United States
Environmental Protection
Agency
Office of Acid Deposition,
Environmental Monitoring and
Quality Assurance
Washington DC 20460
EPA/600/4-89/031
April 1989
Research and Development
Direct/Delayed Response
Project: Quality
Assurance Plan for
Preparation and Analysis
of Soils from the
Mid-Appalachian
Region of the
United States
-------
EPA/600/4-89/031
April 1989
Direct/Delayed Response Project:
Quality Assurance Plan for Preparation
and Analysis of Soils from the
Mid-Appalachian Region of the United States
by
M.L. Papp, R.D. Van Remortel, C.J. Palmer, G.E. Byers,
B.A. Schumacher, R.L. Slagle, J.E. Teberg, and M.J. Miah
A Contribution to the
National Acid Precipitation Assessment Program
U.S. Environmental Protection Agency
Region 5, Library (5PL-16)
230 S. Dearborn Street, Eaom 167-0
tgaicago, JL 60604
U.S. Environmental Protection Agency
Ollice 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
-------
Notice
The information in this document has been funded wholly or in part by the U.S. Environmental
Protection Agency under Contract Number 68-03-3249 to Lockheed Engineering and Sciences
Company and Cooperative Agreement Number 81-2189-03 to the Environmental Research Center of
the University of Nevada at Las Vegas. Additional cooperation has been provided under Contract
Number 68-03-3246 to NSI Technology Services Corporation. It has been subject to the Agency's
peer and administrative review and it has been approved for publication as an EPA document.
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 Direct/Delayed Responce
Project, Mid-Appalachian Soil Survey. The complete document set includes the major data report,
quality assurance plan, laboratory analysis handbook, field and laboratory operations reports, and
quality assurance reports. 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:
Papp1, M. L, R. D. Van Remortel1, C. J. Palmer2, G. E. Byers1, B. A Schumacher1, R. L Slagle1,
J. E. Teberg1, and M. J. Miah1. 1989. Direct/Delayed Response Project: Quality Assurance
Plan for Preparation and Analysis of Soils from the Mid-Appalachian Region of the United
States. EPA/600/4-89/031. U.S. Environmental Protection Agency, Las Vegas, Nevada.
1 Lockheed Engineering and Sciences Company. Las Vegas, Nevada 89119.
2 Environmental Research Center, University of Nevada, Las Vegas, Nevada 89114.
-------
Abstract
The Direct/Delayed Response Project is designed to address the concern over potential
acidification of surface waters by atmospheric sulfur deposition within the United States. The Mid-
Appalachian Soil Survey is being conducted 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. The survey has benefited from information gained
during two previous, similar surveys. This document addresses the design and implementation of
a quality assurance program and the verification of the analytical data base for the Mid-Appalachian
Soil Survey. It is addressed primarily to 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 Mid-Appalachian region, the third and final region characterized during the project.
Data quality is assessed by addressing detectability, precision, accuracy, representativeness,
completeness, and comparability of the data.
This plan is submitted in partial fulfillment of Contract Number 68-03-3249 by Lockheed
Engineering and Sciences Company, Las Vegas, Nevada, under sponsorship of the U.S.
Environmental Protection Agency.
in
-------
-------
Contents
Page Revision
Notice ii 1
Abstract iii 1
List of Figures vii 1
List of Tables viii 1
Acknowledgments ix 1
List of Abbreviations x 1
Executive Summary xii 1
1. Introduction 1 of 2 1
1.1 Directives 1 of 2 1
1.2 Sources of Information 1 of 2 1
2. Project Description 1 of 6 1
2.1 Project Overview 1 of 6 1
2.2 Project Organization 2 of 6 1
2.2.1 Project Management 2 of 6 1
2.2.2 Laboratory Responsibilities 5 of 6 1
2.2.3 Primary Cooperators 5 of 6 1
3. Sampling Strategy 1 of 2 1
3.1 Watershed Selection and Mapping 1 of 2 1
3.2 Selection of Sampling Classes 2 of 2 1
4. Field and Laboratory Operations 1 of 8 1
4.1 Field Operations 1 of 8 1
4.1.1 Personnel 1 of 8 1
4.1.2 Sampling Activities 1 of 8 1
4.2 Preparation Laboratory Operations 2 of 8 1
4.2.1 Personnel 3 of 8 1
4.2.2 Preparation Activities 3 of 8 1
4.3 Analytical Laboratory Operations 5 of 8 1
4.3.1 Personnel 5 of 8 1
4.3.2 Laboratory Activities 5 of 8 1
4.4 Sample Custody 8 of 8 1
5. Quality Assurance Objectives 1 of 17 1
5.1 Overview of Quality Objectives 1 of 17 1
5.2 Design Characteristics 2 of 17 1
5.2.1 Quality Evaluation Samples 3 of 17 1
5.2.2 Quality Control Samples 7 of 17 1
5.3 Description of Measurement Quality Objectives 10 of 17 1
5.3.1 Field Sampling and Characterization 11 of 17 1
5.3.2 Sample Preparation 13 of 17 1
5.3.3 Laboratory Analysis 13 of 17 1
-------
Contents (Continued)
Page Revision
6. Quality Assurance Implementation 1 of 10 1
6.1 Control of Data Quality 1 of 10 1
6.1.1 Field Sampling and Characterization 1 of 10 1
6.1.2 Sample Preparation 2 of 10 1
6.1.3 Laboratory Analysis 3 of 10 1
6.1.4 On-Site Systems Audits 3 of 10 1
6.2 Data Verification 5 of 10 1
6.2.1 Field Sampling and Characterization Data 5 of 10 1
6.2.2 Preparation Laboratory Data 5 of 10 1
6.2.3 Analytical Laboratory Data 6 of 10 1
7. Data Quality Assessment and Reporting 1 of 3 1
7.1 Statistical Design 1 of 3 1
7.1.1 Assessment of Detectability 1 of 3 1
7.1.2 Assessment of Precision 1 of 3 1
7.1.3 Assessment of Accuracy 1 of 3 1
7.1.4 Assessment of Representativeness 2 of 3 1
7.1.5 Assessment of Completeness 2 of 3 1
7.1.6 Assessment of Comparability 2 of 3 1
7.2 Overview of Validation Procedures 2 of 3 1
7.3 Quality Assurance Reports to Management 3 of 3 1
7.3.1 Status Reporting 3 of 3 1
7.3.2 Formal Reporting 3 of 3 1
8. Data Management System 1 of 3 1
8.1 Overview of the Data Bases 1 of 3 1
8.1.1 Field Sampling Data 1 of 3 1
8.1.2 Preparation Laboratory Data 1 of 3 1
8.1.3 Analytical Laboratory Data 3 of 3 1
8.2 Data Base Audits 3 of 3 1
References 1 of 2 1
Appendices
A. SCS-SOI-232 Field Data Form and Codes 1 of 14 1
B. Preparation Laboratory Data Forms 1 of 11 1
C. Analytical Parameters Measured in the Mid-Appalachian Soil Survey ... 1 of 5 1
D. Analytical Laboratory Verification Criteria 1 of 17 1
E. Analytical Laboratory Pre-Award Evaluation Scoring Sheet 1 of 7 1
F. Analytical Laboratory On-Site Evaluation Questionnaire 1 of 54 1
G. Field Data Verification Procedures 1 of 18 1
H. Preparation Laboratory Verification Procedures 1 of 12 1
I. Preparation Laboratory On-Site Evaluation Questionnaire 1 of 9 1
J. Quality Assurance Reanalysis Templates 1 of 21 1
K. Analytical Data Verification and Validation Flags 1 of 5 1
VI
-------
Figures
Figure Section Page Revision
2-1 DDRP Regional Surveys 2 1 of 6 1
2-2 DDRP Major Operations 2 3 of 6 1
2-3 DDRP Operational Management Structure 2 4 of 6 1
2-4 DDRP Quality Assurance Management Structure at EMSL-LV ... 2 5 of 6 1
5-1 Assessment Samples Used in Mineral Sample Batches 5 4 of 17 1
5-2 Assessment Samples Used in Organic Sample Batches 5 5 of 17 1
8-1 DDRP Data Management Operations Flowchart 8 2 of 3 1
VII
-------
Tables
Table Section Page Revision
1-1 Quality Assurance Subjects in Supporting Documents 1 1 of 2 1
4-1 Preparation Laboratory Parameters 4 4 of 8 1
4-2 Analytical Laboratory Parameters 4 6 of 8 1
4-3 Laboratory Data Qualifiers 4 7 of 8 1
4-4 Decimal-Place Reporting Requirements 4 7 of 8 1
5-1 Status/Assessment of Measurement Samples 5 6 of 17 1
5-2 Quality Control Requirements for Analytical Batches 5 8 of 17 1
5-3 Within-batch Precision Objectives for Field Samples 5 12 of 17 1
5-4 Within-batch Precision Objectives for Preparation Samples 5 14 of 17 1
5-5 Preparation Laboratory Measurement Quality Objectives 5 15 of 17 1
5-6 Within-batch Precision Objectives for Analytical Samples 5 16 of 17 1
VIII
-------
Acknowledgments
External peer reviews by the following individuals are gratefully acknowledged: D. R. DeWalle,
School of Forest Resources, Pennsylvania State University, University Park, Pennsylvania; D. W.
Johnson, Oak Ridge National Laboratory, Oak Ridge, Tennessee; W. K. Smith, Department of
Statistics, Temple University, Philadelphia, Pennsylvania; C. M. Thompson, U.S. Department of
Agriculture (USDA) Soil Conservation Service (retired), Temple, Texas; and W. J. Walker, Department
of Land and Water Resources, University of California, Davis, California.
The authors wish to acknowledge the following individuals for their technical assistance
during the development of this document: J. J. Lee, U.S. Environmental Protection Agency, Corvallis,
Oregon; D. A. Lammers, USDA Forest Service, Corvallis, Oregon; D. L Cassell, D. S. Coffey, M. G.
Johnson, J. S. Kern, and P. W. Shaffer, NSI Technology Services Corporation, Corvallis, Oregon; R.
S. Turner, C. C. Brandt, and M. B. Adams, Oak Ridge National Laboratory, Oak Ridge, Tennessee;
D. D. Schmoyer, Martin Marietta Energy Systems, Oak Ridge, Tennessee; P. A. Gowland, Automated
Sciences Group, Oak Ridge, Tennessee; T. H. Starks, University of Nevada, Las Vegas, Nevada; and
M. H. Bartling, W. H. Cole. J. V. Burton, J. K. Bartz, R. L. Tidwell, R. W. Gerlach, K. C. Shines, G. A.
Raab, D. V. Peck, R. C. Metcalf, and J. L. Engels, Lockheed Engineering and Sciences Company, Las
Vegas, Nevada.
The following individuals provided editorial and logistical support and are gratefully
acknowledged: D. W. Sutton, D. J. Chaloud, L. A. Stanley, K. M. Howe, R. B. Corse, S. L Pierett,
J. R. Baker, L. K. Marks, J. D. Hunter, L. M. Mauldin, P. F. Showers, and A. Tippett, Lockheed
Engineering and Sciences Company, Las Vegas, Nevada. B. N. Cordova, Lockheed Engineering and
Sciences Company, Las Vegas, Nevada is acknowledged for exceptional word processing and
graphics support.
Finally, we appreciate the support of the technical director, M. R. Church, and our technical
monitor, L. J. Blume, during the development of this quality assurance plan.
IX
-------
List of Abbreviations
AA atomic absorption spectrometry
AC_BACL barium chloride triethanolamine exchangeable acidity
AD~ analytical duplicate
AERP Aquatic Effects Research Program
AL_AO acid oxalate extractable aluminum
AL_CD citrate dithionite extractable aluminum
AL~CL exchangeable aluminum in unbuffered ammonium chloride
AL_CL2 aluminum in 0.002M calcium chloride
AL~PYP pyrophosphate extractable aluminum
ALSOW analytical laboratory statement of work
ANOVA analysis of variance
BD_CLD clod bulk density
BD_KV known volume bulk density
CA_CL exchangeable calcium in unbuffered ammonium chloride
CA~CL2 calcium in 0.002M calcium chloride
CA OAC exchangeable calcium in buffered ammonium acetate
CEC cation exchange capacity
CEC_CL unbuffered ammonium chloride cation exchange capacity
CEC OAC buffered ammonium acetate cation exchange capacity
CLA7 total clay fraction
CLP Contract Laboratory Program
CRDL contract-required detection limit
DDRP Direct/Delayed Response Project
DL-QCCS detection limit quality control check sample
DQO data quality objective
EMSL-LV Environmental Monitoring Systems Laboratory at Las Vegas, Nevada
EPA U.S. Environmental Protection Agency
ERL-C Environmental Research Laboratory at Corvallis, Oregon
FAL low-range field audit sample
FAO field audit organic sample triplicate
FAP field audit sample pair
FD field duplicate sample
FE_AO acid oxalate extractable iron
FE_CD citrate dithionite extractable iron
FE_CL2 iron in 0.002M calcium chloride
FE~PYP pyrophosphate extractable iron
FIA" flow injection analysis
FP flame photometry
1C ion chromatography
ICC internal consistency checks
ICP inductively coupled plasma atomic emission spectrometry
IDL instrument detection limit
IFB invitation for bid
IQR interquartile range
IR infrared detection
K CL exchangeable potassium in unbuffered ammonium chloride
K~CL2 potassium in 0.002M calcium chloride
K~OAC exchangeable potassium in buffered ammonium acetate
LAL low-range laboratory audit sample
LAO laboratory audit organic sample triplicate
-------
List of Abbreviations (Continued)
LAP laboratory audit sample pair
LEVIS Laboratory Entry and Verification Information System
MASS Mid-Appalachian Soil Survey
MG_CL exchangeable magnesium in unbuffered ammonium chloride
MG_CL2 magnesium in 0.002M calcium chloride
MG_OAC exchangeable magnesium in buffered ammonium acetate
MOIST_A air-dry soil moisture at the analytical laboratory
MOIST_P air-dry soil moisture at the preparation laboratory
MQO measurement quality objective
MS manager's sample
NA_CL exchangeable sodium in unbuffered ammonium chloride
NA_CL2 sodium in 0.002M calcium chloride
NA OAC exchangeable sodium in buffered ammonium acetate
NCC National Computer Center
NSS National Stream Survey
NSWS National Surface Water Survey
OEPER Office of Environmental Processes and Effects Research
OM_LOI organic matter by loss-on-ignition
ORNL Oak Ridge National Laboratory
PD preparation duplicate sample
PE performance evaluation
PH_002M pH in 0.002M calcium chloride
PH_01M pH in 0.01M calcium chloride
PHJH2O pH in deionized water
PH MP field-moist pH in deionized water
PLSOP preparation laboratory standard operating procedures
QA quality assurance
QART quality assurance reanalysis template
QC quality control
QCAS quality control audit sample
QCCS quality control check sample
QE quality evaluation
RF_FG fine gravel rock fragments
RF_MG medium gravel rock fragments
RSD relative standard deviation
SAND total sand fraction
SAS Statistical Analysis Systems, Inc.
SBRP Southern Blue Ridge Province
SCR soil chemistry relationship
SCS Soil Conservation Service
SD standard deviation
SDL system detection limit
SILT total silt fraction
SI_AO acid oxalate extractable silicon
SMO Sample Management Office
SO4_0 zero mg S/L sulfate isotherm parameter
S04_2 two mg S/L sulfate isotherm parameter
SO4_4 four mg S/L sulfate isotherm parameter
SO4_8 eight mg S/L sulfate isotherm parameter
SO4_16 sixteen mg S/L sulfate isotherm parameter
S04_32 thirty-two mg S/L sulfate isotherm parameter
S04_H2O water extractable sulfate
SO4_PO4 phosphate extractable sulfate
SOP standard operating procedures
SOR SCS state office representative
SOW statements of work
TC thermal conductivity
USDA U.S. Department of Agriculture
XI
-------
Executive Summary
The quality assurance (QA) policy of the U.S. Environmental Protection Agency (EPA) requires
every monitoring and measurement project to have a written and approved quality assurance project
plan. The purpose of this quality assurance plan is to specify the policies, organization, objectives,
and the quality evaluation and quality control activities needed to achieve the data quality
requirements of the Direct/Delayed Response Project (DDRP), Mid-Appalachian Soil Survey (MASS).
These specifications are used to assess and control measurement errors that may enter the
system at various phases of the project, e.g., during soil sampling, preparation, and analysis.
Project Description
The DORP is conducted as part of the interagency, federally mandated National Acid Precipita-
tion Assessment Program which addresses the concern over potential acidification of surface
waters by atmospheric deposition within the United States. The overall purpose of the project is
to characterize geographic regions of the United States by predicting the long-term response of
watersheds and surface waters to acidic deposition.
The EPA is assessing the role that atmospheric deposition of sulfur plays in controlling long-
term acidification of surface waters. Recent trend analyses have indicated that the rate of sulfur
deposition is either unchanging or slowly declining in the Northeastern United States, but is increa-
sing in the Southeastern United States. If a "direct" response exists between sulfur deposition and
surface water alkalinity, then the extent of current effects on surface water probably would not
change much at current levels of deposition, and conditions would improve as the levels of
deposition decline. If surface water chemistry changes in a "delayed" manner, e.g., due to chemical
changes in the watershed, then future changes in water chemistry (even with level or declining rates
of deposition) become difficult to predict. This range of potential effects has clear and significant
implications to public policy decisions on sulfur emissions control strategies.
The DDRP focuses on regions of the United States that have been identified as potentially
sensitive to surface water acidification. The MASS is the third of three DDRP regionaj surveys and
includes portions of Pennsylvania, Virginia, and West Virginia. Surface water acidification in the
Mid-Appalachian region was studied during the Eastern Lakes Survey in 1984 and in the National
Stream Survey-Phase I, in 1985. Information gained during these and other surveys has been used
to optimize the quality assurance program in the MASS.
Field and laboratory data collected in the DDRP are included in the system description
modeling. These data are then used to assess the key processes that regulate the dynamics of
base cation supply and sulfur retention within watersheds. Integrated watershed data are used
to calibrate three dynamic simulation models that predict future regional responses to acidic
deposition. Uncertainty and error propagation estimates are an important part of all levels of
analysis.
The DDRP staff at the EPA Environmental Research Laboratory in Corvallis, Oregon, is involved
in all aspects of the MASS. Responsibilities of this laboratory include the development of an
experimental design, QA oversight for soil mapping and sampling, and data analysis and
interpretation. The DDRP staff at the EPA Environmental Monitoring Systems Laboratory in Las
Vegas, Nevada, is involved in matters relating to QA implementation, logistics, and analytical
services. The responsibilities of this laboratory include the development and implementation of
operational procedures and quality evaluation/control criteria for soil sample preparation and
analysis, and the development of data verification procedures for field and laboratory data.
The use of interagency agreements by EPA has allowed the DDRP to engage support from
several groups that have expertise in areas of importance to the project. The DDRP staff at Oak
Ridge National Laboratory is involved in managing, manipulating, and structuring large data bases
to satisfy DDRP data analysis requirements, me soil survey staff of the United States Department
of Agriculture, Soil Conservation Service is involved in DDRP soil mapping, characterization, and
sampling. Laboratory analysis support is solicited through competitive bid on an invitation for bid
by independent contract laboratories.
xii
-------
Field and Laboratory Operations
Field mapping and sampling operations in the MASS are conducted by Soil Conservation
Service soil scientists under the supervision of EPA representatives. Approximately five sampling
crews, each composed of three to four qualified persons, are selected. These crews are
responsible for the description of soil and site characteristics, sample collection, and shipment of
samples to the preparation laboratory. Members of each crew are soil scientists that are
experienced with the universally recognized National Cooperative Soil Survey characterization
procedures. The crew leader is responsible for selecting the sampling site and documenting all field
data.
