United States
Environmental Protection
Agency
Environmental Monitoring
Systems Laboratory
Las Vegas NV 89193-3478
Research and Development
EPA/600/S4-89/037 June 1990
Project Summary
Direct/Delayed Response
Project: Quality Assurance
Report for Physical and
Chemical Analyses of Soils
from the Northeastern United
States
G. E. Byers, R. D. Van Remortel, J. E. Teberg, M. J. Miah, C. J. Palmer, M.
L. Papp, W. H. Cole, A. D. Tansey, D. L. Cassell, and P. W. Shaffer
The Northeastern soil survey was
conducted during 1985 as a synoptic
physical and chemical survey to
characterize a statistical sampling of
watersheds in a region of the United
States believed to be susceptible to
the effects of acidic deposition. This
document addresses the
implementation of a quality
assurance program and the
verification of the analytical data
base for the Northeastern Soil
Survey. It is focused primarily
towards the users of the data base
who will be analyzing the data and
making various assessments and
conclusions relating to the effects of
acidic deposition on the soils of the
Northeastern region of the United
States. The quality assurance
program is evaluated in terms of its
success in identifying potential
problems that could have an effect
on the quality of the data. Verification
procedures used to analyze
laboratory data are described. Quality
is assessed by describing the
detectability, precision, accuracy
(interlaboratory differences), repre-
sentativeness, completeness, and
comparability of the data for the
quality assurance samples used
throughout the soil survey. Detection
limits and two-tiered precision data
quality objectives were established
for most of the parameters. A step-
function statistical approach was
used to assess precision.
This Project Summary was
developed by EPA's Environmental
Monitoring Systems Laboratory, Las
Vegas, NV, to announce key findings
of the research project that is fully
documented in a separate report of
the same title (see Project Report
ordering information at back).
Introduction
The U.S. Environmental Protection
Agency (EPA), as a participant in the
National Acid Precipitation Assessment
Program, has designed and implemented
a research program to predict the long-
term response of watersheds and surface
waters in the United States to acidic
deposition. Based on this research, a
sample of watershed systems will be
classified according to the time scale in
which each system will reach an acidic
steady state, assuming current levels of
acidic deposition. The Direct/Delayed
Response Project (DORP) was designed
as the terrestrial component of the EPA
Aquatic Effects Research Program.
The mapping for the DORP
Northeastern Soil Survey was conducted
during the Spring and Summer of 1985
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and the sampling took place during the
Summer and Fall of 1985. These
activities initiated the first full-scale
regional survey of the DDRP. The
physical and chemical properties that
were measured in the soil samples are
listed below.
Data Quality Objectives and
Assessment
The quality assurance (QA) program,
soil sampling techniques, sample
preparation, and analytical methods, were
designed to satisfy the data quality
objectives (DQOs) for field and analytical
data and to assess the variability of
sampling, preparation, and analytical
performance. The DQOs for this survey
were directed toward detectability,
precision, and completeness. Accuracy,
representativeness, and comparability of
the data were also assessed, although
specific DQOs were not imposed on
these attributes.
Detectability
The instrument detection limit is the
lowest value that an analytical instrument
can reliably detect above instrument
background concentrations and was
calculated for each analyte as three times
the standard deviation of a low level
quality control check sample, under the
assumption that its variability would
approximate the variability of an
analytical blank sample. The system
detection limit indicates the variability of
a blank sample resulting from sample
collection, processing, extraction, and
analysis. The system detection limit was
estimated for each parameter as the
variability in the ten percent of field
duplicates that had the lowest
concentration of the analyte of interest.
Contracts with the analytical laboratories
established maximum allowable
instrument detection limits, defined as
contract-required detection limits;
however, no DQOs were established for
system detectability.
Precision
Precision is the degree of scatter of
repeated measurements. Measurement
imprecision is distinct from the overall
variability in the population itself.
Determination of measurement
imprecision and its sources in the
Northeastern Soil Survey relied strongly
on the analyses of the QA samples and
was a function of the intralaboratory
within-batch precision DQOs defined in
the QA Plan. Overall variability
(measurement and population) was
estimated from the routine data. No
DQOs were established for the sampling
or preparation phases of the survey.
The precision DQOs were charac-
terized by use of a two-tiered system.
