLC
R
R
R
R
R
R
R
B
R
R
R
TO
R
R
R
R
D
R
Official
verified
value
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
_
CURRENT
CURRENT
CURRENT
CURRENT
—
—
—
CURRENT
—
NO WIND
—
CURRENT
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
260CT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
26OCT1986
260CT1986
26OCT1986
26OCT1986
26OCT1986
CURRENT
CURRENT
CURRENT
CURRENT
CURRENT
Modified
verified
value
RAIN
«•*
RAIN
D
D
—
—
—
D
RAIN
0
CURRENT
RAIN
—
D
LIGHT
RAIN
—
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
27OCT1986
—
—
—
H
—
Comment
a
s
a
a
a
a
£
a
s
a
a
a
a
£
a
£
a
a
a
a
a
a
a
b
b
b
b
t>
b
b
b
b
b
b
b
b
b
b
b
b
b
b
a
a
a
£
a
a
(Continued)
88
-------
Table A-Z Continued
Official Modified
Sample verified verified
Batch ID Sample 10 Variable type value value Comment
3722 1 PREC01D R — O a
3722 13 PREC010 R — PREV *
• This change was made as the result of review of the field form information.
* This change was made as the result of a transcription error that occurred during the creation
of the raw data base.
89
-------
Table A-3. All X Flag' Changes Applied to the Official Verified Data Base to Create the Modified Verified Data
Bate (For Form 11 Only)
Batch 10
3508
3508
3508
3508
3515
3515
3717
3717
3721
Sample ID
4
5
6
8
5
6
6
10
8
Variable
CL11F
CL11F
CL11F
CL11F
CL11F
CL11F
ALKA11F
ALKA11F
ALKA11F
Sample
type
R
R
0
LN8
FL15
R
LS3
R
B
Official
verified
flag*
_
_
—
^ _
—
_
—
—
Modified
verified
flag*
XO
XO
XO
XO
XO
XO
XO
XO
XO
Comment
c
c
c
c
c
e
c
e
c
* An X flag is applied when a value is deemed invalid. It is recommended that this value should not be used in
statistical analyses.
* Only X flags are provided. Other flags which may be in the data bases are not shown.
c X flag added to modified verified data base.
90
-------
Appendix B
Results of the Decision Process for
Chloride, Sulfate, and Nitrate Assessment
Results of the decision process using the accuracy and precision estimates for chloride,
suifate, and nitrate are provided in tables B-1 through B-6.
The audit percent differences (accuracy estimates) were calculated using the following
equation:
% Relative Difference
(reference - measured)
reference
x100
The reference concentration for a given audit sample is the median value of all the laboratory
data and, where possible, the National Streams Survey (NSS-I) data. The measured value refers
to the measured concentration of a given audit sample.
The percent differences for the routine/duplicate pairs (precision estimates) were calculated
using the following equation:
% Relative Difference =
(routine - duplicate)
x100
routine
The legend for tables B-1 through B-6 is presented below:
FN7 - Seventh Lake field natural audit sample.
FN8 - Big Moose Lake field natural audit sample.
FL - Field synthetic audit sample.
LL - Laboratory synthetic audit sample.
Analysis 1 - The first sample analysis, performed in March and April of 1986, consists of
all the Spring Seasonal subsurvey chloride, suifate, and nitrate determina-
tions.
Analysis 2 - The second sample analysis, performed in May and June of 1986, consists
of all the Spring Seasonal subsurvey chloride, suifate, and nitrate
determinations.
Analysis 3 - The third sample analysis, performed in May of 1987 (during data
verification), consists of selected samples from the Spring Seasonal
subsurvey chloride, suifate, and nitrate determinations.
Indecisive - When a conclusive decision could not be made between the two (sometimes
three) analyses, the Analysis 2 data remained in the data base.
91
-------
Table B-1. Results of the Decision Process Using the Accuracy Estimates For Chloride Baeed on the Audit
Sample Data; Spring Seasonal Subsurvey, Eastern Lake Survey-Phase II
Audit
typs
FN7
LN7
FN7
FL14
FN7
FN7
LN7
FN8
LN8
FN8
FN7
LN7
FN8
LN8
FN7
LN8
FN8
FL13
FL14
LL14
FL14
FN8
FL15
FL15
LL15
FL7
FN7
LN7
FN8
FL15
FN8
LN8
LN8
FN8
LL15
FL15
FN8
LN8
FN7
LN7
FN7
FN7
FN8
Batch
ID
3500
3501
3501
3502
3503
3504
3504
3505
3506
3506
3507
3507
3508
3508
3509
3510
3510
3511
3512
3512
3513
3514
3515
3516
3516
3517
3518
3518
3519
3520
3521
3521
3522
3522
3523
3523
3524
3524
3525
3526
3526
3527
3528
Reference
value (mg/L)
£950
£950
£950
0.330
£950
£950
£950
0.412
0.412
0.412
£950
£950
0.412
0.412
£950
0.412
0.412
0.330
0.330
0.330
0.330
0.412
0.330
0.330
0.330
£950
£950
£950
0.412
0.330
0.412
0.412
0.412
0.412
0.330
0.330
0.412
0.412
£950
£950
£950
£950
0.412
Percent difference
for Analysis 1
30.51
67.76
47.88
100.00
44.04
50.82
48.13
5320
35.43
54.85
1£47
16.47
50.00
59.70
7.79
50.00
4029
55.15
34.84
35.75
43.33
50.00
78.18
23.63
36.36
14.16
1220
1023
8.73
16.97
5.82
3.88
47.57
9.70
13.33
6.66
16.99
£42
1.15
32.40
40.47
1£13
44.41
Percent difference
for Analysis 2
39.66
85.39
1.83
20.00
197.93
29.32
84.30
77.42
58.71
6529
16.81
5.11
4126
3£76
10.37
7.52
1£62
0.00
20.90
6.97
16.36
9.95
10.60
26.97
43.03
3.89
51.69
£16
11.16
10424
45.63
77.42
23.78
23.05
6.97
10.30
15.53
4.12
£37
5.72
0.33
5.35
16.50
Percent difference
for Analysis 3
b
b
b
b
44.10
b
b
0.72
4.61
4.51
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
31.55
0.72
b
b
b
b
b
b
b
b
b
b
b
Decision based
on accuracy'
Indecisive
Indecisive
Indecisive
Analysis 2
Indecisive
Indecisive
Indecisive
Analysis 3
Analysis 3
Analysis 3
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Indecisive
Indecisive
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 2
Analysis 2
Analysis 2
Analysis 2
' A decision based on accuracy estimates was made on a batch-by-batch basis.
" Reanalyses were not required for these batches.
92
-------
Tabla B-2. Reautta of tha Decision Proeaaa Ualng tha Precision Eatlmatea for Chlorld* Baaad on tha Routlna/
Duplteata Palrad Data, Spring Saaaonal Subaurvay, Eaatam Lafca SurvayPhaaa II
Batch ID
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
Paroant diff aranea
for Analysis 1
0.86
33.66
36.64
475.28
2.01
61.85
49.08
10.85
33.51
10.86
16.52
2.64
0.00
48.60
0.00
0.00
0.48
370.00
221
0.35
4.76
5.55
42.63
0.89
3.84
7.38
23.43
0.00
6.51
Pareant diffaranca Paroant difference Decision baaed
for Analysis 2 for Analysis 3 on precision
31.51
1.104.77
24.64
12629
43.52
58.10 1
5328 1
21.04
7.18
320
0.54
6.80
17.82
31.83
6.40
0.00
0.33
40.06
40.00
4.46
25.95
38.83 3(
6.13
3.60
4.99
1.99
71.05
7.77
Analysis 1
Analysis 1
Analysis 2
.19 Analysis 3
Analysis 1
.95 Analysis 3
.12 Analysis 3
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Analysis 2
Analysis 1
Indecisive
Indecisive
Analysis 2
Analysis 1
Indecisive
Analysis 1
.96 Analysis 1
Analysis 2
Indecisive
Indecisive
Analysis 2
Analysis 1
Analysis 1
4.95 Indecisive
• Reanalyses wara not required for thaaa batchaa.
93
-------
Table B-3. Results of the Decision Process Using the Accuracy Estimates For Suit ate Based on the Audit
Sample Data; Spring Seasonal Subsurvey. Eastern Lake Survey-Phase II
Audit
type
FN7
LN7
FN7
FL14
FN7
FN7
LN7
FN8
LN8
FN8
FN7
LN7
FN8
LN8
FN7
LN8
FN8
FL13
FL14
LL14
FL14
FN8
FL15
FL15
LL15
FN7
FN7
LN7
FN8
FL15
FN8
LN8
LN8
FN8
LL15
FL15
FN8
LN8
FN7
LN7
FN7
FN7
FN8
Batch
ID
3500
3501
3501
3502
3503
3504
3504
3505
3506
3506
3507
3507
3508
3508
3509
3510
3510
3511
3512
3512
3513
3514
3515
3516
3516
3517
3518
3518
3519
3520
3521
3521
3522
3522
3523
3523
3524
3524
3525
3526
3526
3527
3528
Reference
value (mg/L)
6.850
6.850
6.850
2235
6.850
6.850
6.850
6.335
6.335
6.335
6.850
6.850
6.335
6.335
6.850
6.335
6.335
2235
2235
2235
2235
6.335
2235
2235
2235
6.850
6.850
6.850
6.335
2235
6.335
6.335
6.335
6.335
2235
2235
6.335
6.335
6.850
6.850
6.850
6.850
6.335
Percent difference
for Analysis 1
26.42
67.88
1529
82.99
0.90
1.19
429
11.94
1029
9.39
8.47
10.91
1122
8.08
5.96
11.46
12.45
12.98
34.04
3624
36.64
5.03
11.89
13.91
5.14
8.67
5.19
3.62
£95
621
4.56
3.99
60.56
0.67
4.87
5.68
4.65
1£39
23.35
8.26
8.05
11.76
4£71
Percent difference
for Analysis 2
4.65
8.75
5.91
5.54
5.34
11.19
1525
22.71
3.33
3.66
20.52
0.37
9.18
23.55
8.94
17.60
18.50
11.67
429
3.62
5.68
5.82
£01
7.33
9.61
4.05
18.05
42.18
7.19
4.34
£20
4£38
20.91
21.62
720
10.69
0.12
0.59
0.01
3.85
1.97
3.40
4.62
Percent difference
for Analysis 3
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
8.19
7.35
b
b
b
b
b
b
b
b
b
b
b
b
b
8.19
5.66
b
b
b
b
b
b
b
b
b
b
b
Decision based
on accuracy'
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 3
Analysis 3
Indecisive
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 2
Indecisive
Indecisive
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
' A decision based on accuracy estimates was made on a batch-by-batch basis.
0 Reanalyses were not required for these batches.
94
-------
Table B-4. Results of the Decision Process Using the Precision Eatlmatee for Sulfate Baaed on the Routine/
Duplicate Paired Data, Spring Seasonal Subsurvsy, Eaatem Laka Survey-Phase II
Batch ID
3500
3501
3502
3503
3504
3505
3506
3507
3508
3508
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
Percent difference
for Analysis 1
11.17
3.79
35.00
8.12
1.18
2.13
2.88
2.46
0.94
0.67
8.01
728
16.54
4.38
1.78
1.13
2.39
0.50
0.42
0.52
1.53
3.33
176.80
0.30
129
4.52
0.68
1.32
1.45
Percent difference Percent difference Decision based
for Analysis 2 for Analysis 3 on precision
1.18
1.13
0.19
12.50
8.58
15.69
029
16.03
18.94
022
10.37
1.37
2.03
0.42
0.83
0.62
7.67
1.91
26.09
0.93
3.66
37.80
23.46
424
4.56
1.87
0.93
1.13
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 1
Analysis 1
Indecisive
.88 Analysis 3
Analysis 2
Analysis 2
Analysis 2
Indecisive
Indecisive
Analysis 1
Indecisive
Analysis 1
Indecisive
Indecisive
.69 Analysis 3
Analysis 2
Analysis 1
Analysis 1
Analysis 2
Indecisive
Indecisive
0.39 Indecisive
' Rssnalysss were not required for these batches.
95
-------
Table B-5. Results of the Decision Process Using the Accuracy Estimates For Nitrate Based on the Audit
Sample Data; Spring Seasonal Subsurvey, Eastern Labs Survey-Phase II
Audit
type
FN7
LN7
FN7
FL14
FN7
FN7
LN7
FN8
LN8
FN8
FN7
LN7
FN8
LN8
FN7
LN8
FN8
FL13
FL14
LL14
FL14
FN8
FL15
FL15
LL15
FN7
FN7
LN7
FN8
FL15
FN8
LN8
LN8
FN8
LL15
FL15
FN8
LN8
FN7
LN7
FN7
FN7
FN8
Batch
ID
3500
3501
3501
3502
3503
3504
3504
3505
3506
3506
3507
3507
3508
3506
3500
3510
3510
3511
3512
3512
3513
3514
3515
3516
3516
3517
3518
3518
3519
3520
3521
3521
3522
3522
3523
3523
3524
3524
3525
3526
3526
3527
3528
Reference
value (mg/L)
1264
1264
1264
0.471
1264
1264
1264
1200
1200
1200
1264
1264
1200
1200
1264
1200
1200
0.471
0.471
0.471
0.471
1200
0.471
0.471
0.471
1264
1264
1264
1200
0.471
1200
1200
1200
1200
0.471
0.471
1200
1200
1264
1264
1264
1264
1200
Percent difference
for Analysis 1
17.48
10.75
32.04
16.77
0.63
3.71
2.05
28.34
12.40
12.05
12.68
14.77
15.55
14.03
8.33
14.93
1625
1626
30.74
41.74 *
57.36
7.00
21.86
9g?5
20.00
10.53
9.09
10.17
8.86
11.88
9.43
9.88
70.61
1.00
13.80
10.10
6.11
11.80
21.83
11.42
9.52
13.40
38.02
Percent difference
for Analysis 2
8.63
8.38
6.45
1025
8.06
11.36
34.59
26.60
328
3.66
20.93
0.15
7.75
2229
13.73
20.85
23.51
0.97
0.59
28.59
2724
8.70
49.66
520
35.32
166.66
40.78
30.75
13.59
59.15
2.07
66.37
23.87
22.63
7.43
7.43
12.83
7.99
10.96
2.86
7.58
£86
2.65
Percent difference
for Analysis 3
b
2025
10.98
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
15.06
8.75
b
b
b
b
b
b
b
b
b
b
b
Decision based
on accuracy*
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 1
Analysis 1
Analysis 1
Indecisive
Analysis 2
Analysis 2
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Indecisive
Indecisive
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Analysis 1
Indecisive
Indecisive
Analysis 2
Analysis 2
Indecisive
Indecisive
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
* A decision based on accuracy estimates was made on a batdvby-batch basis.
* Rsanalyses were not required for these batches.
96
-------
Table B-6. Reeulte of tha Daelalon Proeaaa Ualng the Precision Eatlmataa for Nltrata Based on th« Routine/
Duplicate Paired Data, Spring Seasonal Subsurvey, Eastern Lake Survoy-Phaeo II
Batch ID
3500
3501
3502
3503
3504
3505
3506
3507
3508
3609
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
Percent difference
for Analysis 1
3.89
12.40
15.91
0.00
0.00
0.00
£86
1.98
7.63
10.13
39.00
34.28
36.75
25.17
6.55
45.00
2g,2?
0.16
2.73
0.70
0.93
151
0.00
0.00
0.00
0.00
12*7
14.92
3.69
Percent difference Percent difference Decision based
for Analysis 2 for Analysis 3 on precision
1.96 0.00 Indecisive
2.42
0.00
3,709.09
0.00
0.00
4.77
19.84
28.34
1.40
1954
5.16
2.84
4.40
0.60
5.16
9.20
0.67
50.36
0.94
254
16.63
0.00
0.00
0.00
0.00
34.46
82.47
5.48
Analysis 2
Analysis 2
Analysis 1
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 1
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Analysis 2
Indecisive
Analysis 1
Indecisive
Indecisive
00 Indecisive
Indecisive
Indecisive
Indecisive
Indecisive
Analysis 1
Analysis 1
Indecisive
' Rsanalysss were not required for these batches.
97
-------
Appendix C
Summary Statistics for Natural
Audit Sample Data
Summary statistics for estimates of accuracy and precision based on the natural audit
samples are provided in tables C-1 through C-6. These tables present estimates for all 30 (6
processing laboratory and 24 analytical laboratory) analytes. The processing laboratory, by design,
does not analyze laboratory natural audit samples. Therefore, there are no lab target values for
processing laboratory variables (Al-dis, Al-org, DIC-closed, pH-closed, true color, and turbidity) in
tables C-1, C-3, C-4, and C-6. For the same reason there are also no summary statistics for
laboratory natural audits for processing laboratory variables.
Terms and abbreviations used in tables C-1 through C-6 are defined as follows:
Field target: These reference values' for Seventh Lake and Big Moose Lake are the
medians of all the available Seventh Lake and Big Moose Lake field natural
audit sample data, respectively.
Lab target: These reference values' for Seventh Lake and Big Moose Lake are the
medians of all the available Seventh Lake and Big Moose Lake laboratory
natural audit sample data, respectively.
N - Number of audit samples
Median - Median of N data points
Mean • Mean of N data points
Std - Standard deviation of N data points
Q1 - Lower quartile or 25th percentile: the value below which 25% of all data falls.
Q3 • Upper quartile or 75th percentile: the value below which 75% of all data falls.
P10 - 10'" percentile: the value below which 10% of all data falls.
P90 - 90th percentile: the value below which 90% of all data falls.
Subsurvey-lab:
Spring-1 = Spring Seasonal subsurvey analysis performed by Laboratory 1.
Summer-1 = Summer Seasonal subsurvey analysis performed by Laboratory 1.
Summer-2 = Summer Seasonal subsurvey analysis performed by Laboratory 2.
Summer-1+2 = Summer Seasonal subsurvey analyses; Laboratory 1 and Laboratory 2 data
combined.
Fall-2 - Fall Seasonal subsurvey analysis performed by Laboratory 2.
Reference values were generated from all the ELS-II data (including the Spring Variability Pilot
Study) for the Seventh Lake natural audit data. Reference values for Big Moose Lake audit
data were determined from the ELS-II and NSS-I data.
98
-------
Spring-PL - Spring Seasonal subsurvey analysis performed by the processing
laboratory.
Summer-PL * Summer Seasonal subsurvey analysis performed by the processing
laboratory.
Fall-PL * Fall Seasonal subsurvey analysis performed by the processing
laboratory.
99
-------
Table C-1. Summary Statistic* for Seventh Lake Field Natural Audit*, Eastern Lake Survey - Phase II
8
Analyte
Al-ext
AJ-ext
Al-ext
Al-ext
Al-ext
Al-total
Al-total
Al-total
Al-total
Al-total
Al-dis
Al-dis
Al-dis
Al-org
Al-org
Al-org
ANC
ANC
ANC
ANC
ANC
BNC
BNC
BNC
BNC
BNC
Ca
Ca
Ca
Ca
Ca
ci-
ci-
ci-
cr
ci-
Units
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
0»qA-)
Owq/L)
0»q/L)
fcwq/L)
0«q/L)
G»q/L)
0»q/L)
fcieq/L)
O^q/L)
(/*»q/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-14-2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.0163
0.0163
0.0163
0.0163
0.0163
0.0627
0.0627
0.0627
0.0627
0.0627
0.0212
0.0212
0.0212
0.0165
0.0165
0.0165
155.0
155.0
155.0
155.0
155.0
36.1
36.1
36.1
36.1
36.1
5.050
5.050
5.050
5.050
5.050
2.940
2.940
2.940
2.940
2.940
Lab
target
0.0302
0.0302
0.0302
0.0302
0.0302
0.0682
0.0682
0.0682
0.0682
0.0682
_
—
—
_
— .