The sampling protocols specify the procedures for ensuring the integrity of the samples.
Specific instructions on excavating and draining (if necessary) soil pits are given. Crews are
provided with standard containers for sample collection. Samples are kept as cool as possible in
the field and during transport to the preparation laboratory. In order to maintain their integrity and
to reduce the possibility of biological degradation, samples are stored at a temperature of 4°C
within 24 hours of sampling. It is anticipated that 150 pedons are to be sampled during the MASS,
with each pedon yielding an average of six routine samples from its component horizons.
To allow the determination of bulk density, natural soil clods are collected in triplicate, where
possible from each soil horizon. Where clods cannot be collected, known volumes of soil are
collected in duplicate. Using a volume replacement method, the volume of soil is estimated by
replacing a small excavated soil cavity with a known volume of foam beads. If this method does
not yield satisfactory samples, a volume filling method is attempted by filling a container of known
volume with soil and attempting to pack it to the same density normally encountered in the horizon
being sampled.
The preparation laboratory is the intermediate link between the sampling crews and the
analytical laboratories. The laboratory is located at the EPA Environmental Monitoring Systems
Laboratory in Las Vegas, Nevada, although previous DDRP surveys utilized the services of multiple
university laboratories for sample preparation. It was thought that consolidating the preparation
activities at a single laboratory might result in better sample handling, improved sample integrity,
and higher data quality. The laboratory serves as a central location for soil processing and for
introducing double-blind measurement quality samples into the sample flow. The soil samples
collected by the sampling crews are sent via overnight courier to the preparation laboratory, where
the laboratory staff processes the samples and prepares homogeneous, anonymous subsamples
that are shipped to the analytical laboratories. To accomplish these tasks successfully, the
preparation laboratory must uniformly track, process, and store all samples.
After a bulk soil sample is air dried, the soil is carefully disaggregated and sieved. The less
than 2-mm soil is retained in a labeled plastic bottle and placed in cold storage when not
undergoing processing. The rock fragments are retained for gravimetric analysis. In order to
obtain representative volumes of soil, it is necessary to prepare homogeneous subsamples from
the less than 2-mm soil fraction using a riffle splitter. The subsamples are placed in labeled
plastic bottles and are organized in batches that are shipped to separate contract analytical
laboratories for general and elemental analyses. Each batch normally contains 40 samples but may
have as many as 42 samples. With the exception of a quality control audit sample, all samples
are randomly placed within a batch and cannot be distinguished by the recipient laboratory. The
unused portions of the bulk soil samples are archived in cold storage.
All raw data obtained are recorded on a series of preparation laboratory raw data forms and
are immediately entered into a personal computer to ensure proper tracking, processing, and eval-
uation of the samples. The computer is directly linked to the QA staff and allows real-time data
evaluation and tracking of samples by both parties. The precision and accuracy criteria for
measurement quality samples are checked while the samples are being processed, permitting the
prompt identification of any discrepancies.
During shipment, the samples received by the analytical laboratories can undergo segregation
both by particle size and by density; therefore, each sample must be rehomogenized by thorough
mixing prior to the removal of aliquots for analysis. The raw data generated during sample analysis
are entered on a personal computer using a specially designed entry and verification system. Data
xiii
-------
reports are then generated to assist in the evaluation of data quality. Before a batch of analytical
data is accepted all completeness and quality control requirements must be met. In addition, the
following documents must be updated constantly at the analytical laboratory and must be available,
upon request, to the analysts and the supervisor involved in the project: standard operating
procedures, laboratory QA plan, list of in-house samples, instrument performance data, control
charts for all check samples and detection limit check samples, and quality control data reports.
Frequent communications, i.e., two or three contacts each week, are maintained with each
analytical laboratory in order to obtain current sample status and to discuss any problems that may
occur during analyses. These discussions are to be recorded in a logbook by the QA staff and the
laboratory staff. Preliminary and final data arc available for access via electronic transfer. The
preliminary data are reviewed for anomalies and if a problem is identified, the laboratory is notified.
Corrective action or reanalysis may be suggested.
Legal chain-of-custody procedures are not required for this study; however, sample custody
must be documented. The sampling crew leader is responsible for all samples in the field as well
as the sample shipments. As soil shipments are received in the preparation laboratory, they are
immediately logged in and checked against the packing slip and the pedon description forms for
proper labeling and quantity. If any discrepancies are found, the soil sampling task leader and the
field crew leader are notified. Following the receipt of samples, the appropriate laboratory
managers are responsible for sample integrity until the samples are archived.
Quality Assurance Program
The data collection criteria provide a balance between constraints of time and cost and the
quality of data necessary to achieve the DDRP research objectives. The MASS QA Plan is designed
to accomplish the following general objectives:
• establish the QA criteria used to control and assess data collected in the survey,
• provide standardized sampling, preparation, and analytical methods and procedures,
• utilize assessment samples and procedures to verify the quality of the data,
• perform field and laboratory on-site audits to ensure that all activities are properly performed
and that discrepancies are identified and resolved,
• evaluate the data and document the results in QA reports to EPA management.
The raw soil characterization data for the DDRP surveys are collected during four major
operational phases consisting of mapping, sampling, preparation, and analysis. A certain amount
of data measurement uncertainty is expected to enter the system at each phase. Grouping of the
data, e.g., by sampling class/horizon configurations, also increases uncertainty. The sampling
population itself is a source of confounded uncertainty that is extremely difficult to quantify.
Generally, the data quality objectives for the MASS encompass the overall allowable
uncertainty from sample measurement and from the sampling population that the data users are
willing to accept in the analytical results. Because of the many possible confounded sources of
uncertainty and the data collection focus of this QA plan, overall data quality objectives for the
survey are not described herein. Rather, the plan focuses on the definition, implementation, and
assessment of measurement quality objectives (MQOs) that are specified for the entire sample
preparation and analysis phases of data collection and portions of the field sampling phase. The
MQOs are more or less specific goals defined by the data users that clearly describe the data
quality that is sought for each of the measurement phases. The MQOs are defined according to
the following six attributes:
• Detectability - the lowest concentration of an analyte that a specific analytical procedure can
reliably detect.
• Precision - the level of agreement among multiple measurements of the same characteristic.
• Accuracy ~ the difference between an observed value and the true value of the parameter being
estimated.
• Representativeness - the degree to which the data collected accurately represent the
population of interest.
• Completeness -- the quantity of data that is successfully collected with respect to the amount
intended in the experimental design.
xiv
-------
• Comparability - the similarity of data from different sources included within individual or
multiple data sets; the similarity of analytical methods and data from related projects across
regions of concern.
An attempt has been made to define each quality attribute individually for each measurement
phase of the program (sampling, preparation, analysis) or collectively for overall measurement
system uncertainty on a parameter-by-parameter basis. The term "uncertainty is used as a generic
term to describe the sum of all sources of error associated with a given portion of the
measurement system. In order to reduce measurement uncertainty in the final data values, errors
occurring in each phase must be identified and rectified during that phase of the survey.
Measurement uncertainty for the MASS is controlled through well-defined protocols, system audits,
and the introduction of measurement quality samples at each measurement phase.
Initial MQOs were established on the basis of the requirements of EPA data users and on
the selection of appropriate methods to obtain the data. The MQOs were reviewed by persons
familiar with analytical methods and soil characterization techniques, including soil chemists and
laboratory personnel. Modifications to the MQOs and to the protocols were implemented based
on information gained from the DDRP Northeastern region and Southern Blue Ridge Province
surveys, from peer review comments, or according to the limitations of a particular analytical proce-
dure or instrument.
Measurement quality samples are placed at a rate of about one sample in four in each batch
of analytical samples to determine measurement uncertainty. The measurement quality samples
are used during various phases of the survey and allow the QA staff to control and assess the
quality of the data collection process. Included in each batch of samples are: audit samples that
have known ranges of analyte concentration (detectability, precision, and accuracy), duplicates of
routine samples introduced at each measurement phase (precision and representativeness), and
an audit sample for use in laboratory quality control (accuracy). There are separate analytical
within-batch precision and accuracy MQOs for organic and mineral soils because of differences in
methodology and the expected wide variability in analyte concentrations. For this reason, mineral
and organic samples are organized and analyzed in separate batches.
Status and Assessment of Measurement Quality Samples
Status of sample'
Sample type
Low-range field audit*
Field audit pair**
Field duplicate
Low-range lab audit*
Lab audit pair**
Preparation duplicate
Quality control audit
Manager's sample
#per
batch
1
2
1
1
2
2
1
—
QA Sampling
staff crew
K
K
-
K
K
-
K
K
B
B
B
-
-
-
-
—
Preparation
laboratory
B
B
B
-
-
B
-
B
Analytical
laboratory
DB
DB
DB
DB
DB
DB
B
—
Assessment purpose'
System
D.A
P,A
P
-
-
-
-
—
Sampling
D,A
P.A
P,R
—
--
..
-
-
Preparation
D.A
P.A
P
-.
-
P.R
—
A
Analysis
A
P.A
P
D
P,A
P
A
-
• K = known concentration, B = blind, DB = double blind.
* D = detectability/contamination, P = precision, A = accuracy. R = representativeness.
* Not placed in organic soil batches.
** Triplicate in organic soil batches.
The quality evaluation samples are those samples which are known to the QA staff but are
either blind or double blind to the sampling crews, preparation laboratory, or analytical laboratory.
A blind sample has a concentration range that is unknown to the analyst, whereas a double blind
sample cannot be distinguished from a routine sample and has a concentration range that is
unknown to the analyst. These samples provide an independent check on the analytical process
and can be used to evaluate whether the MQOs have been met for any given run or batch, or for
all batches, i.e., overall measurement uncertainty. Important characteristics of the audit samples
xv
-------
include their similarity to routine samples in matrix type and concentration level, their homogeneity
and stability, and their defined accuracy windows. Every quality evaluation sample has a specific
purpose in the data assessment scheme.
In order to produce data of consistently high quality, the contract laboratories are required
to analyze certain types of quality control samples that are known to the laboratory staff and that
can be used by the analysts to identify and control analytical measurement uncertainty. Each of
these samples has certain specifications that must be met before data for the parameter or batch
are accepted. The control samples are non-blind samples procured under contract to assist the
laboratories in meeting laboratory MQOs and include soil samples, e.g., analytical duplicates, and
non-soil samples, e.g., reagent blanks. The samples allow the laboratory manager and the QA staff
to assess whether the physical and chemical analysis is under control.
Quality Assurance Implementation
The quality assurance program is implemented through training, on-site systems audits,
independent assessments, and other procedures used to control and assure the quality of the data
being collected. Verification of these data is accomplished through a series of computer and
manual checks of data quality.
Corrective action for errors made at the preparation and analytical laboratories is
accomplished primarily through the application of a QA reanalysis template for each analysis of
interest. The templates provide a precision and accuracy checklist of the measurement quality
samples for each batch and are useful in deciding whether to reanalyze a particular parameter.
The analytical data verification is a multi-faceted, computerized approach to provide a concise
and consistent assessment of the data. The overall process is highlighted by the Laboratory Entry
and Verification Information System (LEVIS). The LEVIS programs are implemented on personal
computers and facilitate the data entry and quality control sample evaluation at the analytical
laboratories as well as the evaluation of laboratory performance by the QA staff. The system is
a menu-driven product that is designed as a two-phase operation, where phase one is an analytical
laboratory system and phase two is a quality assessment system. The LEVIS initiative was
pursued because previous surveys had shown that manual verification of the data was both labor-
and time-intensive, and allowed only a limited amount of real-time corrective action on the part of
the laboratories.
An internal consistency program is used to generate routine data outliers in each sample
batch. Analytical data from each parameter are correlated against corresponding data from all
other analytical parameters measured in the MASS. For each parameter, the parameter pair with
the strongest linear relationship is identified and evaluated. Soil chemistry relationships are another
tool used to examine the internal consistency of the routine sample data. It is expected that
approximately 10 percent of the data will not comply with the relationships; these anomalous data
are examined by a staff soil chemist who either qualifies them or assigns appropriate flags
signifying the discrepancy.
Data Quality Assessment and Reporting
The assessment of detectability is accomplished on a parameter basis at four levels: (1)
compliance with contract-required detection limits; (2) calculation of actual instrument detection
limits; (3) calculation of estimated system detection limits; and (4) identification of routine samples
having concentrations below the system detection limits. The results can be grouped in tabular
form to allow comparisons among the values for any parameter of interest.
A statistical evaluation procedure that has been developed by the QA staff and data users
is applied to the data in order to assess precision as a function of confounded data collection
uncertainty. An additive step-function model is used, where an observed value of any soil
characteristic is considered as the sum of the "true" or accepted value and an error term. Precision
is evaluated for each variance segment of the range of concentration for a given analyte.
xvi
-------
The accuracy windows for the laboratory audit samples are based on previous interlaboratory
analyses of the same sample material by the same protocols. The objective of setting windows
is to specify a range of acceptable single values based on a mean and standard deviation
computed from a number of previously observed values. The prediction intervals used for the
accuracy windows are generally determined with confidence intervals constructed around the mean
of these values, using appropriate weighting factors.
The sampling aspect of representativeness is assessed by comparing the individual site and
pedon classifications with the component sampling classes to which the soils are assigned.
Representativeness of the measurement quality samples is assessed by comparing the
concentration ranges of data from the duplicate samples to the overall concentration range of the
routine sample data. Representativeness of the analytical samples is identified by assessing the
homogenization and subsampling procedures at the preparation and analytical laboratories using
precision estimates from the duplicate samples.
Sampling completeness is assessed by comparing the actual number of soil pedons and
associated horizons sampled to that number specified in the MASS design. Completeness of the
sample collection, preparation, and analysis is calculated using data from the verified data base,
while completeness of the data from a data user's perspective, i.e., amount of usable data, is
determined using the validated data base.
Following completion of the MASS, a comparison is made across the Northeastern, Southern
Blue Ridge, and Mid-Appalachian regions that focuses on method differences, audit sample results,
laboratory effects, and other QA features of the surveys. Comparison of the DDRP data bases to
other similar data bases may also be undertaken. Summary statistics are used to collate individ-
ual values into groups that enable the data users to discern trends of interest among the surveys.
Task leaders for the various stages of the MASS provide a written summary of operations
to the project managers on a quarterly basis. These reports describe the kinds of data collected
as well as summarize the QA activities associated with the data. The summary of QA activities
includes the following:
overview of QA activities,
list of changes to the QA program,
results of system and performance audits,
assessment of data quality based on the verified data bases,
documentation of unfavorable incidents and corrective actions,
distribution of updated control charts, and
results of special studies.
Reports relating to data quality assessment are also prepared by QA staff. These reports
include interpretation of performance audits and replicate sample data, estimates of measurement
error, and identification of any major discrepancies found during the assessment. In addition to
this QA Plan, the QA staff produces laboratory operations manuals for use in the preparation and
analysis of soil samples. Upon completion of the data verification activities, summary QA reports
of the sample preparation and sample analysis data are produced and distributed to all cooperat-
ing DDRP staff and data users.
Data Management System
The purpose of data base management for the MASS is to facilitate the collection, entry,
review, modification, and distribution of all data associated with the survey. Additional tasks
include the data analysis for report generation, statistical analysis, verification, and data file
security. The MASS final data base contains three progressive versions of the data gathered: (1)
the raw data base, (2) the verified data base, and (3) the validated data base.
XVII
-------
-------
Section 1
Revision 1
Date: 4/89
Page 1 of 2
Section 1
Introduction
1.1 Directives
The quality assurance policy of the
U.S. Environmental Protection Agency (EPA)
requires every monitoring and measurement
project to have a written and approved quality
assurance (QA) project plan (Costle, 1979a and
1979b). This requirement applies to all environ-
mental monitoring and measurement efforts
authorized or supported by EPA through regu-
lations, grants, contracts, or other formal
means. The purpose of this QA plan is to
specify the policies, organization, objectives,
and the quality evaluation (QE) and quality
control (QC) activities needed to achieve the
data quality requirements of the Direct/Delayed
Response Project (DDRP), Mid-Appalachian Soil
Survey (MASS). These specifications are used
to assess and control measurement errors
that may enter the system at various phases
of the project, e.g., during soil sampling, prep-
aration, and analysis.
All project personnel are expected to be
familiar with the policies and objectives out-
lined in this QA plan to ensure proper interac-
tions between field and laboratory operations
and data management.
1.2 Sources of Information
The EPA Quality Assurance Management
Staff guidelines (Stanley and Verner, 1985)
state that the QA project plan should address,
in detail or by reference, each of the 14 items
listed in Table 1-1. Method-specific discus-
sions presented in the DDRP-MASS Soil Sam-
pling Manual (Kern et a!., 1988), the DDRP-
MASS Preparation Laboratory Standard Opera-
ting Procedures (Bartling et al., 1988), or the
DDRP-MASS Analytical Laboratory Statements
of Work (USEPA, 1988) might not be repeated
in this project plan. In these cases, Table 1-1
serves as an index to the appropriate refer-
ences.
Table 1-1. Sections In This Quality Assurance Project Plan and In Other DDRP Documents Where Quality
Assurance Subjects are Addressed
Section Number
Subject
Project Description
Project Organization
QA Objectives for Measurement
Data
Sampling Procedures
Sample Custody and Storage
Calibration Procedures
Analytical Procedures
QA Project
Plan
2
2
5
3,4
4
—
4
Soil Sampling
Manual!
1,2
1,2
7
7,8
—
*—
Preparation
Laboratory
SOP
1
1
2
—
2
2,3
3
Analytical
Laboratory
sow:
1
1
3
—
2
3
4
(continued)
-------
Table 1-1. Continued
Section 1
Revision 1
Date: 4/89
Page 2 of 2
Section Number
Subject
Data Reduction, Validatbn, and
Reporting
QC Standards and Checks
Performance and System Audits
Preventive Maintenance
Data Quality Assessment
Corrective Actions
QA Reports
QA Project
Plan
7.8
5.6
7
6
6,7
6.1
7
Soil Sampling
Manual:
6,7
—
2.6
—
.
2.6
2
Preparation
Laboratory
sor
2.3
2.3
1
3
2,3
2.3
2
Analytical
Laboratory
sow:
4.5
3.4
6
3.4
3.4
3.4
3
• Kern et al., 1988
1 Bartling et al., 1988
1 USEPA, 1988
-------
Section 2
Revision 1
Date: 4/89
Page 1 of 6
Section 2
Project Description
2.1 Project Overview
The DDRP is an integral part of the
Aquatic Effects Research Program (AERP) of
the EPA and is conducted as part of the
interagency, federally mandated National Acid
Precipitation Assessment Program (NAPAP)
which addresses the concern over potential
acidification of surface waters by atmospheric
deposition within the United States. The
overall purpose of DDRP is to characterize
geographic regions of the United States by
predicting the long-term response of water-
sheds and surface waters to acidic deposition.