Below a specific concentration, called the
knot, precision is defined as a standard
deviation in absolute reporting units;
above the knot, precision is defined as a
percent relative standard deviation. In
order to address the issue of
concentration-dependent variance, the
range of soil analyte concentrations was
divided into appropriate intervals
(windows) within which the error variance
was relatively constant. A step function
was fitted across the windows to
represent the error variance for the entire
concentration range. Different step
functions were used to assess the
variability for each QA sample type. The
data uncertainty in the routine samples
due to collection error was also
measured.
Accuracy (Interlaboratory
Differences)
Accuracy is the ability of a
measurement system to approximate a
true value. Accuracy could not be
determined because the audit samples
used in the survey were natural soil
samples with chemical composition and
physical properties that were not known
with any confidence. Due to this lack of
acceptable values for accuracy
estimates, the data were assessed only
for interlaboratory differences.
Three types of comparisons made
were: (1) the use of a pair-wise statistical
test for significance between laboratories;
(2) the pooling of audit sample data for
each laboratory for direct interlaboratory
comparisons; and (3) the pooling of
laboratory data for each audit sample for
comparison of laboratory performance
among audit samples.
Representativeness
The representativeness objectives of
the survey were qualitative in nature. The
general objectives were that: (1) the soil
samples collected by the field crews be
representative of the soil sampling class
characteristics, (2) the samples be
homogenized and subsampled properly
by the preparation laboratory, and (3) the
QA duplicate samples adequately
represent the range of analyte
concentrations found in the routine
samples.
Completeness
The 100 percent completen
objectives of the survey were
determine whether (1) all ped
designated for sampling were acti
sampled by the field crews, (2)
samples received by the prepara
laboratories were prepared and analy.
and (3) all samples received by
analytical laboratories were analyzed
that 90 percent of the requi
measurements were made on
samples. Enough data were provide<
allow statistically significant conclus
to be drawn. Data qualifiers, or flags,
completeness were inserted in the <
base to indicate any missing values.
Comparability
Data comparability objectives w
qualitative in nature. The goal was
comparability of data from the
surveys within the DDRP and for
DDRP surveys to be comparable to o
similar programs. The stated objec
was the uniform use of known, accep
and documented procedures for
location, soil sample collection, sanr
preparation, extraction, analysis, ,
standard reporting units for
Northeastern Soil Survey. Unif(
QA/QC protocols and on-site inspect!
assured that these procedures w
implemented properly. The resull
analytical data should be comparable
those from other surveys.
Quality Assurance and Qualit
Control Samples
Quality assurance samples were u;
to independently assess data quality ,
monitor the internal quality control ((
procedures. The composition and ider
of the QA samples were unknown to
analyst. Three types of QA samples w
used in the Northeastern Soil Survey:
field duplicates (soil samples w
collected by each sampling crew fr
one horizon of one pedon each day ;
were placed randomly in the sam
batch with the other samples from
same pedon); (2) preparation duplicc
(one soil sample per sample batch \
selected and split by the preparal
laboratory and placed randomly in
sample batch); and (3) natural at
samples (one duplicate sample pair fr
a homogenized bulk soil samp
representing one of five soils typical
the eastern United States were pla<
randomly in the sample batch).
The composition of QC samples w
known and the analytical results fr
each laboratory were required to
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Physical and Chemical Properties Measured in the Direct/Delayed Response Project Northeastern Soil Survey
Air-dry Moisture Content
Specific Surface Area
Total Sand
Very Coarse Sand
Coarse Sand
Medium Sand
Fine Sand
Very Fine Sand
Total Silt
Coarse Silt
Fine Silt
Total Clay
pH in Deionized Water
pH in 0.002M CaCI2
pHin0.01MCaCI2
Ca in 1 .OM Ammonium Chloride
Mg in 1 .OM Ammonium Chloride
K in 1 .OM Ammonium Chloride
Na in 1 .OM Ammonium Chloride
Ca in 1 .OM Ammonium Acetate
Mg in 1 .OM Ammonium Acetate
K in 1 .OM Ammonium Acetate
Na in 1.0M Ammonium Acetate
CEC in 1 .OM Ammonium Chloride
CEC in 1 .OM Ammonium Acetate
Ex. Acidity by f.OM KCl
Ex. Acidity by BaCI2-TEA
Ext. Aluminum in 1 .OM KCl
Ca in 0.002M Calcium Chloride
Mg in 0.002M Calcium Chloride
K in 0.002M Calcium Chloride
Na in 0.002M Calcium Chloride
Fe in 0.002M Calcium Chloride
Al in 0.002M Calcium Chloride
Ext. Fe in Pyrophosphate
Ext. Al in Pyrophosphate
Ext. Fe in Ammonium Oxalate
Ext. Al in Ammonium Oxalate
Ext. Fe in Citrate Dithionite
Ext. Al in Citrate Dithionite
Ext. Sulfate in Deionized Water
Ext. Sulfate in Sodium Phosphate
Sulfate Isotherm 0 mg sulfur/L
Sulfate Isotherm 2 mg sulfur/L