—
151.1
151.1
151.1
151.1
151.1
35.6
35.6
35.6
35.6
35.6
4.842
4.842
4.842
4.842
4.842
2.943
2.943
2.943
2.943
2.943
N
11
3
4
7
11
11
3
4
7
11
8
7
11
8
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
Median
0.0227
-0.0025
0.0163
0.0137
0.0117
0.0651
0.0875
0.0517
0.0648
0.0607
0.0226
0.0200
0.0259
0.0223
0.0055
0.0150
148.3
155.2
153.6
154.0
155.7
51.2
36.1
36.5
36.1
32.8
4.908
5.023
4.993
5.023
5.158
2.940
2.393
2.915
2.875
2.970
Mean
0.0205
•0.0006
0.0160
0.0089
0.0132
0.0578
0.1408
0.0502
0.0891
0.0699
0.0225
0.0209
0.0246
0.0226
0.0088
0.0153
150.0
153.1
165.6
160.4
158.9
66.5
36.0
37.5
36.9
35.0
4.909
4.990
5.007
4.999
5.165
3.175
2.366
2.910
2.677
2.913
Std
0.0074
0.0036
0.0017
0.0092
0.0043
0.0165
0.1126
0.0176
0.0820
0.0419
0.0050
0.0058
0.0034
0.0063
0.0095
0.0034
17.7
6.2
27.2
20.7
8.7
58.5
5.2
7.2
6.0
7.6
0.116
0.171
0.079
0.114
0.095
1.103
0.079
0.025
0.295
0.181
01
0.0114
-0.0029
0.0142
-0.0025
0.0103
0.0410
0.0648
0.0330
0.0404
0.0478
0.0179
0.0174
0.0216
0.0184
0.0027
0.0125
139.3
146.1
150.5
149.6
154.4
42.4
30.7
31.1
30.7
28.1
4.817
4.804
4.938
4.930
5.101
2.582
2.277
2.885
2.393
2.920
03
0.0260
0.0035
0.0174
0.0171
0.0170
0.0706
0.2702
0.0660
0.0875
0.0720
0.0272
0.0264
0.0270
0.0250
0.0151
0.0183
153.7
158.0
193.4
158.0
170.3
60.9
41.1
44.9
41.1
43.4
4.928
5.142
5.089
5.110
5.231
4.251
2.428
2.930
2.915
2.975
P10
0.0095
•0.0029
0.0137
-0.0029
0.0069
0.0324
0.0648
0.0306
0.0306
0.0353
0.0149
0.0129
0.0196
0.0129
-0.0003
0.0100
125.4
146.1
149.6
146.1
147.8
26.5
30.7
30.0
30.0
25.0
4.783
4.804
4.930
4.804
5.026
1.571
2.277
2.875
2.277
2.485
P90
0.0289
0.0035
0.0175
0.0175
0.0199
0.0768
0.2702
0.0670
0.2702
0.1678
0.0294
0.0304
0.0291
0.0347
0.0272
0.0196
185.7
158.0
206.5
206.5
172.5
205.8
41.1
47.0
47.0
46.6
5.157
5.142
5.110
5.142
5.304
5.137
2.428
2.935
2.935
3.043
(Continued)
-------
Table C-1. Continued
Analyte
Cond
Cond
Cond
Cond
Cond
DIC-closed
DIC-closed
DIC-closed
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-initial
DIC-initial
DIC-initial
DIC-initial
DIC-initial
DOC
DOC
DOC
DOC
DOC
F'-total
F'-total
F--total
F'-total
F'-total
Fe
Fe
Fe
Fe
Fe
K
K
K
K
K
Units
Subsurvey-
lab
(pS/cm) Spring-1
(pS/cm) Summer-1
(pS/cm) Summer-2
(pS/cm) Summer-1-1-2
-------
Table C-1. Continued
to
• - *•,' . - " ' . „ --.-• - Subsurvey-
Analyte ^ Units < lab
Mg (mj
Mg (mj
Mg (mj
Mg (mj
Mg (mi
j/L) Spring-1
j/L) Summer-1
j/L) Summer-2
I/L) Summer-1+2
j/L) Fall-2
Mn (mg/L) Spring-1
Mn (mg/L) Summer-1
Mn (mg/L) Summer-2
Mn (mg/L) Summer-1+2
Mn (mg/L) Fall-2
Na (mg/L) Spring-1
Na (mg/L) Summer-1
Na (mg/L) Summer-2
Na (mg/L) Summer-1+2
Na (mg/L) Fall-2
NH. (mg/L) Spring-1
NH. (mg/L) Summer-1
NH. (mg/L) Summer-2
NH. (mg/L) Summer-1+2
NH. (mg/L) Fall-2
NO,- (mg/L) Spring-1
NO,' (mg/L) Summer-1
NO,- (mg/L) Summer-2
NO," (mg/L) Summer-1+2
NO,' (mg/L) Fall-2
P-total (mg/L) Spring-1
P-total (mg/L) Summer-1
P-total (mg/L) Summer-2
P-total (mg/L) Summer-1+2
P-total (mg/L) Fall-2
pH-ANC (pH units) Spring-1
pH-ANC (pH units) Summer-1
pH-ANC (pH units) Summer-2
pH-ANC (pH units) Summer-1+2
pH-ANC (pH units) Fall-2
Field
target
0.824
0.824
0.824
0.824
0.824
0.005
0.005
0.005
0.005
0.005
2.176
2.176
2.176
2.176
2.176
0.010
0.010
0.010
0.010
0.010
1.2725
1.2725
1.2725
1.2725
1.2725
0.0022
0.0022
0.0022
0.0022
0.0022
7.02
7.02
7.02
7.02
7.02
tab
target
0.800
0.800
0.800
0.800
0.800
0.005
0.005
0.005
0.005
0.005
2.181
2.181
2.181
2.181
2.181
0.019
0.019
0.019
0.019
0.019
1.2853
1.2853
1.2853
1.2853
1.2853
0.0012
0.0012
0.0012
0.0012
0.0012
6.97
6.97
6.97
6.97
6.97
N
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
Median
0.829
0.82S
0.795
0.805
0.822
0.005
0.005
0.001
0.003
0.005
2.150
2.142
2.158
2.142
2.185
0.010
0.017
0.021
0.017
0.013
1.2720
1.3118
1.2663
1.2746
1.2645
0.0030
0.0023
0.0016
0.0018
0.0025
7.09
6.97
7.03
7.01
7.00
Mean
0.824
0.830
0.793
0.809
0.824
0.004
0.005
0.001
0.003
0.005
2.138
2.142
2.277
2.220
2.203
0.009
0.019
0.021
0.020
0.016
1.2557
1.3131
1.2682
1.2874
1.2416
0.0030
0.0028
0.0020
0.0024
0.0025
7.04
6.98
7.03
7.01
7.00
Std
0.016
0.011
0.012
0.023
0.012
0.003
0.000
0.001
0.002
0.001
0.082
0.039
0.322
0.240
0.067
0.016
0.004
0.012
0.009
0.017
0.1085
0.0391
0.0130
0.0342
0.0739
0.0024
0.0014
0.0012
0.0012
0.0010
0.10
0.03
0.08
0.06
0.08
vV-
0.810
0.822
0.781
0.794
0.815
0.004
0.005
0.000
0.001
0.004
2.033
2.104
2.061
2.104
2.158
•0.006
0.017
0.010
0.015
0.002
1.1490
1.2746
1.2568
1.2602
1.2500
0.0010
0.0018
0.0012
0.0015
0.0021
6.93
6.95
6.96
6.95
6.99
- 'Or- .->
0.834
0.843
0.803
0.825
0.826
0.005
0.005
0.002
0.005
0.006
2.200
2.181
2.613
2.203
2^77
0.019
0.023
0.033
0.028
0.019
1.3599
1.3528
1.2815
1.3118
1.2736
0.0050
0.0044
0.0033
0.0038
0.0031
7.13
7.01
7.11
7.04
7.03
^
0.796
0.822
0.777
0.777
0.814
0.000
0.005
0.000
0.000
0.003
2.007
2.104
2.044
2.044
2.110
-0.011
0.017
0.008
0.008
-0.002
1.1090
1.2746
1.2557
1.2557
1.0644
0.0000
0.0018
0.0011
0.0011
0.0008
6.91
6.95
6.94
6.94
6.82
P90
0.84S
0.843
0.805
0.843
0.852
0.009
0.005
0.003
0.005
0.008
2^38
£181
2.750
2.750
2.313
0.037
0.023
0.035
0.035
0.051
1.3967
1.3528
1.2845
1.3528
1.2764
0.0074
0.0044
0.0038
0.0044
0.0044
7.16
7.01
7.13
7.13
7.09
(Continued)
-------
TabtoC-1. Continued
Analyte
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-closed
pH-closed
pH-closed
pH-eq
pH-eq
pH-eq
pH-eq
pH-eq
SiO,
SiO,
SiO,
SiO,
SiO,
so.2-
so42-
so4J-
so.J-
sota-
True color
True color
True color
Turbidity
Turbidity
Turbidity
Units
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(mg/Li
(mg/L
(mg/L
(mg/L
(mg/L ;
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(PCU)
(PCU)
(PCU)
(NTU)
(NTU)
(NTU)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Field
target
7.07
7.07
7.07
7.07
7.07
6.89
6.69
6.89
7.28
7.28
7.28
7.28
7.28
4.820
4.820
4.820
4.820
4.820
6.850
6.850
6.850
6.850
6.850
15.0
15.0
15.0
0.100
0.100
0.100
Lab
target
7.01
7.01
7.01
7.01
7.01
_
—
—
7.28
7.28
7.28
7.28
7.28
4.738
4.738
4.738
4.738
4.738
6.730
6.730
6.730
6.730
6.730
_
_
—
_
_
—
N
11
3
4
7
11
11
7
11
11
3
4
7
11
11
3
4
7
11
11
3
4
7
11
11
7
10
11
7
10
Median
7.07
6.94
7.10
7.01
7.05
6.92
6.68
6.90
7.28
7.19
7.34
7.31
7.05
4.820
4.733
4.863
4.849
4.776
6.849
7.072
6.895
6.950
6.770
15.0
20.0
15.0
0.160
0.100
0.100
Mean
7.05
6.96
7.09
7.03
7.05
6.91
6.68
6.90
7.28
7.22
7.37
7.30
7.07
4.760
4.793
4.856
4.829
4.641
6.798
6.791
6.942
6.877
6.456
15.0
19.3
16.0
0.212
0.100
0.120
Std
0.14
0.05
0.10
0.10
0.09
0.03
0.09
0.04
0.06
0.12
0.13
0.14
0.14
0.194
0.158
0.044
0.102
0.453
0.321
0.739
0.232
0.464
0.904
4.5
3.5
3.2
0.198
0.058
0.042
Q1
6.91
6.92
6.99
6.94
6.97
6.89
6.62
6.87
7.27
7.11
7.26
7.19
7.01
4.630
4.673
4.811
4.733
4.735
6.494
5.952
6.750
6.720
6.615
10.0
15.0
15.0
0.120
0.100
0.100
03
7.18
7.01
7.18
7.14
7.12
6.94
6.74
6.92
7.33
7.35
7.50
7.38
7.16
4.880
4.972
4.894
4.900
4.820
7.128
7.348
7.182
7.260
6.930
20.0
20.0
20.0
0.180
0.100
0.125
P10
6.85
6.92
6.97
6.92
6.92
6.87
6.52
6.83
7.18
7.11
7.25
7.11
6.81
4.346
4.673
4.798
4.673
3.549
6.303
5.952
6.720
5.952
4.346
10.0
15.0
10.5
0.104
0.000
0.100
P90
7.21
7.01
7.19
7.19
7.19
6.96
6.81
6.95
7.36
7.35
7.54
7.54
7.29
4.956
4.972
4.900
4.972
4.903
7.238
7.348
7.260
7.348
6.982
20.0
25.0
20.0
0.684
0.200
0.200
-------
Table C-2. Summery Statistics for Seventh Lake Lab Natural Audits, Eastern Lake Survey - Phase II
Analyte
Al-ext
Al-ext
Al-ext
Al-ext
Al-ext
Al-total
Al-total
Al-total
Al-total
Al-total
ANC
ANC
ANC
ANC
ANC
BNC
BNC
BNC
BNC
BNC
Ca
Ca
Ca
Ca
Ca
ci-
ci-
ci-
cr
cr
Cond
Cond
Cond
Cond
Cond
Units
(mg/L)
(mg/L)
(mg/L)
mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(ueq/L)
(ueq/L)
(ueq/L)
(«eq/L)
(ueq/L)
(ueq/L)
(ueq/L)
(uaq/L)
(ueq/L)
(ueq/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(uS/cm)
(uS/tem)
(uS/cm)
(uS/cm)
(uS/cm)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Sprlng-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.0163
0.0163
0.0163
0.0163
0.0163
0.0827
0.0627
0.0627
0.0627
0.0627
155.0
155.0
155.0
155.0
155.0
36.1
36.1
36.1
36.1
36.1
5.050
5.050
5.050
5.050
5.050
2.940
2.940
2.940
2.940
2.940
47.0
47.0
47.0
47.0
47.0
Lab
target
0.0302
0.0302
0.0302
0.0302
0.0302
0.0682
0.0682
0.0682
0.0682
0.0682
151.1
151.1
151.1
151.1
151.1
35.6
35.6
35.6
35.6
35.6
4.842
4.842
4.842
4.842
4.842
2.943
2.943
2.943
2.943
2.943
47.3
47.3
47.3
47.3
47.3
N
5
2
4
6
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
5
15
6
Median
0.0335
0.0152
0.0277
0.0272
0.0259
0.0640
0.0725
0.0584
0.0698
0.0553
145.1
138.9
153.6
149.5
156.5
51.6
35.7
36.9
35.7
35.4
4.808
4.778
4.877
4.821
4.991
2.464
2.880
2.965
2.918
2.952
47.7
48.4
46.7
48.0
47.1
Mean
0.0328
0.0152
0.0278
0.0236
0.0258
0.0603
0.0817
0.0571
0.0708
0.0557
146.2
138.9
154.7
145.9
168.5
49.6
35.3
37.9
36.5
35.1
4.801
4.786
4.887
4.831
5.014
2.196
3.547
2.979
3.294
2.945
47.7
48.3
46.1
475
47.1
Std
0.0050
0.0081
0.0008
0.0075
0.0025
0.0174
0.0257
0.0104
0.0235
0.0171
6.6
8.7
3.4
10.5
24.4
14.8
3.4
5.5
4.5
11.2
0.036
0.043
0.072
0.076
0.122
0.938
3.139
0.099
2.303
0.059
0.4
0.5
1.2
1.3
0.3
Q1
0.0280
0.0095
0.0272
0.0181
0.0233
0.0454
0.0698
0.0470
0.0597
0.0426
140.2
133.6
152.1
136.9
155.7
35.3
34.1
33.3
34.1
25.6
4.765
4.747
4.825
4.763
4.899
1240
1597
2.897
2.825
2.880
47.3
47.9
44.9
46.8
46.8
03
0.0372
0.0210
0.0287
0.0282
0.0280
0.0732
0.0821
0.0670
0.0728
0.0711
152.6
143.8
156.3
153.9
182.1
62.9
38.1
432
38.5
42.4
4.832
4.817
4.928
4.875
5.145
3.016
3.669
3.075
3.100
2.994
48.0
48.5
46.9
48.5
47.3
P10
0.0258
0.0095
0.0271
0.0095
0.0228
0.0326
0.0625
0.0439
0.0466
0.0295
137.7
123.5
151.5
130.9
154.3
34.3
27.6
31.6
31.1
20.2
4.747
4.739
4.818
4.740
4.897
0.951
1.394
2.840
1.399
2.880
47.0
47.4
44.1
45.1
46.7
P90
0.0390
0.0210
0.0289
0.0289
0.0294
0.0798
0.1441
0.0700
0.1062
0.0788
154.4
153.4
162.0
157.0
216.7
70.5
38.9
46.8
44.8
53.1
4.834
4.860
5.034
4.946
5.171
3252
11.309
3.130
4.610
3.025
48.1
49.1
47.0
48.9
47.4
(Continued)
-------
Table C-2. Continued
Subsurvey-
Analyte Units lab
OlC-eq (mg/L) Spring-1
DIC-eq (mg,
DIC-eq (mg
DIC-eq (mg
DIC-«q (mg
IL) Summer-1
IL) Summer-2
IL) Summer-1+2
IL) Fall-2
DIC-initial (mg/L) Spring-1
DIC-initial (mg
DIC-initial (mg
DIC-initial (mg
DIC-initial (mg
DOC (mg
DOC (mg
DOC (mg
DOC (mg
DOC (mg
IL) Summer-1
IL) Summer-2
IL) Summer-1+2
(L) Fall-2
0.) Spring-1
IL) Summer-1
IL) Summer-2
IL) Summer-1+2
fL) Fall-2
FMotal (mg/L) Spring-1
F'-total (mg/L) Summer-1
F'-total (mg/L) Summer-2
F'-total (mg/L) Summer-1+2
F'-total (mg/L) Fall-2
Fe (mg
Fe (mg
Fe (mg
Fe (mg
Fe (mg
IL) Spring-1
IL) Summer-1
IL) Summer-2
IL) Summer-1+2
IL) Fall-2
K (mg/L) Spring-1
K (mg/L) Summer-1
K (mg/L) Summer-2
K (mg/L) Summer-1+2
K (mg/L) Fall-2
Mg (mg
Mg (mg
Mg (mg
Mg (mg
Mg (mg
IL) Spring-1
IL) Summer-1
IL) Summer-2
IL) Summer-1+2
IL) Fall-2
Field
target
1.725
1.725
1.725
1.725
1.725
2.018
2.018
2.018
2.018
2.018
3.56
3.56
3.56
3.56
3.56
0.0661
0.0661
0.0661
0.0661
0.0661
0.026
0.026
0.026
0.026
0.026
0.490
0.490
0.490
0.490
0.490
0.824
0.824
0.824
0.824
0.824
Lab
target
1.668
1.668
1.668
1.668
1.668
1.965
1.965
1.965
1.965
1.965
3.56
3.56
3.56
3.56
3.56
0.0639
0.0639
0.0639
0.0639
0.0639
0.027
0.027
0.027
0.027
0.027
0.503
0.503
0.503
0.503
0.503
0.800
0.800
0.800
0.800
0.800
N
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
Median
2.117
2.475
1.562
£016
1.616
2.280
2.580
1.931
2.231
1.923
3.50
3.49
3.56
3.55
3.61
0.0576
0.0642
0.0642
0.0642
0.0673
0.030
0.021
0.027
0.026
0.023
0.484
0.517
0.495
0.514
0.483
0.801
0.799
0.797
0.797
0.799
Mean
2.184
£291
1.570
1.971
1.611
2.280
2.373
1.909
2.167
1.906
4.13
3.43
3.61
3.51
3.63
0.0586
0.0640
0.0668
0.0653
0.0684
0.030
0.022
0.027
0.024
0.025
0.490
0.516
0.491
0.505
0.475
0.612
0.799
0.791
0.795
0.802
Std
0.223
0.748
0.073
0.659
0.080
0.085
0.824
0.079
0.646
0.068
1.72
0.23
0.18
0.23
0.10
0.0047
0.0043
0.0078
0.0061
0.0057
0.003
0.005
0.002
0.005
0.005
0.015
0.006
0.016
0.017
0.018
0.017
0.012
0.015
0.013
0.011
Q1
1.997
2.350
1.500
1.554
1.537
2.197
2.487
1.862
1.918
1.870
3.19
3.19
3.49
3.40
3.54
0.0543
0.0602
0.0638
0.0616
0.0639
0.028
0.018
0.026
0.020
0.021
0.480
0.517
0.480
0.496
0.452
0.800
0.791
0.777
0.786
0.794
03
2.404
2.584
1.633
2.503
1.683
2.363
2.731
1.966
2.657
1.946
5.38
3.61
3.66
3.62
3.70
0.0635
0.0659
0.0658
0.0657
0.0730
0.033
0.028
0.028
0.028
0.029
0.501
0.517
0.502
0.517
0.490
0.830
0.808
0.801
0.805
0.811
P10
1.974
0.421
1.484
1.356
1.508
2.170
0.299
1.745
1.576
1.779
3.18
3.05
3.47
3.15
3.52
0.0536
0.0596
0.0620
0.0600
0.0620
0.027
0.016
0.025
0.017
0.021
0.480
0.501
0.462
0.477
0.450
0.800
0.778
0.766
0.775
0.791
P90
2.518
2.840
1.692
2.708
1.706
£370
£835
1.982
2.786
1.978
7.18
3.72
4.02
3.75
3.80
0.0640
0.0730
0.0859
0.0749
0.0777
0.035
0.029
0.031
0.029
0.035
0.516
0.522
0.512
0.518
0.490
0.836
0.817
0.810
0.811
0.821
(Continued)
-------
Table C-2. Continued
Analyte
Mn
Mn
Mn
Mn
Mn
Na
Na
Na
Na
Na
NH/
NH,*
NH4*
NH/
NH4*
N
-------
Table C-2. Continued
Subsurvey-
Analyta Units lab
pH-«q (pH units) Spring-1
pH-eq (pH units) Summer-1
pH-eq (pH units) Summer-2
pH-aq (pH units) Summer-1+2
pH-eq (pH units) Fall-2
SiO, (m)
SiO, (m
SiO, (mi
SiO, (mi
SiO, (m
B/L) Sprlng-1
B/L) Summer-1
B/L) Summer-2
j/L) Summer-1+2
B/L) Fall-2
SO." (mg/L) Spring-1
S04 - (m,
SO4 - (mj
S04 - (mi
SO. - (mi
B/L) Summer-1
B/L Summer-2
j/L Summer-1+2
B/L Fall-2
Field
target
7.28
7.28
7.28
7.28
7.28
4.820
4.820
4.820
4.820
4.820
6.850
6.850
6.850
6.850
6.850
Lab
target
7.28
7.28
7.28
7.28
7.28
4.738
4.738
4.738
4.738
4.738
6.730
6.730
6.730
6.730
6.730
N
5
10
8
18
6
5
10
8
18
6
5
10
8
18
6
Median
7.27
7.30
7.14
7.27
7.05
4.840
4.643
4.638
4.643
4.856
6.602
6.444
6.565
6.444
6.950
Mean
7.27
7.27
7.10
7.20
7.08
4.832
4.610
4.651
4.628
4.835
6.777
6.405
6.586
6.485
7.166
Std
0.05
0.11
0.21
0.18
0.06
0.164
0.170
0.119
0.147
0.071
0.527
0.145
0.354
0.267
0.482
Q1
132.