The DDRP has been designed under the
concept of regionalized, integrative surveys
which initially are approached from a large
region of study and lead to the selection and
study of regionally characteristic systems.
These systems can be assessed through
detailed, process-oriented research which aid
in the understanding of the underlying mecha-
nisms responsible for observed effects. The
projected responses of watershed systems
typical of the regional population can then be
extrapolated to a regional or national scale.
The EPA is assessing the role that
atmospheric deposition of sulfur plays in
controlling long-term acidification of surface
waters (USEPA, 1985). Recent trend analyses
have indicated that the rate of sulfur deposi-
tion is either unchanging or slowly declining in
the Northeastern United States, but is increa-
sing in the Southeastern United States. If a
"direct" response exists between sulfur deposi-
tion and surface water alkalinity, then the
extent of current effects on surface water
probably would not change much at current
levels o* deposition, and conditions would
improve as the levels of deposition decline. If
surface water chemistry changes in a "delayed"
manner, e.g., due to chemical changes in the
watershed, then future changes in water
chemistry (even with level or declining rates of
deposition) become difficult to predict. This
range of potential effects has clear and signifi-
cant implications to public policy decisions on
sulfur emissions control strategies.
The DDRP focuses on regions of the
United States that have been identified as
potentially sensitive to surface water acidifica-
tion. The Northeastern Soil Survey was con-
ducted in 1985 in the New England states of
Maine, New Hampshire, Vermont, Massachu-
setts, Connecticut, Rhode Island, and portions
of New York and Pennsylvania. The Southern
Blue Ridge Province Soil Survey was conducted
in 1986 in portions of Virginia, Tennessee,
North Carolina, South Carolina, and Georgia.
The Mid-Appalachian Soil Survey (MASS)
includes portions of Pennsylvania, Virginia, and
West Virginia (see Figure 2-1). Surface water
acidification in these regions was studied
during the Eastern Lakes Survey in 1984 and in
the National Stream Survey, Phase-I, in 1985.
Northeast
Region
Mid-Appalachian
Region
Southern Blue Ridge
Province
Figure 2-1. Regional Surveys of the Direct/Delayed
Response Project (from Lee et al., 1989)
-------
Section 2
Revision 1
Date: 4/89
Page 2 of 6
Specific goals of the DORP are to: (1)
characterize the variability of soil and water-
shed attributes across these regions; (2)
determine which of the soil and watershed
characteristics are most strongly related to
surface-water chemistry; (3) estimate the
relative importance of key watershed proces-
ses in controlling surface water chemistry
across the regions of concern; and (4) classify
the sample of watersheds with regard to their
response characteristics and extrapolate the
results from the sample of watersheds to the
regions of concern (USEPA, 1989). Figure 2-2
displays a general outline of the DDRP activi-
ties in support of these goals.
A variety of data sources and methods
of analysis are used to address the objectives
of DDRP. In addition to the data collected
during DDRP, other data sources include the
following data bases:
• National Surface Water Survey (NSWS)
[water chemistry data],
• Acid Deposition Data Network (ADDNET),
including GEOECOLOGY [atmospheric
precipitation chemistry data],
• Soil Conservation Service (SCS) Soils-5
[soil physical and chemical data],
• Topographic and Acid Deposition System
(ADS) [total sulfur deposition data],
• U.S. Geological Survey [runoff data].
Also, data from EPA long-term monitoring
sites, episodic event monitoring sites, and
intensively studied watersheds are used in the
data analysis. The data that are collected are
analyzed at three levels. Level I involves the
system description and statistical analysis,
Level II provides single factor response-time
estimates, and Level III involves dynamic
systems modeling.
Field and laboratory data collected in the
DDRP and NSWS are included in the Level I
system description. These data are used in
Level II models to assess the key processes
that regulate the dynamics of base cation
supply and sulfur retention within watersheds.
Integrated watershed data are used in Level
III to calibrate three dynamic simulation mod-
els, MAGIC (Cosby et al., 1984). ILWAS (Chen
et al., 1984), and Trickle-Down (Schnoor et al.,
1984), that predict future regional responses to
acidic deposition. Uncertainty and error prop-
agation estimates are an important part of all
three levels of analysis (USEPA, 1989).
2.2 Project Organization
Figure 2-3 illustrates the operational
management structure of the MASS and Figure
2-4 illustrates the local organizational manage-
ment of the QA staff. The director of the
Office of Environmental Processes and Effects
Research (OEPER) is the EPA official who has
overall responsibility for programs within EPA
that address the effects of acidic deposition.
2.2.1 Project Management
The EPA management structure for the
DDRP surveys is described in the following
subsections.
2.2.1.1 Program Director-
The program director of the Aquatic
Effects Research Program is the EPA Head-
quarters representative for DDRP and is the
liaison between the headquarters staff, the
laboratory directors, and NAPAP. Questions
regarding general management and resources
should be forwarded to the program director
through the technical director.
2.2.1.2 Technical Director-
The DDRP technical director is assigned
tasks at the discretion of the AERP director.
The primary role of the technical director is to
maintain the integrity of program objectives, to
integrate components of the program, and to
ensure that deadlines are met. The technical
director coordinates and integrates the DDRP
activities of the EPA Environmental Research
Laboratory at Corvallis, Oregon (ERL-C) and
the Environmental Monitoring Systems Labor-
atory at Las Vegas, Nevada (EMSL-LV). The
technical director coordinates peer reviews,
resolves issues of responsibility, and dissemi-
nates information to the public. The techni-
cal director represents the program director as
-------
Section 2
Revision 1
Date: 4/89
Page 3 of 6
WATERSHED SELECTION
(ERL-C)
WATERSHED MAPPING
(SCS)
SAMPLING CLASS/PEDON
SELECTION
(ERL-C/SCS)
SOIL SAMPLING
(SCS)
SOIL PREPARATION
(EMSL-LV)
SOIL ANALYSIS
(EMSL-LV)
DATA VERIFICATION
(EMSL-LV)
DATA VALIDATION
(ERL-C)
rERIFICATIOH
(ERL-C)
INAGEMENT
(ORNL)
DATA ANALYSIS
(ERL-C)
Figure 2-2. Major Operations In the Direct/Delayed Response Project Mid-Appalachian Soil Survey
necessary and informs the program director
of EPA laboratory activities, progress, and
performance.
2.2.1.3 Technical Monitor
A technical monitor at each EPA labora-
tory is responsible for specific DDRP tasks
assigned by the technical director. Responsi-
bilities include the employment and coordina-
tion of groups of individuals to accomplish the
assigned tasks. The technical monitor per-
forms reviews and audits, resolves issues of
responsibility, and informs EPA management
of progress and performance. The ERL-C
technical monitor has the primary responsibility
for soil mapping and sampling activities. The
EMSL-LV technical monitor has the primary
responsibility for soil preparation and physical
/chemical analysis activities.
-------
Section 2
Revision 1
Date: 4/89
Page 4 of 6
Director of Office of
Environmental Processes
and Effects Research
Director of Acid Deposition
and Atmospheric Research
Division
Director of Aquatic Effects
Research Program
ERL-Corvallis
Mapping Supervision
Happing/Sampling QA
Sampling Supervision
Data Validation
Data Interpretation
Reporting
EMSL-Las Vegas
Sampling Logistics
Soil Preparation
Analysis Supervision
Preparation/Analysis QA
Data Entry
Data Verification
Reporting
Figure 2-3. Operational management structure for the Mid-Appalachian Soil Survey of the Direct/Delayed
Response Project.
2.2.1.4 Quality Assurance Officer
The QA officer is responsible for ensur-
ing that each project within an EPA laboratory
satisfies the agency's requirements for QA
programs. The QA officer evaluates QA plans,
coordinates and supervises system audits,
disseminates information, and is responsible
for the coordination and oversight of the
division QA officers.
2.2.1.5 Division Quality Assurance Officer
The division QA officer is tasked with
overseeing the QA operations of specific tasks
or projects. Responsibilities include facilitating
the attainment of quality objectives, coordinat-
ing system audits, and resolving QA issues
relating to a specific project. The division QA
officer acts as a liaison with the QA staff and
technical monitor in addressing QA issues.
-------
Section 2
Revision 1
Date: 4/89
Page 5 of 6
Division QA Officer
EMSL-LV
Figure 2-4. Organizational management structure of
the Quality Assurance Staff at the
Environmental Monitoring Systems
Laboratory, Las Vegas, Nevada
2.2.2 Laboratory Responsibilities
A summary of the different DDRP staff
responsibilities at each cooperating laboratory
is provided below.
2.2.2.1 Environmental Research
Laboratory, Corvallis, Oregon
The DDRP staff at ERL-C is involved
in all aspects of the DDRP soil surveys.
Responsibilities of ERL-C personnel for the
overall program include:
• developing the experimental design and
QA oversight for soil mapping and sam-
pling,
• collecting supplemental historical and
other available data on each sampling site,
• coordinating the temporary storage of
samples in the field and their transfer to
the preparation laboratory,
• analyzing and validating data (jointly with
EMSL-LV),
• interpreting data,
• preparing reports (final and progress
reports with contributions from the other
laboratories relative to their responsibilities),
• assessing and resolving all science-
related issues other than QA or data
management (jointly with other laborato-
ries as necessary), and
• coordinating survey activities with NAPAP
management staff.
2.2.2.2 Environmental Monitoring Systems
Laboratory, Las Vegas, Nevada
The DDRP staff at EMSL-LV has exper-
tise in matters relating to QA implementation,
logistics, and analytical services. The respon-
sibilities of personnel at EMSL-LV include:
• developing QE/QC procedures for all
sample preparation and analysis compo-
nents,
• developing and implementing preparation
laboratory and analytical laboratory oper-
ating procedures,
• developing and implementing analytical
methods,
• coordinating logistical support and equip-
ment needs for all field and preparation
laboratory operations,
• distributing processed samples to ana-
lytical laboratories,
• developing and implementing the verifica-
tion and management procedures for
sampling, preparation, and analytical data,
• developing and implementing the QA
project plan and corresponding laboratory
manuals,
• analyzing data (jointly with ERL-C),
• preparing QA data reports for preparation
laboratory and analytical laboratory opera-
tions, and
• resolving issues pertaining to QE/QC,
logistics, and analytical services.
2.2.3 Primary Cooperators
The use of interagency agreements by
EPA has allowed the DDRP to tap support
from several groups that have expertise in
areas of importance to the project, as outlined
below.
-------
Section 2
Revision 1
Date: 4/89
Page 6 of 6
2.2.3.1 Oak Ridge National
Laboratory, Oak Ridge,
Tennessee
The DDRP staff at the Oak Ridge Nation-
al Laboratory (ORNL) has expertise in manag-
ing, manipulating, and restructuring large data
bases to satisfy data analysis needs. ERL-C
oversees the activities of ORNL, which has
responsibilities for:
• developing and maintaining a data base
management system,
• preparing computer-generated summary
tables, statistics, and graphics for reports,
and
• assisting with data validation and inter-
pretation activities.
2.2.3.2 Soil Conservation Service
The United States Department of Agricul-
ture (USDA), Soil Conservation Service (SCS)
has expertise in soil mapping and characteriza-
tion on a national scale. Under the supervi-
sion of ERL-C, the SCS has responsibilities
for:
• soil mapping of the MASS watersheds,
• site selection and characterization, and
• soil sampling of selected pedons.
-------
Section 3
Revision 1
Date: 4/89
Page 1 of 2
Section 3
Sampling Strategy
Following is a brief summary of the
considerations involved in designing the MASS
sampling strategy. Additional details are pro-
vided in documents prepared by the DDRP
staff at ERL-C, who had the primary responsi-
bility for sampling in the MASS.
3.1 Watershed Selection and
Mapping
Since the objectives of the DDRP are
focused on making regional inferences, the
watersheds selected for mapping of soils and
watershed characteristics must constitute a
representative sample of a region. After
eliminating watersheds having known acid
mine drainage and prohibitively large water-
sheds exceeding 3,000 hectares in area, 79
watersheds included in the Pilot Survey of the
National Stream Survey-Phase I (NSS-I) pro-
vided an excellent starting point from which to
draw the subsample for MASS. These water-
sheds satisfied the requirements because: (1)
the NSS-I pilot survey stream reaches were
selected according to a rigorous probability
sampling method, i.e., the reaches were strati-
fied by three subregions and three alkalinity
classes within each subregion; and (2) water
chemistry information was available from NSS-
I for the reaches (Messer et al., 1986).
The preliminary procedure for selecting
the MASS watersheds consisted of eliminating
certain watersheds according to strictly
defined criteria, as follows:
• acid-neutralizing capacity (ANC) of greater
than 200 microequivalents per liter,
• suspected residual effects of acid mine
drainage,
• a geomorphic history of Pleistocene glacia-
tion within the northern portion of the Mid-
Appalachian region.
Twenty-three watersheds having high
ANC were deleted because they were consi-
dered to be at low risk for future acidification.
Twelve watersheds identified as harboring
mine spoils or other disturbances were deleted
because of their anticipated inordinate effect
on chemical characteristics of the soils. Seven
watersheds with a history of glaciation were
deleted because it was decided that soils in
these areas would exhibit characteristics
similar to those soils previously sampled in
the DDRP Northeastern Soil Survey. Hence, 37
watersheds from NSS-I remained for potential
sampling in the MASS.
The 37 watersheds were mapped during
the autumn of 1987 and the spring of 1988
according to the mapping protocols (Lammers
et al., 1988). With the exception of certain
highlighted modifications, the protocols are
based on standard procedures developed for
the National Cooperative Soil Survey (NCSS)
and also describe the preparation of uniform
soil profile descriptions, mapping legends, and
the QA program for mapping. A second order
soils map at a 1:24,000 scale is specified.
Mapping units are mostly consociations and
complexes, and the components of the map-
ping units are phases of soil series, higher
categories, or miscellaneous areas. Contras-
ting inclusions are limited to a maximum of 20
percent for soil components and 15 percent for
miscellaneous areas.
Field review and evaluation of the map-
ping by the soil mapping task leader, a Soil
Conservation Service (SCS) state office repre-
sentative (SOR), and an independent regional
correlator/coordinator (RCC) ensures the
consistent application of the mapping proto-
cols and the correlation of mapping units
among the mapping parties. Soil map adequa-
cy is qualitatively evaluated for soil features
relevant to the DDRP, such as permeability
or slope class. Correctness of the mapping
is determined by point observations along
selected transects using an approach similar
to that outlined in Steers and Hajek (1979).
-------
Section 3
Revision 1
Date: 4/89
Page 2 of 2
Both the degree of error and the number of
errors are considered for a designated size
area. Key mapping and supervisory personnel
attend a final correlation workshop to review
and correlate the mapping units and delinea-
tions across the region.
3.2 Selection of Sampling
Classes
Sampling each of the soil mapping units
is not necessarily the best way to describe the
chemistry of the soils in a region. A more
straightforward procedure is to combine the
mapping units into groups, or sampling clas-
ses, which are either known to have or are
expected to have similar characteristics. Each
of these sampling classes can be sampled
from a number of watersheds, and the mean
characteristics of each sampling class can be
computed. The mean and variance values can
be used to construct area-weighted or volume-
weighted estimates of the characteristics for
each watershed. For this procedure to work,
at least five samples must be taken to char-
acterize the variability of each sampling class.
The objective is to develop a method of group-
ing the large number of soils into a reasonable
number of sampling classes. The spatial
distribution of mapping units and the relative
proportion of the components of the mapping
units are used to aggregate the watershed
attributes.
Specific information relating to sampling
classes can be found in the sampling proto-
cols (Kern et al., 1986).
-------
Section 4
Revision 1
Date: 4/89
Page 1 of 9
Section 4
Field and Laboratory Operations
This section outlines the operational and
logistical activities required for the sampling,
preparation, and analysis of soils for the
MASS. Specific information on the individual
phases can be found in the soil sampling
manual (Kern et al., 1988), the preparation
laboratory standard operating procedures
(Bartling et al., 1988), and the analytical
laboratory statements of work (USEPA, 1988),
respectively.
4.1 Field Operations
Field operations are conducted under the
supervision of ERL-C staff. The primary parti-
cipants are SCS soil scientists tasked through
an interagency agreement. Complete details
of the MASS field operations can be found in
the mapping protocols (Lammers et al., 1988)
and the sampling manual (Kern et al., 1988).
4.1.1 Personnel
Approximately five sampling crews, each
composed of three to four qualified persons,
perform the field sampling operations for the
MASS. These crews are responsible for the
description of soil and site characteristics,
sample collection, and shipment of samples to
the preparation laboratory. Members of each
crew are soil scientists experienced in the
National Cooperative Soil Survey characteriza-
tion procedures. The crew leader is responsi-
ble for selecting the sampling site and docu-
menting all field data.
4.1.2 Sampling Activities
Several steps are necessary to complete
soil sampling activities at a particular site;
descriptions of these activities follow.
4.1.2.1 Description of Sampling Site
and Soil Profile
After the sampling site is located, a pit
large enough for sampling all major horizons is
excavated to a depth of 2.0 meters or to
bedrock. The soil profile is described accord-
ing to the sampling protocols and the data are
recorded on an SCS-SOI-232 field data form
(see Appendix A). Other descriptive informa-
tion, e.g., site characteristics or herbicide
contamination, also is recorded on the field
data form.
4.1.2.2 Sampling Procedure
For each soil horizon greater than 3
centimeters thick identified on the field data
form, the sampling crew collects a soil sample
weighing approximately 5.5 kilograms after
sieving to exclude rock fragments exceeding 20
millimeters in diameter. A sufficient quantity of
sample should be collected to ensure that a
minimm of 2 kg of less than 2-mm material is
available after sample preparation. Sample
bags are labeled with DDRP Label A (see
Appendix A) which identifies the date the
sample was taken, the crew that collected the
sample, and the appropriate site, sample, and
set identification (ID) codes. The thirteen-
character sample code is an alpha-numeric
coding of the sample type, e.g., routine, audit,
or field duplicate; the number of bags per
sample; the SCS state and county code; and
the pedon/horizon code. The identification and
sample numbering scheme yields unique alpha-
numeric labels for each pedon and for each
sample collected from the pedon.
The sampling protocols specify the
procedures for ensuring the integrity of the
samples. Specific instructions on excavating
-------
Section 4
Revision 1
Date: 4/89
Page 2 of 9
and draining (if necessary) soil pits are given.
Crews are provided with standard containers
for sample collection. Samples are kept as
cool as possible in the field and during trans-
port to the preparation laboratory. To maintain
their integrity, samples are to be stored at an
ambient air temperature of 4°C within 24
hours of sampling. This can be accomplished
by temporarily storing the samples in rented
cold lockers or in styrofoam coolers with
frozen gel packs.
It is anticipated that 150 pedons are to
be sampled during the MASS, with each pedon
yielding an average of six routine samples
from the component horizons.
4.1.2.3 Collection of the Field
Duplicate Sample--
Each sampling crew samples one horizon
in duplicate from every third pedon. The type
of horizon chosen for this field duplicate
(FD) sample is based on random selection,
although the crew is instructed to avoid contin-
uous sampling of the same type of horizon to
ensure that FOs for each crew are sampled
across the complete range of possible hori-
zons.
The sampling procedure specifies that
the FD and its corresponding routine sam-
ple are collected simultaneously. Alternate
trowels-full of soil are removed from the hori-
zon and are placed into separate sample bags
until two samples of fine earth material equal
to approximately 5.5 kg each are collected.