Sulfate Isotherm 4 mg sulfur/L
Sulfate Isotherm 8 mg sulfur/L
Sulfate Isotherm 16 mg sulfur/L
Sulfate Isotherm 32 mg sulfur/L
Total Carbon
Total Nitrogen
Total Sulfur
compared with the accepted values as
the samples are analyzed. This
immediate feedback on the functioning of
the analytical system allowed analytical
and sample processing problems to be
resolved quickly, with the result that error
from that source was minimized. Six
types of QC samples were used in the
Northeastern Soil Survey: (1) calibration
blanks were used as a check for sample
contamination and for baseline drift in the
analytical instrument immediately after
calibration; (2) reagent blanks underwent
the same treatment as the routine
samples and served as a check for
reagent contamination; (3) QC check
samples contained the analyte of interest
in the mid-calibration range and served
as a check on the accuracy and
consistency of the calibration of the
instrument throughout the analysis of the
sample batch; (4) detection limit QC
check samples were low concentration
samples that eliminated the necessity of
determining the detection limit every day
and allowed accuracy to be determined
at the low end of the calibration range; (5)
matrix spikes were sample aliquots to
which known quantities of analyte are
added for determining the sample matrix
effect on analytical measurements; and
(6) analytical duplicates were splits of a
single sample and were used to estimate
analytical within-batch precision.
In addition to the use of QC samples
for quality control, two system audits, or
on-site evaluations were also conducted,
one immediately after award of the
contract to the laboratories and the
econd after sample analysis had begun.
Internal Consistency Checks
An internal consistency computer
program provided a meaningful check of
routine data by identifying values that
differed from the majority of observed
values and that might have gone
unnoticed had the check not been made.
The checks uncovered errors in data
entry and transcription as well as errors
that occurred on an analytical batch
basis. In this study, a correlation
approach was used to assess internal
consistency in which the coefficients of
determination were obtained by
performing weighted linear regressions.
From the regressions, studentized
residuals and DFFITS statistics were
calculated to identify extreme data values
that could be considered outliers.
Outliers determined by the computer
program and representing only about 1
percent of the data, were checked for
transcription errors. For a few analyses, a
significant number of outliers were
present. There were some parameters
that did not correlate well with any of the
other parameters.
Data Management
The field sampling and analytical data
were entered into the Northeastern
survey data bases at Oak Ridge National
Laboratory in Tennessee. Both data
bases progressed through three phases:
raw, verified, and validated. The QA staff
at the EPA Environmental Monitoring
Systems Laboratory at Las Vegas,
Nevada, verified the two data bases. The
field sampling data were entered into
data sets from specialized forms, visually
checked, and frozen as the official raw
data base. The analytical data were
entered into data sets and visually
checked, thereby allowing errors in
transcription to be identified corrected.
The verification stage was accomplished
by a systematic evaluation of
completeness and coding accuracy, and
flags were used in the data base to note
discrepancies. The verified data base
was used to assess data for this QA
report. The validation stage identified,
confirmed, and flagged data values that
warrant special attention or caution when
used for data analysis.
Results and Discussion
Detectability
The calculated instrument detection
limits were less than the contract-
required detection limits in the majority of
the cases of parameters for which
detection limits were established. The
DQOs established for detectability were
not met for the cation exchange capacity
parameters in ammonium chloride and
ammonium acetate, aluminum in
potassium chloride, total carbon, and total
nitrogen. The calculated instrument
detection limits were an order of
magnitude above the DQOs for the cation
exchange parameters.
Precision
The analytical within-batch precision
objectives were satisfied for most of the
parameters. These included clay, the pH
parameters, exchangeable cations, cation
exchange capacity and acidity,
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extractable iron and aluminum, the
extractable sulfates and sulfate isotherm
parameters, and total carbon, nitrogen,
and sulfur. Occasionally, an objective was
not met for an upper or lower tier of a
parameter. There were some instances
where the DQOs were slightly exceeded
for these parameters either above or
below the knot. The DQOs were not met
for total sand and silt, extractable cations
in calcium chloride, and sulfate zero
isotherm parameters. When the two tiers
were pooled over the total concentration
range for each of the parameters, a
precision index showed that the particle
size and extractable cations in calcium
chloride did not meet the overall DQOs.