7.20
6.92
7.10
7.00
4.705
4.558
4.543
4.552
4.766
6.344
6.370
6.300
6.351
6.879
03
7.31
7.36
7.28
7.32
7.11
4.955
4.668
4.757
4.724
4499
7.297
6.496
6.899
6.573
7.575
P10
7.20
7.04
6.73
6.86
6.98
4.590
4.254
4.511
4.431
4.720
6.103
6.056
6.080
6.075
6.710
P90
7.33
7.37
7.31
7.36
7.16
5.050
4.868
4.815
4.822
4.899
7.450
6.510
7.040
6.914
8.025
-------
Table C-3. Summary Statistics for Seventh Lake Field and Lab Natural Audits Pooled, Eastern Lake Survey - Phase II
Subsurvey-
Analyte Units lab
Al-ext (mg/L) Spring-1
Al-ext (mg/L) Summer- 1
Al-ext (mg/L) Summer-2
Al-ext (mg/L) Summer-1+2
Al-ext (mg/L) Fall-2
Al-total (mg/L) Spring-1
Al-total (mg/L) Summer-1
Al-total (mg/L) Summer-2
Al-total (mg/L) Summer-1+2
Al-total (mg/L) Fall-2
Al-dis (mg/L) Spring-PL
Al-dis (mg/L) Summer-PL
Al-dis (mg/L) Fall-PL
Al-org (mg/L) Spring-PL
Al-org (mg/L) Summer-PL
Al-org (mg/L) Fall-PL
ANC (f/eq/L) Spring-1
ANC (peq/L) Summer-1
ANC (peq/L) Summer-2
ANC (peq/L) Summer-1+2
ANC (peq/L) Fall-2
BNC G«q/L) Spring-1
BNC (peq/L) Summer-1
BNC (peq/L) Summer-2
BNC (peq/L) Summer-1 +2
BNC (peq/L) Fall-2
Ca (mg/L) Spring-1
Ca (mg/L) Summer-1
Ca (mg/L) Summer-2
Ca (mg/L) Summer-H-2
Ca (mg/L) Fall-2
Cl" (m(
C\- (mj
Cl" (mi
Cl~ (m|
Cl" (ITH
j/L) Spring-1
I/L) Summer-1
I/L) Summer-2
I/L) Summer-1+2
j/L) Fall-2
Field
target
0.0163
0.0163
0.0163
0.0163
0.0163
0.0627
0.0627
0.0627
0.0627
0.0627
0.0212
0.0212
0.0212
0.0165
0.0165
0.0165
155.0
155.0
155.0
155.0
155.0
36.1
36.1
36.1
36.1
36.1
5.050
5.050
5.050
5.050
5.050
2.940
2.940
2.940
2.940
2.940
Lab
target
0.0302
0.0302
0.0302
0.0302
0.0302
0.0682
0.0682
0.0682
0.0682
0.0682
—
—
—
_
—
—
151.1
151.1
151.1
151.1
151.1
35.6
35.6
35.6
35.6
35.6
4.842
4.842
4.842
4.842
4.842
2.943
2.943
2.943
2.943
2.943
N
16
5
8
13
17
16
13
12
25
17
8
7
11
8
7
11
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
Median
0.0259
0.0035
0.0223
0.0171
0.0170
0.0645
0.0726
0.0584
0.0679
0.0574
0.0226
0.0200
0.0259
0.0223
0.0055
0.0150
147.9
142.4
153.6
151.8
156.5
51.4
35.7
36.9
35.7
34.3
4.839
4.804
4.916
4.835
5.114
2.813
£781
2.927
2.915
2.970
Mean
0.0243
0.0057
0.0219
0.0157
0.0177
0.0585
0.0953
0.0548
0.0759
0.0649
0.0225
0.0209
0.0246
0.0226
0.0088
0.0153
148.8
142.2
158.4
150.0
162.3
61.2
35.5
37.8
36.6
35.0
4.875
4.833
4.927
4.878
5.112
2.869
3574
2.956
3.121
2.925
Std
0.0088
0.0099
0.0065
0.0112
0.0072
0.0163
0.0573
0.0128
0.0463
0.0352
0.0050
0.0058
0.0034
0.0063
0.0095
0.0034
14.9
10.1
15.4
15.1
16.0
49.0
3.7
5.8
4.8
8.7
0.110
0.119
0.092
0.116
0.126
1.125
2.767
0.087
1.964
0.147
Q1
0.0177
•0.0027
0.0160
0.0065
0.0108
0.0420
0.0698
0.0446
0.0578
0.0474
0.0179
0.0174
0.0216
0.0184
0.0027
0.0125
139.6
135.1
152.1
141.5
154.7
37.9
33.8
33.3
33.8
27.7
4.799
4.752
4.840
4.797
5.035
2.153
1.970
2.896
2.604
2.900
Q3
0.0300
0.0152
0.0278
0.0272
0.0242
0.0702
0.0945
0.0663
0.0730
0.0703
0.0272
0.0264
0.0270
0.0250
0.0151
0.0183
153.6
150.8
156.3
154.6
170.4
59.8
38.1
43.2
38.8
41.1
4.921
4.849
5.008
4.950
5.219
3.295
3.275
3.016
3.002
2.979
P10
0.0098
-0.0029
0.0137
-0.0027
0.0091
0.0324
0.0629
0.0335
0.0425
0.0319
0.0149
0.0129
0.0196
0.0129
-0.0003
0.0100
132.3
126.3
150.2
133.2
149.9
30.1
28.5
30.5
30.4
23.6
4.770
4.739
4.820
4.746
4.899
1.301
1.396
2.850
1.558
2.778
P90
0.0365
0.0210
0.0289
0.0285
0.0279
0.0787
0.2216
0.0694
0.1204
0.1012
0.0294
0.0304
0.0291
0.0347
0.0272
0.0196
175.3
156.9
193.1
159.6
181.5
127.5
40.3
46.9
45.5
48.1
5.065
5.094
5.088
5.065
5.295
4.653
8.797
3.118
3.690
3.041
(Continued)
-------
Table C-3. Continued
8
Analyte
Cond
Cond
Cond
Cond
Cond
DIC-closed
DlC-closed
DIC-closed
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-initial
DIC-initial
DIC-initial
DIC-initial
DIC-initial
DOC
DOC
DOC
DOC
DOC
F'-total
F'-total
F'-total
F'-total
F'-total
Fe
Fe
Fe
Fe
Fe
K
K
K
K
K
Unite
Subsurvey-
lab
0/S/cm) Spring-1
(pS/cm) Summer-1
(pS/cm) Summer-2
(pS/cm) Summer-1 +2
(pS/cm) Fall-2
(mg/L) Spring-PL
(mg/L) Summer-PL
(mg/L) Fall-PL
(mg/L) Spring-1
(mg/L) Summer-1
(mg/L) Summer-2
(mg/L) Summer-1+2
(mg/L) Fall-2
(mg/L) Sprlng-1
(mg/L) Summer-1
(mg/L) Summer-2
(mg/L) Summer-1+2
(mg/L) Fall-2
(mg/L) Spring-1
(mg/L) Summer-1
(mg/L) Summer-2
(mg/L) Summer-1+2
(mg/L) Fall-2
(mg/L) Spring-1
(mg/L
(mg/L
(mg/L
(mg/L
Summer-1
Summer-2
Summer-1+2
Fall-2
(mg/L) Spring-1
(mg/L) Summer-1
(mg/L) Summer-2
(mg/L) Summer-1+2
(mg/L) Fall-2
(mg/L) Spring-1
(mg/L) Summer-1
(mg/L) Summer-2
(mg/L) Summer-1+2
(mg/L) Fall-2
Field
target
47.0
47.0
47.0
47.0
47.0
2.047
2.047
2.047
1.725
1.725
1.725
1.725
1.725
2.018
2.018
2.018
2.018
2.018
3.56
3.56
3.56
3.56
3.56
0.0661
0.0661
0.0661
0.0661
0.0661
0.026
0.026
0.026
0.026
0.026
0.490
0.480
0.480
0.490
0.490
Lab
target
47.3
47.3
47.3
47.3
47.3
m m m
_
—
1.668
1.668
1.668
1.668
1.668
1.965
1.965
1.965
1.965
1.965
3.56
3.56
3.56
3.56
3.56
0.0639
0.0639
0.0639
0.0639
0.0639
0.027
0.027
0.027
0.027
0.027
0.503
0.503
0.503
0.503
0.503
N
16
13
9
22
17
11
7
11
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
Median
47.9
48.4
46.6
47.8
46.9
2.081
2.014
2.036
2.181
2.453
1.577
1.700
1.572
2.284
2.515
1.913
2.023
1.900
3.51
3.57
3.58
3.56
3.56
0.0580
0.0643
0.0645
0.0644
0.0686
0.029
0.024
0.027
0.026
0.023
0.485
0.517
0.489
0.506
0.483
Mean
48.0
48.3
462
47.4
46.5
2.052
2.004
1.980
2.165
2.328
1.589
1.974
1.579
2.281
2.392
1.891
2.151
1.902
3.71
3.50
3.61
3.55
3.55
0.0598
0.0648
0.0672
0.0659
0.0693
0.028
0.023
0.027
0.025
0.023
0.491
0.514
0.492
0.504
0.468
Std
0.7
0.4
0.9
1.3
1.3
0.069
0.105
0.362
0.209
0.653
0.071
0.598
0.102
0.119
0.716
0.101
0.571
0.087
0.97
024
0.15
021
0.13
0.0098
0.0044
0.0068
0.0058
0.0046
0.004
0.005
0.002
0.004
0.004
0.015
0.008
0.019
0.018
0.048
Q1
47.6
48.0
45.7
46.7
46.3
1.982
1.921
1.996
1.977
2.370
1.549
1.571
1.501
2.162
2.450
1.816
1.901
1.849
3.35
327
3.50
3.47
3.52
0.0538
0.0604
0.0638
0.0624
0.0658
0.026
0.018
0.025
0.022
0.021
0.480
0.511
0.480
0.489
0.452
03
48.3
48.5
46.7
48.5
47.1
£105
2.073
2.099
2.319
2.563
1.650
2.475
1.668
2.366
2.702
1.966
2.554
1.958
3.74
3.70
3.66
3.68
3.63
0.0626
0.0676
0.0670
0.0671
0.0723
0.030
0.028
0.028
0.028
0.023
0.506
0.517
0.502
0.517
0.495
P10
47.1
47.5
44.1
45.7
44.8
1.968
1.823
1.155
1.874
1.061
1.484
1.485
1.445
2.080
0.974
1.706
1,723
1.774
3.03
3.09
3.47
3.19
3.30
0.0518
0.0598
0.0621
0.0602
0.0641
0.022
0.016
0.023
0.018
0.018
0.475
0.499
0.464
0.475
0.366
P90
49.1
48.9
47.0
48.7
47.3
2.154
2.144
2.360
2.489
2.791
1.698
2.624
1.720
2.393
2.817
2.011
2.741
2.010
4.94
3.78
3.92
3.76
3.70
0.0727
0.0727
0.0829
0.0746
0.0781
0.034
0.030
0.030
0.029
0.028
0.512
0.521
0.527
0.519
0.516
(Continued)
-------
Table O-3. Continued
Anatyte
Mg
MO
Mg
Mg
Mg
Mn
Mn
Mn
Mn
Mn
Na
Na
Na
Na
Na
NH,
NH4
NH<
NH4
NH«
NO,-
NO,-
NO/
NO/
N
-------
Table C-3. Continued
Analyte
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-closed
pH-closed
pH-closed
pH-eq
pH-eq
pH-eq
pH-eq
pH-eq
SiO,
SiO,
SiOa
SiO,
SiO,
so4-
so -
so -
SO -
so;-
True color
True color
True color
Turbidity
Turbidity
Turbidity
Units
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(PCU)
(PCU)
(PCU)
(NTU)
(NTU)
(NTU)
Subsurvey-
lab
Sprlng-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Sprlng-1
Summer-1
Summer-2
Summer-1 + 2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Field
target
7.07
7.07
7.07
7.07
7.07
6.88
6.88
6.88
7.28
7.28
7.28
7.28
7.28
4.820
4.820
4.820
4.820
4.820
6.650
6.850
6.850
6.850
6.850
15.0
15.0
15.0
0.100
0.100
0.100
Lab
target
7.01
7.01
7.01
7.01
7.01
_
_
—
7.28
7.28
7.28
7.28
7.28
4.738
4.738
4.738
4.738
4.738
6.730
6.730
6.730
6.730
6.730
—
_
—
__
.«
—
N
16
13
12
25
17
11
7
11
16
13
12
25
17
16
13
12
25
17
16
13
12
25
17
11
7
10
11
7
10
Median
7.03
6.98
7.01
7.00
7.06
6.82
6.68
6.80
7.28
7.28
7.26
7.28
7.05
4.820
4.653
4.756
4.673
4.814
6.782
6.451
6.800
6.485
6.824
15.0
20.0
15.0
0.160
0.100
0.100
Mean
7.04
6.86
7.04
7.00
7.06
6.81
6.68
6.80
7.28
7.26
7.18
7.23
7.07
4.782
4.652
4.718
4.685
4.708
6.782
6.484
6.705
6.585
6.707
15.0
18.3
16.0
0.212
0.100
0.120
Std
0.12
0.07
0.07
0.08
0.08
0.03
0.08
0.04
0.05
0.11
0.22
0.17
0.12
0.183
0.180
0.140
0.162
0.373
0.378
0.368
0.354
0.370
0.840
4.5
3.5
32
0.188
0.058
0.042
01
6.86
6.82
7.00
6.85
6.88
6.88
6.62
6.87
7.25
7.18
7.08
7.15
7.01
4.662
4.613
4.560
4.583
4.740
6.517
6.355
6.362
6.365
6.727
10.0
15.0
15.0
0.120
0.100
0.100
03
7.18
7.02
7.06
7.03
7.12
6.84
6.74
6.82
7.32
7.35
7.31
7.34
7.15
4.875
4.723
4.840
4.806
4.856
7.140
6.506
6.837
6.887
6.860
20.0
20.0
20.0
0.180
0.100
0.125
P10
6.87
6.84
6.87
6.88
6.82
6.87
6.52
6.83
7.18
7.06
6.77
6.86
6.84
4.487
4.320
4.518
4.438
4.268
6218
5.882
6.140
6.058
5.142
10.0
15.0
10.5
0.104
0.000
0.100
P90
7.21
7.04
7.17
7.10
721
6.86
6.81
6.85
7.35
7.37
7.48
7.37
7.20
4.887
4.937
4.893
4.891
4.903
7.313
7238
7.194
7.147
7.545
20.0
25.0
20.0
0.684
0200
0.200
-------
Table C-4. Summary Statistic* for Big Moos* Lak* Fl*ld Natural Audits, Eastern Lak* Survey - Phase II
ro
Subsurvey-
Analyte Units lab
Al-ext (m
Al-ext (rr
Al-ext (rr
Al-ext (m
Al-ext (m
ig/L) Spring-1
ig/L) Summer-1
ig/L) Summer-2
ig/L) Summer-1+2
ig/L) Fall-2
Al-total (mg/L) Spring-1
Al-total (mg/L) Summer-1
Al-total (mg/L) Summer-2
Al-total (mg/L) Summer-1+2
Al-total (mg/L) Fall-2
Al-dis (rr
Al-dis (m
Al-dis (rr
ig/L) Spring-PL
ig/L) Summer-PL
ig/L) Fall-PL
Al-org (mg/L) Spring-PL
Al-org (mg/L) Summer-PL
Al-org (mg/L) Fall-PL
ANC (ueq/L) Spring-1
ANC (peq/L) Summer-1
ANC (M*q/L) Summer-2
ANC C*q/L) Summer-H-2
ANC (peq/L) Fall-2
BNC (peq/L) Spring-1
BNC (/Jeq/L) Summer-1
BNC (peq/L) Summer-2
BNC (peq/L) Summer-1 +2
BNC (peq/L) Fall-2
Ca (mg/L) Spring-1
Ca (mg/L) Summer-1
Ca (mg/L) Summer-2
Ca (mg/L) Summer-1 +2
Ca (mg/L) Fall-2
Cr (rr
CI" (rr
C\~ (rr
ig/L) Sprlng-1
ig/L) Summer-1
ig/L) Summer-2
Cr (mg/L) Summer-1 +2
CI- (mg/L) Fall-2
Field
target
0.1658
0.1658
0.1658
0.1658
0.1656
0.2650
0.2650
0.2650
0.2650
0.2650
0.1831
0.1831
0.1831
0.0554
0.0554
0.0554
-3.3
-3.3
-3.3
-3.3
•3.3
73.1
73.1
73.1
73.1
73.1
1.821
1.821
1.821
1.821
1.821
0.411
0.411
0.411
0.411
0.411
Lab
target
0.2106
02106
02106
02106
0.2106
0.2641
0.2641
0.2641
0.2641
0.2641
M
_
—
_
—
—
-3.4
-3.4
-3.4
-3.4
-3.4
73.6
73.6
73.6
73.6
73.6
1.875
1.875
1.875
1.875
1.875
0.408
0.408
0.408
0.408
0.408
N
8
5
6
11
10
8
5
6
11
10
10
12
11
10
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
Median
0.1540
0.1407
0.1728
0.1630
0.1362
0.2848
0.2522
0.2406
0.2484
0.2427
0.1864
0.1858
0.1840
0.0618
0.0255
0.0530
-4.8
0.7
-5.2
-4.5
-2.1
78.7
73.0
72.8
72.8
76.7
1.835
1.842
1.811
1.813
1.860
0.433
0.350
0.402
0.400
0.413
Mean
0.1417
0.1158
0.1728
0.1470
0.1372
0.2844
02607
0.2333
0.2458
02458
0.1885
0.1783
0.1863
0.0604
0.0224
0.0526
-42
1.0
-5.4
-2.7
•£1
80.1
68.5
73.8
71.7
77.3
1.828
1.818
1.805
1.811
1.868
0.406
0.375
0.404
0.382
0.411
Std
0.0287
0.0661
0.0228
0.0538
0.0170
0.0218
0.0188
0.0284
0.0274
0.0366
0.0126
0.0185
0.0081
0.0088
0.0136
0.0080
3.8
32
0.8
3.8
1.8
14.1
8.6
5.0
7.4
8.1
0.041
0.053
0.023
0.037
0.041
0.077
0.048
0.010
0.034
0.008
Q1
0.1072
0.0528
0.1481
0.1407
0.1230
0.2786
02450
0.1886
0.2320
0.2178
0.1875
0.1628
0.1920
0.0531
0.0085
0.0480
-62
•1.6
-6.1
-5.6
-3.5
65.7
58.8
70.8
68.3
71.1
1.884
1.862
1.883
1.875
1.843
0.357
0.337
0.387
0.361
0.408
Q3
0.1652
0.1664
0.1868
0.1800
0.1518
0.3023
02807
0.2581
0.2620
0.2674
02130
0.1842
0.1860
0.0682
0.0315
0.0578
-22
3.8
-4.6
0.5
-12
85.7
75.8
76.5
75.8
86.7
1.863
1.863
1.817
1.841
1.883
0.467
0.426
0.412
0.410
0.415
P10
0.0850
0.0083
0.1453
0.0268
0.1152
0.2365
02406
0.1884
0.1887
0.1886
0.1640
0.1408
0.1884
0.0443
0.0023
0.0366
-8.5
-3.3
-6.6
-6.4
•5.1
63.8
53.6
68.8
56.8
62.3
1.881
1.852
1.868
1.857
1.815
0.248
0.328
0.384
0.333
0.383
P80
0.1658
0.1688
0.1888
0.1880
0.1654
0.3036
0.2844
0.2620
0.2828
0.3167
0.2182
0.1847
02130
0.0705
0.0430
0.0640
4.1
5.6
-4.5
4.5
0.5
100.7
75.8
83.7
81.5
88.5
1.882
1.865
1.837
1.864
2.048
0.480
0.447
0.423
0.440
0.420
(Continued)
-------
Table C-4. Continued
Analyte
Cond
Cond
Cond
Cond
Cond
QIC-closed
DIC-closed
DlC-closed
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-lnitial
Die-initial
DIC-initial
DIC-initial
DIC-initial
DOC
DOC
DOC
DOC
DOC
F--total
F--total
F--total
F'-total
F'-total
Fe
Fe
Fe
Fe
Fe
K
K
K
K
K
Units
(pS/cm
(fJS/cit
(pS/cm
(pS/cm
(JiS/em
(mg/L)
(mg/L)
(mg/L)
(mg/Li
(mg/L
(mg/L
(mg/L
(mg/Lj
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
Subsurvey-
lab
) Spring-1
) Summer-1
) Summer-2
} Summer-1+2
) Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
25.2
25.2
25.2
25.2
25.2
0.549
0.549
0.549
0.121
0.121
0.121
0.121
0.121
0.338
0.338
0.338
0.338
0.338
3.55
3.55
3.55
3.55
3.55
0.0740
0.0740
0.0740
0.0740
0.0740
0.047
0.047
0.047
0.047
0.047
0.411
0.411
0.411
0.411
0.411
Lab
target
252
252
252
252
252
-
_
—
0.162
0.162
0.162
0.162
0.162
0.424
0.424
0.424
0.424
0.424
3.50
3.50
3.50
3.50
3.50
0.0740
0.0740
0.0740
0.0740
0.0740
0.051
0.051
0.051
0.051
0.051
0.409
0.409
0.409
0.409
0.409
N
10
5
7
12
11
10
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
Median
24.6
25.9
24.9
25.0
25.3
0.547
0.563
0.582
0.182
0.332
0.073
0.128
0.075
0.438
0.341
0.455
0.447
0.466
3.53
3.60
3.41
3.49
3.41
0.0718
0.0781
0.0776
0.0778
0.0798
0.051
0.045
0.046
0.045
0.039
0.401
0.414
0.408
0.408
0.383
Mean
24.8
25.9
24.8
25.3
25.3
0.545
0.574
0.568
0250
0.363
0.085
0201
0.085
0.459
0.612
0.449
0.517
0.451
3.49
3.50
3.42
3.46
3.37
0.0715
0.0769
0.0641
0.0694
0.0794
0.054
0.044
0.045
0.045
0.037
0.406
0.414
0.413
0.413
0.379
Std
0.6
0.3
02
0.6
02
0.033
0.066
0.050
0.142
0.198
0.043
0.189
0.042
0.097
0.522
0.050
0.328
0.051
0.20
0.25
0.09
0.17
0.15
0.0051
0.0056
0.0261
0.0207
0.0047
0.007
0.003
0.006
0.005
0.004
0.014
0.010
0.022
0.017
0.043
CM
24.5
25.7
24.6
24.7
252
0.513
0.544
0.519
0.171
0.179
0.045
0.065
0.055
0.407
0.310
0.420
0.345
0.394
3.27
3.30
3.35
3.35
3.21
0.0685
0.0716
0.0451
0.0705
0.0761
0.048
0.041
0.045
0.043
0.033
0.396
0.404
0.394
0.399
0.350
03
24.9
262
25.0
25.9
25.5
0.580
0.632
0.600
0.305
0.562
0.128
0.305
0.116
0.496
1.049
0.483
0.505
0.482
3.65
3.65
3.52
3.59
3.50
0.0748
0.0815
0.0800
0.0797
0.0802
0.062
0.046
0.049
0.048
0.040
0.418
0.423
0.437
0.424
0.405
P10
242
25.6
24.6
24.6
24.9
0.498
0.451
0.507
0.153
0.134
0.029*
0.034
0.028
0.320
0.286
0.358
0.301
0.386
3.19
3.06
3.30
3.13
3.14
0.0625
0.0693
0.0120
0.0219
0.0714
0.046
0.040
0.033
0.035
0.031
0.388
0.401
0.394
0.394
0298
P90
26.0
26.4
25.0
26.3
25.5
0.590
0.641
0.655
0.578
0.607
0.129
0.580
0.162
0.666
1.524
0.512
1.239
0.522
3.80
3.66
3.56
3.65
3.53
0.0794
0.0843
0.0801
0.0830
0.0881
0.064
0.048
0.051
0.050
0.044
0.426
0.426
0.449
0.445
0.442
(Continued)
-------
Table C-4. Continued
Analyte
Mg
Mg
MO
Mg
Mg
Mn
Mn
Mn
Mn
Mn
Na
Na
Na
Na
Na
NH/
NH4*
NH.*
NH/
NH4*
NO,-
NCV
NO,-
NO;-
NO,-
P-total
P-total
P-total
P-total
P-totat
pH-ANC
pH-ANC
pH-ANC
pH-ANC
pH-ANC
Unite
(mgA.)
(mgA
(mgA,
(mgA.