The FDs are processed and analyzed in the
same manner as the routine soil samples. The
analytical results are used in the assessment
of measurement variability attributed to sam-
pling, preparation, and analysis that contribute
to the overall measurement uncertainty.
4.1.2.4 Handling of the Field Audit
Samples-
Field audit samples are supplied to the
crews by the QA staff. On each day that
crews sample the FD, they are required to
sieve, rebag, and label three audit samples
while at the sampling site. The samples are
sent with the routine samples to the prepara-
tion laboratory for processing and subsequent-
ly to the analytical laboratories for analysis.
The results are used to assess the overall
measurement uncertainty and system-wide
detectability.
4.1.2.5 Collection of the Bulk Density
Samples-
To allow the determination of bulk densi-
ty, natural soil clods are collected in triplicate,
where possible, from each soil horizon. The
clods are placed in hairnets and dipped in a
Saran solution to preserve their structural
integrity during transport to the preparation
laboratory. The clods are carefully packed in
clod boxes with cushioned packing material to
absorb excess shock and vibration during
transport.
Where clods cannot be collected, known
volumes of soil are collected in duplicate. The
volume of soil is estimated by replacing a
small excavated cavity with a known volume of
foam beads. If the clod method or volume
replacement method do not yield satisfactory
samples, a volume filling method is initiated by
filling a container of known volume with soil
and attempting to pack it to the same density
normally encountered in the horizon being
sampled.
4.1.2.6 Sample Shipments-
Sampling crews are responsible for the
shipment of soil samples and pedon descrip-
tions to the preparation laboratory. Shipments
must include a packing slip detailing the num-
ber of boxes sent and the contents of the
shipment, i.e., number of samples and forms.
An overnight courier service is used for ship-
ment of all bulk samples. Clods are shipped
in clod boxes packed inside of styrofoam
coolers.
4.2 Preparation Laboratory
Operations
The preparation laboratory is located at
EMSL-LV and is the intermediate link between
-------
Section 4
Revision 1
Date: 4/89
Page 3 of 9
the sampling crews and the analytical labora-
tories. The bulk soil samples collected by the
sampling crews are sent via overnight courier
to the preparation laboratory, where the labor-
atory staff processes the samples and pre-
pares homogeneous, anonymous subsamples
that are shipped to the analytical laboratories.
To accomplish these tasks successfully, the
preparation laboratory must uniformly track,
process, and store all samples. Complete
details of the preparation laboratory activities
are prescribed in the Preparation Laboratory
Standard Operating Procedure (PLSOP)
(Bartling et al., 1988).
4.2.1 Personnel
Preparation laboratory operations are
performed by approximately six laboratory
analysts under the supervision of the prepara-
tion laboratory manager. The manager must
be an experienced soil scientist and has the
responsibility of training and supervising the
analysts. Each analyst is responsible for
certain processing activities and is required to
uniformly enter raw data on specified data
forms.
4.2.2 Preparation Activities
Sample preparation involves a number of
activities in order to produce homogeneous
and representative subsamples of soil for
analysis, as described below.
4.2.2.1 Sample Receipt-
The sampling crew member responsible
for shipping samples notifies the laboratory
manager when samples are shipped from the
sampling sites to the preparation laboratory.
Bulk samples and completed packing lists are
shipped via overnight courier directly to the
EMSL-LV cold storage facility. All incoming
soil samples are received by preparation
laboratory personnel, who record information
for the samples on the Sample Receipt Raw
Data Form (see Appendix B). Additional notes
must be kept in a logbook that is submitted to
the QA manager at the end of the survey.
Each shipment is checked upon arrival
to ensure that all samples sent have been
received. The condition of the samples is
noted on the receipt form and any samples
that are damaged or missing are reported to
the QA manager. Samples are grouped in sets
that are maintained throughout storage, pro-
cessing, and shipping.
The original field data forms and photo-
copies of the appropriate sampling forms are
included in the sample shipment. Information
on these forms is used to check sample
labeling and to document the receipt of sam-
ples. The forms are then forwarded to the QA
manager.
4.2.2.2 Sample Storage--
All soil samples are placed in cold stor-
age immediately upon receipt and remain there
until they undergo further processing. After air
drying, the samples are returned to cold stor-
age and remain there when not undergoing
processing. Cold storage is required to main-
tain sample integrity and to minimize microbial
degradation. The laboratory analyst responsi-
ble for sample storage organizes the samples
for easy and efficient access. The cold stor-
age area is maintained at a temperature
of 4°C and is monitored regularly.
4.2.2.3 Sample Processing and
Analysis-
Sample integrity is protected during all
steps of sample handling. The unique identity
of each sample is maintained by labeling with
the sample code, by documenting the status
of each sample through all processing and
analyses, and by avoiding physical and chemi-
cal contamination during each processing step.
The analyses specified in Table 4-1 and in
Appendix C are accomplished at the prepara-
tion laboratory. These include field-moist pH,
air-dry moisture, organic matter by loss-on-
ignition, rock fragments, and bulk density.
Bulk samples are allowed to air dry in
a contamination-protected room until they
achieve a prescribed moisture content. Any
observations of microbial growth or other
-------
Section 4
Revision 1
Date: 4/89
Page 4 of 9
unusual characteristics are noted in a logbook.
If the calculated moisture content of a compo-
site subsample is below 2.5 percent for miner-
al soils or below 6.0 percent for organic soils,
the sample is considered air dry.
Table 4-1. Preparation Laboratory Parameter* and
Corresponding Analytical Technique*
Parameter
Technique
PH_MP Combination electrode/millivoltmeter
MOIST_P Gravimetric
OM_LOI Loss-on-ignition
RF_FG, RF_MG Sieve/gravimetric
BD_CLD Displacement/gravimetric
BD_KV Replacement/gravimetric
If samples labeled as organic soil have
less than 20 percent organic matter or sam-
ples labeled as mineral soil have greater than
20 percent, the QA manager and the soil
sampling task leader are notified immediately
for further instructions concerning the affected
samples.
After a bulk sample is air dry, the soil is
carefully disaggregated and passed through
nested 4.75-mm and 2-mm sieves. After
sieving, the less than 2-mm soil is retained in
a labeled, plastic bottle and placed in cold
storage when not undergoing processing. The
rock fragments are retained for gravimetric
analysis (NOTE: fragments exceeding 20 mm
in diameter are removed in the field). The
fragments are then placed in a pre-labeled,
plastic bag and are archived by set.
In order to obtain representative volumes
of soil, it is necessary to prepare homogen-
eous subsamples from the less than 2-mm
fraction of the bulk samples. Two subsamples
of each bulk sample are obtained through use
of a Jones-type riffle splitter. The subsamples
are placed in plastic bottles labeled with DORP
Label B (see Appendix B) and are organized by
batch in cold storage to await shipment to
separate contract analytical laboratories for
general and elemental analyses. The unused
portions of the bulk soil samples are stored in
labeled plastic bottles and are archived by set
in cold storage.
Bulk density is defined as weight per unit
volume expressed in grams per cubic centi-
meter (g/cm3). Generally, three clod samples
or two known volume samples are extracted
from each horizon. The mean of the replicate
bulk density values is accepted as the bulk
density of a particular horizon.
All raw data obtained are recorded on a
series of preparation laboratory raw data
forms (see Appendix B). All data obtained are
immediately entered into the computer to
ensure proper tracking, processing, and eval-
uation of the samples. Calculated data from
all of the preparation laboratory analyses are
recorded on the summary data form, Form 101,
which is compiled during the batching of
samples. The raw data recorded in logbooks
or on data forms must be submitted to the QA
manager upon completion of the MASS prepar-
ation activities.
4.2.2.4 Data Entry-
Preparation laboratory personnel have
access to a personal computer directly linked
to the QA staff that allows real-time data
evaluation and tracking of samples by both
parties. Data entry screens are developed for
entering information from the raw data forms
used by the analysts. All raw data obtained
during sample receipt and sample analysis are
entered into a temporary file in the computer
by laboratory personnel. The verification
programs are run and printouts of the calcula-
ted data and suspect values are generated
(see Section 6.2 and Appendix D). The preci-
sion and accuracy criteria for QE/QC samples
are checked while the samples are being
processed, permitting the prompt identification
of any discrepancies.
The laboratory manager ensures that
computer entry is current at all times and
checks the calculated data for outliers and
-------
Section 4
Revision 1
Date: 4/89
Page 5 of 9
other possible discrepancies. A computer-
generated spreadsheet shows the status of all
samples received, samples being processed,
and samples shipped. The computer program
produces summary data forms that contain
the final data values.
4.2.2.5 Batch Formation and Sample
Shipment-
Batches of processed soil samples are
assembled as specified in the PLSOP and
shipped to the designated analytical laborato-
ries. Each batch normally contains 40 sam-
ples but may have as many as 42 samples.
With the exception of the QC audit sample
(QCAS), all samples are randomly placed
within a batch and cannot be distinguished by
the recipient laboratory.
The site ID, sample code, and set ID for
each sample are recorded on the DDRP Form
101. This form assigns a unique batch/sample
number to each soil sample. The shipping
form, DDRP Form 102 (see Appendix B), is
completed and sent to the laboratories toge-
ther with the samples. Form 102 uses the
batch/sample information from the Form 101 to
identify each sample while keeping the catego-
ry of sample, e.g., audit or routine, unknown to
the recipient.
4.2.2.6 Sample Tracking-
Sample tracking at the preparation
laboratory is initiated with the computer entry
of sample receipt information upon delivery of
samples to the EMSL-LV cold storage facility.
Since the number of pedons to be sampled in
the MASS is known before sampling begins, a
tally of the pedons received at cold storage
can be useful in assessing field sampling
progress. As the preparation laboratory raw
data are entered into the computer, the QA
staff monitors the proportion of samples that
have undergone processing and analysis to the
total number of samples at the preparation
laboratory. In addition, the preparation labora-
tory entry/verification system is used to track
the samples as batches are shipped to analyti-
cal laboratories. Sample tracking reports
about the sampling, preparation, and analysis
phases of the program are prepared for EPA
management every two weeks.
4.3 Analytical Laboratory
Operations
The sample analysis is conducted by
contract analytical laboratories through a
series of activities outlined in the following
subsections. Specific information on the
analytical operations can be found in the
Analytical Laboratory Statement of Work
(ALSOW) (USEPA, 1988).
4.3.1 Personnel
Analytical laboratory operations are
performed by a group of experienced labora-
tory analysts under the supervision of a labor-
atory manager. The manager must have
several years of experience in laboratory
analysis of soils and has the responsibility of
training and supervising the analysts. Each
analyst is responsible for specific activities
and is required to uniformly enter raw data on
specified data forms. The laboratory must
employ sufficient personnel to ensure delivery
of acceptable data within the time constraints
established in the contract.
4.3.2 Laboratory Activities
A brief description of the analytical
laboratory activities is provided in the following
subsections. Specific information is contained
in the ALSOW.
4.3.2.1 Sample Receipt-
All samples delivered to a contracted
analytical laboratory are checked in by a
receiving clerk who: (1) records on the ship-
ping form the date the samples are received,
(2) evaluates the condition of the samples
received, (3) checks the samples to identify
discrepancies with the shipping form, and (4)
sends copies of the completed shipping forms
to the EPA Sample Management Office (SMO)
and the QA manager or designee. If there are
any discrepancies or problems such as lea-
kage in shipping or an insufficient amount of
-------
Section 4
Revision 1
Date: 4/89
Page 6 of 9
sample, the QA manager must be notified
immediately. The receiving clerk retains a copy
of the completed Form 102 for the laboratory
records. The samples are refrigerated at 4°C
as soon as they are logged in and must be
kept refrigerated when not in use.
4.3.2.2 Sample Homogenization-
The samples received by the analytical
laboratories have been air dried and disaggre-
gated to pass through a 2-mm sieve at the
preparation laboratory. During shipment, the
sample material within each container segre-
gates both by particle size and by density;
therefore, each sample must be re-homoge-
nized by thorough mixing prior to the removal
of aliquots for analysis. Previous research by
the QA staff has been useful in specifying the
homogenization procedures (Schumacher et al.,
in preparation).
4.3.2.3 Measurement of Analytical
Parameters--
Procedures specified in the ALSOW are
to be followed exactly for each parameter.
Table 4-2 summarizes the parameters to be
measured and the corresponding analytical
techniques, and Appendix C contains a more
detailed description of the parameters.
4.3.2.4 Contractual Compliance-
Before a batch of sample data is submit-
ted to the QA staff for verification, certain
completeness and QC requirements must be
met. The batch is not considered acceptable
for complete payment until these requirements
are met (see Section 5.0).
4.3.2.5 Data Entry and Reporting-
The raw data generated during sample
analysis are entered on a personal computer
using a specially designed entry and verifica-
tion system (see Section 6.2.3.1). Data reports
are then generated to assist in the evaluation
of data quality.
In addition, the following documents
must be updated constantly at the analytical
laboratory and must be available, upon re-
quest, to the analysts and the supervisor
involved in the project:
Table 4-2. Analytical Laboratory Parameter* and
Correepondlng Analytical Technique*
Parameter
Technique!
MOIST_A
SAND (and all fractions)
SILT (and all fractions)
CLAY
PH_H2O. PH_002M, PH_01M
CA_CL, CA_OAC. CA_CL2
MG_CL, MGJDAC. MG_CL2
K_OAC. K_CL. K_CL2
NA_CL. NA_OAC. NA_CL2
CEC_CL, CEC_OAC
ACJBACL
FE CL2. FE PYP, FE_AO.
Ft CD. AL CL2. AL PYP.
AL>O, ALjCD, SI_AO,
AL_CL
SO4JH2O, SO4 PO4,
SO4JO-32
CJTOT, N_TOT
S TOT
Gravimetric
Sieve/gravimetric
Pipet/gravimetric
Pipet/gravimetric
Combination electrode/
millivolt meter
AA or ICP
AA or ICP
AAor FP
AA, ICP, or FP
Autotitration or FIA
Titrimetric
ICP
Ion chromatography
oxidation/combustion
with IR or TC
oxidation/combustion
with IR
AA = atomic absorption spectroscopy
ICP= inductively coupled plasma atomic emission
spectroscopy
FP = flame emission photometry
FIA = flow injection analysis
IR = infrared detection
TC = thermal conductivity
• Standard operating procedures - detailed
instructions about the laboratory and
instrument operations.
-------
Section 4
Revision 1
Date: 4/89
Page 7 of 9
Laboratory QA plan - clearly defined
laboratory protocol, including personnel
responsibilities and use of QC samples.
List of in-house samples ~ includes dates
for completion of analyses, and allowing
the analysts to schedule further analyses.
Instrument performance data - informa-
tion about baseline noise, calibration
standard response, precision as a function
of concentration, and detection limits;
used by analysts and supervisor to evalu-
ate daily instrument performance.
QC charts - plots of warning and control
limits for all quality control check samples
(QCCS) and detection limit QCCS (DL-
QCCS) generated and updated for each
batch; the same QCCS stock solution
should be used throughout the term of
sample analysis in order to ensure conti-
nuity of the control chart. (NOTE: the
purpose of preparing QCCS control charts
is to ensure that the actual control limits
do not exceed the limits defined by the
contract.)
QC data report -- a computer printout
generated by the laboratory manager that
reviews QC results for each parameter;
specifies flags (Table 4-3) that are used to
document all results outside statistically
established QC limits and to identify sam-
ples that require reanalysis before data
are submitted.
Table 4-3. Laboratory Data Qualifier*
Data qualifier
Indication
A Instrument unstable
B Reanalyzed, first reading not acceptable
F Result outside criteria with consent of QA
manager
G Result obtained from method of standard
additions
J Result not available; insufficient sample
volume shipped to laboratory
L Result not available because of interfer-
ence
M Result not available; sample lost or
destroyed by laboratory
N Result outside QA criteria
P Result outside criteria, but insufficient
volume for reanalysis
R Result from reanalysis
S Contamination suspected
T Container broken
U Result unnecessary, or not required by
procedure
X No sample
Y [Available for miscellaneous comments]
Z Result from approved alternative method
-------
Section 4
Revision 1
Date: 4/89
Page 8 of 9
Examples of the data reporting forms
used by the analytical laboratory are provided
in the ALSOW, although examples of certain
QE/QC reporting forms are provided in Appen-
dix D. When applicable, data are annotated
with the appropriate data qualifiers listed in
Table 4-3. Results must be reported to the
number of decimal places listed in Table 4-4.
Table 4-4. Decimal-Place Reporting Requirement*
Reporting Decimal
Parameter units Places
MOIST A
SAND (and all fractions)
SILT (and coarse silt)
CLAY (and fine silt)
PHJH20, PH_002M, PH_01M
CA CL. MG CL. K CL, NA CL,
AL_CL
CA OAC. MG OAC. K OAC,
NA_OAC.
CEC_CL. CEC_OAC, AC_BACL
wt. %
wt. %
wt. %
wt. %
pH units
meq/100g
meq/100g
meq/100g
2
2
2
4
2
3
3
3
CA CL2, MG CL2, K_CL2,
NA_CL2. FE_CL2. AL_CL2 meq/100g
FE PYP. AL PYP, FE AO, AL AO.
SI_AO, FE_CD. ALjCD
SO4_H2O. SO4_PO4
SO4_0-32
C_TOT, N_TOT
S_TOT
wt. %
mg S/kg
mg S/L
wt. %
wt. %
3
3
3
3
4
All deviations from the analytical proto-
cols must be documented. All original raw
data such as data system printouts, chroma-
tograms, logbooks, individual data sheets, QC
charts, and standard preparation data should
be retained by the analytical laboratory and be
available upon request.
4.3.2.6 Evaluation of Quality Control
Data-
A data entry and verification system (see
Section 6) that is provided to the analytical
laboratories includes programs that evaluate
the QC data and report the information for
each parameter in a summary report. The
objective of these summary reports is to keep
the QA manager informed of the status of the
internal QC checks at the laboratory in order
to identify and resolve any problems that may
arise. Before laboratories submit a batch of
data to the QA staff, all parameters must
satisfy the QC requirements outlined in the
contract. The laboratory managers are ex-
pected to keep the QA manager apprised, on
a regular basis, of the evaluation of the QC
data either by phone or by sending preliminary
data electronically to the QA staff for similar
evaluation.
4.3.2.7 Communications-
Frequent communications, i.e., two or
three contacts each week, are maintained with
each analytical laboratory in order to obtain
current sample status and to discuss any
problems that may occur during analyses.
These discussions are to be recorded in a
logbook by the QA staff and the laboratory
staff. Preliminary and final data are available
for access via electronic transfer. The prelimi-
nary data are reviewed for anomalies and if a
problem is identified, the laboratory is notified.
Corrective action or reanalysis may be sug-
gested. Contractual issues are referred to the
QA manager and to the contract officer. Major
technical issues are referred to the QA manag-
er.