Although no DQOs were set for the
preparation laboratory phase, the
preparation laboratory within-batch
precision also met the DQOs set for
analytical within-batch precision for all
parameters except sand and silt, calcium
and magnesium in ammonium acetate,
the two cation exchange capacities,
acidity in barium chloride, cations in
calcium chloride, extractable sulfate in
water, sulfate zero isotherm, and total
nitrogen. This indicates that the
preparation laboratories performed
relatively well in subsampling the bulk
samples.
Within-batch imprecision estimates
increased, as expected, from analytical to
sample preparation to field sampling. The
between-batch precision estimates were
generally low.
Accuracy (Interlaboratory
Differences)
The Scheffe's pair-wise multiple
comparison test showed that about 5
percent of the interlaboratory differences
were significantly different and 0.8
percent were highly significantly different.
The latter could be considered to be of
concern to the data user. Highly
significant differences were shown mostly
for the cations in calcium chloride. About
half the cases were in the Bw audit
sample.
The lowest interlaboratory differences
were shown for pH and the highest
differences were shown for the cation
exchange capacity parameters, calcium
in calcium chloride, and cations in
ammonium chloride. The mean
interlaboratory differences for laboratories
1,2,3, and 4 were 9.5, 9.3, 11.6, and 6.9
percent, respectively.
Among the audit samples, the
laboratories showed the highest
differences overall for the C horizon audit
sample and the lowest differences for the
A audit sample. The mean interlaboratory
differences for the Oa, A, Bs, Bw, and C
audit samples were 13.8, 9.3, 14.5, 10.7,
and 15.8 percent, respectively.
No single laboratory was consistently
superior to the others for all parameters
or parameter groups regarding low
differences. Each laboratory appears to
have individual strengths for specific
analytical methods. This is probably a
reflection of the combination of
experience, instrumentation, and
laboratory management practices within
each laboratory. This resulted in a
patchwork of differences on a parameter
group basis.
Representativeness
The field duplicates were repre-
sentative of the range of concentration in
the range of the routine samples for most
parameters. The natural audit samples
and the preparation duplicates were
rarely representative of the routine
samples. The preparation duplicates
consistently represented only the
extreme lower range of routine sample
concentrations for a given parameter. The
preparation laboratory personnel
apparently selected bulk samples for
duplication based not on random
selection but on the quantity of each
sample available. The sampling crews
were able to collect the largest amount of
sample from the thicker horizons
normally found in the lower portion of the
soil pedon. Horizon type selection
appears to be highly skewed (73 percent)
toward the transitional B and C horizons
which typically have very low analyte
concentrations in their extractions.
Completeness
Ninety-six percent of the designated
pedons were sampled. Although this
does not fully satisfy the DQO of 100
percent for sampling completeness, a
sufficient number of pedons were
sampled to enable estimates and
conclusions to be drawn from the data.
The requested soil sampling and sample
processing tasks were performed on 100
percent of the samples received by the
preparation laboratory which satisfies the
DQO of 100 percent. The analytical
completeness level exceeded 99 percent
for all parameters. Sufficient data were
generated to make conclusions for each
parameter in the data bases, with the
possible exception of iron in the calcium
chloride extract.
Comparability
The verified data bases were used
the assessment of data quality for t
the Northeastern and the Southern E
Ridge Province soil surveys. The c
bases from each survey therefore can
compared to each other. Flags w
applied consistently.
Sufficient audit sample data w
available from the DDRP contr
laboratory analyses to provide
estimate of the audit sample composi
for use in the assessment of precis
interlaboratory differences, £
comparability. Data from each ai
sample can be compared between
two surveys for any given parame
Significant differences can thus
attributed to differing amounts
measurement error. Reanalyses h,
corrected all data significantly affected
methods amendments which occurred
the survey progressed.
Identical soil preparation methods w
used in preparing soil samples for
two surveys. The procedure for selecl
the preparation duplicate was refined
the Southern Blue Ridge survey, result
in better representativeness of
preparation duplicate.