(mgA-i
(mgA.)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA
(mgA
(mgA
(mgA
(mgA
(mgA)
(mgA)
(mgA)
(mgA.)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(mgA)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1-f2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Field
target
0.326
0.326
0.326
0.326
0.326
0.073
0.073
0.073
0.073
0.073
0.606
0.606
0.606
0.606
0.606
0.057
0.057
0.057
0.057
0.057
1.2231
1.2231
1.2231
1.2231
1.2231
0.0016
0.0016
0.0016
0.0016
0.0016
5.12
5.12
5.12
5.12
5.12
Lab
target
0.320
0.320
0.320
0.320
0.320
0.072
0.072
0.072
0.072
0.072
0.612
0.612
0.612
0.612
0.612
0.058
0.058
0.058
0.058
0.058
1.2156
1.2156
1.2156
1.2156
1.2156
0.0016
0.0016
0.0016
0.0016
0.0016
5.09
5.09
5.09
5.09
5.09
N
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
Median
0.328
0.326
0.316
0.317
0.331
0.069
0.072
0.068
0.069
0.075
0.594
0.606
0.598
0.601
0.632
0.039
0.059
0.044
0.048
0.051
1.0664
1.2348
1.2120
1.2231
1.2003
0.0010
0.0023
0.0016
0.0022
0.0012
5.13
5.13
5.05
5.06
5.11
Mean
0.326
0.327
0.314
0.319
0.332
0.072
0.072
0.068
0.070
0.075
0.608
0.605
0.595
0.599
0.623
0.039
0.059
0.042
0.049
0.056
1.0834
1.2363
1.2006
1.2155
1.2038
0.0041
0.0023
0.0043
0.0034
0.0015
5.16
5.12
5.05
5.08
5.11
Std
0.010
0.004
0.004
0.008
0.013
0.005
0.002
0.001
0.003
0.004
0.040
0.016
0.025
0.022
0.041
0.016
0.009
0.020
0.018
0.025
0.1392
0.0140
0.0293
0.0296
0.0311
0.0081
0.0005
0.0061
0.0046
0.0010
0.09
0.02
0.02
0.04
0.02
Q1
0.317
0.323
0.309
0.314
0.319
0.069
0.070
0.067
0.068
0.071
0.580
0.591
0.579
0.583
0.587
0.026
0.052
0.035
0.042
0.038
0.9858
1.2255
1.1892
1.2028
1.1660
0.0010
0.0018
0.0013
0.0016
0.0006
5.08
5.10
5.04
5.05
5.10
03
0.334
0.331
0.317
0.325
0.342
0.076
0.074
0.069
0.072
0.079
0.637
0.617
0.606
0.610
0.662
0.056
0.066
0.049
0.060
0.061
1.2348
1.2477
1.2209
1.2333
1.2164
0.0032
0.0026
0.0066
0.0029
0.0026
5.22
5.14
5.05
5.12
5.12
P10
0.313
0.322
0.308
0.307
0.313
0.067
0.070
0.066
0.066
0.070
0.558
0.580
0.552
0.560
0.557
0.014
0.046
0.003
0.013
0.031
0.8856
1.2253
1.1412
1.1556
1.1557
0.0001
0.0016
-0.0008
•0.0002
0.0002
5.07
5.09
5.02
5.03
5.08
P90
0.339
0.332
0.317
0.331
0.350
0.080
0.075
0.070
0.074
0.080
0.673
0.624
0.634
0.631
0.668
0.059
0.072
0.071
0.071
0.111
1.2984
1.2598
1.2290
1.2525
1.2653
0.0247
0.0030
0.0171
0.0139
0.0029
5.30
5.14
5.08
5.14
5.14
(Continued)
-------
Table C-4. Continued
Analyte
pH-BNC <
pH-BNC
pH-BNC
pH-BNC
pH-BNC
Units
[pH units
pH units
pH units
pH units
pH units
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
pH-ctosed (pH units) Spring-PL
pH-closed (pH units) Summer-PL
pH-closed (pH units) Fall-PL
pH-eq (pH units) Spring-1
pH-eq (pH units) Summer-1
pH-eq (pH units) Summer-2
pH-eq (pH units) Summer-1+2
pH-eq (pH units) Fall-2
SiO, (mg/L)
SiO, (mg/L)
SiO, (mg/L)
SiO,
mg/L)
SiO, (mg/L)
SO." (mg/L)
SO.'- (mg/L)
SO,'- (mg/L)
S04'- (mg/L)
S04'- (mg/L)
True color (PCU)
True color (PCU)
True color (PCU)
Turbidity (NTU)
Turbidity (NTU)
Turbidity (NTU)
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Field
target
5.15
5.15
5.15
5.15
5.15
5.13
5.13
5.13
5.16
5.16
5.16
5.16
5.16
4.386
4.386
4.386
4.386
4.386
6.363
6.363
6.363
6.363
6.363
15.0
15.0
15.0
0.130
0.130
0.130
Lab
target
5.12
5.12
5.12
5.12
5.12
_
—
—
5.15
5.15
5.15
5.15
5.15
4.347
4.347
4.347
4.347
4.347
6.360
6.360
6.360
6.360
6.360
___
_
—
_
_
—
N
10
5
7
12
11
10
12
11
10
5
7
12
11
10
5
7
12
11
10
5
7
12
11
10
11
11
10
11
11
Median
5.16
5.12
5.07
5.09
5.17
5.15
5.11
5.13
5.14
5.15
5.12
5.13
5.01
4.440
4.372
4.376
4.374
4.407
6.097
6.536
6.320
6.400
6.335
12.5
15.0
15.0
0.175
0.100
0.100
Mean
5.18
5.12
5.08
5.09
5.19
5.16
5.11
5.13
5.17
5.14
5.41
5.30
5.03
4.385
4.408
4.394
4.400
4.317
6.082
6.454
6.529
6.498
6.364
13.0
17.3
16.4
0.189
0.091
0.127
Std
0.06
0.03
0.02
0.03
0.07
0.04
0.06
0.03
0.09
0.09
0.76
0.58
0.04
0.122
0.097
0.075
0.081
0.418
0.570
0247
0.635
0.494
0228
4.8
4.1
3.9
0.083
0.030
0.047
Q1
5.13
5.09
5.07
5.07
5.14
5.14
5.08
5.09
5.12
5.06
5.10
5.10
5.01
4.260
4.322
4.343
4.335
4.374
5.612
6240
6.255
6.259
6.145
10.0
15.0
15.0
0.120
0.100
0.100
03
521
5.14
5.09
5.12
521
5.18
5.15
5.15
5.19
520
520
5.20
5.07
4.482
4.511
4.461
4.491
4.468
6.601
6.627
6.425
6.543
6.545
16.3
20.0
20.0
0.225
0.100
0200
P10
5.09
5.09
5.05
5.06
5.12
5.09
5.01
5.09
5.10
4.99
5.08
5.02
4.99
4.194
4.312
4.289
4.296
3.337
5.026
6.049
6.105
6.066
6.091
5.5
11.0
10.0
0.111
0.020
0.100
P90
5.30
5.16
5.11
5.15
5.33
5.25
5.22
5.19
5.39
5.21
7.14
6.56
5.10
4.526
4.521
4.505
4.516
4.629
6.791
6.708
7.950
7.577
6.737
20.0
24.0
20.0
0.375
0.100
0.200
-------
Table OS. Summary Statlatlea for Big MOOM Lake Lab Natural Audit*, Eaatem Lake Sunwy - Phaao II
o>
Analyte
Al-ext
Al-ext
Al-ext
Al-ext
Al-ext
Al-total
AMotal
Al-total
Al-total
Al-total
ANC
ANC
ANC
ANC
ANC
BNC
BNC
BNC
BNC
BNC
Ca
Ca
Ca
Ca
Ca
ci-
ci-
ci-
ci-
ci-
Cond
Cond
Cond
Cond
Cond
Units
(mg/L)
(mg/L
(mg/L
(mg/L
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
0«q/L)
(f»q/L)
foeq/L)
fe«q/L)
0»q/L)
Owq/Li
(ueq/L
fcjeq/L
(W/L
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/Lj
(mg/L
(mg/L
(mg/L
(mg/L)
(fJSIcm)
(j/S/cm)
(pS/cm)
(pS/cm)
(pS/cm)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.1658
0.1658
0.1658
0.1658
0.1658
0.2650
0.2650
0.2650
0.2650
0.2650
-3.3
-3.3
-3.3
-3.3
-3.3
73.1
73.1
73.1
73.1
73.1
1.921
1.921
1.921
1.921
1.921
0.411
0.411
0.411
0.411
0.411
25.2
25.2
25.2
25.2
25.2
Lab
target
02106
02106
02106
02106
0.2106
0.2641
0.2641
0.2641
0.2641
02641
-3.4
•3.4
•3.4
-3.4
-3.4
73.6
73.6
73.6
73.6
73.6
1.875
1.875
1.875
1.875
1.875
0.409
0.409
0.409
0.409
0.409
25.2
25.2
25.2
25.2
252
N
5
3
2
5
6
6
8
5
13
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
5
9
7
16
6
6
9
7
16
6
Median
0.1682
0.1649
0.2038
0.1971
0.1983
0.2847
0.2638
0.2223
0.2378
0.2694
-2.6
-0.4
-5.8
-4.4
-2.7
76.0
73.3
75.5
75.1
69.4
1.887
1.821
1.869
1.829
1.944
0.395
0.344
0.439
0.418
0.405
25.0
26.2
24.7
25.3
25.1
Mean
0.1706
0.1223
02038
0.1549
0.1783
0.2359
0.2352
0.2256
0.2315
02760
-1.9
-0.8
-5.6
-2.9
-2.3
772
78.8
76.5
77.8
68.7
1.885
1.824
1.866
1.842
1.937
0.390
0.382
0.440
0.407
0.418
25.7
25.7
24.6
25.2
25.3
Std
0.0109
0.1422
0.0095
0.1102
0.0682
0.1034
0.0601
0.0129
0.0467
0.0528
5.8
3.7
0.9
3.7
1.5
9.8
11.4
4.9
9.0
5.3
0.041
0.028
0.036
0.038
0.055
0.047
0.180
0.021
0.135
0.038
1.8
1.1
0.6
1.0
0.6
Q1
0.1638
-0.0364
0.1971
0.0642
0.1532
0.1607
0.2006
02154
0.2154
02331
•7.3
-3.4
•62
•5.9
-3.3
67.1
702
712
71.4
632
1.841
1.808
1.829
1.818
1.877
0.347
0264
0.423
0.336
0.398
24.6
25.5
23.8
24.5
24.9
03
0.1786
02383
02106
02244
0.2204
0.3015
02741
0.2374
0.2687
0.3099
32
1.5
-4.9
-0.2
-0.8
88.3
90.9
81.1
81.9
72.6
1.924
1.835
1.904
1.878
1.982
0.429
0.444
0.454
0.452
0.431
27.0
26.4
25.0
262
25.6
P10
0.1599
-0.0364
0.1971
-0.0364
0.0415
0.0395
0.1055
0.2098
0.1386
02195
-6.1
•7.3
-6.8
-6.9
-4.0
66.7
67.3
69.8
68.1
61.9
1.836
1.780
1.818
1.799
1.873
0.314
0.189
0.414
0210
0.391
242
23.1
23.8
23.6
24.9
P90
0.1890
02383
02106
02383
0.2206
0.3050
0.2755
0.2445
0.2751
0.3700
7.3
5.5
-4.3
2.9
0.0
89.0
962
82.2
932
76.4
1.939
1.881
1.911
1.906
2.015
0.431
0.790
0.475
0.600
0.493
29.0
26.6
25.2
26.5
26.4
(Continued)
-------
Table C-5. Continued
Analyte
DIC-«q
DIC-«q
OlC-eq
DIC-eq
DIC-eq
QIC-Initial
DIC-initial
DIC-initial
DIC-initial
DIC-initial
DOC
DOC
DOC
DOC
DOC
F'-total
F'-total
F'-total
F'-total
F'-total
Fe
Fe
Fe
Fe
Fe
K
K
K
K
K
Mg
Mg
Mg
Mg
Mg
Unite
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
SummeM+2
FaH-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.121
0.121
0.121
0.121
0.121
0.338
0.338
0.338
0.338
0.338
3.55
3.55
3.55
3.55
3.55
0.0740
0.0740
0.0740
0.0740
0.0740
0.047
0.047
0.047
0.047
0.047
0.411
0.411
0.411
0.411
0.411
0.326
0.326
0.326
0.326
0.326
Lab
target
0.162
0.162
0.162
0.162
0.162
0.424
0.424
0.424
0.424
0.424
3.50
3.50
3.50
3.50
3.50
0.0740
0.0740
0.0740
0.0740
0.0740
0.051
0.051
0.051
0.051
0.051
0.409
0.409
0.409
0.409
0.409
0.320
0.320
0.320
0.320
0.320
N
6
9
7
16
6
6
9
7
16
6
6
9
7
18
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
Median
0.237
0.295
0.128
0.194
0.101
0.447
0.520
0.457
0.466
0.473
3.34
3.58
3.49
3.54
3.38
0.0715
0.0724
0.0766
0.0731
0.0804
0.061
0.051
0.052
0.051
0.046
0.393
0.420
0.398
0.420
0.374
0.316
0.317
0.316
0.317
0.326
Mean
0236
0285
0.124
0214
0.097
0.488
0.484
0.462
0.474
0.455
3.27
3.41
3.50
3.45
3.40
0.0710
0.0751
0.0653
0.0708
0.0813
0.059
0.107
0.051
0.083
0.046
0.389
0.419
0.405
0.413
0.360
0.317
0.318
0.314
0.316
0.326
Std
0.046
0.086
0.052
0.109
0.033
0.152
0.090
0.042
0.071
0.054
0.22
0.40
0.19
0.32
0.16
0.0029
0.0071
0.0289
0.0197
0.0038
0.009
0.163
0.002
0.122
0.004
0.011
0.006
0.024
0.018
0.040
0.009
0.004
0.005
0.004
0.010
Q1
0.199
0211
0.056
0.134
0.066
0.405
0.401
0.434
0.435
0.406
3.01
3.20
3.42
3.25
329
0.0690
0.0709
0.0711
0.0711
0.0789
0.050
0.046
0.049
0.048
0.041
0.382
0.416
0.388
0.400
0.327
0.309
0.314
0.309
0.313
0.315
O3
0272
0.354
0.162
0.314
0.129
0.549
0.542
0.470
0.541
0.496
3.46
3.72
3.67
3.67
3.48
0.0729
0.0772
0.0787
0.0786
0.0837
0.067
0.066
0.054
0.054
0.050
0.397
0.423
0.427
0.425
0.389
0.325
0.321
0.317
0.320
0.336
P10
0.169
0.162
0.054
0.055
0.046
0.355
0.326
0.420
0.350
0.365
2.95
2.51
3.15
2.96
3.23
0.0660
0.0704
0.0000
0.0493
0.0773
0.048
0.045
0.049
0.046
0.040
0.369
0.407
0.367
0.382
0288
0.308
0.313
0.309
0.309
0.313
P90
0.302
0.423
0.188
0.380
0.131
0.785
0.589
0.547
0.560
0.503
3.48
3.76
3.68
3.76
3.69
0.0744
0.0925
0.0788
0.0833
0.0883
0.070
0.540
0.054
0218
0.050
0.397
0.426
0.437
0.430
0.396
0.326
0.323
0.321
0.322
0.338
(Continued)
-------
Table C-5. Continued
Analyte
Mn
Mn
Mn
Mn
Mn
Na
Na
Na
Na
Na
NH.*
NH/
NH/
NH*
NH4*
NO,-
NO-
NO-
NO-
NO;-
P-total
P-total
P-total
P-total
P-total
pH-ANC
pH-ANC
pH-ANC
pH-ANC
pH-ANC
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-BNC
Units
(mfl/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.073
0.073
0.073
0.073
0.073
0.606
0.606
0.606
0.606
0.606
0.057
0.057
0.057
0.057
0.057
1.2231
1.2231
1.2231
1.2231
1.2231
0.0016
0.0016
0.0016
0.0016
0.0016
5.12
5.12
5.12
5.12
5.12
5.15
5.15
5.15
5.15
5.15
Lab
target
0.072
0.072
0.072
0.072
0.072
0.612
0.612
0.612
0.612
0.612
0.058
0.058
0.058
0.058
0.058
1.2156
1.2156
1.2156
1.2156
1.2156
0.0016
0.0016
0.0016
0.0016
0.0016
5.09
5.09
5.09
5.09
5.09
5.12
5.12
5.12
5.12
5.12
N
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
Median
0.069
0.072
0.068
0.071
0.072
0.603
0.624
0.598
0.616
0.590
0.048
0.051
0.046
0.051
0.111
1.0565
1.2566
1.2126
1.2445
1.1808
0.0015
0.0028
0.0014
0.0019
0.0021
5.17
5.08
5.02
5.04
5.10
5.18
5.05
5.07
5.07
5.16
Mean
0.073
0.072
0.068
0.071
0.073
0.613
0.620
0.599
0.610
0.600
0.038
0.097
0.049
0.076
0.113
1.0971
1.3071
1.2018
1.2610
1.1846
0.0062
0.0031
0.0016
0.0024
0.0021
5.15
5.07
5.03
5.05
5.12
5.18
5.05
5.08
5.06
5.18
Std
0.007
0.001
0.002
0.002
0.003
0.028
0.015
0.016
0.019
0.033
0.025
0.143
0.033
0.109
0.037
0.1441
0.1348
0.0444
0.1157
0.0225
0.0112
0.0032
0.0014
0.0026
0.0003
0.07
0.05
0.02
0.05
0.05
0.05
0.05
0.02
0.04
0.06
Q1
0.067
0.071
0.067
0.068
0.070
0.590
0.610
0.583
0.593
0.570
0.010
0.043
0.035
0.040
0.082
0.9940
1.2445
1.1616
1.2127
1.1654
0.0010
0.0004
0.0005
0.0005
0.0019
5.07
5.02
5.02
5.02
5.10
5.13
4.99
5.07
5.05
5.14
Q3
0.081
0.073
0.070
0.073
0.076
0.644
0.632
0.616
0.624
0.636
0.058
0.064
0.070
0.065
0.143
1.2535
1.3027
1.2312
1.2692
1.2055
0.0095
0.0044
0.0033
0.0034
0.0023
5.22
5.10
5.05
5.09
5.14
5.22
5.09
5.10
5.10
5.21
P10
0.066
0.070
0.067
0.067
0.069
0.590
0.589
0.576
0.581
0.569
0.003
0.026
•0.003
0.017
0.066
0.9135
1.2354
1.1403
1.1552
1.1590
0.0010
•0.0004
-0.0004
•0.0004
0.0016
5.06
4.97
5.02
5.00
5.09
5.12
4.98
5.05
4.99
5.13
P90
0.082
0.073
0.071
0.073
0.076
0.649
0.637
0.616
0.633
0.647
0.059
0.476
0.105
0.216
0.167
1.2959
1.6604
1.2720
1.4184
1.2160
0.0290
0.0101
0.0036
0.0068
0.0023
5.24
5.15
5.06
5.11
5.22
5.27
5.11
5.10
5.11
5.31
(Continued)
-------
Table C-5. Continued
Analyte
pH-eq
pH-eq
pH-eq
PH*q
pH-eq
SiO,
SiO,
SiO,
SiO,
SiO,
so4J-
_ -_ *•
SO,''
so.2-
so4J-
so4a-
Units
Subsurvey-
lab
(pH units) Spring-1
(pH units
(pH units
(pH units
(pH units
(tng/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(tng/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
Summer-1
Summer-2
Summer-1+2
Fail-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Falt-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Field
target
5.16
5.16
5.16
5.16
5.16
4.386
4.386
4.386
4.386
4.386
6.363
6.363
6.363
6.363
6.363
Lab
target
5.15
5.15
5.15
5.15
5.15
4.347
4.347
4.347
4.347
4.347
6.360
6.360
6.360
6.360
6.360
N
6
9
7
16
6
6
9
7
16
6
6
9
7
16
6
Median
5.13
5.15
5.12
5.14
5.04
4.305
4.292
4.371
4.322
4.424
6.227
6.148
6.265
6.210
6.439
Mean
5.22
5.15
5.09
5.12
5.05
4.330
4.343
4.354
4.348
4.427
6.115
6.194
6219
6.205
6.341
Std
0.21
0.06
0.13
0.10
0.05
0.113
0.147
0.120
0.132
0.048
0.649
0.301
0.169
0.245
0.314
Q1
5.12
5.14
5.07
5.12
5.01
4.230
4262
4.216
4.257
4.388
5.620
5.922
6.060
5.949
6.212
03
5.30
5.19
5.14
5.19
5.09
4.447
4.395
4.477
4.445
4.463
6.623
6.514
6.360
6.403
6.497
P10
6.11
5.00
4.81
4.94
5.00
4.230
4.192
4.183
4.189
4.363
5.010
5.895
5.940
5.902
5.720
P90
5.65
5.19
521
520
5.15
4.470
4.685
4.495
4.552
4.503
6.854
6.654
6.430
6.625
6.610
-------
Table C-e. Summary Statlatlce for Big Mooce lake Field and Ub Natural Audlta Pooled, Eaatem Lake Survey - Phaae II
Analyte
Al-ext
Al-ext
Al-ext
Al-ext
Al-ext
Al-total
Al-total
Al-total
Al-total
Al-total
Al-dis
Al-dis
Al-dis
Al-org
Al-org
Al-org
ANC
ANC
ANC
ANC
ANC
BNC
BNC
BNC
BNC
BNC
Ca
Ca
Ca
Ca
Ca
ci-
ci-
ci-
cr
ci-
Unita
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
0«q/L)
0*>q/L)
foeq/L)
0»q/L)
foeq/L)
(ueq/L)
0*q/L)
(ueq/L)
0*q/L)
(peq/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
Subsurvey-
lab
Sprlng-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1-1-2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Sprlng-1
Summer-1
Summer-2
Summer-1-1-2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
SummeM+2
Fall-2
Field
target
0.1658
0.1658
0.1658
0.1658
0.1658
0.2650
0.2650
0.2650
0.2650
0.2650
0.1931
0.1931
0.1931
0.0554
0.0554
0.0554
-3.3
-3.3
-3.3
-3.3
•3.3
73.1
73.1
73.1
73.1
73.1
1.921
1.921
1.921
1.921
1.921
0.411
0.411
0.411
0.411
0.411
Lab
target
0.2106
0.2106
0.2106
0.2106
0.2106
0.2641
0.2641
0.2641
0.2641
0.2641
»
_
—
_
—
—
-3.4
-3.4
-3.4
-3.4
-3.4
73.6
73.6
73.6
73.6
73.6
1.875
1.875
1.875
1.875
1.875
0.409
0.409
0.409
0.409
0.409
N
13
8
8
16
16
14
13
11
24
16
10
12
11
10
12
11
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
15
14
14
28
17
Median
0.1633
0.1518
0.1880
0.1653
0.1447
0.2848
0.2630
0.2303
0.2469
0.2578
0.1964
0.1858
0.1940
0.0618
0.0255
0.0530
-4.6
-0.1
•5.4
-4.5
-2.3
78.3
73.1
74.5
73.7
75.9
1.904
1.835
1.894
1.871
1.958
0.428
0.348
0.418
0.403
0.411
Mean
0.1528
0.1183
0.1806
0.1494
0.1526
0.2636
0.2451
0.2298
0.2381
0.2571
0.1995
0.1783
0.1963
0.0604
0.0224
0.0526
-3.3
-0.2
-5.5
-2.8
-2.2
78.0
75.1
75.2
75.2
74.2
1.912
1.857
1.886
1.872
1.957
0.402
0.379
0.422
0.401
0.413
Std
0.0277
0.0910
0.0244
0.0720
0.0463
0.0706
0.0489
0.0220
0.0390
0.0443
0.0126
0.0195
0.0081
0.0088
0.0136
0.0080
4.6
3.5
0.8
3.7
1.6
12.4
11.6
4.9
8.7
8.8
0.045
0.060
0.036
0.050
0.047
0.067
0.144
0.024
0.103
0.022
Q1
0.1439
0.0311
0.1542
0.1418
0.1241
0.2681
0.2392
0.2098
0.2213
0.2198
0.1875
0.1628
0.1920
0.0531
0.0095
0.0490
•6.3
-2.5
•6.1
•5.8
-3.3
66.8
68.1
70.8
70.8
66.4
1.884
1.816
1.863
1.826
1.928
0.371
0.323
0.401
0.347
0.401
03
0.1680
0.1686
0.1991
0.1868
0.1863
0.3014
0.2750
0.2493
0.2643
0.2762
0.2130
0.1942
0.1960
0.0682
0.0315
0.0578
-1.3
1.9
-4.8
•0.0
-1.1
88.8
79.6
80.6
79.5
80.9
1.846
1.886
1.813
1.911
1.983
0.448
0.415
0.441
0.437
0.415
P10
0.0957
-0.0364
0.1453
-0.0044
0.0927
0.1203
0.1386
0.1987
0.1933
0.2087
0.1840
0.1408
0.1884
0.0443
0.0023
0.0366
•8.6
-5.9
•6.7
•6.6
•4.3
64.1
58.9
69.3
67.0
61.9
1.841
1.793
1.823
1.809
1.877
0.285
0.204
0.395
0.300
0.393
P90
0.1807
0.2363
0.2108
0.2189
02205
0.3044
0.2815
0.2612
0.2763
0.3364
0.2192
0.1947
02130
0.0705
0.0430
0.0640
5.5
5.5
-4.4
2.4
0.4
100.6
94.6
82.9
90.9
87.3
1.978
1.963
1.927
1.944
2.024
0.478
0.654
0.464
0.479
0.435
(Continued)
-------
Table C-6. Continued
ro
Analyte
Cond
Cond
Cond
Cond
Cond
QIC-closed
DIC-closed
DIC-closed
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-eq
DIC-initial
DIC-initlal
DIC-initial
DIC-initial
DIC-initial
DOC
DOC
DOC
DOC
DOC
F'-total
F'-total
F'-total
F'-total
F'-total
Fe
Fe
Fe
Fe
Fe
K
K
K
K
K
Units
Subsurvey-
lab
(pS/cm) Spring-1
(pS/cm
(pS/cm
(pS/em
(pS/crr
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L;
(mg/L;
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
) Summer-1
) Summer-2
) Summer-1+2
) Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
25.2
2S.2
25.2
25.2
25.2
0.548
0.549
0.549
0.121
0.121
0.121
0.121
0.121
0.338
0.338
0.338
0.338
0.338
3.55
3.55
3.55
3.55
3.55
0.0740
0.0740
0.0740
0.0740
0.0740
0.047
0.047
0.047
0.047
0.047
0.411
0.411
0.411
0.411
0.411
Lab
target
25.2
25.2
25.2
25.2
25.2
^ _
—
—
0.162
0.162
0.162
0.162
0.162
0.424
0.424
0.424
0.424
0.424
3.50
3.50
3.50
3.50
3.50
0.0740
0.0740
0.0740
0.0740
0.0740
0.051
0.051
0.051
0.051
0.051
0.409
0.409
0.409
0.409
0.409
N
16
14
14
28
17
10
12
11
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
Median
24.8
25.9
24.8
25.1
25.3
0.547
0.583
0.582
0.210
0.307
0.127
0.162
0.075
0.438
0.509
0.456
0.460
0.468
3.43
3.59
3.46
3.50
3.41
0.0717
0.0733
0.0771
0.0754
0.0798
0.053
0.047
0.049
0.048
0.040
0.397
0.420
0.403
0.417
0.377
Mean
25.1
25.8
24.7
252
25.3
0.545
0.574
0.568
0.245
0.313
0.104
0.209
0.089
0.470
0.530
0.455
0.493
0.453
3.41
3.44
3.46
3.45
3.38
0.0713
0.0757
0.0647
0.0702
0.0801
0.056
0.084
0.048
0.066
0.040
0.400
0.417
0.409
0.413
0.372
Std
1.2
0.9
0.4
0.9
0.4
0.033
0.066
0.050
0.113
0.134
0.050
0.145
0.039
0.116
0.305
0.045
0.217
0.050
0.23
0.35
0.15
0.26
0.15
0.0043
0.0065
0.0265
0.0197
0.0044
0.008
0.131
0.005
0.093
0.006
0.015
0.008
0.023
0.017
0.042
Q1
24.5
25.6
24.6
24.7
25.0
0.513
0.544
0.519
0.176
0.217
0.055
0.127
0.062
0.415
0.339
0.430
0.420
0.397
3.27
3.20
3.36
3.35
327
0.0690
0.0711
0.0646
0.0711
0.0781
0.049
0.045
0.046
0.045
0.036
0.391
0.411
0.394
0.399
0.345
03
252
26.4
25.0
26.0
25.5
0.580
0.632
0.600
0273
0.377
0.135
0.314
0.122
0.487
0.550
0.477
0.533
0.492
3.57
3.66
3.58
3.65
3.49
0.0739
0.0789
0.0787
0.0788
0.0617
0.064
0.052
0.052
0.052
0.044
0.413
0.421
0.429
0.425
0.394
P10
242
24.2
23.8
23.8
24.9
0.498
0.451
0.507
0.155
0.148
0.037
0.053
0.041
0.342
0.306
0.389
0.334
0.380
3.01
2.79
322
3.14
3.14
0.0648
0.0698
0.0060
0.0418
0.0748
0.046
0.041
0.039
0.042
0.031
0.382
0.404
0.377
0.393
0.288
P90
272
26.5
25.1
26.4
25.7
0.590
0.641
0.655
0.453
0.562
0.175
0.432
0.145
0.714
1.056
0.529
0.576
0.521
3.73
3.76
3.67
3.69
3.56
0.0780
0.0884
0.0800
0.0805
0.0885
0.067
0.310
0.054
0.057
0.050
0.425
0.426
0.443
0.437
0.433
(Continued)
-------
Table C-6. Continued
Analyte
Mg
Mg
Mg
Mg
Mg
Mn
Mn
Mn
Mn
Mn
Na
Na
Na
Na
Na
NH4*
NH4*
NH/
NH4*
NH4*
NO-
NO-
NO-
NO-
NO,-
P-total
P-total
P-total
P-total
P-total
pH-ANC
pH-ANC
pH-ANC
pH-ANC
pH-ANC
Units
(mg/L)
(mgA-)
(mgA.)