4.4 Sample Custody
Legal chain-of-custody procedures are
not required for this study; however, sample
custody must be documented. The field crew
leader is responsible for all samples in the
-------
Section 4
Revision 1
Date: 4/89
Page 9 of 9
field as well as the sample shipments. As soil the soil sampling task leader and the field
shipments are received in the preparation crew leader are notified. Following receipt of
laboratory, they are immediately logged in and samples, the appropriate laboratory managers
checked against the packing slip and the are responsible for sample integrity until the
pedon description forms for proper labeling samples are archived.
and quantity. If any discrepancies are found,
-------
-------
Section 5
Revision 1
Date: 4/89
Page 1 of 17
Section 5
Quality Assurance Program
This section describes the MASS QA
program which is designed to allow both
control and assessment of measurement
uncertainty during the sampling, preparation,
and analysis phases of the survey.
5.1 Overview of Quality
Objectives
The data collection criteria provide a
balance between constraints of time and cost
and the quality of data necessary to achieve
the DDRP research objectives. The MASS QA
Plan is designed to accomplish the following
general objectives:
• establish the QE/QC criteria used to
control and assess data collected in the
survey;
• provide standardized sampling, prepar-
ation, and analytical methods and proce-
dures;
• utilize assessment samples and proce-
dures to verify the quality of the data;
• perform field and laboratory on-site audits
to ensure that all activities are properly
performed and that discrepancies are
identified and resolved; and
• evaluate the data and document the
results in QA reports to EPA management.
To aid in this effort, it is necessary to
identify both qualitative and quantitative esti-
mates of the quality of data needed by the
DDRP data users. Guidelines established by
the EPA Quality Assurance Management Staff
(Stanley and Verner, 1985) encourage the data
users to clearly identify the decisions that will
be made and to specify the calculations,
statistical and otherwise, that are applied to
the data.
The raw data for the DDRP surveys are
collected during four major operational phases
consisting of mapping, sampling, preparation,
and analysis. A certain amount of data mea-
surement uncertainty is expected to enter the
system at each phase. Grouping of the data,
e.g., by sampling class/horizon configurations,
also increases uncertainty. The sampling
population itself is a source of confounded
uncertainty that is extremely difficult to quanti-
fy. Generally, the data quality objectives
(DQOs) encompass the overall allowable
uncertainty from sample measurement and
from the sampling population that the data
users are willing to accept in the analytical
results (Taylor, 1987). Because of the many
confounded sources of uncertainty, overall
DQOs for the MASS have not been defined.
This QA Plan focuses on the definition,
implementation, and assessment of measure-
ment quality objectives (MQOs) that are speci-
fied for the entire sample preparation and
analysis phases of data collection, as well as
for the verification of the field sampling phase.
The MQOs are more or less specific goals
defined by the data users that clearly describe
the data quality that is sought for each of the
measurement phases. The MQOs are defined
according to the following six attributes:
• Detectability ~ the lowest concentration of
an analyte that a specific analytical proce-
dure can reliably detect.
• Precision — the level of agreement among
multiple measurements of the same char-
acteristic.
• Accuracy - the difference between an
observed value and the true value of the
parameter being estimated.
• Representativeness - the degree to which
the data collected accurately represent the
population of interest.
• Completeness - the quantity of data that
is successfully collected with respect to
the amount intended in the experimental
design.
• Comparability - the similarity of data from
different sources included within individual
or multiple data sets; the similarity of
analytical methods and data from related
projects across regions of concern.
-------
Section 5
Revision 1
Date: 4/89
Page 2 of 17
An attempt has been made later in this
section to define each quality attribute individu-
ally for each measurement phase of the pro-
gram (sampling, preparation, analysis) or
collectively for overall measurement system
uncertainty on a parameter-by-parameter
basis. The term "uncertainty" is used as a
generic term to describe the sum of all sour-
ces of error associated with a given portion of
the measurement system. In order to reduce
measurement uncertainty in the final data
values, error occurring in each phase must be
identified and rectified during that phase of the
survey. Measurement uncertainty for the
MASS is controlled through well-defined proto-
cols, system audits, and the introduction of
measurement quality samples at each mea-
surement phase.
Initial MQOs were established on the
basis of the requirements of EPA data users
and on the selection of appropriate methods
to obtain the data. The MQOs were reviewed
by persons familiar with analytical methods
and soil characterization techniques, including
soil chemists and laboratory personnel.
Modifications to the MQOs and to the proto-
cols were implemented based on information
gained from the DDRP Northeastern region and
Southern Blue Ridge Province surveys, from
peer review comments, or according to the
limitations of a particular analytical procedure
or instrument.
If the data quality goals cannot be met
during the course of the project, the actual
level of quality is used to reassess the intend-
ed use of the data. A lower than desired
attainment of data quality could require differ-
ent approaches to be used in data analysis or
may result in modifications to the levels of
confidence assigned to the data.
5.2 Design Characteristics
The design of the DDRP QA Program
incorporates the concept of batch sample
analysis, where the soil samples collected in
the field are combined into groups called
batches. Within these batches a series of
different types of measurement quality
samples are included which are used to evalu-
ate and, possibly, control various types of
measurement uncertainty. There are two
distinct groups of measurement quality sam-
ples used in the MASS design: quality evalua-
tion (QE) samples and quality control (QC)
samples. The QE samples allow an indepen-
dent assessment by the QA staff, while the
QC samples enable a laboratory to control
measurement error and to meet contractual
requirements. In order to assess the MQOs,
a series of both types of samples must be
analyzed together with the routine samples in
a manner that is statistically relevant and in
which conclusions concerning the quality of
data can be made. There are a number of
assumptions upon which this design has been
based:
• Complete attainment of the analytical
MQOs can be accomplished if each batch
meets the within-batch MQOs set for the
data quality attributes.
• A two-tiered approach to measuring ana-
lytical precision allows the low concentra-
tion samples to be assessed separately
from the mid-range and high concentration
samples; a knot value separates the two
tiers and is determined by dividing the
precision objective for the lower tier by the
precision objective for the upper tier and
multiplying by 100.
• Approximately 25 percent of the overall soil
analysis effort is applied to the analysis of
QE/QC samples required for assessment
purposes; normally there are 40 samples
in a batch, hence, about 10 of these are
measurement quality samples.
• Measurement uncertainty at any given
level, e.g., analytical within-batch precision,
should be evaluated by the analysis of a
minimum of 20 QE samples of any given
type; since 30 to 35 batches are expected
to be analyzed during MASS, at least one
sample used in the evaluation of overall
measurement uncertainty is included in
each batch.
• The primary sources of measurement
uncertainty to be identified, controlled, and
assessed are from sampling, preparation,
and analysis.
-------
Section 5
Revision 1
Date: 4/89
Page 3 of 17
• Each of the primary sources of uncertainty
may be considered to be a combination of
several smaller sources of uncertainty,
e.g., the analytical component can consist
of between-laboratory, between-batch,
within-laboratory, and within-batch uncer-
tainties.
• Accuracy includes characteristics of audit
sample control windows, bias in relation
to other laboratories, and trends relative
to the accepted "true" value.
To date, it has not been feasible to pro-
vide one QE or QC sample type whose entire
variability is a reflection of overall measure-
ment uncertainty. However, the combination
of two or more different types of measurement
quality samples which include the quantifiable
components of system measurement uncer-
tainty can provide a reasonable and defensible
estimate of overall data quality.
Figures 5-1 and 5-2 graphically illustrate
the QE/QC samples that are placed in each
mineral or organic batch of analytical samples
to determine measurement uncertainty. There
are separate analytical within-batch precision
MQOs for organic and mineral soils because
of the expected wide variability in analyte
concentrations. For this reason, mineral and
organic samples are organized and analyzed
in separate batches.
5.2.1 Quality Evaluation Samples
The QE samples are those samples
which are known to the QA staff but are either
blind or double blind to the sampling crews,
preparation laboratory, or analytical laboratory.
A blind sample has a concentration range that
is unknown to the analysts, whereas a double
blind sample cannot be distinguished from a
routine sample and has a concentration range
that is unknown to the analyst (Taylor, 1987).
These samples provide an independent check
on the QC process and can be used to evalu-
ate whether the MQOs have been met for any
given run or batch, or for all batches, i.e.,
overall measurement uncertainty. Impor-
tant characteristics of the QE audit samples
include their similarity to routine samples in
matrix type and concentration level, their
homogeneity and stability, and their defined
accuracy windows. Every QE sample has a
specific purpose in the data assessment
scheme, as described below and in Table 5-1.
5.2.1.1 Field Duplicate (FD) Sample-
From every third pedon sampled, a FD
sample is collected from a horizon selected at
random by the sampling crew leader. The
crew places alternate trowels-full of soil into
separate bags to produce a pair of samples
(one routine, one duplicate) from one specific
horizon within a specific pedon. Individual
pairs are used primarily to assess the overall
within-batch precision. These estimates are
pooled to provide the within-batch component
of overall system measurement uncertainty.
5.2.1.2 Field Audit (FAP or FAO)
Sample--
The FAP sample is a median-range
mineral audit sample sent in pairs by the QA
staff to the sampling crews. The FAO sample
is a median range organic audit sample sent
in triplicate by the QA staff to the sampling
crews. The FAP and FAO samples are treated
as if they had just been obtained from the
excavated pit, i.e., the samples are sieved and
bagged during the collection of a FD sample.
These samples are then handled as if they
were routine samples and undergo all of the
associated soil preparation and analysis
steps. The FAP and FAO samples are used to
identify within-run and within-batch system
precision and accuracy for mineral and organic
batches, respectively. The variability, i.e.,
imprecision, is reported as the mean square
error by analysis of variance (ANOVA) and the
bias, i.e., inaccuracy, is calculated as the
difference between an actual measured value
and the reference value established by previ-
ous DDRP analysis. The analytical data from
each pair are also pooled from all batches
to provide an estimate of other sources of
measurement uncertainty, such as between-
batch or between-laboratory system uncertain-
ty.
-------
If
' V
o
w
o_
w
•o
(0
5; SAMPLING
PI
B
O
e
0
PD/routin* PD/FD
prep lab within batch
preciaion
FD/routine
ayetem
within batch
precision
•yetern
detection
luait
ayaten
within/between
batch precision
and accuracy
analyt. lab laboratory
within/between detection
precision/accuracy lu&it
7,8 »
10
within/
between lab
accuracy
11
7D " field duplicate; FAL • low-range field audit; FAP « field audit pair; PD « preparation duplicate; LAL • low-range laboratory audit; LAP *• laboratory
audit pair; QCAS • quality control audit •ample; AD " analytical duplicate.
0) Q) (D
'
5 » g-
(A
O
-------
5
U
[1
c
£
U
Si
V)
i:S
IQ
1
i
i
j
>
c
b
it
i
> -» 4
4 *-.*
4
14
: B
' g
•4 •
SAMPLING
n
•-*
4J
s
V
M
O.
o
B
*>
8
U
— 1
y o
_J
i
2?i
•t
4
O — • <
^
h
A
o
ft.
PREPARATION
— 1
r— -1
~
t
1
r
3
«^
— i
_j
»
i
o — <
1
o ~<
(h« *- J
4
**
0.
Q
0,
ANALYSIS
| ^sS
*) • 9 0
— J * ** o
~< j: •
x: U-H o
>Ss
B
J3 e^-
« * e >,
L— , ^ W 0 U
tl e-^ • A
B • a m u -
: > B u u -
j -H-I c u r-
i— • « JB K •
c *> a
B
* "X,
> C >i
i— . g 4J o u to
n 3 J-H *
» pZ • K M
3 X6 O O «
J «-^ • U
I — 1 J3 H «
•p a
. la
B « O
•H fl J3 -*4
O • -H o n
h X,J •
^ • *J M
£ «
a
-5 B
fi •
*> • Q>
2"1
0 rt
M Oi
^ •
at a
3lic»t«
*)
JJ
-H
•0
3
x>r«tory i
•^
I
!T
+j
«
u
-*4
r^
3
•o
e
o
V
« •
u o
&!i
•!
Q
.. U
<•> ~4
B '-I
S> B
29
i ?
a a*
•tf 3
+J *J
-^ -*
• 2
-( O
-H 4J
, 8
O X
^
2 1
S i
-H
r4 (fl
a. 2
3 O
•o a
•o •-
^i — *
• u
-* -*4
-------
Section 5
Revision 1
Date: 4/89
Page 6 of 17
Table 5-1. Statua and Assessment of Measurement Quality Samples
Status of sample*
Sample QA
type staff
FAL K
FAP K
FAO K
FD
LAL K
LAP K
LAO K
PD
QCAS K
MS K
Sampling
crew
B
B
B
B
Preparation
laboratory
B
B
B
B
B
B
Analytical
laboratory
DB
DB
DB
DB
DB
DB
DB
DB
B
* K - known concentration, B - blind, DB « double blind
. D - detectability/contamination, P - precision, A - accuracy, R
* For preparation laboratory parameters only.
System
D.A
P.A
P,A
P
Assessm
Sampling
D.A-
P.A-
P.A-
P.R
ant ouroose"
Preparation
D.A-
P.A-
P.A-
P
P.R
A*
Analysis
A
P.A
P.A
P
D
P.A
P,A
P
A
• representativeness
5.2.1.3 Low-Range Field Audit (FAL)
Sample-
The FAL sample is sent with the FAP
samples to the sampling crews by the QA
staff and is handled in the same manner as
the FAP samples. The FAL is used to identify
any system contamination and as an addition-
al check on preparation and system accuracy
of mineral soil samples. Pooled data from all
FAL samples provide an estimate of the sys-
tem detection limit (SDL), which is calculated
as three times the pooled standard deviation
of the FAL samples.
5.2.1.4 Preparation Duplicate (PD)
Sample--
A pair of PD samples, one split from the
FD sample and one split from its associated
routine sample, are created at the preparation
laboratory immediately following sample homo-
genization and are placed in each batch. The
PDs are used to identify the within-batch
preparation uncertainty component of the
overall measurement uncertainty and as an
independent check on the within-batch preci-
sion estimates provided by the analytical
laboratories. The samples characterize mea-
surement uncertainty introduced during or
after homogenization/subsampling at the
preparation laboratory, but do not identify the
uncertainty related to sample drying or sieving,
or any contamination occurring prior to sample
homogenization. The uncertainty attributed to
sample preparation is calculated by ANOVA
and is derived after identifying and isolating
the uncertainty from sample analysis.
5.2.1.5 Laboratory Audit (LAP or LAO)
Sample--
The LAP samples are mineral audit sam-
ples, identical to the FAP samples, that are
sent in pairs by the QA staff directly to the
preparation laboratory for inclusion in each
mineral batch sent to an analytical laboratory.
The LAO samples are organic audit samples,
identical to the FAO samples, that are sent in
triplicate by the QA staff directly to the prepa-
ration laboratory for inclusion in each organic
batch sent to an analytical laboratory. The
LAP and LAO samples are used to assess
analytical within-batch precision and accuracy
and are used in combination with the FAP and
FAO samples to identify sources of bias, e.g.,
contamination or method error. Comparing
the LAP/LAO data across batches allows the
QA staff to calculate interlaboratory differ-
ences and to assess between-laboratory and
between-batch components of analytical mea-
surement uncertainty. The precision compo-
nents of measurement uncertainty are calcu-
lated by ANOVA and the bias is calculated as
the difference between an actual measured
value and the reference value established
-------
Section 5
Revision 1
Date: 4/89
Page 7 of 17
by previous DORP analysis. The LAP/LAO
samples are also used to assess between-
batch and between-laboratory uncertainty.
5.2.1.6 Low-Range Laboratory Audit
(LAL) Sample-
The LAL is a low-range natural audit
sample, identical to the FAL, that is sent by
the QA staff directly to the preparation
laboratory for inclusion in each mineral batch
sent to an analytical laboratory. When used in
conjunction with the FAL sample, the LAL is
helpful in calculating instrument detection
limits (IDLs) as well as in identifying the
magnitude and source of sample contamina-
tion. The calculated IDL for a given parameter
is estimated as three times the pooled stan-
dard deviation of the LAL samples. The IDL is
used as an independent check on laboratory
detection limits.
5.2.1.7 Manager's Sample (MS)»
The MS is a natural audit sample used
only at the preparation laboratory that allows
the QA staff to independently assess the
accuracy of data produced during the prepara-
tion laboratory analyses. One MS subsample
is placed in each run of analysis and must fall
within the control limits established in the
sample preparation MQOs.
5.2.2 Quality Control Samples
In order to produce data of consistently
high quality, the contract laboratories are
required to analyze certain types of QC sam-
ples that are known to the laboratory staff
and that can be used by the analysts to
identify and control analytical measurement
uncertainty. Each QC sample has certain
specifications that must be met before data
for the parameter or batch are accepted, as
listed in Table 5-2. The QC samples are non-
blind samples procured under contract to
assist the laboratories in meeting laboratory
MQOs and include both soil samples, e.g.,
analytical duplicates, and non-soil samples,
e.g., reagent blanks. The QC samples are
analyzed by each laboratory and allow the
laboratory manager and the QA staff to as-
sess whether the physical and chemical analy-
sis is under control. The QC samples used in
the MASS are detailed in the following subsec-
tions.
5.2.2.1 Quality Control Audit Sample
(QCAS)-
The QCAS is a natural audit sample
provided directly to the analytical laboratories
along with the corresponding reference value
obtained by previous DDRP analysis. The
QCAS is used to control bias and reduce
between-laboratory and between-batch compo-
nents of measurement uncertainty. Data for
the QCAS are evaluated by batch on a control
chart to ensure that the result are within
acceptable accuracy limits as defined by the
QA staff. If the QCAS does not meet the
accuracy window criteria, the batch of sam-
ples is reanalyzed for the parameter in ques-
tion.
5.2.2.2 Analytical Duplicate (AD)
Sample-
A duplicate subsample of a specified
routine sample, i.e., the 25th sample in each
batch, is selected as the AD sample at the
analytical laboratory and is used to ensure
that within-batch precision MQOs are being
satisfied. Precision is calculated as a stan-
dard deviation or relative standard deviation,
depending on concentration, and is evaluated
to ensure that the result is within acceptable
limits set forth in the laboratory contract. If
the precision objectives for the AD are not met,
the batch of samples is reanalyzed for the
parameter in question.
5.2.2.3 Calibration Blank-
One calibration blank per batch is ana-
lyzed immediately after the initial calibration
to check for baseline drift. The calibration
blank is defined as a zero mg/L standard and
contains only the matrix of the calibration
standards with no analyte present. During
batch sample analysis, a calibration blank
is run at the beginning, after every 10 sam-
ples, and at the end of each analyzed batch.
-------
Section 5
Revision 1
Date: 4/89
Page 8 of 17
Tabla 5-2. Quality Control Requirements for Analytical Batch**
Sample type
Parameter
Requirement
QC audit sample (QCAS)
Analytical duplicate (AD)
Calibration blank
number run
measured
Reagent blank (mean)
QC check sample (QCCS)
number run
measured
Detection limit QCCS
(DL-QCCS)
Matrix spike recovery
Spike solution
Ion chromatography (1C)
Instrument detection limit (IDL)
Sample weight
Sample volume
ALL
ALL
ALL''"
ALL'-c'd
PH H20, PH 002M, PH 01M
SO4 2-16
SO4 32
ALL OTRERSd
ALL
SO4 0-32
ALL OTHERS"
ALLa
S04 0-32
ALL OTHERS"
S04 0
SO4_2-16
S04 32
CA_CL2
SO4_H2O. SO4_PO4. SO4JO-32
ALL''0
ALL
ALL
Within accuracy window
Satisfies precision limits in Table 5-5
((samples in batch/10) + 1)
< CRDL
4.5 <_ mean <_ 7.5
± 3% of theoretical
± 2% of theoretical
< CRDL
((samples in batch/10) + 1)
i 5% of theoretical
± 10% of theoretical
± 20% of theoretical
95% < x <. 105%
90% < x <. 110%
<.CRDL
i 3% theoretical value
± 2% theoretical value
± 5% theoretical value
Resolution >. 60%
;< CRDL
Measured = ± 5% method
Measured = method
' Except moisture, particle size, and pH parameters.