Due to an inconsistent application
the sampling of the field duplicates in
Northeastern survey, the variances
the field duplicates tend to fluctu
among the pedons. Overall within-ba
variability was greater in the South
Blue Ridge survey than in 1
Northeastern survey. This suggests t
the measurement error in t
Northeastern survey may have be
somewhat underestimated. However
does not mean that the routine d
between regions are not comparable. 1
same methodology was used in i
routine soil sampling for each surv
There were no deviations from I
sampling protocols that woi
compromise the integrity of the rout
data. These field sampling discrepanc
that could affect data comparability w>
documented during the Northeast*
survey and were resolved. The fii
sampling audit team did not report <
deviations from the sampling protoc
that would compromise the integrity
the routine data for the Southern B
Ridge Province Soil Survey.
As part of the DDRP, an interlaborat
methods comparison study w
conducted which compared the analy
of soils for two laboratories using '
DDRP methods to 16 statistically chos
external laboratories. These laborator
used their own methods which w<
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similar, but not identical, to the DDRP
methods. The results of the study will
show the comparability of the DDRP data
with other similar surveys using other
laboratories.
Conclusions and
Recommendations
Analysis of data from the Northeastern
Soil Survey indicate areas where
improvement is needed in the QA
program. The quality assurance data are
presented in a manner considered to be
the most appropriate for use by the
primary data users. The development of
this approach resulted from regular
interactions with the data users. In
addition, the statistical approaches taken
and the formats used were assessed in
depth by several external reviewers to
ensure agreement in the final
presentations. A great deal of information
is included in the many figures and
tables. Each user has a subjective
conception for data quality as well as a
need for a specific level of data quality
desired for his/her own use. The user is
therefore encouraged to become familiar
in detail with the text, figures, and tables
in order to best assess the data for
his/her specific needs.
A computerized data entry and
verification system should be developed
that will calculate the final data values
and produce a list of flags and data entry
errors. A computer link between the
laboratories and the quality assurance
staff should be established that will
enable the transfer of preliminary and
final data. The verification program
should be designed to evaluate the
quality control checks and other
contractual requirements, thereby
inducing the laboratories to assume
much of the responsibility for
identification and correction of errant
data. Evaluation of the blind audit
samples should be made part of the
computer verification system and a better
procedure for checking these values
should be developed. The internal
consistency checks should become an
integral part of the verification process.
Attention should be given to improving
detectability in future surveys. Both
instrument detection and system
detection limits should be addressed in
the data quality objectives for future
surveys.
The DQOs for total sand and total silt
should be increased from 1.0 percent to
3.0 percent. A two-tiered precision
objective should be defined for the
extractable cations in calcium chloride.
Specific data quality objectives should be
defined for system wide measurement.
Low concentration audit samples,
entered into the system during the
sampling phase, should be utilized as
substitutes for soil blank samples. A
quality control soil audit sample should
be incorporated into the quality
assurance program to better monitor the
analytical results of the laboratories. The
laboratories should be required to report
the analytical results of the analyses on a
batch-by-batch basis to the QA staff
immediately after the analysis of each
batch. The laboratory protocols should
specify a statistically valid method for
selecting the preparation duplicate.
An effort should be made to locate an
uncontaminated filter material for the
determination of the basic cations or
modify the pretreatment procedure for
the filter material used. Additional
methods details should be reviewed and
provided where appropriate in order to
reduce within-laboratory analytical
variability.
The DDRP staff should consider the
possibility of choosing laboratories to
perform analyses on a parameter basis
for those parameters or parameter
groups that revealed inherently high
differences or where specialized
instrumentation is used, e.g., total carbon.
nitrogen, and sulfur. A more stringent
laboratory selection procedure should be
adopted in the pre-evaluation process for
the selection of contract laboratories.
The issue of accuracy should be
addressed because the current approach
using interlaboratory differences has
limited utility. Data quality objectives for
accuracy should be established.
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G. £ flyers, R. D. Van Remortel, J. E. Teberg, M. J, Miah, M. L Papp, W. H. Cole
and A. D. Tansey are with Lockheed Engineering and Sciences Company, Las
Vegas, NV 89119; C. J. Palmer is with Environmental Research Center,
University of Nevada, Las Vegas, NV 89114; and D. L. Cassell and P. W. Shaffer
are with NSI Technology Services Corporation, Corvallis, OR 97333.
L J. Blume is the EPA Project Officer (see below)
The complete report, entitled "Direct/Delayed Response Project: Quality Assurance
Report for Physical and Chemical Analyses of Soils from the Northeastern
United States," (Order No. PB90-219 395IAS; Cost: $31.00, subject to change)
will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Las Vegas, NV 89193-3478
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
Official Business
Penalty for Private Use $300
US. OFFICIAL MAiL"
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