•ra
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mgA.)
(mgA.)
(mgA.)
(mgA.)
(mg/L)
(mgA.)
(mgA.)
(mgA.)
(mgA.)
(mg/L)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
Subsurvey-
lab
Sprlng-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 4 2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Sprlng-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Field
target
0.326
0.326
0.326
0.326
0.326
0.073
0.073
0.073
0.073
0.073
0.606
0.606
0.606
0.606
0.606
0.057
0.057
0.057
0.057
0.057
1.2231
1.2231
1.2231
1.2231
1.2231
0.0016
0.0016
0.0016
0.0016
0.0016
5.12
5.12
5.12
5.12
5.12
Lab
target
0.320
0.320
0.320
0.320
0.320
0.072
0.072
0.072
0.072
0.072
0.612
0.612
0.612
0.612
0.612
0.058
0.058
0.058
0.058
0.058
1.2156
1.2156
1.2156
1.2156
1.2156
0.0016
0.0016
0.0016
0.0016
0.0016
5.09
5.09
5.09
5.09
5.09
N
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
Median
0.324
0.321
0.316
0.317
0.328
0.069
0.072
0.068
0.070
0.073
0.594
0.619
0.598
0.606
0.627
0.039
0.056
0.045
0.050
0.061
1.0637
1.2494
1.2122
1.2301
1.1985
0.0010
0.0023
0.0015
0.0022
0.0020
5.15
5.09
5.04
5,05
5.10
Mean
0.323
0.321
0.314
0.317
0.329
0.072
0.072
0.068
0.070
0.074
0.610
0.614
0.597
0.605
0.615
0.039
0.083
0.046
0.065
0.076
1.0885
1.2818
1.2012
1.2415
1.1970
0.0049
0.0028
0.0030
0.0029
0.0017
5.16
5.09
5.04
5.06
5.11
Std
0.010
0.006
0.004
0.006
0.012
0.006
0.001
0.001
0.002
0.004
0.035
0.017
0.020
0.020
0.039
0.019
0.114
0.027
0.083
0.040
0.1363
0.1118
0.0362
0.0912
0.0292
0.0091
0.0026
0.0045
0.0036
0.0009
0.08
0.05
0.02
0.05
0.03
Q1
0.314
0.316
0.309
0.313
0.318
0.068
0.071
0.067
0.068
0.071
0.584
0.602
0.582
0.592
0.579
0.026
0.046
0.035
0.041
0.045
1.0071
1.2352
1.1768
1.2121
1.1773
0.0010
0.0014
0.0011
0.0013
0.0007
5.08
5.07
5.02
5.02
5.10
03
0.333
0.324
0.317
0.322
0.339
0.079
0.073
0.069
0.072
0.077
0.639
0.626
0.611
0.623
0.648
0.058
0.064
0.056
0.062
0.109
1.2375
1.2684
1.2229
1.2574
1.2122
0.0030
0.0031
0.0034
0.0032
0.0023
5.20
5.13
5.05
5.10
5.12
P10
0.309
0.313
0.307
0.309
0.313
0.067
0.070
0.066
0.067
0.070
0.570
0.584
0.564
0.579
0.564
0.010
0.033
-0.000
0.024
0.032
0.9037
12255
1.1407
1.1596
1.1570
0.0007
-0.0001
-0.0006
•0.0004
0.0002
5.07
4.99
5.02
5.02
5.09
P90
0.337
0.331
0.319
0.326
0.346
0.081
0.074
0.070
0.073
0.079
0.671
0.634
0.625
0.632
0.667
0.059
0.274
0.088
0.075
0.141
1.2984
1.4875
1.2516
1.2932
1.2353
0.0276
0.0077
0.0118
0.0069
0.0028
5.30
5.14
5.07
5.14
5.16
(Continued)
-------
Table C-6. Continued
Analyte
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-BNC
pH-ctosed
pH-closed
pH-closed
pH-eq
PH-eq
pH-eq
pH-«q
pH-eq
SiO,
SiO,
SiO,
SiO,
SiO,
so,a-
so4'-
so4'-
so4J-
so42-
True color
True color
True color
Turbidity
Turbidity
Turbidity
Units
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(pH units)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(mg/L)
(PCU)
(PCU)
(PCU)
(NTU)
(NTU)
(NTU)
Subsurvey-
lab
Spring-1
Summer-1
Summer-2
Summer-H-2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Field
target
S.15
5.15
5.15
5.15
5.15
5.13
5.13
5.13
5.16
5.16
5.16
5.16
5.16
4.386
4.386
4.386
4.386
4.386
6.363
6.363
6.363
6.363
6.363
15.0
15.0
15.0
0.130
0.130
0.130
Lab
target
5.12
5.12
5.12
5.12
5.12
_
_
—
5.15
5.15
5.15
5.15
5.15
4.347
4.347
4.347
4.347
4.347
6.360
6.360
6.360
6.360
6.360
w
_
—
_
_
—
N
16
14
14
28
17
10
12
11
16
14
14
28
17
16
14
14
28
17
16
14
14
28
17
10
11
11
10
11
11
Median
5.17
5.08
5.07
5.07
5.17
5.15
5.11
5.13
5.14
5.15
5.12
5.14
5.03
4.400
4.322
4.373
4.348
4.420
6.115
6.311
6.267
6.267
6.430
12.5
15.0
15.0
0.175
0.100
0.100
Mean
5.18
5.07
5.08
5.07
5.19
5.16
5.11
5.13
5.19
5.14
5.25
5.20
5.04
4.364
4.366
4.374
4.370
4.356
6.094
6.287
6.374
6.331
6.356
13.0
17.3
16.4
0.189
0.091
0.127
Std
0.06
0.05
0.02
0.04
0.06
0.04
0.06
0.03
0.14
0.07
0.55
0.39
0.04
0.118
0.131
0.098
0.114
0.336
0.579
0.302
0.474
0.393
0.252
4.8
4.1
3.9
0.083
0.030
0.047
Q1
5.14
5.04
5.07
5.05
5.14
5.14
5.08
5.09
5.12
5.13
5.09
5.11
5.01
4.230
4.287
4.321
4.292
4.378
5.674
5.967
6.187
6.071
6.170
10.0
15.0
15.0
0.120
0.100
0.100
Q3
621
5.11
5.09
5.10
620
5.18
5.15
5.15
5.18
5.19
5.15
5.19
5.07
4.467
4.469
4.465
4.460
4.459
6.562
6.562
6.387
6.431
6.538
16.3
20.0
20.0
0.225
0.100
0.200
P10
5.11
4.98
5.05
5.00
5.13
5.09
5.01
5.09
5.11
4.99
4.94
5.00
5.00
4.218
4.222
4.199
4.214
4.104
4.996
5.900
6.000
5.936
6.016
5.5
11.0
10.0
0.111
0.020
0.100
P90
528
5.14
5.10
5.12
5.32
5.25
5.22
5.19
5.47
5.20
6.17
521
5.12
4.502
4.603
4.500
4.507
4.536
6.817
6.681
7.190
6.659
6.643
20.0
24.0
20.0
0.375
0.100
0.200
-------
Appendix D
Confidence Interval and Scatter
Plots for Natural Audit Sample Data
Plots illustrating the 95% confidence intervals and the scatter of the Seventh Lake (FN7) and
Big Moose Lake (FN8) field natural audit sample data are presented in figures D-1 through D-60.
Plots are provided for all 30 (6 processing laboratory and 24 analytical laboratory) analytes. These
plots can be used in conjunction with the summary statistics provided for the field natural audit
data in Appendix C. The legend for the plots is as follows:
o - analytical result for a single audit sample, Spring Seasonal subsurvey.
° » analytical result for a single audit sample, Summer Seasonal subsurvey.
A » analytical result for a single audit sample, Fall Seasonal subsurvey.
124
-------
.04-
.035-
.03
.025
.02
.015
.01
.005
0
-.005
r- -.01
!E -.ots
.035.
.03.
.025.
.02.
.015.
.01.
.005.
0.
-.005.
-.01.
-.015-
34
j
< 1
I 1
1 1
o
<0 o
o o
0 ¥ fcA
<$>° A£*
A
a
a
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
FIgura D-1. ExtraetabI* aluminum (Al-ext). Seventh Lake audit aamplea.
125
-------
oo
5
i
AS.
.4,
.35
.3
.25
.2
.15
.1
.05
0
.5
.45
.4-
.35
.3
33
2.
.15
.1
.05.
0-
34
i 1
•
o •
&* D * ^ ^
CO D
a
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Flgura D-2. Extraetabla aluminum (Al-axt), Big Mooaa Uka audit aamplaa.
126
-------
.25.
2..
.13.
.1.
.05.
i
.25.
.15
.05,
D
A
3450 3500 3550 3600 3650 3700
Spring Summer Fall
3750
Figure 0-3. Total aluminum (AMotal), Seventh Lake audit sample*.
127
-------
s
.4,
.35
.3
.25
.2
.15.
.1.
.05.
0.
.4
.35.
.3.
.2.
.15
.1.
.05
3450
'
O CD
A
* £
„_
OP
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Flgura D-4. Total aluminum (Al-total), Big Mooaa Laka audit aamptoa.
128
-------
.055,
.05.
.045.
.04
.035
.03
.025
.02
.015
.01
.005
1
'
.05.
.045.
.04
.035
.03
.025
.02
.015
.01
.005
S
0
3450
3500 3550
Spring
3600 3650 3700
Summer Fall
3750
FIgura 0-5. DUMlwtf monoHMrte aluminum (AMU), S«v*nth Uto audit •arnpl**.
129
-------
00
.4,
.35
.3
.23
2
.15
.1.
.05
0.
.4
.35.
.3.
35.
.2.
.15.
.1.
.05.
0.
3450
i
\
3500 3550 3600 3650 3700 3750
Spring Summer Fall
0-9. Dissolved mononwrte aluminum (AMII»), Big Meow Uk» audit ••mpl««.
130
-------
.05
.045
.04
.035
03
.025
.02
.015
.01
.005
0
-.005
-.01
-.015
.055<
.05
.045.
.04
.035.
.03
.025
.02
.015.
.01-
.005.
0.
-.005.
-.01.
-.015.
34
1
1 1
O
•o
<*> A
O A£&
0 D * jfi
00
.................. ..n.l
50 3500 3550 3600 3630 3700 3750
Spring Summer Fall
0-7.
monoiiwrlc •huMmim (AKorg), S*vwith Lake audit
131
-------
00
£
I
.09.
.08.
.07.
.06.
.05.
.04.
.03.
.02.
.01.
0
.09.
.08.
.07
.06.
.05.
.04.
.03.
.02.
.01
0
34
I
1 I
j
<>
9, 0
o li
Q. 4bk
^D «*" A
O
nD A
D r$>
Cfa
D
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
FIgura 0-8. Nenanehangaabla monomarle aluminum (Al-org), Big Mooa* Laka audit aamplaa.
132
-------
1
o
240
220
200
180
160
140
120
100
260.J
240.
220.
200.
180.
160.
140
120.
100.
34
j
1 1
D
O
0 A *
C> O Drtp v^ttft
O
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Figure D-9. Acid neutralizing capacity (ANC), Seventh Lake audit eamplee.
133
-------
oo
20
13-1
10.
5
0
-5
-10
-15
-20
U
20
15
10
5
0
-5
-10
-15
-20
3450
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure D-10. Acid neutralizing capacity (ANC), Big Moot* Lak* audit sampl««.
134
-------
1
i
g
«
240.
220
200
180
160
140
120
100
80
60
40
20
o
230,
225.
200
175
150.
125.
too.
75.
50.
25.
o.
1 i
1 * i
0
o
0* °C gfr AM-
3475 3500 3525 3550 3575 3600 3625 3650 3675 3700 3725 3750
Spring Summer Fall
Figure 0-11. Baae neutralizing capacity (BNC), Seventh Lafca audit samples.
135
-------
140
120.
100.
80.
60
40
20
0
1
CO
140
120.
100.
80
60
40.
20
O O
O
-------
6,
5.5
5.
4.5.
4.
3.5.
3.
2.5.
2.
6
5.5
5
4.5
4.
3.5
3-1
2.5
o
o
2-1
3450
3500 3550 3600 3650 3700 3750
Spring Summer Fall
Figure D-13. Calcium (Ca), Seventh Lake audit •ample*.
137
-------
oo
3
2.8
2.6
2.4
2.2
2
1.8
1.6
1.4
1.2
1.
3
2.8.
2.6
2.4
2
1.8.
1.6.
1.4.
1.2.
1.
3450
3500 3550 3600 3650
Spring Summer
3700
3750
Fall
FIgura D-14. Calcium (Ca), Big Moos* Lake audit samples.
138
-------
r»
E
MM
s~*>
0
5.5
5
4.5
4
3.5
2.5
2
1.5
1
.5
0
1 i
6 .-...-._.
5.5.
4.5.
4.
3.5.
3.
2.5
2.
1.5-
1.
.5-
0-
o
0
o % IP ^tftfP
o
0
o
3450 3500 3550 3600 3650 3700 3750
Spring Summer Fall
Figure D-15. Chloride (CO, Seventh Lake audit eamplee.
139
-------
oo
.5,
.45.
.4.
.35.
.3.
.25.
.2.
.15.
.1
.05.
0.
.5
.45-
.4.
.35.
.3.
.25.
.2.
.15.
.1.
.05
0
3450
CO
B
3500 3550
Spring
3600 3650
Summer
3700
Fall
3750
Figure D-16. Chloride (CO, Big Moot* Lake audit camples.
140
-------
60,
55
50.
45
40
35
30
25
20
$
60
55.
50.
45.
40.
35.
30.
25.
20.
3450
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Flgur* D-17. Conductivity (Cond). S«v«nth Lake audit •ample*.
141
-------
oo
30
29.
28.
27.
26.
25
24.
23
22
21
20
30
29
28
27^
26
23
24
23
22
21
20-I
3450
O
O
3500
Spring
3550
3600
Summer
3650
3700
3750
Fall
Flgur* D-18. Conductivity (Cond), Big Moos* Lak* audit samples.
142
-------
2.5.
2
1.5
.5
! D
2.5.
2.
1.5.
1.
.5.
3450
i
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure D-19. Dissolved Inorganic carbon-closed (DlC-closed), Seventh Lake audit samples.
143
-------
i
1.
.9
.8
.7
.6
.5.
.4
.3
.2
.1.
OJ
.9.
.8.
.7
.6.
.5.
.4.
.3.
.2.
.1.
0.
3450
B
3500 3550 3600 3650
Spring Summer
3700 3750
Fall
Figure D-20. Dissolved Inorganic carbon-closed system (DIC-closed), Big Moose Lake audit samples.
144
-------
2.5
2
1.5.
1.
i
I,,,
2
1.5.
1
.5.
3450 3500 3550
Spring
3600 3650 3700
Summer Fall
3750
Figure D-21. Dissolved Inorganic carbon-equilibrated (DIC-eq), Seventh Lake audit samples.
145
-------
.9.
.8.
.7
.6.
.5.
.4
.3
.2
oo
5 •'•
«J 0
H !i
y -9.
Q
.8.
.7
.6
.5
.4
.3
.2
.1
0
34
j
<
4 1
1 5
O D
D
O
a
o
°
-------
2.5
1.3
.5
2.3.
1.5.
.5.
3450 3500 3550 3600 3650 3700
Spring Summer Fall
3750
Figure D-23. Dissolved Inorganic carbon-Initial (DIC-lnltlal), Seventh Lake audit samples.
147
-------
A*
1.8.
1.6
1.4
1.2
1
.8
.6
.4
i *
3 o
I
1 2.,
i •*
1.6.
1.4.
1.2.
1.
.8.
.6
.4.
.2
0
34
I " i
D
O
o0^>° 3£ ^f^
50 3500 3550 3600 3650 3700 3750
Spring Summer Fall
FIgurs D-24. Dissolved Inorganic carbon-Initial (DIC-lnltlal), Big Moos* Lake audit samples.
148
-------
5,
4.5.
4.
3.3
3
2.5
2
1.5
1.
.5.
0
5
4.5.
4.
3.5.
3
2.5
2
1.5
1
.5,
0
3450
3500 3550
Spring
3600
Summer
3650
3700
Fall
3750
FIgura D-25. Dlsaoved organic carbon (DOC), Seventh Lake audit samples.
149
-------
oo
£
1'
"
3.5
3.
2.5.
2
1.5
1
.5
0
41
3.5.
3
2.5-
2-
1.5
1
.5
0
34
* i i
O
0<8° ^"Sj AAavA
IQr A ^
D A
50 3500% 3550 3600 3650 3700 37!
Spring Summer Fall
FIgura 0-26. Dlaaolvad organic carbon (DOC), Big Moose Lak« audit «ampl««.
150
-------
i
.055,
.05
.045
.04
.035
.03
.025
.02
.015
.01
.005
0.
.055
.05
.045-1
.04
.035
.03-1
.025
.02
.015-1
.01
.005
0-1
3450
A*
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
FIgura D-27. Iron (Fa), Savanth Laka audit aamplaa.
151
-------
oo
E
!
.09
.08
.07
.06
.05
.04.
.03.
.02.
.01
0.
.1.1
.09.
.08.
.07.
.06.
.05.
.04.
.03.
.02.
.01.
0.
34!
I
I
*
0
0
D £A
50 3500 3550 3600 3650 3700 3750
Spring Summer Fall
FIgura D-28. Iron (F«), Big Moos* Lak* audit samples.
152
-------
i
!
.09.
.08.
.07
.06
.03.
.04-
.03.
.02.
.01
0
•'1
.09.