" Except SO4 2-32.
c Except AC SACL
d Except FSi; CLAY, and AC_BACL.
e Except moisture, particle size, AC_BACL, and SO4_0,4,16 parameters.
These calibration blanks are used to check
for significant baseline drift and should have
values less than the CRDL, except for exchang-
eable acidity. The within-batch data from the
calibration blanks are used to calculate long-
term IDLs.
All calibration standards are prepared as
specified in the appropriate procedure. A
calibration curve for each analytical method is
established by using a minimum of three
points within the dynamic linear range. The
use of at least a three-point calibration curve
is required in place of the manufacturer's
recommendations for the instrumentation,
unless those recommendations specify more
than three points within the linear range. The
concentration of standards must bracket the
expected sample concentration without exceed-
ing the dynamic linear range of the instrument.
The lowest standard should not be greater
than 10 times the CRDL. If the concentration
of a sample falls outside the range during
analysis, the sample is diluted and reanalyzed.
-------
Section 5
Revision 1
Date: 4/89
Page 9 of 17
For the MASS, an IDL is defined as three
times the standard deviation of at least 15
nonconsecutive calibration blank analyses run
on three separate days, based on a similar
formula defined by Winefordner and Long
(1983). In some analyses, such as ion chro-
matography, a signal may or may not be
obtained for a blank analysis; in these cases,
the IDL is defined as three times the standard
deviation of 15 nonconsecutive replicate analy-
ses of a standard whose concentration is four
times the lesser of the actual detection limit or
the CRDL
Acceptable initial IDLs must be estab-
lished prior to any sample analysis. Ongoing
IDLs are determined and reported monthly or
on a per-batch basis upon batch submission,
whichever is more frequent, for all except the
moisture, particle size, and pH parameters.
The raw data for the initial IDL determinations
must be reported to the QA manager. Initial
IDLs are valid for one month following their
establishment; after this, new IDLs must be
established prior to any sample analysis for
the first batch of samples. Ongoing IDLs are
determined only for those parameters being
analyzed during a given reporting period.
After the validity of the initial IDLs has
been established, the first batch of samples is
analyzed and the IDLs reported for this batch
are determined as composites of the calibra-
tion blanks from the batch and the calibration
blanks run on the second and third day of the
initial IDL determination. If a given IDL is
invalid, then reanalysis of the batch for the
parameter of interest is required. Similarly, the
IDLs for other sample batches will be estab-
lished as composites of the calibration blanks
from the batch being analyzed and the calibra-
tion blanks from the two previous batches
such that the calibration blanks have been
determined over three separate days. It is
essential that each laboratory analyzes the
batches sequentially in order to establish the
proper IDL determinations; any exceptions to
this practice must be reported immediately to
the QA manager.
5.2.2.4 Reagent Blank-
When an analytical method requires
sample preparation, a reagent blank for each
group of samples processed is prepared and
analyzed. A reagent blank is defined as a
sample composed of all the reagents, in the
same quantities, used in preparing an actual
sample for analysis. The reagent blank under-
goes the same digestion and extraction proce-
dures as an actual sample.
For most parameters, the analyte con-
centration of a reagent blank must be less
than or equal to the CRDL; if not, the source
of contamination must be investigated and
eliminated. A new reagent blank is then pre-
pared and analyzed, and the same criteria are
applied. All samples associated with the
"high" blank must be reprocessed and reana-
lyzed after the contamination has been elimi-
nated. Approximately seven samples and a
reagent blank are analyzed prior to the matrix
spike and duplicate analyses so that approxi-
mate endogenous sample concentrations may
be determined.
5.2.2.5 Quality Control Check Sample
(QCCS)-
Immediately after calibration of an instru-
ment, a QCCS containing the analyte of inter-
est at a concentration in the mid-calibration
range is analyzed. The QCCS is analyzed to
verify the calibration curve prior to any sample
analysis, after every 10 samples, and after the
last sample of a batch. The sample must be
selected from a stock solution different than
that used for the instrument calibration.
The QCCS data are plotted at the labora-
tory on a control chart, which warns when the
concentrations are outside of established QC
limits. The same QCCS stock solution must
be used to establish all values on a given
control chart to ensure continuity. The ob-
served concentration for each QCCS is plotted
-------
Section 5
Revision 1
Date: 4/89
Page 10 of 17
on the chart and the 95 and 99 percent confi-
dence intervals (warning and control limits,
respectively) are developed. The control limits
must not differ from the theoretical value by
more than the limits given in Table 5-2. A
value outside the control limits is considered
unacceptable, hence, the instrument is recali-
brated and all samples up to the last accept-
able QCCS are reanalyzed.
After each day of analysis, the control
charts are updated. Cumulative means and
new warning and control limits are calculated.
If bias for a given analysis is indicated, i.e., at
least seven successive points occur on one
side of the cumulative mean, sample analysis
must cease until an explanation is found and
the system is brought under control.
5.2.2.6 Detection Limit Quality
Control Check Sample
(DL-QCCS)-
The DL-QCCS is a low-range QC sample
that contains the analyte of interest at concen-
trations specified in the respective method.
The purpose of the DL-QCCS is to eliminate
the necessity of formally determining the
detection limit on a daily basis. The sample is
run once per batch and the measured value
must be within 20 percent of the theoretical
concentration listed in Table 5-2. If this criteri-
on is not met, the source of error must be
identified and corrected, and an acceptable
result must be obtained before routine sample
analysis is resumed.
5.2.2.7 Matrix Spike Sample-
For liquid samples, e.g., cation extrac-
tions, one matrix spike sample is prepared for
each procedure by spiking an aliquot of a
solution with a known quantity of analyte prior
to analysis. The spike concentration must be
approximately equal to the endogenous level or
10 times the detection limit, whichever is
larger. Also, the volume of the added spike
must be negligible, i.e., less than or equal to
one percent of the sample aliquot volume.
For solid samples, e.g., aliquots for total
carbon, one matrix spike sample is prepared
for each procedure by adding a known weight
of material containing the analyte of interest to
a sample of known weight. The spike concen-
tration should be twice the endogenous level
or 10 times the detection limit, whichever is
larger. The concentration of the matrix spike
must not exceed the linear range of the instru-
ment. Although it is not negligible, the weight
of the spike material should be considered
negligible for the purposes of calculation.
The spike recovery must be within the
limits shown in Table 5-2. If these criteria are
not satisfied, then two additional (different)
samples are spiked with the analyte in ques-
tion and are analyzed. If the recovery from
either sample does not meet requirements, the
entire batch must be analyzed by the method
of standard additions. The method of stan-
dard additions is performed by analyzing the
sample, analyzing the sample plus a spike at
about the endogenous level, and analyzing the
sample plus a spike at about twice the endo-
genous level. The concentration of the matrix
spike sample must not exceed the linear range
of the instrument; if this is not possible, the
spiked sample is diluted before analysis.
5.2.2.8 Ion Chromatography (1C)
Resolution Sample--
An 1C resolution test is performed once
per analytical run by analyzing a standard that
contains concentrations of approximately 1
mg/L each for sulfate, phosphate, and nitrate.
The analysis is run to provide evidence of
"clean" peak separation for proper quantifica-
tion of each of the sulfate parameters. If the
resolution does not exceed 60 percent, the
column should be replaced, and the resolution
test should be repeated.
5.2.2.9 Internal Duplicate-
The internal duplicate is a duplicate
subsample used only during the preparation
laboratory analyses. It is characterized in
essentially the same manner as the AD sample
described above. The internal duplicate is
helpful in assessing within-run precision during
sample preparation.
-------
Section 5
Revision 1
Date: 4/89
Page 11 of 17
5.3 Description of Measurement
Quality Objectives
The following text describes the data
quality attributes and MQOs as they apply to
the sampling, preparation, and analysis pha-
ses of the MASS. Implementation of the
MQOs during the survey is described in Sec-
tion 6.
The structure of the three analytical MQO
tables in this section (tables 5-3, 5-4, and
5-6) is as follows:
• Parameter - data variable name of
parameter being analyzed (see Appendix
C).
• Reporting units ~ analytical units in
which the laboratory data should be
reported.
• CRDL - contract-required detection limit
expressed in reporting units and in parts
per million.
• Precision at the lower limit ~ a guideline
to define the acceptable absolute standard
deviation for low concentrations that are
difficult to reproduce; a knot, or cutoff
value, is reported for which data below the
knot are assessed separately from data
above the knot.
• Precision at the upper limit -- serves as a
guideline to define the acceptable percent
relative standard deviation for concentra-
tions above the knot.
5.3.1 Field Sampling and
Characterization
Soil sampling includes the physical
removal of soil samples from an excavated pit
as well as the characterization of the soil
pedon and the sampling site. Because the
MQOs developed for the analytical operations
are not applicable to the sampling activities,
specific objectives for soil sampling were
developed to ensure that field operations, e.g.,
sampling site location, profile description, and
sampling, are conducted in a consistent man-
ner. 'Tie objectives are intended to reduce the
errors inherent in collecting soils data and to
provide an estimate of the variability among
sampling crews.
The goal of the MASS sampling is to
describe and collect soil samples from repre-
sentative pedons of established sampling
classes. The classes are intended to repre-
sent the soil characteristics of the Mid-Appala-
chian region. The field sampling produces
both qualitative data from pedon characteriza-
tion, e.g., soil color, and quantitative data from
analysis of the soil samples, e.g., soil pH.
5.3.1.1 Precision and Accuracy-
Due to the subjective nature of pedon
characterization, values for precision and
accuracy cannot be determined. However, by
the use of multiple descriptions by a number
of individuals at the same pedon, variability of
descriptions can be assessed. Control and
assessment of the pedon characterization are
accomplished through various auditing tech-
niques discussed in later sections.
Field sampling precision and accuracy
are assessed using data from the QE field
samples to estimate the system measurement
uncertainty and comparing this uncertainty to
that identified in the QE preparation and
analytical samples. The field samples are
expected to contain the largest amount of
confounded error of all QE/QC samples. The
MQOs for precision of the physical and chemi-
cal analyses of the field samples are pres-
ented in Table 5-3. Accuracy is assessed by
comparing the laboratory data to the audit
window criteria defined for the audit samples.
It should be noted that the MQOs for field
sampling are not intended to control field
sampling error but are used to assess this
error and to control within-laboratory error at
the preparation laboratory and within- and
between-laboratory error occurring at the
analytical laboratories.
5.3.1.2 Representativeness-
Soil types identified in the Mid-Appala-
chian region are combined into sampling
classes that are known to have, or are ex-
pected to have, similar physical and chemical
characteristics. Each of the sampling classes
can be sampled across a number of water-
sheds in which they occur. In this approach,
-------
Table 5-3. Analytical Laboratory Wlthln-Batch Precision Objectives for Field Samples
Precision for
mineral samples'*
Parameter
MOIST A
SAND ~
SILT
CLAY
PH H2O
PH 002M
PHJ)1M
CA CL
MG CL
K Cl
NACL
ALjCL
CA OAC
MG~OAC
K OAC
NA_OAC
CEC CL
CEC'OAC
AC_§ACL
CA CL2
MG~CL2
K Cl2
NA CL2
FE~CL2
ALJ3L2
FE PYP
AL PYP
FE~AO
AL AO
SI AO
FE~CD
AL~CD
SO4 H2O
SO4 PO4
SO4J>-32
C TOT
N TOT
S~TOT
Reporting
units
wt. %
H
H
"
pH units
H
"
meq/100g
H
H
H
"
meq/lOOg
M
H
H
meq/100g
N
H
meq/100g
•
M
H
H
M
wt. %
H
H
H
H
II
N
mg S/kg
it
mg S/L
wt. %
M
M
CRDL"
units
—
—
—
_
—
—
0.003
0.005
0.002
0.003
0.007
0.003
0.005
0.002
0.003
0.14
0.14
0.25
0.005
0.0008
0.0003
0.0004
0.0005
0.0011
0.005
0.005
0.005
0.005
0.005
0.002
0.002
0.50
0.50
0.025
0.01
0.005
0.001
ppm
—
—
—
—
—
0.05
0.05
0.05
0.05
0.10
0.05
0.05
0.05
0.05
1.050'
1.050-
0.005?
0.50
0.05
0.05
0.05
0.10
0.10
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.025
0.025
0.025
100.0
50.0
10.0
SD
(lower)
0.3
6.0
6.0
4.0
0.20
0.20
0.20
0.04
0.04
0.04
0.04
0.40
0.04
0.04
0.04
0.04
0.50
0.50
2.0
0.10
0.01
0.01
0.01
0.02
0.10
0.06
0.06
0.06
0.06
0.06
0.06
0.06
3.0
3.0
0.20
0.10
0.03
0.004
RSD
(upper)
2.5%
—
—
—
_
—
—
30%
30%
30%
30%
45%
30%
30%
30%
30%
30%
30%
45%
15%
30%
30%
30%
45%
45%
30%
30%
30%
30%
30%
30%
30%
30%
30%
15%
30%
30%
30%
Knot
12.0
—
—
—
»
—
—
0.13
0.13
0.13
0.13
0.89
0.13
0.13
0.13
0.13
1.7
1.7
4.4
0.67
0.03
0.03
0.03
0.04
0.22
0.20
0.20
0.20
0.20
0.20
0.20
0.20
10.0
10.0
1.3
0.33
0.10
0.01
Section 5
Revision 1
Date: 4/89
Page 12 of 17
Precision for
oraanic samples'"
SD
(lower)
0.3
—
—
—
0.20
0.20
0.20
0.20
0.20
0.20
0.20
1.0
0.20
0.20
0.20
0.20
0.50
0.50
2.0
0.40
0.10
0.05
0.05
0.10
0.40
0.06
0.06
0.06
0.06
0.06
0.06
0.06
3.0
3.0
—
0.10
0.03
0.004
RSD
(upper)
2.5%
—
—
—
__
—
—
30%
30%
30%
30%
45%
30%
30%
30%
30%
30%
30%
45%
15%
30%
30%
30%
45%
45%
30%
30%
30%
30%
30%
30%
30%
30%
30%
—
30%
30%
30%
Knot
12.0
—
—
—
_
—
—
0.67
0.67
0.67
0.67
2.2
0.67
0.67
0.67
0.67
1.7
1.7
4.4
2.7
0.33
0.17
0.17
0.22
0.89
0.20
0.20
0.20
0.20
0.20
0.20
0.20
10.0
10.0
—
0.33
0.10
0.01
* Contract-required detection limit in reporting units and parts per million, respectively.
b Within-batch precision by standard deviation below the knot and by percent relative standard deviation above the knot.
c For FIA only, for titrations, the value is 0.0075.
d Units are meq for this parameter; for organic soils, value is 0.025 meq (1.25 meq/100g in reporting units).
-------
Section 5
Revision 1
Date: 4/89
Page 13 of 17
a given soil sample does not represent the
specific watershed from which it was collect-
ed; rather, it contributes to a set of samples
which collectively represent a specific sampling
class for all watersheds within the sampling
region. The sampling crew leader selects
a sampling site representing the designated
sampling class and vegetation class within the
designated watershed according to the sam-
pling protocols. Both the qualitative and
quantitative data collected are intended to be
representative of soils in the region.
5.3.1.3 Completeness--
The sampling completeness objective can
be met by ensuring that 100 percent of the
designated pedons and their component hori-
zons are sampled, with the exception of cer-
tain undecomposed organic horizons, intermit-
tent horizons, or horizons less than three
centimeters thick. Only the soil sampling task
leader at ERL-C can authorize pedons to be
excluded from sampling.
5.3.1.4 Comparability-
Soils are sampled and characterized in
the same general manner as in previous DDRP
surveys. Improvements in methods and the
use of consistent sampling protocols and
personnel with equivalent experience should
provide data of equal or better quality.
5.3.2 Sample Preparation
The goal of sample preparation is to
process bulk soil samples collected by the
sampling crews and to provide homogeneous
subsamples to the analytical laboratories for
soil analysis.
5.3.2.1 Precision and Accuracy-
A portion of the measurement error
incurred at the preparation laboratory is
assessed by the inclusion of preparation
duplicate samples created during the
homogenization/subsampling stage of sample
preparation. The samples are used in each
batch to assess and, in some cases, control
imprecision during this stage of sample prepa-
ration. The MQOs for the preparation samples
are specified in Table 5-4.
During sample preparation, the precision
and accuracy of analysis are estimated by
comparing physical and chemical data from
the QE field samples (all analyses), the QC
internal duplicates (PH MP, MOIST P, and
OM_LOI only), the clod "replicates (BD_CLD),
and the known volume replicates (BD_KV).
The specific MQOs for precision and accuracy
of these samples during the preparation labor-
atory analysis are outlined in Table 5-5.
5.3.2.2 Representativeness-
Laboratory personnel ensure that the
integrity of the samples is maintained during
the soil preparation activities. Adherence to
QC procedures outlined in the PLSOP is moni-
tored by the QA staff. Homogenization of the
bulk soil samples allows representative analyti-
cal subsamples to be prepared.
5.3.2.3 Completeness-
The preparation laboratory is expected to
complete the specified analyses and process-
ing tasks for 100 percent of the samples
received. Each batch of samples sent to a
contracted analytical laboratory must include
the specified configuration of routine, dupli-
cate, and QE/QC samples.
5.3.2.4 Comparability-
The preparation laboratory processes
bulk samples according to protocols docu-
mented in the PLSOP. Strict adherence to the
protocols should result in comparability of the
MASS preparation laboratory with the previous
DDRP preparation laboratories.
5.3.3 Laboratory Analysis
The analysis phase of measurement
allows the most quantitative evaluation of data
quality, as outlined below.
-------
Table 5-4. Analytical Laboratory Wlthln-batch Precision Objectives for Preparation Sample*
Precision for
mineral samples
Parameter
MOIST A
SAND"
SILT
CLAY
PH H2O
PH 002M
PHJ01M
CACL
MG CL
KCL
NA CL
AL~CL
CAOAC
MG'OAC
KOAC
NAjOAC
CEC CL
CEC'OAC
AC_§ACL
CACL2
MG CL2
KCl2
NA CL2
FE~CL2
AL_CL2
FE PYP
AL PYP
FE~AO
AL~AO
SI~AO
FE~CD
ALjCD
S04 H20
SO4"PO4
SO4JO-32
C TOT
N~TOT
S TOT
Reporting
units
wt. %
H
H
H
pH units
M
"
meq/100g
M
*
N
N
meq/100g
"
«
"
meq/100g
M
II
meq/lOOg
N
II
H
H
"
Wt. %
M
•
«
H
N
"
mg S/kg
H
mg S/L
wt. %
"
N
CRDL*
units
_
_
—
_
_
—
0.003
0.005
0.002
0.003
0.007
0.003
0.005
0.002
0.003
0.14
0.14
0.25
0.005
0.0008
0.0003
0.0004
0.0005
0.0011
0.005
0.005
0.005
0.005
0.005
0.002
0.002
0.50
0.50
0.025
0.01
0.005
0.001
ppm
_
_
—
__
—
—
0.05
0.05
0.05
0.05
0.10
0.05
0.05
0.05
0.05
1.050C
1.050C
0.005"
0.50
0.05
0.05
0.05
0.10
0.10
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.025
0.025
0.025
100.0
50.0
10.0
SO
(lower)
0.3
3.0
3.0
2.0
0.10
0.10
0.10
0.02
0.02
0.02
0.02
0.20
0.02
0.02
0.02
0.02
0.25
0.25
1.0
0.05
0.005
0.005
0.005
0.01
0.05
0.03
0.03
0.03
0.03
0.03
0.03
0.03
1.5
1.5
0.10
0.05
0.015
0.002
RSO
(upper)
2.5%
__
__
—
___
_
—
15%
15%
15%
15%
22%
15%
15%
15%
15%
15%
15%
22%
8%
15%
15%
15%
22%
22%
15%
15%
15%
15%
15%
15%
15%
15%
15%
8%
15%
15%
15%
Knot
12.0
_
_
—
_
_
—
0.13
0.13
0.13
0.13
0.91
0.13
0.13
0.13
0.13
1.7
1.7
4.5
0.62
0.03
0.03
0.03
0.05
0.23
0.20
0.20
0.20
0.20
0.20
0.20
0.20
10.0
10.0
1.3
0.33
0.10
0.01
Section 5
Revision 1
Date: 4/89
Page 14 of 17
Precision for
oraanic samples"
SO
(lower)
0.3
_
_
—
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.50
0.10
0.10
0.10
0.10
0.25
0.25
1.0
0.20
0.05
0.025
0.025
0.05
0.20
0.03
0.03
0.03
0.03
0.03
0.03
0.03
1.5
1.5
—
0.05
0.015
0.002
RSO
(upper)
2.5%
_
_
_
__
_
—
15%
15%
15%
15%
22%
15%
15%
15%
15%
15%
15%
22%
8%
15%
15%
15%
22%
22%
15%
15%
15%
15%
15%
15%
15%
15%
15%
—
15%
15%
15%
Knot
12.0
_
_
—
—
_
—
0.67
0.67
0.67
0.67
2.3
0.66
0.66
0.66
0.66
1.7
1.7
4.5
2.5
0.33
0.17
0.17
0.23
0.91
0.20
0.20
0.20
0.20
0.20
0.20
0.20
10.0
10.0
—
0.33
0.10
0.01
* Contract-required detection limit in reporting units and parts per million, respectively.
b Within-batch precision by standard deviation below the knot and by percent relative standard deviation above the knot.
c For FIA only; for titrations, the value is 0.0075.
° Units are meq for this parameter; for organic soils, value is 0.025 meq (1.25 meq/100g in reporting units).
-------
Table 5-5. Preparation Laboratory Wlthln-Run Measurement Quality Objectives
Section 5
Revision 1
Date: 4/89
Page 15 of 17
Parameter
PH_MP
MOIST_P
OMJ.OI
RF_FG
RF_MG
BD_CLD
BD_KV
* The internal
Sample
type
Reporting
format
Buffer (QCCS) 0.01
Int dup"
FD
FAL(C)
FAP(B)
FAP (Bw)
FAO (0)
MS
Int dup"
FD
FAL(C)
FAP(B)
FAP (Bw)
FAO (0)
MS
Int dup"
FD
FAL(C)
FAP(B)
FAP (Bw)
FAO (0)
MS
FD
FD
Reps"
Dupsc
duplicate is a
b The replicate bulk density
0.01
0.01
pH units
wt. %
wt. %
0.1 wt. %
0.1 wt. %
0.01 g/cm3
0.01 g/cm3
Expected
range
3.90-4.10
2.50-7.50
2.50-7.50
4.80-6.00
4.40-5.40
4.70-5.70
3.70-4.50
4.10-5.00
0.50-6.00
0.50-6.00
0.00-2.50
1.00-4.00
1.00-4.00
4.00-8.00
0.50-6.00
0.00-90.00
0.00-90.00
0.00-3.00
5.00-15.00
1.00-9.00
65.00-85.00
5.00-15.00
0.0-60.0
0.0-60.0
0.60-2.00
0.40-2.50
Acceptable
accuracy
3.95-4.05
—
_
as specified
as specified
as specified
as specified
as specified
_»
_
control limits
control limits
control limits
control limits
control limits
_
_
control limits
control limits
control limits
control limits
control limits
—
—
—
—
Acceptable
precision
_
0.15 SD
0.20 SD
—
0.15 SD
0.15 SD
0.15 SD
—
15% RSD
20% RSD
—
15% RSD
15% RSD
15% RSD
—
15% RSD
20% RSD
—
15% RSD
15% RSD
15% RSD
—
20% RSD**
20% RSD**
20% RSD **
20% RSD**
replicate subsample that is randomly selected from one soil sample per analytical run.
samples are clod samples
0 The duplicate bulk density samples are known volume
** Since these
anticipated;
duplicates or
in which one to three clods per horizon
samples in which
have been sampled.
one or two samples per horizon are sampled.
replicates are measurements of different samples within the same horizon, variability is
however, when variability is >20% RSD,
these samples are checked for possible
measurement error.
5.3.3.1 Detectability-
The data users have determined specific
levels of instrument detection for the parame-
ters being analyzed. The contract-required
detection limits (CRDLs) for these parameters
are listed in Table 5-6. The low concentration
QE samples (PAL and LAL) are used to assess
contamination issues and to calculate the
instrument and system detection limits for a
specific parameter.
5.3.3.2 Precision and Accuracy-
The MQOs for precision of the physical
and chemical analyses of soil samples are
presented in Table 5-6. Accuracy is assessed
by comparing the analytical data to the audit
window criteria defined for the laboratory audit
samples (see Section 7.1.3).
-------
Table 5-6. Analytical Laboratory Wlthln-Batch Precision Objectives for Analytical
Parameter
MOIST A
SAND ~
SILT
CLAY
PH H20
PH~002M
PhfOlM
CA CL
MG CL
K CL
NACL
AL'CL
CA OAC
MG~OAC
K OAC
NA_OAC
CEC CL
CEC~OAC
AC_BACL
CA CL2
MG CL2
K CL2
NA CL2
FE~CL2
AL~CL2
FE PYP
AL PYP
FE AO
AL~AO
SI~AO
FE~CD
ALJJD
S04 H2O
S04~PO4
SO4JO-32
C TOT
N~TOT
S~TOT
Reporting
units
wt. %
M
H
H
pH units
M
H
meq/100g
H
H
H
H
meq/100g
M
H
M
meq/100g
«
H
meq/100g
•
N
11
H
N
wt. %
"
"
H
H
H
"
mg S/kg
"
mg S/L
wt. %
H
N
CRDL"
units
_
_
—
--
—
—
0.003
0.005
0.002
0.003
0.007
0.003
0.005
0.002
0.003
0.14
0.14
0.25
0.005
0.0008
0.0003
0.0004
0.0005
0.0011
0.005
0.005
0.005
0.005
0.005
0.002
0.002
0.50
0.50
0.025
0.01
0.005
0.001
ppm
—
_
—
___
—
—
0.05
0.05
0.05
0.05
0.10
0.05
0.05
0.05
0.05
1.050C
1.050C
0.005d
0.5
0.05
0.05
0.05
0.10
0.10
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.025
0.025
0.025
100.0
50.0
10.0
Precision for
mineral samples'1
SD
(lower)
0.3
3.0
3.0
2.0
0.10
0.10
0.10
0.02
0.02
0.02
0.02
0.20
0.02
0.02
0.02
0.02
0.25
0.25
1.0
0.05
0.005
0.005
0.005
0.01
0.05
0.03
0.03
0.03
0.03
0.03
0.03
0.03
1.5
1.5
0.10
0.05
0.015
0.002
RSD
(upper)
2.5%
__
_
—
fmm
—
—
10%
10%
10%
10%
15%
10%
10%
10%
10%
10%
10%
15%
5%
10%
10%
10%
15%
15%
10%
10%
10%
10%
10%
10%
10%
10%
10%
5%
10%
10%
10%
Knot
12.0
_
_
—
_
_
—
0.20
0.20
0.20
0.20
1.3
0.20
0.20
0.20
0.20
2.5
2.5
6.7
1.0
0.05
0.05
0.05
0.07
0.33
0.30
0.30
0.30
0.30
0.30
0.30
0.30
15.0
15.0
2.0
0.50
0.15
0.02
Sample*
Section 5
Revision 1
Date: 4/89
Page 16 of 17
Precision for
orqanic samples'"
SD
(lower)
0.3
_
_
—
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.50
0.10
0.10
0.10
0.10
0.25
0.25
1.0
0.20
0.05
0.025
0.025
0.05
0.20
0.03
0.03
0.03
0.03
0.03
0.03
0.03
1.5
1.5
—
0.05
0.015
0.002
RSD
(upper)
2.5%
—
_
—
m m —
_
—
10%
10%
10%
10%
15%
10%
10%
10%
10%
10%
10%
15%
5%
10%
10%
10%
15%
15%
10%
10%
10%
10%
10%
10%
10%
10%
10%
—
10%
10%
10%
Knot
12.0
—
—
—
u
__
—
1.0
1.0
1.0
1.0
3.3
1.0
1.0
1.0
1.0
2.5
2.5
6.7
4.0
0.50
0.25
0.25
0.33
1.3
0.30
0.30
0.30
0.30
0.30
0.30
0.30
15.0
15.0
—
0.50
0.15
0.02
a Contract-required detection limit in reporting units and parts per million, respectively.
b Within-batch precision by standard deviation below the knot and by percent relative standard deviation above the knot.
c For FIA only; for titrations, the value is 0.0075.
d Units are meq for this parameter; for organic soils, value is 0.025 meq (1.25 meq/100g in reporting units).
-------
Section 5
Revision 1
Date: 4/89
Page 17 of 17
5.3.3.3 Representativeness-
The integrity of the analytical samples is
to be maintained during soil analysis activities.
Homogenization of the samples at the prepar-
ation laboratory in conjunction with re-homo-
genization of the samples at the analytical
laboratory ensures that a uniform stock of
sample is available from which to select
aliquots for analysis.
5.3.3.4 Completeness--
The completeness objective for all sam-
ples collected is 90 percent or better for all
analytical parameters. It is possible to attain
100 percent completeness if a sufficient quanti-
ty of each sample is available to complete all
analyses and reanalyses that may be request-
ed.
5.3.3.5 Comparability-
Analytical data from the MASS are ex-
pected to be comparable to the DDRP North-
eastern region and Southern Blue Ridge Prov-
ince (SBRP) soil surveys. Improvements in the
analytical protocols could result in data of
higher quality than attained in the previous
surveys.
-------
-------
Section 6
Revision 1
Date: 4/89
Page 1 of 10
Section 6
Quality Assurance Implementation
6.1 Control of Data Quality
The following subsections describe
the methods used to control the quality of
data produced during the various data col-
lection phases and to ensure that the MQOs
described in Section 5 are met.
6.1.1 Field Sampling and
Characterization
Control of data quality in the sampling
phase requires frequent checks on the crews'
compliance with the sampling protocols, as
described below.
6.1.1.1 Precision and Accuracy-
All prospective field sampling personnel
are trained by ERL-C personnel at a workshop
held prior to the initiation of sampling. The
sampling crew leaders have the responsibility
for training personnel that are added to the
project after sampling has begun. The staff is
encouraged to hold an additional hands-on
training session for all sampling crew mem-
bers, although this session is optional. Fur-
ther ensurance of precision and accuracy in
sampling is accomplished with independent
pedon descriptions.
A representative of the Soil Conservation
Service (SCS) state soils staff independently
describes a minimum of one pedon per sam-
pling crew. The soil profile is described on the
same face of the soil pit as described by the
sampling crew. The state office representative
(SOR) makes the assessment while the crew
is describing and sampling the pedon.
The Regional Coordinator/Correlator
(RCC) must be a qualified soil scientist with
several years experience in soil profile descrip-
tion, mapping, and correlation. The RCC
performs an independent profile description at
least once for each sampling crew. At least
one site in each state is monitored with the
SOR; the remaining pedons may be monitored
independently. The RCC also ensures that the
SORs perform independent profile descriptions.
The independent pedon descriptions are
used to assess the variability in site descrip-
tions among soil scientists. Because the
pedon can be characterized anywhere along
the pedon face, this information cannot be
used to evaluate the accuracy of characteriza-
tion at a specific point, e.g., soil color at a
specific location, but it can be used to evalu-
ate the general variability of independent
pedon characterization.
6.1.1.2 Representativeness-
The soil sampling information provided
on the field data forms is used to determine
whether the pedons sampled are within the
range of morphological characteristics that
identify the respective sampling classes. Data
are analyzed by ERL-C staff to determine the
population characteristics of the sampling
classes.
6.1.1.3 Completeness
Soil sampling protocols specify the
sampling of 100 percent of the designated
pedons and their corresponding horizons. If
samples are lost, spilled, or mislabeled, it may
be possible to return to the same site and
resample the pedon. If a sampling site is
inaccessible, the reason for excluding the site
must be formally documented by the sampling
crew and immediately reported to the ERL-C
soil sampling task leader and QA auditor via
the SOR and RCC.
6.1.1.4 Comparability--
Trie consistent use of standard NCSS
methods, specified protocols, and forms for
-------
Section 6
Revision 1
Date: 4/89
Page 2 of 10
the sampling phase provides field and
analytical data that are comparable to data
generated from other studies which have used
these standardized methods. Specifically, the
MASS data are expected to be comparable to
data collected during the DORP Northeastern
region and SBRP soil surveys.
6.1.2 Sample Preparation
Control of data quality during sample
preparation requires frequent checks of the
laboratory's ability to comply with the proto-
cols.
6.1.2.1 Precision and Accuracy-
The preparation laboratory combines
sets of routine and QE/QC soil samples into
batches, each containing approximately 40
samples. After processing a FD sample and
its associated routine sample, two subsam-
ples are split out of each sample and are
included in a batch as the PD/FD and PD/
routine pairs. Comparison of physical and
chemical data from each pair allows evaluation
of preparation within-batch precision. Since
the precision data for some of these samples
are evaluated before soil preparation is com-
pleted, the data may be used to control prepa-
ration laboratory uncertainty.
As data from the physical and chemical
analyses become available, the preparation
duplicates are evaluated in terms of the MQOs
listed previously in Table 5-4. Reanalysis of
soil parameters at the analytical laboratories
may be justified on the basis of data from the
preparation duplicates. If reanalysis confirms
the original values, the sample preparation
phase may be a suspected source of error.
The analyses that take place at the
preparation laboratory must satisfy the preci-
sion and accuracy criteria previously outlined
in Table 5-5. If the QE/QC objectives are not
met, the data undergo confirmation and the
affected run of samples may be reanalyzed as
specified in the PLSOP (Bartling et al., 1988).
The preparation laboratory manager is
responsible for providing the QA manager with
bimonthly reports summarizing the progress
of sample preparation. These reports address
issues relating to the tracking of samples and
any QC discrepancies in the resulting data.
6.1.2.2 Representativeness-
Each bulk soil sample is processed by a
preparation laboratory in order to produce a
thoroughly homogeneous sample. Homogen-
ization is accomplished by passing the sample
through a Jones-type riffle splitter at least five
times in succession. The riffle splitter is
subsequently used for subsampling homogen-
eous analytical samples. All samples not
being processed are stored at 4°C in the
preparation laboratory. Because the FO sam-
ples are collected according to a random
selection method, it is assumed that the PD
samples split from the FD/routine pair (see
Section 5.2.4) are representative of the ranges
of concentration for the MASS routine soil
samples. This aspect of representativeness is
assessed by the Kolmogorov-Smirnov test
(Conover, 1980).
6.1.2.3 Completeness-
All soil samples that satisfy the prepara-
tion laboratory sample receipt criteria are
processed and analyzed as specified in the
PLSOP. One pair of FAP samples and one FAL
sample (or three FAO samples), one pair of
FD/routine samples, and one pair of LAP
samples, one LAL sample (or three LAO sam-
ples), two pairs of PD/routine samples, one
QCAS, and appropriate sets of routine sam-
ples are grouped into approximately 40 sam-
ples per batch.
6.1.2.4 Comparability-
By adhering to the PLSOP, sample prep-
aration should be comparable to that of the
DDRP Northeastern region and SBRP soil
surveys. System audits and periodic routine
audits are undertaken by appropriate QA
representatives to ensure consistent laboratory
performance. The MQOs for analysis within
the preparation laboratory, as well as MQOs
for the PD samples, are used to assess and
-------
Section 6
Revision 1
Date: 4/89
Page 3 of 10
control measurement uncertainty affecting
comparability of the data.
6.1.3 Laboratory Analysis
Before an analytical laboratory is awar-
ded a contract to analyze samples, the labora-
tory must demonstrate its ability to meet
certain requirements relating to on-site facili-
ties, instrumentation, personnel, and the pro-
duction and quality of data. Analytical labora-
tories respond to an invitation for bid (IFB)
and are sent copies of the ALSOW, which
contains all contractual requirements including
the methods for soil analysis. A group of pre-
award performance evaluation (PE) soil sam-
ples, consisting of characterized audit samples
used in previous DDRP surveys, is sent to the
participating laboratories for analysis. Each
laboratory is rated according to the pre-award
scoring evaluation sheet (see Appendix E).
Laboratories that submit the lowest bids and
pass the PE criteria are then observed during
a pre-award audit visit (see Appendix F),
leading to a final selection of contract labora-
tories by EPA management.
6.1.3.1 Detectability-
The analytical laboratories are required to
make repeated determinations of the IDLs
before analysis begins. The IDLs serve as an
estimate of the lowest concentration of an
analyte that an instrument can reliably detect.
In addition, the laboratories must satisfy the
CRDLs outlined previously in Table 5-2 and are
required to demonstrate with control charts
that the analytical system is under control at
all times during analysis. The data derived
from the DL-QCCS, FAL, and LAL samples are
used in assessing these objectives and in
calculating IDLs and SDLs.
6.1.3.2 Precision and Accuracy-
Upon satisfactory completion of the
contractual requirements by the analytical
laboratories, a data package for each batch of
samples is submitted to the QA operations
staff for evaluation. The data are verified and
evaluated in accordance with the precision and
accuracy MQOs for the various QE/QC sam-
ples and with the appropriate soil chemistry
relationships described later in this section.
Precision for each type of assessment sample
must meet the objectives outlined previously in
the MQO tables of Section 5, insofar as the
objectives relate to the confirmation/reanalysis
considerations outlined later in Section 6.2.
Data from the LAP/LAO and LAL samples must
fall within the accuracy windows defined by
the QA staff and data users. Reanalysis may
be requested for certain parameters or
batches on the basis of imprecise or inaccu-
rate results from the assessment samples.
6.1.3.3 Representativeness-
Upon receipt of each analytical sample
from the preparation laboratory, the analysts
are required to re-homogenize the soil thor-
oughly to ensure the representativeness of
aliquots used in the analyses. All samples not
in use are stored at 4°C in the analytical
laboratory.
6.1.3.4 Completeness--
The completeness objective for all sam-
ples collected is 90 percent or better for all
parameters. A level of 100 percent complete-
ness is possible if a sufficient amount of all
samples is available to complete all routine
analyses and reanalyses.