.08
.07.
.06.
.05.
.04.
.03.
.02.
.01.
o.
34!
1
0
A
D ° A A
Q OL D D
o o
O
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
FIgura D-29. Total fluoride (F-total), Seventh Lake audit samples.
153
-------
OO
Js,
t
B-
.1,
.09.
.08.
.07.
.06.
.05.
.04
.03.
.02
.01
0
.09.
.08
.07
.06.
.05.
.04-
.03
.02
.01
0-
34
i 1
.
•
A
o D JOD A
°0 o a A
o
D
D
50 3500 3550 3600 3650 3700 375
Spring Summer Fall
Figure D-30. Total fluoride (F'-total), Big Moose Lake audit samplea.
154
-------
s
.55.
.5.
.45.
.4.
.35.
.3.
.25.
.2.
.15.
.1
.05
0.
•61
.55.
.5.
.45.
.4.
.35.
.3
.25.
.2
.15.
.1.
.05.
o.
34
I
t
'
D
^y D A,
A
A
A
50 3500 3550 3600 3650 3700 375
Spring
Summer
Fall
Figure 0-31. Potassium (K), Seventh Lake audit samples.
155
-------
oo
i
.5,
.45
.4
.35
.3
.25.
.2
.15.
.1
.05.
0
.45H
.4
.35
.3,
.25
.2
.15
.1.
.05.
0.
3450
i
3500
Spring
3550
3600
Summer
3650
3700
3750
Fall
Figure D-32. Potassium (K), Big Moose Lake audit samples.
156
-------
r-
£
a
.95.
.9.
.85.
.8.
.75.
.7.
.65.
.6.
.55.
.5.
I..
.95.
.9.
.85
.8.
.75.
.7
.65
.6
.55
.5
34
I
Q ft n
50 3500 3550 3600 3650 3700 37!
Spring
Summer
Fall
Figure D-33. Magnesium (Mg), Seventh Lake audit samples.
157
-------
00
i
60
s
.5
.45
.4
.35
.3
.25.
.2
.45.
.4.
.35.
.3.
.25.
.2.
3450 3500 3550 3600 3650
Spring Summer
3700 3750
Fall
Figure D-34. Magnesium (Mg), Big Moose Lake audit samples.
158
-------
.025
.02.
.015.
.01
.005
0
t
I
-.005
-.01
.025
.02
.015
.01.
.005.
-.005.
-.01
I
00
OO
OD
3450 3500 3550 3600 3650
Spring Summer
3700
3750
Fall
Figure 0-35. Manganese (Mn), Seventh Lake audit samples.
159
-------
09
£
i
.1.
.095
.09
.085
.08
.075
.07
.065
.06
.055
.05
.1051
.1.
.095
.09,
.085.
.08.
.075-
.07.
.065.
.06.
.055
.05
34
f i '
•
.
•
00 £
on A\
«X 00 Sag, ® *
"
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
FIgura D-36. Manganese (Mn), Big Moose Lake audit samples.
160
-------
I
at
2
2.8
2.6.
2.4.
2.2
2
1.8
1,6
1.4
1.2
1
3
2.8.
2.6.
2.4.
2.2
2.
1.8.
1.6.
1.4.
1.2.
1
3450
3500
Spring
3550
3600
Summer
3650
3700
3750
Fall
Figure D-37. Sodium (Na), Scwnth Lake audit •ample*.
161
-------
oo
£
1
CO
Z
1.
.95
.9
.85
.8.
.75.
.7.
.65.
.6.
.55.
.5.
l-i
.95-
.9-
.85-
.8
.75-
.7-
.65-
.6
.55-
.5-
34!
,
1 I *
* ^
° o^° i£a ^ ^
o° ° D £
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Figure D-38. Sodium (Na), Big Moose Lake audit sample*.
162
-------
.1.
.08.
.06.
.04.
.02
I
-.02J
.1
.08-
.06
.04
.02-
-.02
3450
O
O
-------
oo
£
I
Jj,
s
25
.18.
.16
.14
.12
.1
.08
.06
.04
.02
o
T ]
I I {
2 ...
.18.
.16.
.14.
.12.
.1.
.08
.06
.04
.02
Q
A
DO A
^ 0 ^ A £*
0 0 °^ AA
^J ^P^ ™™
O
n
3450 3500 3550 3600 3650 3700 3750
Spring Summer Fall
Figure D-40. Ammonium (NH/), Big Moose Lake audit samples.
164
-------
f>
§
1.8.
1.6.
1.4.
1.2.
1.
.8.
.6.
.4.
.2
0.
2.
1.8.
1.6.
1.4.
1.2.
1.
.8.
.6.
.4.
.2.
0.
34
i ' i
* $
-------
oo
£
2
£
CO
o
z
4b
1.8.
1.6.
1.4.
1.2.
1.
.8.
.6.
.4.
.2
0.
i
1.8.
1.6.
1.4.
1.2.
1.
.8.
.6.
.4.
.2.
0.
o
«j>^>
o
3450 3500 3550 3600 3650 3700 3750
Spring Summer Fall
Figure D-42. Nitrate (NO3')t Big Moose Uke audit samples.
166
-------
i
1
§
.02
.018.
.016.
.014
.012
.01
.008
.006
.004
.002
0
- 002
022
.02^
.018.
.016.
.014.
.012
.01
.008
.006
.004.
.002-
0.
- 002
34
.
I !
O
° § ° Q *
^ ^ A^lA
50 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Figure D-43. Total dissolved phosphorus (P-total), Seventh Lake audit samples.
167
-------
oo
i
2
.04.
.035.
.03.
.025.
.02.
.015
.01
.005
Q
-.005
- 01
045 j
.04.
.035-
.03.
.025
.02
.015
.01
.005
-.005
- 01
34
• •
..__" I. . .
o
D
n
SO 3500 3550 3600 3650 3700 3"
Spring Summer Fall
•
rso
Figure D-44. Total dissolved phosphorus (P-total), Big Moose Lake audit samples.
168
-------
£
Is
7.5
7.
6.5.
6.
5.5.
u
7.5J
7.
6.3-
6.
5.5.
5-
3450
3500
3550
Spring
3600
Summer
3650
3700
375C
Fall
Figure D-45. pH, Initial for acid titratlon for ANC (pH-ANC), Seventh Lake audit samples.
169
-------
6.5
6
5.S,
oo 4.5.
U
6-5
5.5
5-
4.5
4
3450
O O
3500
Spring
3550
3600
Summer
3650
3700
3750
Fall
Figure D-46. pH, Initial for acid titratlon for ANC (pH-ANC), Big Moose Lake audit samples.
170
-------
7.5.
7.
6.5
6
5.5
s
CQ
7.5
7
6.5-
5.5.
3450
«
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Flgur* D-47. pH, Initial for acid titratlon for BNC (pH-BNC), Seventh Lake audit samples.
171
-------
6.3
6.
5.5.
5.
oo 4.5,
S3
•a
I
CQ
6.5.
6.
5.5.
5.
4.5.
3450 3500 3550
Spring
3600 3650
Summer
3700 3750
Fall
Figure D-48. pH, Initial for acid titratlon for BNC (pH-BNC), Big Moos* Lake audit samples.
172
-------
7.5
7.
6.5.
5.5.
i
s
7.5
7-
6.5-
5.5
3450
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure 0-49. pH-elosed system (pH-closed), Seventh Lake audit samples.
173
-------
6.3.
6.
3.3.
5.
oo
1
I
NW'
IS
4.5.
6.5.
5.5 J
5.
4.3-
3450
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
FIgurs D-50. pH-clossd system (pH-closed), Big Moose Lake audit samples.
174
-------
w
7.5
7.
6.5
6
t- 5.5
g
a"
•g 5
1
*§,
_: 8
'3
'a. 7.5.
7.
6.5.
6.
5.5.
5-
a
^ AVA
A
3450 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Figure D-51. pH-equlllbrated (pH-eq), Seventh Lake audit samples.
175
-------
7.5
7.
6.5.
6.
5.5.
5.
2S 4.5J
53
•a 4
K
a
—• 7.5
6.5.
6.
5.5.
5.
4.5-
4.
3450
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure D-52. pH-equlllbrated (pK-eq), Big Moose Lake audit samples.
176
-------
2
O
e/3
6,
5.5
5.
4.5
4
3.5
3,
2.5
2
1.5
1.
.5
5
4.5
4
3.5
2.5.
2
1.5^
1.
0.
3450
I
3500
3550
Spring
3600
Summer
3650
3700
3750
Fall
Figure D-53. Silica (SIO2), Seventh Lake audit samples.
177
-------
5.5.
5.
4.5
4
3.5
oo
g
1
§
bo
5.5
5
4.5
3.5
3
3450
3500 3550
Spring
3600 3650 3700
Summer Fall
3750
Figure D-54. Silica (SIO2), Big Moose Lake audit samples.
178
-------
r-
£
3
l
8,
7
6.
5.
4.
3
2
1
0
81
7
6.
5.
4.
3.
2.
1.
o.
34!
i .
i I
I
*° °% B* ^^
D
A
A
50 3500 3550 3600 3650 3700 375
Spring Summer Fall
Figure 0-55. Sulfate (SO42'), Seventh Lake audit samples.
179
-------
oo
E
i
n.
5
CO
9
8
7
6
5
4
3
2
1
o
1 *
•
10
9.
81
7-
6.
3.
4.
3.
2
1
o
a
cfb fi Ap4
<©o d^6 £flr™
CO
0
3450 3500 3550 3600 3650 3700 37!
Spring Summer Fall
Figure D-56. SulfaU (SOt2'), Big Moo«« Lake audit camples.
180
-------
30,
25
20
13
10
30
25.
20.
15.
10.
3.
0.
3450
O 00
OD O
O2> O
DD
3500
Spring
3550
3600
Summer
3650
3700 3750
Fall
Figure 0-57. True Color, Seventh Lake audit samples.
181
-------
30,
25.
20.
15.
10
oo
B
30
25.
20.
15.
10
O O
O OO
OOOO
DDOU
ii ii ii i
AA/WA
A. A
3450 3500 3550
Spring
3600 3650
Summer
3700 3750
Fall
Figure D-58. True Color, Big Moos* Lake audit sample*.
182
-------
.8.
.6.
.4.
.2
-.2
1
>.
'•B
.8
.6
.4-
-.2
3450
a
rm
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure D-59. Turbidity, Seventh Lake audit samples.
183
-------
.8
.6
.2
-.2
S
.8-
.6.
.4.
0 .
-.2
3450
o
III IIIIIII
... rri.......
3500
Spring
3550
3600
Summer
3650
3700
Fall
3750
Figure D-60. Turbidity, Big Moose Lake audit samples.
184
-------
Appendix E
Summary Statistics and Plots for
Field Routine-Duplicate Sample Data
System-level precision estimates based on field routine-duplicate pair sample data are
presented in Table E-1. Precision estimates are provided for various concentration ranges because
of concentration-dependent effects. Figures E-1 through E-30 present all the field duplicate pair
sample mean concentrations plotted against the relative standard deviation or standard deviation
of the pair. The statistics presented in Table E-1 are defined below.
The pooled standard deviation (Sp) is calculated by:
Sp = \/Zs>
'-1
where:
Sp = pooled standard deviation,
n - number of field routine-duplicate pairs, and
S,2 * variance of the field routine-duplicate pair.
The percent relative standard deviation (%RSDp) is calculated by dividing Sp by the grand mean
of the sample pairs and multipying by 100 and is expressed as:
%RSD, - — - - 1 100
where:
Sp =• pooled standard deviation,
x; = mean of a sample pair, and
n = number of field routine-duplicate pairs.
A detailed discussion of these formulas and statistical approach can be found in Taylor
(1987).
The following terms are used in the tables in this appendix:
Subsurvey-lab:
Spring-1 = Spring Seasonal subsurvey analysis performed by Laboratory 1.
Spring-PL = Spring Seasonal subsurvey analysis performed by processing laboratory.
185
-------
Summer-1 » Summer Seasonal subsurvey analysis performed by Laboratory 1.
Summer-2 = Summer Seasonal subsurvey analysis performed by Laboratory 2.
Summer-1+2 ~ Summer Seasonal subsurvey analyses; Laboratory 1 and Laboratory 2 data
combined.
Summer-PL = Summer Seasonal subsurvey analysis performed by processing laboratory.
Fall-2 - Fall Seasonal subsurvey analysis performed by Laboratory 2.
Fall-PL « Fall Seasonal subsurvey analysis performed by processing laboratory.
The following symbols are used in the plots in this appendix:
A * Analytical result for a single mean concentration for a field routine-duplicate pair
measured by Laboratory 1.
a - Analytical result for a single mean concentration for a field routine-duplicate pair
measured by Laboratory 2.
e » Analytical result for a single mean concentration for a field routine-duplicate pair
measured by processing laboratory.
X - Indicates % RSD for Laboratory 1 results is out of range.
+ - Indicates % RSD for Laboratory 2 results is out of range.
Y » Indicates % RSD for processing laboratory results is out of range.
186
-------
Table E-1. Summary Statistics for Field Routine-Duplicate Pair Sample Data, Eaatern Lake Survey - Phaae II
Subsurvev-Lab
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Concentration
ranges
< 0.02
0.02 - 0.05
>0.05
All data
<0.02
0.02 - 0.05
>0.05
All data
<0.02
0.02 - 0.05
>0.05
All data
< 0.025
0.025 - 0.05
>0.05
All data
< 0.025
>0.05
All data
< 0.025
All data
< 0.025
0.05
All data
< 0.025
0.025 - 0.05
>0.05
All data
<0.05
0.05 - 0.1
> 0.1
All data
< 0.05
0.05 - 0.1
> 0.1
All data
< 0.05
> 0.05 - 0.1
All data
< 0.05
0.05 - 0.1
> 0.1
All data
< 0.05
0.05 - 0.1
>0.1
All data
Number of
pairs
Al-dls (mg/L)
4
13
11
28
10
5
2
17
5
12
8
25
Al-ext (mg/L)
14
4
10
28
3
1
4
11
11
14
1
15
16
3
7
26
AMot (mg/L)
9
7
12
28
1
1
2
4
9
2
11
10
3
2
15
14
5
7
26
Grand
mean
0.014
0.030
0.125
0.065
0.014
0.026
0.210
0.041
0.017
0.028
0.143
0.063
0.007
0.032
0.099
0.043
-0.008
0.128
0.026
0.008
0.008
0.005
0.128
0.013
0.007
0.030
0.096
0.034
0.033
0.076
0.212
0.121
0.039
0.068
0.260
0.157
0.025
0.057
0.031
0.026
0.060
0.260
0.064
0.022
0.082
0.228
0.089
Pooled
s,
0.001
0.001
0.004
0.003
0.002
0.001
0.006
0.003
0.001
0.002
0.004
0.003
0.005
0.006
0.040
0.024
0.003
0.034
0.017
0.002
0.002
0.002
0.034
0.009
0.002
0.002
0.006
0.004
0.004
0.021
0.013
0.014
0.015
0.007
0.082
0.059
0.012
0.004
0.011
0.012
0.005
0.082
0.032
0.005
0.041
0.066
0.039
%RSDp
5.035
4.140
3.537
4.466
16.750
2.651
2.708
6.572
3.818
7.288
2.960
4.472
67.036
19.604
40.327
55.775
-37.293
26.609
65.414
27.499
27.499
51.106
26.609
71.075
31.820
7.047
6.374
11.031
12.785
27.864
6.145
11.465
39.000
11.055
31.539
37.407
48.428
7.297
36.025
47.167
9.084
31.539
49.294
21.753
49.207
28.794
43.297
(Continued)
187
-------
Table E-1. Continued
Subsurvey-Lab
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-H-2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-H-2
Fall-2
Concentration
ranges
< 0.02
0.02 - 0.05
>0.05
All data
<0.02
All data
<0.02
0.02 - 0.05
>0.05
All data
< 5
5-25
25-100
> 100
All data
<5
5-25
25-100
All data
5-25
25-100
> 100
All data
< 5
5-25
25-100
> 100
All data
<5
5-25
25-100
> 100
All data
0-50
>50
All data
0-50
>50
All data
0-50
> 50
All data
0-50
> 50
All data
0-50
>50
All data
Number of
pairs
Al-org (mg/L)
5
17
6
28
17
17
18
4
3
25
ANC (fieq/L)
9
5
11
4
29
3
1
2
6
1
2
8
11
3
2
4
8
17
6
8
2
9
25
BNC (peq/L)
8
21
29
4
2
6
9
2
11
13
4
17
16
10
26
Grand
mean
0.015
0.031
0.078
0.038
0.008
0.008
0.011
0.036
0.075
0.022
-3.128
17.220
62.164
193.687
52.293
-15.000
16.250
59.700
15.108
7.150
42.875
257.162
195.473
-15.000
11.700
51.287
257.162
131.815
-13.708
10.675
70.125
245.078
93.964
38.119
66.529
58.691
36.700
100.450
57.950
31.928
58.450
36.750
33.396
79.450
44.232
36.462
84.955
55.113
Pooled
s,
0.001
0.002
0.004
0.003
0.002
0.002
0.001
0.005
0.003
0.002
3.389
4.557
5.135
5.422
4.605
5.405
1.909
0.500
3.911
0.071
2.072
8.275
7.112
5.405
1.351
1.507
8.275
6.175
5.650
1.037
9.903
5.334
5.108
11.378
16.231
15.049
5.656
7.710
6.414
5.000
11.904
6.799
5.211
10.029
6.666
7.076
19.678
13.407
%RSD,
8.854
7.645
5.322
7.127
31.628
31.628
9.726
14.908
3.384
11.080
-108.36
26.461
8.261
2.799
8.805
-36.031
11.749
0.838
25.886
0.989
4.832
3.218
3.638
-36.031
11.546
2.938
3.218
4.684
-41.218
9.713
14.122
2.177
5.437
29.849
24.397
25.641
15.412
7.676
11.069
15.661
20.366
18.499
15.603
12.623
15.069
19.407
23.163
24.326
(Continued)
188
-------
Table E-1. Continued
Subsurvey-Lab
Concentration
ranges
Number of
pairs
Grand
mean
Pooled
%RSD.
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
8ummer-1
Summer-2
Summer-1+2
Fall-2
<2
2-3
3-5
>5
All data
< 2
2-3
All data
<2
2-3
3-5
>5
All data
<2
2-3
3-5
>5
All data
<2
2-3
3-5
> 5
All data
< 1
1-7
>7
All data
< 1
1-7
>7
All data
< 1
1-7
>7
All data
< 1
1 -7
>7
All data
< 1
1-7
> 7
All data
Ca (mg/L)
14
11
1
3
29
4
2
6
1
2
4
4
11
5
4
4
4
17
13
2
6
5
26
Cl- (mg/L)
11
8
9
28
2
1
3
6
4
4
3
11
6
5
6
17
12
6
8
26
Cond (f/S/cm)
1.351
2.519
3.035
6.953
2.431
1.304
2.339
1.649
0.711
2.933
3.418
7,237
4.472
1.185
£636
3.418
7.237
3.476
1.167
2.290
3.855
6.936
£983
0.063
0.016
0.064
0.057
0.050
0.064
0.012
0.053
0.004
0.009
0.093
0.067
0.069
0.058
0.011
0.093
0.067
0.064
0.015
0.030
0.027
0.058
0.032
0.519
£551
14.630
5.635
0242
4.121
10.461
5.998
0.566
£765
11.130
4.247
0.458
3.036
10.796
4.865
0.451
1.768
11207
4.064
0.150
0.102
£000
1.139
0.053
4.777
1.013
£078
0.012
0.022
0.239
0.126
0.032
£136
0.736
1.238
0.025
0.039
0.317
0.178
4.650
0.640
£097
0.825
£049
4.935
0.510
3.213
0.626
0.304
£718
0.926
1.547
4.859
0.400
£718
0.926
1.839
1.327
1.307
0.710
0.838
1.066
29.012
3.982
13.668
20.211
21.751
115.892
9.686
34.639
2.070
0.788
2.151
2.965
6.947
70.356
6.820
25.458
5.563
£224
£827
4.369
Spring-1
< 25
25-50
>50
All data
11
10
8
29
21.181
36.985
77.612
42.198
0.507
0.761
1.340
0.890
2.396
2.056
1.727
£110
(Continued)
189
-------
Table E-1. Continued
Subsurvey-Lab
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-PL
Summer-PL
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Concentration
ranges
< 25
25-50
>50
All data
<25
25-50
>50
All data
< 25
25-50
>50
All data
< 25
25-50
>50
All data
<0.5
0.5-1
1-2
>2
All data
< 0.5
0.5-1
1-2
>2
All data
< 0.5
0.5-1
1-2
> 2
All data
<0.5
0.5-1
1-2
> 2
All data
< 0.5
0.5-1
1-2
All data
<0.5
0.5-1
1-2
> 2
All data
<0.5
0.5-1
1-2
> 2
All data
Number of
pairs
Cond (pS/cm)(contlnued)
1
3
2
6
1
6
4
11
2
9
6
17
13
5
8
26
DIC-closed (mg/L)
5
5
16
3
29
4
4
3
6
17
3
9
6
8
26
DIC-eq (mg/L)
11
10
5
3
29
3
1
2
6
2
2
1
6
11
5
3
3
6
17
Grand
mean
24.350
39.100
58.900
43.242
23.450
35.750
74.637
48.773
23.900
36.867
69.392
46.821
19.505
38.780
75.700
40.502
0.405
0.725
1.521
3.085
1.353
0.292
0.682
1.351
3.562
1.725
0.480
0.712
1.438
3.525
1.718
0.268
0.704
1.525
3.301
0.949
0.290
0.660
1.347
0.704
0.210
0.669
1.410
3.146
2.004
0.258
0.666
1.368
3.146
1.545
Pooled
SP
0.071
8.921
0.000
6.308
0.071
0.365
0.943
0.630
0.071
5.159
0.770
3.782
0.567
0.265
0.496
0.500
0.026
0.065
0.045
0.058
0.048
0.047
0.012
0.029
0.176
0.108
0.024
0.024
0.055
0.062
0.046
0.032
0.201
0.071
0.104
0.127
0.107
0.179
0.141
0.133
0.020
0.082
0.006
0.089
0.075
0.084
0.123
0.115
0.089
0.099
%RSDp
0.290
22.816
0.000
14.588
0.302
1.021
1.263
1.291
0.296
13.994
1.109
8.077
2.909
0.682
0.656
1.235
6.418
8.898
2.978
1.873
3.554
15.917
1.696
2.130
4.932
6.239
4.993
3.344
3.792
1.759
2.687
12.029
28.509
4.639
3.140
13.434
36.936
27.085
10.433
18.867
9.695
12.297
0.451
2.839
3.754
32.509
18.498
8.391
2.839
6.435
(Continued)
190
-------
Table E-1. ContlniMd
Subsurvey-Lab
Fall-2
Sprlng-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Concentration
ranges
<0.5
0.5-1
1-2
>2
All data
<0.5
0.5-1
1-2
>2
All data
< 0.5
0.5-1
1-2
All data
< 0.5
0.5-1
1-2
>2
All data
< 0.5
0.5-1
1-2
> 2
All data
< 0.5
0.5- 1
1-2
> 2
All data
< 3
3-5
> 5
All data
<3
3-5
> 5
All data
< 3
3-5
>5
All data
< 3
3-5
> 5
All data
<3
3-5
>5
All data
Number of
pairs
DIC-eq (mg/L)(contlnued)
14
2
3
7
26
DIC-lnltlal (mg/L)
1
8
16
4
29
2
2
2
6
1
2
2
6
11
3
4
4
6
17
5
8
5
8
26
DOC (mg/L)
12
12
5
29
3
2
1
6
2
5
4
11
5
7
5
17
8
13
5
26
Grand
mean
0.112
0.765
1.417
2.755
1.025
0.290
0.671
1.446
3.182
1.432
0.404
0.568
1.411
0.795
0.374
0.643
1.392
3.397
2.257
0.394
0.606
1.402
3.397
1.741
0.411
0.668
1.295
3.230
1.528
1.820
3.513
7.051
3.423
1.284
3.985
5.800
2.937
2.605
4.050
7.236
4.946
1.812
4.031
6.949
4.237
2.036
4.323
9.282
4.573
Pooled
sp
0.021
0.177
0.024
0.096
0.072
0.014
0.147
0.174
0.159
0.162
0.210
0.062
0.087
0.136
0.016
0.011
0.058
0.100
0.078
0.172
0.045
0.074
0.100
0.102
0.054
0.042
0.071
0.075
0.062
0.075
0.131
0.843
0.363
0.603
0.112
1.152
0.638
0.139
0.165
0.233
0.189
0.475
0.152
0.556
0.408
0.051
0.357
0.276
0.281
%RSD,
18.616
23.116
1.700
3.471
7.017
4.877
21.957
12.063
5.010
11.324
52.026
10.919
6.146
17.140
4.343
1.775
4.171
2.938
3.459
43.621
7.362
5.266
2.938
5.883
13.114
6.299
5.509
2.333
4.051
4.115
3.732
11.953
10.611
46.975
2.806
19.858
21.725
5.347
4.083
3.222
3.821
26.229
3.770
7.997
9.640
2.504
8.253
2.975
6.150
(Continued)
191
-------
TabtoE-1. Continued
Subsurvay-Lab
Concentration
ranges
Number of
pairs
Grand
mean
Pooled
%RSD,
Spring-1
Summer-1
Summer-2
Summer-1-1-2
Fall-2
Sprlno-1
Summer-1
Summer-2
Summer-1+2
Fall-2
< 0.025
0.025 - 0.05
>0.05
All data
0.025 - 0.05
>0.05
All data
< 0.025
0.025 - 0.05
>0.05
All data
< 0.025
0.025 - 0.05
>0.05
All data
< 0.025
0.025 - 0.05
> 0.05
All data
< 0.025
0.025 - 0.05
0.05 - 0.1
>0.1
All data
< 0.025
0.025 - 0.05
0.05 • 0.1
>0.1
All data
< 0.025
0.025 - 0.05
0.05 - 0.1
>0.1
All data
< 0.025
0.025 - 0.05
0.05 - 0.1
> 0.1
All data
< 0.025
0.025 - 0.05
0.05 - 0.1
> 0.1
All data
FMotal (mg/L)
8
13
8
29
1
5
6
2
4
5
11
2
5
10
17
4
8
14
26
F» (mg/L)
9
5
7
8
29
2
1
2
1
6
7
1
2
1
11
9
2
4
2
17
8
5
4
9
26
K (mg/L)
0.019
0.041
0.092
0.049
0.026
0.074
0.066
0.022
0.037
0.064
0.047
0.022
0.035
0.069
0.053
0.020
0.039
0.096
0.067
0.013
0.035
0.061
0.137
0.063
0.018
0.031
0.084
0.127
0.061
0.012
0.033
0.075
0.895
0.106
0.013
0.032
0.080
0.511
0.090
0.009
0.039
0.065
0.251
0.107
0.002
0.001
0.009
0.005
0.000
0.003
0.003
0.001
0.002
0.002
0.002
0.001
0.002
0.003
0.002
0.003
0.002
0.004
0.003
0.004
0.005
0.004
0.013
0.008
0.004
0.001
0.025
0.006
0.015
0.005
0.003
0.007
0.669
0.202
0.005
0.002
0.018
0.473
0.163
0.002
0.007
0.006
0.016
0.010
10.768
3298
9.865
10.181
0.552
3.933
4.031
2.864
4.879
3.187
3.811
2.864
4.650
3.639
3.986
13.871
5.095
3.722
4.558
29.186
13.907
6.854
9.194
12.019
22.869
2.245
29.609
4.991
24.503
42.347
7.737
9.567
74.734
191.082
36.611
5.802
23.118
92.520
181.120
26.081
17.976
8.611
6.291
9.435
Spring-1
< 0.2
0.2 - 0.5
>0.5
All data
3
17
9
29
0.152
0.311
0.792
0.444
0.008
0.005
0.042
0.024
5.086
1.674
5.279
5.356
(Continued)
192
-------
Table E-1. Continued
Subsurvey-Lab
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Concentration
ranges
<02
02 • 0.5
>0.5
All data
02 - 0.5
>0.5
All data
<0.2
0.2 - 0.5
>0.5
All data
< 0.2
0.2 - 0.5
> 0.5
All data
< 0.5
0.5-1
> 1
All data
< 0.5
0.5- 1
> 1
All data
< 0.5
0.5-1
> 1
All data
< 0.5
0.5- 1
> 1
All data
< 0.5
0.5- 1
> 1
All data
< 0.025
0.025 - 0.05
> 0.05
All data
< 0.025
0.025 - 0.05
> 0.05
All data
< 0.025
0.025 - 0.05
> 0.05
All data
Number of
pairs
K (mg/L)(contlnued)
1
4
1
6
7
4
11
1
11
5
17
8
10
8
26
Mg (mg/L)
12
11
6
29
3
2
1
6
1
4
6
11
4
6
7
17
15
6
5
26
Mn (mg/L)
14
10
5
29
3
2
1
6
9
1
1
11
Grand
mean
0.157
0.307
0.534
0.320
0.324
0.784
0.492
0.157
0.318
0.734
0.431
0.159
0.274
0.856
0.418
0.384
0.657
1.419
0.702
0.315
0.557
1.065
0.521
0.333
0.586
1.619
1.126
0.319
0.576
1.540
0.913
0.287
0.711
1.577
0.633
0.015
0.032
0.108
0.037
0.005
0.032
0.128
0.035
-0.002
0.029
0.121
0.012
Pooled
SP
0.000
0.002
0.004
0.002
0.006
0.023
0.015
0.000
0.005
0.021
0.012
0.010
0.056
0.060
0.048
0.004
0.004
0.025
0.012
0.074
0.006
0.014
0.053
0.006
0.004
0.012
0.009
0.064
0.005
0.012
0.032
0.002
0.004
0.014
0.007
0.001
0.001
0.005
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.006
0.002
%RSD,
0.000
0.576
0.661
0.638
1.821
2.964
3.008
0.000
1.519
2.840
^774
6.560
20.306
7.066
11.603
1.008
0.652
1.752
1.691
23.485
1.124
1.328
10.126
1.722
0.644
0.740
0.826
20.076
0.824
0.800
3.529
0.810
0.629
0.878
1.055
6.053
4.231
4.203
5.812
14.630
1.550
0.550
1.866
-37.749
2.896
4.909
16.765
(Continued)
193
-------
Table E-1. Continued
Subsurvey-Lab
Summer-1 +2
Fall-2
Spring-1
Summer-1
Summer-1
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Concentration
ranges
< 0.025
0.025 - 0.05
> 0.05
All data
< 0.025
0.025 - 0.05
>0.05
All data
< 1
1-2
2-5
>5
All data
< 1
2-5
>5
All data
< 1
1-2
2-5
> 5
All data
< 1
1-2
2-5
>5
All data
< 1
1-2
2-5
>5
All data
< 0.01
0.01 - 0.05
> 0.05
All data
< 0.01
0.01 - 0.05
All data
< 0.01
0.01 - 0.05
All data
< 0.01
0.01 - 0.05
All data
<0.01
0.01 - 0.05
>0.05
All data
Number of
pairs
Mn (mg/L)(contlnued)
12
3
2
17
11
12
3
26
Na (mg/L)
9
6
5
9
29
2
2
2
6
3
2
3
3
11
5
2
5
5
17
9
6
3
8
26
NIV (mg/L)
23
3
3
29
5
1
6
7
4
11
12
5
17
12
6
8
26
Grand
mean
-0.001
0.031
0.125
0.020
0.009
0.036
0.105
0.033
0.656
1.295
3.353
7.688
3.435
0.563
3.805
6.802
3.723
0.805
1.478
£735
5.982
2.866
0.708
1.478
3.163
6.310
3.168
0.465
1.357
2.285
7.626
3.084
0.002
0.025
0.079
0.013
0.002
0.023
0.005
-0.006
0.019
0.003
-0.003
0.020
0.004
0.001
0.029
0.143
0.051
Pooled
s.
0.001
0.001
0.004
0.002
0.001
0.002
0.001
0.002
0.008
0.022
0.037
0.171
0.097
0.004
0.019
0.019
0.016
0.010
0.038
0.063
0.333
0.178
0.008
0.038
0.051
0.258
0.143
0.005
0.010
0.044
0.063
0.038
0.007
0.034
0.008
0.013
0.004
0.026
0.011
0.002
0.004
0.003
0.003
0.012
0.007
0.007
0.003
0.010
0.008
%RSDp
-158.89
2.040
3.391
8.239
9.000
6.094
0.908
4.966
1.145
1.718
1.097
2.220
2.822
0.710
0.513
0.279
0.427
1.224
2.604
2.314
5.567
6.207
1.135
£604
1.598
4.093
4.526
1.178
0.741
1.915
0.828
1.248
325.207
133.162
10.686
103.016
217.866
111.332
209.210
-38.030
21.908
103.695
-103.79
60.633
183.639
893.560
10.890
7.078
14.794
(Continued)
194
-------
Table E-1. Continued
Subsurvey-Lab
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-PL
Summer-PL
Concentration
ranges
< 0.05
0.1 -0.5
> 0.5
All data
<0.05
0.05 - 0.1
> 0.1
All data
< 0.05
0.1 -0.5
All data
< 0.05
0.05 - 0.1
>0.1
All data
<0.05
0.05 - 0.1
0.1 - 0.5
> 0.5
All data
< 0.005
0.005- 0.01
> 0.01
All data
< 0.005
0.005- 0.01
>0.01
All data
< 0.005
0.005- 0.01
> 0.01
All data
< 0.005
0.005- 0.01
>0.01
All data
< 0.005
0.005- 0.01
> 0.01
All data
< 6
6-7
> 7
All data
< 6
6-7
> 7
All data
Number of
pairs
NO,- (mfl/L)
8
10
11
29
1
4
1
6
10
1
11
11
4
2
17
11
5
8
2
26
P-total (mo/L)
8
6
15
29
1
4
1
6
4
3
4
11
5
7
5
17
6
7
13
26
pH-closed (pH units)
17
10
2
29
5
8
4
17
Grand
mean
-0.018
0.222
0.984
0.445
0.035
0.064
0.204
0.083
0.016
0.461
0.056
0.017
0.064
0.332
0.065
0.015
0.072
0.227
0.852
0.155
0.003
0.008
0.017
0.011
0.004
0.008
0.019
0.009
0.003
0.007
0.021
0.011
0.003
0.008
0.020
0.010
0.003 .
0.006
0.016
0.010
5.459
6.493
7.132
5.931
5.293
6.584
7.112
6.329
Pooled
s,
0.001
0.045
0.022
0.030
0.020
0.034
0.017
0.029
0.006
0.000
0.006
0.008
0.034
0.012
0.018
0.016
0.017
0.008
0.044
0.018
0.001
0.002
0.009
0.006
0.000
0.004
0.003
0.003
0.001
0.000
0.002
0.001
0.001
0.003
0.002
0.002
0.001
0.002
0.004
0.003
0.028
0.027
0.027
0.028
0.082
0.124
0.043
0.098
%RSD,
-4.485
20.415
2247
6.724
55.602
52.542
8.246
35.640
38.624
0.015
10.273
47.287
52.542
3.580
27.639
106.990
24.126
3.339
5.151
11.625
44223
31.503
50.787
56.567
10.870
46.735
17.631
36.588
32.051
3.371
8.686
11.731
27.940
36.716
10.752
21.892
28.953
27.485
23.516
27287
NA
NA
NA
NA
NA
NA
NA
NA
(Continued)
195
-------
Table E-1. Continued
Subsuvay-Lab
Fall-PL
Spring-1
Summer-1
Summer-2
Summer-1+2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1 +2
Fall-2
Spring-1
Concentration Number of
ranges pairs
pH-closed (pH
< 6
6-7
>7
All data
pH-BNC
<6
6-7
>7
All data
<6
6-7
All data
<6
6-7
>7
All data
< 6
6-7
>7
All data
<6
6-7
>7
All data
pH-ANC
< 6
6-7
>7
All data
< 6
6-7
All data
< 6
6-7
> 7
All data
< 6
6-7
> 7
All data
< 6
6-7
> 7
All data
pH-eq
< 6
6-7
> 7
All data
Grand
mean
Pooled
sp
%RSDp
unlts)(contlnued)
14
9
3
26
(pH units)
12
15
2
29
4
2
6
1
3
7
11
5
5
7
17
11
9
6
26
(pH units)
12
15
2
29
4
2
6
1
5
5
11
5
7
5
17
12
9
5
26
(pH units)
11
12
6
29
5.312
6.698
7.255
6.016
5.423
6.485
7.075
6.086
5.160
6.565
5.628
5.840
6.558
7.228
6.919
5.296
6.561
7.228
6.464
5.254
6.535
7.199
6.146
5.392
6.454
7.072
6.057
5.157
6.587
5.634
5.815
6.699
7.235
6.862
5.289
6.667
7.235
6.429
5.255
6.591
7.195
6.091
5.377
6.582
7.296
6.273
0.024
0.019
0.018
0.022
0.071
0.083
0.061
0.077
0.023
0.035
0.028
0.113
0.036
0.107
0.094
0.055
0.036
0.107
0.077
0.059
0.224
0.029
0.138
0.065
0.072
0.036
0.068
0.042
0.045
0.043
0.092
0.040
0.122
0.091
0.056
0.041
0.122
0.077
0.042
0.225
0.030
0.136
0.067
0.061
0.141
0.086
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
(Continued)
196
-------
Table E-1. Continued
Subsurvey-Lab
Summer-1
Summer-2
Summer-1-1-2
Fall-2
Spring-1
Summer-1
Summer-2
Summer-1-1-2
Fall-2
Spring-1
Summer-1
Summer-2
Concentration
ranges
pH-eq
< 6
6 - 7
>7
All data
6-7
>7
All data
<6
6-7
>7
All data
< 6
6-7
> 7
All data
<0.5
0.5-2
2-5
> 5
All data
< 0.5
0.5-2
2-5
All data
<0.5
0.5-2
2-5
All data
< 0.5
0.5-2
2-5
All data
< 0.5
0.5-2
2-5
> 5
All data
< 5
5-7
> 7
All data
< 5
5-7
All data
< 5
5-7
> 7
All data
Number of
pairs
Grand
mean
Pooled
s,
%RSD,
(pH unlt*)(contlnued)
3
2
1
6
4
7
11
3
6
8
17
11
7
8
26
SIO, (mg/L)
4
3
15
7
29
2
3
1
6
2
4
5
11
4
7
6
17
4
9
10
3
26
SO.1- (mfl/L)
9
15
5
29
2
4
6
6
2
3
11
4.882
6.362
7.155
5.754
6.656
7.535
7.215
4.882
6.558
7.487
6.700
5.207
6.668
7.307
6.247
0.032
1.185
3.516
5.857
3.359
0.232
1.713
2.777
1.397
0.155
1.212
3.306
1.972
0.193
1.427
3.217
1.769
0.361
0.835
3.012
6.134
2.211
3.441
5.947
10.270
5.915
4.767
5.826
5.473
4.022
6.447
8.518
5.689
0.052
0.067
0.276
0.125
0.047
0.157
0.128
0.052
0.055
0.176
0.127
0.061
0.106
0.347
0.204
0.035
0.087
0.251
0.180
0.203
0.011
1.212
0.058
0.858
0.017
0.040
0.048
0.041
0.014
0.794
0.050
0.511
0.019
0.012
0.034
0.119
0.047
0.197
0.069
0.320
0.179
0.162
0.069
0.109
0.070
0.067
0.186
0.114
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
108.786
7.300
7.137
3.081
6.055
4.712
70.751
2.088
61.395
11.137
3.267
1.453
2.075
7.469
55.653
1.549
28.865
5.367
1.422
1.131
1.933
2.110
5.721
1.168
3.115
3.033
3.388
1.183
1.990
1.750
1.043
2.178
1.997
(Continued)
197
-------
Table E-1. Continued
Subsurvey-Lab
Summer-1+2
Fall-2
Spring-PL
Sunifn6r*PL
Fall-PL
Spring-PL
Summer-PL
Fall-PL
Concentration
ranges
<5
5-7
> 7
All data
<5
5-7
>7
All data
< 15
15-30
>30
All data
< 15
15-30
>30
All data
< 15
15-30
> 30
All data
< 1
> 1
All data
< 1
> 1
All data
< 1
> 1
All data
Number of
pairs
SO4*- (mg/L)(contlnued)
8
6
3
17
10
13
3
26
True color (PCU)
11
9
9
29
4
4
9
17
6
6
14
26
Turbidity (NTU)
24
5
29
12
5
17
16
10
26
Grand
mean
4.208
6.033
8.518
5.613
3.378
5.760
8.932
5.210
8.409
20.000
40.833
22.069
11.250
18.125
43.889
30.147
7.917
19.583
53.571
35.192
0.569
1.310
0.697
0.350
3.730
1.344
0.531
1.580
0.935
Pooled
S,
0.101
0.068
0.186
0.112
0.083
0.136
0.176
0.124
1.846
1.667
1.841
1.791
3.536
3.062
2.357
2.844
1.443
2.500
1.890
1.961
0.038
0.332
0.142
0.029
0.055
0.038
0.035
0.045
0.039
%RSDp
2.404
1.133
£178
1.995
£446
2.370
1.965
2.389
21.957
8.333
4.508
8.114
31.427
16.893
5.370
9.434
18.232
12.766
3.528
5.573
6.693
25.366
20.409
8.248
1.468
2.853
6.655
2.830
4.197
198
-------
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AL-org
SPRING
50
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50
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M
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SUMMER
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FALL
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0.12
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Figure E-4. Non*xchang«abl« monomarle aluminum (Al-org), plot of routine-duplicate meant and % RSD.
202
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SUMMER
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FALL
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Mean (mg/L)
Figure E-7. Calcium (Ca), plot of routine-duplicate meant and % RSD.
205
10
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FALL
S°
0.0
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1.5
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Figure E-21. Nitrate (NO,-), plot of routine-duplicate meant and % RSD.
219
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Figure E-23. pH, Initial for acid titratlon for ANC (pH-ANC), plot of routlnc-duplleatt meant and ttd. d«v.
221
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FALL
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Figure E-24. pH, Initial for aeld titratlon for BNC (pH-BNC), plot of routlm-dupllcau nwana and atd. d«v.
222
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Flgur* E-28. pH-«qulllbrat«d (pH-«q), plot of routlnv-dupllcat* m*ana and »td. d»v.
224
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S
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227
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Appendix F
Split Sample Plots for the
Interlaboratory Bias Study
Data from the Interlaboratory Bias experiment conducted as part of the Summer Seasonal
subsurvey are presented in figures E-1 through E-23. Analytical results from Laboratory 1 are
plotted against analytical results from Laboratory 2. All analytes measured in the analytical
laboratories were plotted except for Al-ext which was not included in the Interlaboratory Bias study.
Twenty-six samples are represented in each plot.
The diagonal line in the plots represents a one-to-one relationship in which a Laboratory 1
result equals the Laboratory 2 result. Regression statistics are presented in the caption of each
figure.
229
-------
LABORATORY 1 Cl(m«(L)
"5. —
?
o p n g1 s » a
w o* o ^1 .~ ^ 2
QO I . ^ ^^ "^
•°!S
2*
u\
n
e
a>>
-ig <»
rr8=-
M™
ta M
><
a *°
S- Q
2
99
LABORATORY 1 BNC (ueqflL)
8 & S 9 § i
LABORATORY 1 Al-total (mg/L)
?»
T W
i£i
M H «-o. s. 6 »
O* o » r*. » CT ?0 5 •
fi
w
i?1
s
•ff
>o.£ = S.-2 .
3 J~aC ^
9
|
E
o o
21. -fftl!
- n
*;ji
s 8« o
i rf §
LABORATORY 1 Cowl, (\iSKrri)
8 S S 8 §
^5 "• -~ S S-
o - a- 2. 8
• It M ._, l»
o a-o.
S! ?-
^
LABORATORY 1
5 O M
* O-
T3
*•
1aEM
•^118
I"
a *
" -a -
si o
^ LABORATORY 1 ANC(ueq^)
f J o i i I i 1
-------
01234S«?«
LABORATORY! DIC-equil.(mg/L)
Figure F-7. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= 0.227 + 1.499(Lab 2);
R2 = 0.978; 95% Confidence Interval
for Slope = 1.403 - 1.596
0 2 4 * «
LABORATORY 2 DIC-initial (mg/L)
Figure F-8. Results of the analysis of the split
samples, Summer Seasonal,
Eastern Lake Survey-Phase II:
Lab 1= 0.421 + O01(Lab 2);
R2 = 0.796; 95% Confidence
Interval for Slope = 1.017 - 1.586
10
0.300
I
0.200-
0.100
0.000
0 2 4 6 8 10
LABORATORY 2 DOC (mg/L)
Figure F-9. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= 0.474 + 0.874(Lab 2);
R2 = 0.808; 95% Confidence Interval
for Slope = 0.691 - 1.058
0.1 03
LABORATORY 2 F-tolal (mg/L)
0.3
Figure F-10. Results of the analysis of the split
samples, Summer Seasonal,
Eastern Lake Survey-Phase II:
Lab 1= 0.004 + 0.99(Lab 2);
R2 = 0.914; 95% Confidence
Interval for Slope = 0.862 - 1.118
0.2 0.4 0.6 0.6
LABORATORY 2 Fe(mg/L)
Figure F-ll. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= -0.013 +• 1.355(Lab 2);
R2 = 0.969; 95% Confidence Interval
for Slope = 1.253 - 1.457
2.0-
1.0-
0.0
0.0
1.0 2JD
LABORATORY 2 K(mg/L)
Figure F-12. Results of the analysis of the split
samples, Summer Seasonal,
Eastern Lake Survey-Phase II:
Lab 1= 0.019 + 0.962(Lab 2);
R2 = 0.997; 95% Confidence
Interval for Slope = 0.94 - 0.983
231
-------
3
" t
3 t8
•" S
LABORATORY 1 NO3(ti«/L)
10
LABORATORY 1 Na(mg/L)
3
3
71
Nt
u
» I
8 i
LABORATORY 1 Mg(mg/L)
o
b
M
b
W
b
b'
•<
»>> i
w;pr-
*f- <* ,*_—•
i=.
Kta.
5b
8|SS8fr
IE
8.H
8*'
M'
ft
3
no
LABORATORY 1 P-totol (mg/L)
O O O
2 S 8
LABORATORY 1 NH4(mg/L)
ppoooooo
S2g5gg5§
LABORATORY 1 Mn(mj/L>
o O O O
. I , I Og I0. I . I . I .
a a
-------
4 8 « 7 •
LABORATORY 2 pH-acidily (pH units)
Figure F-19. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= -0.067 + 1.017(Lab 2);
R2 = 0.983; 95% Confidence Interval
for Slope = 0.958 - 1.075
LABORATORY 2 pH-alkalinity (pH units)
Figure F-20. Results of the analysis of the split
samples, Summer Seasonal,
Eastern Lake Survey-Phase II:
Lab 1= 0.112 •(• 0.987(Lab 2);
R2 = 0.988; 95% Confidence
Interval for Slope = 0.941 - 1.033
I •
a
£ H
* .-
4 $ « 1 «
LABORATORY 2 pH-equil. (pH units)
Figure F-21. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= 0.722 + 0.892(Lab 2);
R2 = 0.839; 95% Confidence Interval
for Slope = 0.723 - 1.06
o i a » * s
LABORATORY 2 SiO2(mg/L)
Figure F-22.Results of the analysis of the split
samples, Summer Seasonal,
Eastern Lake Survey-Phase II:
Lab 1= -0.04 + 0.971(Lab 2);
R2 = 0.971; 95% Confidence
Interval for Slope = 0.901 - 1.042
20
0 10 20
LABORATORY 2 SO4(mg/L)
Figure F-23. Results of the analysis of the split
samples, Summer Seasonal, Eastern
Lake Survey-Phase II:
Lab 1= 0358 + 0.896(Lab 2);
R2 = 0.955; 95% Confidence Interval
for Slope = 0.815 - 0.978
233
-------
Glossary
absolute bias
accuracy
acid-neutralizing
capacity (ANC)
acid titrant
air equilibration
aliquot
alkalinity balance
checks
analyte
analytical laboratory
anion
anion-cation balance
The difference between a measured value and the true value.
•accuracy.")
The closeness of a measured value to the true value of an analyte.
(See
Total acid-combining capacity of a water sample determined by titration
with a strong acid. Acid-neutralizing capacity includes alkalinity (carbon-
ate species) as well as other basic species (e.g., berates, dissociated
organic acids, alumino-hydroxy complexes).
Standardized acidic reagent added to a water sample by titration for the
purpose of determining the acid neutralizing capacity of the water.
The process of bringing a sample aliquot to equilibrium with standard air
(300 ppm COj) before analysis; used with some pH and dissolved inorganic
carbon measurements.
Fraction of a sample prepared for the analysis of particular constituents;
sent in a separate container to the analytical laboratory.
See protolyte analysis program.
A chemical species that is measured in a water sample.
In this report, a laboratory under contract with the U. S. Environmental
Protection Agency to analyze water samples shipped from the processing
laboratory.
A negatively charged ion.
In an electrically neutral solution such as water, the total charge of
positive ions (cations) equals the total charge of negative ions (anions).
In this report, anion-cation balance is expressed as percent ion balance
difference and is calculated as follows:
100
Z anions -1 cations + ANC
I anions + Z cations + ANC + 2[H+]
where:
I anions - [Crj + [P] -I- [NOg] + [SO,*],
Z cations - [Na+] + [K+] + [Ca2+] + [Mg2+] + [NH/],
ANC - alkalinity (the ANC value is included in the calculation to
account for the organic tons.
[H+] = (10"pH) x 10a peq/L
234
-------
Glossary (Continued)
ASTMTypel
reagent-grade
water
audit sample
base site
base-neutralizing
capacity (BNC)
batch
batch ID
bias
blank sample
CADAVERS
calculated alkalinity
calculated
conductivity
calculated carbonate
alkalinity
calibration blank
sample
calibration curve
cation
charge balance
closed system
Deionized water that meets American Society for Testing and Materials
(ASTM) specifications for Type I reagent-grade water (ASTM, 1984) and
that has a measured conductance of less than 1 pS/cm at 25 °C. This
water is used to prepare blank samples and reagents.
In this survey, a standardized water sample submitted to an analytical
laboratory for the purpose of checking overall performance in sample
analysis. Natural audit samples in the ELS-II were lake water; synthetic
audit samples were prepared by diluting concentrates of known chemical
composition in ASTM Type I reagent grade water.
A location providing support for sampling personnel during field sampling
operations.
Total base-combining capacity of a water sample due to dissolved CO*
hydronium, and hydroxide; determined by titration with a strong base.
A group of samples processed and analyzed together. A batch consists
of all samples (including quality assurance and quality control samples)
that are assigned a unique batch identification number and that are
processed and sent to one analytical laboratory in one day. In the ELS-
II, a batch did not exceed 30 samples.
The numeric identifier for each batch.
The systematic difference between values or sets of values.
A sample of ASTM Type I reagent-grade water analyzed as a quality
assurance or quality control sample during the ELS-II (see calibration,
reagent, processing laboratory, and field blanks).
See Computer Aided Data Verification System.
The estimate of alkalinity based on the protolyte analysis program.
The sum (as microsiemens per centimeter) of the theoretical specific
conductances of all measured ions in a sample.
See calculated alkalinity.
A sample of ASTM Type I reagent-grade water defined as a 0 mg/L
standard used in standardizing or checking the calibration of analytical
instruments; also used to determine instrument detection limits.
The linear regression of the analytical instrument response to a set of
calibration standards (varying in concentrations) from which the linear
dynamic range is determined.
positively charged ion.
See anion-cation balance.
Method of measurement in which a water sample is collected and ana-
lyzed for pH and dissolved inorganic carbon without exposure to the
atmosphere. These samples were collected in syringes directly from the
sampling apparatus and were analyzed in the processing laboratory.
235
-------
Glossary (Continued)
comparability
completeness
Computer Aided
Verification
System
conductivity
balance
confidence interval
(95% and 99%)
confidence limits
contract-required
detection limit
control limits
Cubitainer
data base
data package
A measure of data quality that allows the similarity within and among data
sets to be established confidently.
A measure of data quality that is the quantity of acceptable data actually
collected relative to the total quantity that was expected to be collected.
A series of data checking programs used by the QA staff to assess data
for reasonableness based upon preset criteria for precision, accuracy,
detectability, internal consistency of the sample and other chemical
relationships, holding times, etc. The system also provides a means for
tracking changes made to the data base.
A comparison of the measured conductance of a water sample (in
microsiemens per centimeter) to the equivalent conductances (in micro-
Siemens per centimeter) of each ion measured in that water sample at
infinite dilution. In this report, conductance balance is expressed as
percent conductance difference and is calculated as follows:
calculated conductivity - measured conductivity
measured conductivity
100
The ions used to calculate conductance are Ca, Cr, CO,9', H+, HCO3', K, Mg,
Na, NO,', OH", NH/, and SO4*.
The range of values, calculated from an estimate of the mean and
deviation, between the confidence limits. This interval has a high proba-
bility (a 95 or 99 percent level of confidence) of containing the true
population value.
Two statistically derived values or points, one below and one above a
statistic, that provide a given degree of confidence that a population
parameter falls between them.
The data quality objective criterion for analytical laboratory detectability for
specified analytes.
Two values between which the analytical measurement of a quality
assurance or quality control sample is expected to be; measurements
outside these limits are suspect.
A 3.8-L container made of semirigid polyethylene used to transport field
samples (routine, duplicate, blank) from the lake site to the processing
laboratory.
All computerized results of the survey, which include the raw, verified,
validated, and enhanced data sets as well as back-up and historical data
sets.
A report, generated by an analytical laboratory for each batch of samples
analyzed, that includes analytical results, acid-neutralizing capacity titratton
data, ion chromatography specifications, analysis dates, calibration and
reagent blank data, quality control check sample results, and analytical
laboratory duplicate results.
236
-------
Glossary (Continued)
data qualifier
data quality
objectives
Data Base 1
Data Base 2
Data Base 3
Data Base 4
detectability
detection limit
quality control
check sample
dissolved inorganic
carbon (DIG)
dissolved organic
carbon (DOC)
dissolved oxygen
(DO)
double-blind
enhanced data set
epilimnetic routine
sample
equivalent
exception
exception program
Annotation applied to a field or analytical measurement related to possi-
ble effects of the quality of the datum. (See definitions for "flag* and
tag.")
Accuracy, detectability, and precision limits established before a sampling
and analytical effort. Also includes comparability, completeness, and
representativeness.
Set of files containing raw data. (See definition for "raw data base.")
Set of files containing verified data. (See definition for "verified data
base.")
Set of files containing validated data. (See definition for "validated data
base.')
Set of files containing final, enhanced lake data. (See definition for
"enhanced data base.')
The capability of an instrument or method to determine a measured value
for an analyte above either zero or background levels.
A quality control check sample that has a specified theoretical concentra-
tion and that is designed to check instrument calibration at the low end of
the linear dynamic range.
A measure of the dissolved carbon dioxide, carbonic acid, bicarbonate and
carbonate anions that constitute the major part of ANC in a lake.
In a water sample, the organic fraction of carbon that is dissolved or
unfilterabte (for this report, the fraction that will pass a filter of 0.45-^m
pore size).
In a water sample, a measure of the dissolved oxygen. For this report, the
measurement was taken in the lake water column (in situ)
Term used for a QA sample for which the analyst does not know either the
source (e.g., type) or the expected concentration.
Data Base 4. Missing values or errors in the validated data set are replace
by substitution values; duplicate values are averaged; negative values
(except for ANC and BNC) are set to zero. Values for field blank, field
duplicate, and performance audit samples are not included in this data set.
A routine lake sample collected in the Summer Seasonal subsurvey at 1.5m
from the surface at the Fall Index Site.
Unit of ionic charge; the quantity of a substance that either gains or loses
one mole of protons or electrons.
An analytical result that does not meet the expected QA or QC criteria and
for which a data flag is generated.
A computer program in CADAVERS that identifies or flags analytical results
classified as exceptions.
237
-------
Glossary (Continued)
extractable aluminum
Fall Seasonal
Fall Variability Study
field audit sample
field blank sample
field duplicate
sample
field duplicate
pair
field natural
audit sample
field synthetic
audit sample
flag
Gran analysis
history file
holding time
Operationally defined aluminum fraction that is extracted by the procedure
used during the ELS-II; this measurement is intended to provide an
indication of the concentration of the aluminum species that may be
available in a form toxic to fish.
See seasonal subsurvey.
A special study conducted in the fall Seasonal subsurvey designed to
estimate the effect of sampling variability of the lake chemistry at the Fall
Index Site.
A standardized water sample submitted to field laboratories to check
overall performance in sample analysis by field laboratories and by
analytical laboratories. Field natural audit samples were lake water; field
synthetic audit samples were prepared by diluting concentrates of known
chemical composition into ASTM Type I reagent-grade water.
A sample prepared at the processing laboratory consisting of ASTM Type
I reagent-grade water and transported to the lake site by the field sam-
pling crews. At the lake site, the blank was processed through the
sampling apparatus. These samples were analyzed at the processing
laboratory (except for pH and DIG) and analytical laboratories and were
employed in the calculation of system decision and system detection limits
and instrument detection limits.
The second sample of lake water collected by the sampling crew at the
same location and depth at the lake site immediately after the routine
sample, in accordance with standardized protocols.
A routine lake water sample and a second sample (field duplicate sample)
collected from the same lake, by the same sampling crew, during the same
visit, and according to the same procedure.
See field audit sample.
See field audit sample.
Qualifier of a data point that did not meet established acceptance criteria
or that was unusual. Flags were assigned during the verification and
validation procedures.
A mathematical procedure used to identify the equivalence point or points
of the titration of a carbonate system and subsequently for ANC and BNC
of that system.
An electronic documentation program in CADAVERS that tracks all the
numeric and character changes form the raw data set to the verified data
set.
(1) In the processing laboratory, the elapsed time between sample
collection and sample preservation. (2) In the analytical laboratory, the
elapsed time between sample processing in the processing laboratory
and final sample analysis or reanalysis.
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in-situ
initial dissolved
inorganic carbon
(DIG)
instrument
detection limit
interlaboratory
bias
ionic strength
lake ID
linear dynamic
range
matrix
matrix spike
maximum required
holding time
measured ANC
member M01
method blank
mobile processing
laboratory
modified verified
data base
Glossary (Continued)
For this survey, any measurements made within the water column of a
lake.
A measurement of dissolved inorganic carbon made on an aliquot
immediately before it is titrated for ANC.
For each chemical variable, a value calculated from laboratory calibration
blank, reagent blank, or field blank samples that indicates the minimum
concentration reliably detectable by the instrument(s) used; calculated as
three times the standard deviation of 10 or more nonconsecutive (i.e., from
different calibrations) blank sample analyses.
Systematic differences in performance between laboratories estimated
from analysis of the same type of samples.
A measure of the interionic effect resulting from the electrical attraction
and repulsion between different ions. In very dilute solutions, ions behave
independently, and the ionic strength can be recalculated from the
measured concentrations of anions and cations present in the solution.
An identification code, assigned to each lake in the survey, which indicates
subregion, alkalinity characteristics, and map coordinates.
The range of analyte concentration for which the calibration curve is best
fitted by a straight line.
The physical and chemical composition of a sample being analyzed.
A QC sample, analyzed at an analytical laboratory, that was prepared by
adding a known concentration of analyte to a sample. Matrix spike
samples can be used to determine possible chemical interferences within
a sample that might affect the analytical result.
The date by which a sample was contractually to be analyzed by the
analytical laboratory.
The ANC result determined in the analytical laboratory with an acid
titration.
A member containing a merge of forms 1,2, and 11. Form 1 is all the lake
data information collected by the sampling crew; Form 2 is all the analyti-
cal, QC, and sample batching data generated by the processing labora-
tory, and Form 11 is only the results of the field sample analysis performed
by the analytical laboratory.
See calibration blank.
See processing laboratory.
The data base created after the Special Data Assessment based upon
changes to the official verified data base.
natural audit sample See audit sample.
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Glossary (Continued)
nephelometric
turbidity unit
official verified
data base
on-site evaluation
open system
outlier
outlier data base
percent conductance
difference calculation
percent ion balance
difference calculation
percent relative
standard deviation
(%RSD)
performance audit
sample
PH
pH-ANC
pH-BNC
platinum cobalt
unit (PCU)
population estimate
precision
A measure of light scatter by a solution of suspended materials detected
at an angle of 90 degrees to an incident light source.
The data base created as a result of data verification based upon changes
to the official verified data base.
A formal on-site review of field sampling, field laboratory, or analytical
laboratory activities to verify that standardized protocols are being
followed.
A measurement of pH or dissolved inorganic carbon obtained from a
sample collected in a beaker and exposed to the atmosphere during
collection, processing, and preparation before measurement.
Observation not typical of the population from which the sample is drawn.
A data set generated as result of the Special Data Assessment.
The 95th percentile of a distribution of blank sample measurements.
A QA procedure used to check that the measured specific conductance
does not differ significantly (outside the acceptance criteria) from the
calculated specific conductance value.
QA procedure used to check that the sum of the anion equivalents equals
the sum of the cation equivalents (see anion-cation balance).
The standard deviation divided by the mean, multiplied by 100, expressed
as percent (sometimes referred to as the coefficient of variation).
See audit sample.
The negative logarithm of the hydrogen-ion activity. The pH scale runs
from 1 (most acidic) to 14 (most alkaline); the difference of 1 pH unit
indicates a tenfold change in hydrogen-ion activity.
A measurement of pH made in the analytical laboratory immediately before
the ANC titration procedure and before the potassium chloride spike has
been added.
A measurement of pH made in the analytical laboratory immediately before
the BNC titration procedure and before the potassium chloride spike has
been added.
A measure of the color of a water sample defined by a potassium
hexachloroplatinate and cobalt chloride standard color series.
A statistical estimate of the number of lakes (target lakes) that have a
particular set of chemical characteristics (i.e., alkalinity class within a
subregion) extrapolated from the number of lakes sampled (probability
sample).
A measure of the capability of a method to provide reproducible measure-
ments of a particular analyte.
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Glossary (Continued)
processing
laboratory
processing
laboratory blank
protolyte
protolyte analysis
program
QC chart
quality assurance
(QA)
quality assurance
sample
quality control
(QC)
quality control
check sample
(QCCS)
quality control
sample
rain water audit
raw data set
reagent
reagent blank
sample
representativeness
required detection
limit
The laboratory that processed samples and measured selected variables.
The ELS-II processing laboratory was located in Las Vegas, Nevada.
An ASTM Type I reagent-grade water sample prepared and processed at
the processing laboratory but analyzed at an analytical laboratory.
That portion of a molecule that reacts with either H+ or OH" in solution.
An exception-generating computer program of CADAVERS that evaluates
in situ, processing laboratory, and analytical laboratory measurements of
pH, QIC, ANC, BNC, and DOC in light of known characteristics of carbon-
ate equilibria.
A graphical plot of test results with respect to time or sequence of
measurement, together with limits within which the results are expected to
lie when the system is in a state of statistical control (Taylor, 1987).
Steps taken to ensure that a study is adequately planned and implemented
to provide data of the highest quality and that adequate information is
provided to determine the quality of the data base resulting from the study.
A sample (other than the routine lake sample) that is analyzed in the
analytical laboratory and that has an origin and composition unknown to
the analyst.
Steps taken during sample collection, processing, and analysis to ensure
that the data quality meets the minimum standards established by the QA
plan.
A sample of known concentration used to verify continued calibration of an
instrument.
Any sample used by the analyst to check immediate instrument calibration
or response; the measurement obtained from a quality control sample is
expected to fall within specific acceptance criteria or control limits.
Standard reference material composed of reverse osmosis water spiked
with various chemical constitutes to simulate rain water.
Data Set 1. The initial data set that received a cursory review to confirm
that data are provided in proper format and are complete and legible.
A substance (because of its chemical reactivity) added to water to
determine the concentration of a specific analyte.
A laboratory blank sample that contained all the reagents required to
prepare a sample for analysis.
The degree to which sample data accurately and precisely reflect the
characteristics of a population.
For each chemical variable, the highest instrument detection limit based on
analyses of laboratory blanks or detection limit check standards allowable
in the analytical laboratory contract.
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Glossary (Continued)
round robin
routine sample
sample ID
sampling crew
SAS
seasonal subsurvey
Special Data
Assessment
specific conductance
spike
split sample
Spring Seasonal
standard deviation
standard operating
statistical
(significant)
difference
subregion
Summer Seasonal
synthetic audit
A method of determining relative laboratory performance with multiple
laboratories measuring samples from the same audit lot.
The first lake sample collected at a site in accordance with standardized
protocols.
The numeric identifier given to each lake sample and to each QA sample
in each batch.
A team of lake sampling personnel who gained access to the lake site on
foot or by vehicle.
Statistical Analysis System, Inc. A statistical data file manipulation
package that has data management, statistical, and graphical analysis
abilities. The ELS-II data base was developed and analyzed primarily
using SAS software and is distributed in SAS format.
One of three (spring, summer, fall) major lake sampling and analytical
components of ELS-II designed to assess the temporal variability of lake
chemistry.
An intensive evaluation of the ELS-II data base that took place after the
creation of the official verified data set.
A measure of the electrical conductance (the reciprocal of the electrical
resistance) or total ionic strength of a water sample expressed as
microsiemens per centimeter at 25 °C.
A known concentration of an analyte introduced into a sample or aliquot.
A replicate portion or subsampte of a total sample obtained in such a
manner that it is not believed to differ significantly from other portions of
the same sample (Taylor, 1987).
See seasonal subsurvey.
The square root of the variance of a given statistic, calculated by the
equation:
standard deviation = [Z(x - X)2/(n - 1)]*
A detailed method that is documented in a step-by-step fashion to ensure
consistency in performance by any operating personnel.
A conclusion based on a stated probability that two sets of measurements
did not come from the same population of measurements.
For this report, geographic portions of Region 1 (northeastern U.S.) denoted
as subregions 1A (Adirondacks), 1B (Poconos/Catskills), 1C (Central New
England), 1D (Southern New England), and 1E (Maine).
See seasonal subsurvey.
See audit sample.
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Glossary (Continued)
synthetic audit
sample
system audit
system blank
system decision
limit
systematic error
tag
target population
target lake
titration data
transaction files
true color
turbidity
validated data base
validation
validation outliers
verification
verified data base
windows
See audit sample.
See on-site evaluation.
See field blank sample.
For each chemical variable except pH, a value that reliably indicates a
concentration above background, estimated as either the 95th percentite
(PJ or as 1.65 times the standard deviation of the field blank sample
measurements.
A consistent difference introduced in the measuring process. Such
differences commonly result in biased estimations.
Code on a data point that is added at the time of sample collection or
analysis to qualify the datum.
In this survey, the lake population of interest that was sampled. This
population was defined by the sampling protocol.
A lake of interest in the target population.
Individual data points from the Gran analysis of ANC and BNC.
Individual electronic numeric or character changes to the working data set.
The color of water that has been filtered or centrifuged to remove parti-
cles that may impart an apparent color; true color ranges from clear blue
to blackish-brown.
A measure of light scattering by suspended particles in an unf iltered water
sample.
Data Base 3. Final product of the validation process in which sample data
are examined in the context of a subregional set of samples, rather than
at the batch and sample level. Outliers are identified and flagged. Data
for field blank, field duplicate, and performance audit samples are included
in this data set.
Process by which data are evaluated for quality with reference to the
intended data use; includes identification of outliers and evaluation of
potential systematic error after data verification.
Data considered suspicious based on data validation.
Process of ascertaining the quality of the data in accordance with the
minimum standards established by the QA plan.
Data Base 2. Final product of the verification process in which each
sample batch and each sample value has been reviewed individually and
all questionable values are corrected or identified with an appropriate flag.
Statistically calculated limits based upon the distribution of a set of data.
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Glossary (Continued)
within-batch The estimate of precision ejected in the analysis of samples in a batch
precision by the same laboratory on any single day. In this report, overall within-
batch precision includes the effects of sample collection, processing, and
analysis; analytical within-batch precision includes the effects of sample
analysis within the analytical laboratories.
working data set The data set containing the most updated changes in the history file prior
to the creation of the verified data set.
X flag A special data qualifier alerting the data user that the value qualified has
been verified but is most likely erroneous or has no documentation or
whose integrity was otherwise violated.
U.S, GOVERNMENT PRINTING OWCE 1989 -6'8-OK) — 000'1 3
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