6.1.3.5 Comparability-
Comparability is assured by the uniform
use of procedures documented in the SOW
and by the use of uniform reporting units as
specified on the data summary forms. The QA
procedures specified for the analytical labora-
tories allow for the determination of measure-
ment uncertainty so that the results can be
compared between laboratories. In addition,
the analytical methods used in the survey can
be compared to methods used in the other
DDRP surveys.
6.1.4 On-Site Systems Audits
A major factor in controlling data quality
is the independent on-site systems audit,
which ensures that all of the survey partici-
-------
Section 6
Revision 1
Date: 4/89
Page 4 of 10
pants are adhering to the protocols in a con-
sistent manner. The on-site field and laborato-
ry audits are described below.
6.1.4.1 Sampling Crews-
The sampling crews are audited by three
separate individuals: the SOR, the RCC, and
the QA field auditor.
The SOR audits a minimum of one pedon
per sampling crew. The SOR monitors adher-
ence to protocol for site selection, sample
collection, and sample labeling. Written re-
views are submitted to the soil sampling task
leader at ERL-C within two weeks. Major
discrepancies are reported verbally within two
working days. The RCC monitors one site per
sampling crew for adherence to National
Cooperative Soil Survey (NCSS) standards and
the MASS sampling manual (Kern et at., 1988).
During the audit the RCC identifies, discusses,
and resolves any significant discrepancies.
The RCC and SOR discuss the independent
pedon descriptions and inform the sampling
crew leader during sampling of any major
deviations from the protocols that could affect
the quality of samples that are collected.
Written reports are submitted to the soil
sampling task leader at ERL-C within two
weeks and the resolution of major problems is
reported verbally within two working days.
The QA field auditor evaluates each
sampling crew at least twice to ensure adher-
ence to the sampling protocols. These audits
are intended to evaluate and clarify the site
selection, profile description, and soil sampling
procedures. A detailed sampling questionnaire
is used in the evaluation (Kern et al., 1988).
Written reports are submitted to the soil
sampling task leader at ERL-C within two
weeks and major discrepancies are reported
verbally within two working days. The field
auditor is responsible for coordinating the
overall QA sampling program and for resolving
any major issues. The auditor ensures that all
sampling crews receive consistent instructions
and clarification of the protocols.
The QA auditor conducts an in-depth
review of all field operations for compliance
with the sampling protocols. This includes
interviewing the sampling crew members while
accompanying the crew on a sampling excur-
sion. If there are any discrepancies, the QA
auditor must attempt to correct them by
reference to or by interpretation of the sam-
pling protocols. The soil sampling task leader
is responsible for conveying information con-
cerning major issues to the DDRP technical
director.
6.1.4.2 Preparation Laboratory-
The preparation laboratory can expect a
minimum of two on-site system audits. The
first on-site audit is performed before initial
receipt of samples in order to assess the
facilities, including the cold storage unit and
the sample drying and processing areas. The
questionnaire in Appendix I is used in this
evaluation. The QA auditor discusses any
discrepancies with the preparation laboratory
manager. All observations are summarized in
an audit report that is submitted to the QA
manager within two weeks following the audit
visit.
The second on-site audit is conducted
approximately one-third of the way through
sample processing. After reviewing the initial
audit report, any changes made since the first
audit are noted on the questionnaire. Also,
any additional discrepancies identified must be
corrected and brought to the attention of the
QA manager. A summary report is written for
this and any additional on-site visits, and is
submitted to the QA manager within two
weeks following the audit.
The QA staff also conducts periodic
audits to inspect the facilities and to assess
the data entry and verification activities.
Reports are submitted to the QA manager on
a regular basis.
6.1.4.3 Analytical Laboratory-
Each analytical laboratory can expect a
minimum of two on-site audits. The first on-
site evaluation is performed after the laborato-
ry has successfully analyzed the PE samples.
The QA auditor conducts an in-depth review of
-------
Section 6
Revision 1
Date: 4/89
Page 5 of 10
all laboratory functions that are pertinent to
the analyses. The questionnaire in Appendix F
is used to document the on-site laboratory
audit. The QA auditor brings any problems to
the attention of the laboratory manager for
corrective action. All observations are summa-
rized in an evaluation report that is submitted
to the QA manager within two weeks following
the audit visit.
The second on-site audit is conducted
approximately one-third of the way through
sample analyses. All changes that have
been implemented since the initial audit are
reviewed. Data from the QE/QC samples
received to date are assessed. An evaluation
report is written for this and any subsequent
on-site audits and is submitted to the QA
manager within two weeks following the audit
visit.
6.2 Data Verification
The intent of this subsection is to des-
cribe the various mechanisms that are used in
defining and implementing the data verification
procedures and the corrective actions that are
taken if the MQOs are not satisfied.
6.2.1 Field Sampling and
Characterization Data
Verifying the quality of the soil character-
ization data entered on the SCS-SOI-232 field
data form (see Appendix A) is a task that
requires a large reference base of information,
which includes the field data form entry codes
and a series of NCSS resource handbooks,
e.g., the Soil Survey Manual (USDA/SCS, 1981).
Some entries are checked against logbooks or
analytical laboratory data and other entries are
compared with soil taxonomic criteria. Certain
general field characteristics may not be possi-
ble to verify.
The field data forms are double-entered
into a personal computer data file upon receipt
of the forms from the sampling crews via the
preparation laboratory manager. A computer
program designed to check every entry on the
form verifies the field data (see Appendix G).
The program checks for appropriate coding
and completeness of entries. Exception pro-
grams examine the internal consistency of the
field data and compare related field parame-
ters, e.g., soil texture modifier versus percent
rock fragments, or compare field and analytical
parameters, e.g., field pH versus laboratory pH.
In addition, certain entry fields on the first
page of the field data form are manually
checked for completeness and correctness
using the pedon code and site identification
information provided by ERL-C personnel.
The program appends the discrepant
data with appropriate field data verification
flags. A form listing the discrepancies (see
Appendix G) is sent to the SCS state office
representative or the appropriate sampling
crew leader for evaluation. The sampling crew
checks the discrepancy form against the field
data form and logbook, corrects the questiona-
ble entry, signs the discrepancy form, and
returns the form to EMSL-LV. The form is
used by the QA staff to edit the local working
copy of the raw data base.
When all of the field data forms have
been properly edited, a final run of the verifica-
tion program is performed on the field data
bases to flag any remaining anomalies.
6.2.2 Preparation Laboratory Data
A computer program has been developed
that allows entry and verification of prepara-
tion laboratory data from the raw data forms
shown in Appendix B. The program also
tracks soil samples through the various sam-
ple processing and analysis activities, and
assigns verification flags to discrepant data.
A remote modem assembly allows concurrent
assessment by the QA staff. A summary of
the preparation laboratory verification proce-
dures and flags is presented in Appendix H.
After the laboratory analysts enter raw
data on the forms, an evaluation procedure is
initiated which identifies coding discrepancies
and those data that do not satisfy the MQO
criteria previously outlined in Table 5-5. The
flags identified in Appendix H are used to
-------
Section 6
Revision 1
Date: 4/89
Page 6 of 10
qualify the discrepancies. All outliers must be
confirmed or corrected before the data are
assigned to the verified preparation laboratory
data base.
The summary data form (DDRP Form
101) is reviewed by QA staff to ensure that the
sample codes correspond to those entered on
the field data and raw data forms. The inclu-
sion of appropriate QE/QC samples is checked
and recorded for each batch. Analytical data
are checked for completeness and correctness.
The preparation laboratory manager is instruc-
ted to acknowledge and correct any errors in
the data while reviewing the raw data forms,
control charts, or logbooks. The shipping form
(Form 102) is compared with the Form 101 to
verify the analytical laboratory destination and
the number of samples shipped. The data
base manager is notified by QA staff of any
changes made to the data base as a result of
these checks. The appropriate analytical
laboratory and the EPA Sample Management
Office (SMO) are notified of any changes
affecting sample analysis or data reporting.
All interactions with the laboratories, ORNL,
SMO, and ERL-C staff are recorded in a log-
book.
Corrective action for the preparation
laboratory is accomplished primarily through
the application of a QA reanalysis template
(QART) for each analysis of interest (see
Appendix J). The QARTs provide an assess-
ment checklist of QE/QC samples for each
analytical run. The templates are used to
make decisions on whether to request reanaly-
sis of a particular parameter and are designed
to assess data on the basis of precision limits
and accuracy windows.
The QARTs are constructed as spread-
sheets, where the row entry fields specify the
evaluation criteria and the column entry fields
represent the magnitude of the measurement
uncertainty that has occurred. The first col-
umn is used to identify major measurement
uncertainty and the second column identifies
minor uncertainty. When major uncertainty
occurs, the QA manager informs the prepara-
tion laboratory manager to check for errors in
the data for the suspect run and parameter
before initiating reanalysis. If the laboratory
confirms the values, then reanalysis may be
requested for all samples in the run. The
following types of measurement uncertainty
are considered major for all parameters
measured at the preparation laboratory
and constitute the basis for requesting
confirmation/reanalysis:
• Precision/accuracy - The pair of FAP
samples and the FAL sample (or the tripli-
cate of FAO samples) are grouped to
allow three evaluations. The FAPs and
FAOs are evaluated for precision and
accuracy, and the FAL is evaluated for
accuracy. Major uncertainty exists if any
two of these three evaluations fail to
satisfy the MQOs.
• QCCS - Major uncertainty exists if a
QCCS is outside of control limits.
• Cumulative minor uncertainty - If three
or more instances of minor uncertainty
occur, this is considered to be comparable
to major uncertainty.
The following types of measurement un-
certainty are considered minor for all para-
meters measured at the preparation labora-
tory:
• Precision/accuracy -- Minor uncertainty
exists if one of the three precision/
accuracy evaluations do not satisfy the
MQOs, or if the FDs, internal duplicate, or
manager's sample do not satisfy the
MQOs.
6.2.3 Analytical Laboratory Data
The analytical data verification is a multi-
faceted, computerized approach to provide a
concise and consistent assessment of the
data. The overall program is highlighted by
the Laboratory Entry and Verification Informa-
tion System (LEVIS); characteristics of the
system and related programs are described
below.
-------
Section 6
Revision 1
Date: 4/89
Page 7 of 10
6.2.3.1 Laboratory Entry and
Verification Information
System (LEVIS)--
The LEVIS programs are implemented
on personal computers and facilitate the
data entry and QC sample evaluation at the
analytical laboratories as well as the evalua-
tion of laboratory performance by the QA staff.
The system is a menu-driven product that is
designed as a two phase operation, where
phase one is an analytical laboratory system
and phase two is a quality assessment sys-
tem.
Phase one is implemented on computers
supplied by the EPA to each of the analytical
laboratories and linked to a central EPA com-
puter located at EMSL-LV. This phase includes
the data entry system and the two laboratory
components of the verification system: the
QC summary reports and the soil chemistry
relationships. Phase two is implemented only
on the central EPA computer and incorporates
two additional verification programs to as-
sess QE samples for accuracy and precision.
This phase first converts the phase one data
base files to the phase two data base struc-
ture before evaluation programs are run. The
central EPA computer maintains data from all
of the analytical laboratories.
Communications software has been
selected that allows the transfer of data files
from the laboratories to the central computer.
The software selected for this operation is PC
Anywhere, produced by Dynamic Microproces-
sor Associates. The software allows the
central computer to operate as a remote
terminal to the laboratory computer when both
are connected by a modem. Commands typed
on the central computer are executed on the
laboratory computer, allowing the QA staff to
review a laboratory's data files, run programs,
and transfer data to the central computer.
The entire LEVIS system must be acces-
sible through the menus by selecting options
displayed on the screen. The three laborato-
ries must operate independently with little
support from the QA staff; therefore, self-
explanatory menus to guide the user are
utilized. Color menu screens are implemented
in both phase one and two to help highlight
options and screen information.
Several key analytical laboratory person-
nel normally participate in the training, inclu-
ding the laboratory manager, analysts, and
data entry operators. To aid in training and
support, a LEVIS User's Guide has been
prepared (SAIC, 1988) and a "test" batch of
data is installed on each computer to evaluate
the verification procedures.
LEVIS phase one facilitates the entry of
the analytical results including all raw data,
e.g., extract volume, collected during sample
analysis. The calculation of the final data,
including the conversion of instrument readings
to reporting units and correction for moisture
content, is performed by the system. The
LEVIS design also facilitates the output to
hard copy printouts of the data files for further
review. Hard copies are made of the input
data files and checked against the laboratory
data sheets by the data entry personnel. The
laboratory manager is required to review the
hard copy outputs.
The analytical laboratories generally
operate as follows: (1) perform soil analysis;
(2) enter results into LEVIS; (3) review data
and correct errors, if necessary; (4) perform
and evaluate verification checks; (5) reanalyze
if necessary (repeat previous steps); (6) select
appropriate run for submission if reanalysis is
performed; (7) submit data to QA staff. This
process is repeated for each parameter until
the entire batch is complete for all parameters
and ready for formal submission. The QA
staff transfers preliminary data by modem and
phase two verification programs are run by the
QA staff to evaluate the data for possible dis-
crepancies. Formal submission data are sent
by the laboratories on floppy disk.
6.2.3.2 Verification of Batch Data-
Using the LEVIS system, the preliminary
data undergo a review against the phases one
and two criteria. Instructions are given to the
laboratory concerning the samples requiring
confirmation of the reported values or reruns
-------
Section 6
Revision 1
Date: 4/89
Page 8 of 10
of the existing extract solutions; re-extraction
normally is not performed at this time. If
there is no extract solution remaining, the
laboratory informs the QA manager and awaits
further instructions. If results change during
confirmation, the laboratory is instructed to
re-enter the correct data into their LEVIS
system and send EMSL-LV a new floppy and
hard copy submission of the data. If confir-
mation fails to correct the discrepancies, then
reanalysis of the batch is requested for the
parameter of interest.
Upon concluding the batch sample analy-
sis, some data values may still have QE/QC
outliers or other discrepancies. The affected
samples can possibly be grouped into a sep-
arate reanalysis batch. The samples are
re-extracted and the results from this second
round of analysis are reviewed and, where
appropriate, edited into the data base.
6.2.3.3 Verification by Internal
Consistency Checks (ICCs)-
An internal consistency program is used,
apart from LEVIS, to generate routine data
outliers in each batch. Analytical data from
each parameter are correlated against corre-
sponding data from all other analytical para-
meters measured in the MASS. For each
parameter, the parameter pair with the strong-
est linear relationship, based on the coefficient
of determination (r2), is identified.
Correlations are not performed on para-
meters within the same extract or from the
same measurement. For example, calcium
values from the ammonium acetate extract are
not correlated with magnesium values from the
ammonium acetate extract, even though the
resulting r2 value might have the highest corre-
lation. The reason for this approach is that
certain errors, e.g., incomplete extraction,
would not be identified by performing correla-
tions within the same extracting solution.
The ICCs are performed by using a
weighted linear regression model (SAS, 1986)
because previous DDRP data have exhibited
heteroscedasticity, i.e., the variances are not
the same for the entire population. The weigh-
ting factor (w) used in the regression is calcu-
lated as the reciprocal of the analyte concen-
tration of the independent variable (w = 1 / x).
The weighting is not performed on logarithmi-
cally defined parameters such as soil pH. A
correlation is run by plotting values for one
parameter on the X-axis and values for another
parameter on the Y-axis. The X- and Y-axes of
each parameter then are reversed and the
regression is repeated.
The results from both regressions are
combined in order to identify the outliers,
which are defined as those points having a
studentized residual (Belsley et al., 1980) of 3.0
or higher. The studentized residual is an
appropriate robust technique, although one
possible limitation of the approach is that the
outlier itself may strongly influence the regres-
sion estimates of the slope or intercept, there-
by affecting the value of the residual. In this
event, an application of the DFFITS statistic
(Belsley et al., 1980) can measure the change
in the predicted value resulting from the exclu-
sion of a specific observation in the regression
analysis. The DFFITS statistic is used to
examine the significance of large differences in
residuals. As with the studentized residual,
division by the estimated error normalizes the
statistic to allow comparison among points of
varying precision. As a result, controlling data
points that might unduly affect the predicted
value of the dependent variable tend to have a
high DFFITS value. The critical point used to
define a high value is the "critical" DFFITS.
A data outlier is identified as any data
point exceeding the critical values defined for
the studentized residual and the DFFITS stati-
stic. These points are temporarily removed
from the set of observations being analyzed.
Using the remaining data, a second regression
is performed on the same parameters. Utili-
zing the regression equation, i.e., slope and
intercept, from the second regression and the
mean and corrected sum of squares from data
points defined as outliers in the first regres-
sion, a residual test Is performed that exam-
ines and returns certain outliers to the set of
"good" or viable data points. Any outliers
failing this test are considered "true" outliers
and undergo additional tests to assess their
-------
Section 6
Revision 1
Date: 4/89
Page 9 of 10
validity. Results are checked for accuracy in
transcription against the values in the data
package and, where necessary, corrections are
made.
The purpose of these evaluations is to
identify routine values for each analytical
parameter that are not consistent with the
majority of values observed. These values
are checked for errors in transcription, data
entry, or editing. If no discrepancies are
encountered, these data values are confirmed
by the analytical laboratory. In the event that
more than 10 outliers occur in any one batch,
reanalysis may be requested. Five to 10
outliers are considered a minor problem but
may be used with other criteria to justify
reanalysis.
6.2.3.4 Verification with Soil
Chemistry Relationships
(SCRs)-
The SCRs are another tool used to
examine the internal consistency of the routine
sample data (see Appendix D). It is expected
that approximately 10 percent of the data will
not comply with these relationships; these
anomalous data are examined by a staff soil
chemist who either qualifies them or assigns
appropriate flags signifying the discrepancy
(see Appendix K).
Data flow for the SCRs uses a separate
LEVIS menu selection, where each program is
selected to run independently, i.e., the menu
selection does not automatically run all the
SCRs at the same time, although that option
is available. The output reports are then
reviewed by the laboratory personnel, and the
raw data for samples that do not meet the
criteria are flagged and then reviewed in order
to identify errors with sample codes, data
entry, dilutions, parameters, or other factors.
Any discrepancies that can be corrected are
edited into the data base, and the QC sum-
mary checks and SCRs are run through the
program again.
If six or more outlying values from one
batch occur for any given SCR, the QA staff
reviews all data relating to the batch to deter-
mine whether there is the possibility of sample
contamination or other systematic source of
error. If the outliers cannot be explained with
this approach, reanalysis is requested for the
parameter of interest. If one to six outlying
values occur, a minor discrepancy is identified
and may be justification for reanalysis if other
batch-wide discrepancies are noted.
6.2.3.5 Quality Assurance Reanalysis
Templates (QARTs)-
The analytical QARTs provide an assess-
ment checklist of QE/QC samples, ICCs, and
SCRs for each batch of data. The templates
are used to make decisions on whether to
request a batch-wide reanalysis of a particular
parameter from an analytical laboratory. The
QARTs are designed to assess data on the
basis of precision limits, accuracy windows,
ICCs, and SCRs (see Appendix J).
The QARTs are constructed as spread-
sheets, where the row entry fields specify the
evaluation criteria and the column entry fields
represent the magnitude of the measurement
uncertainty that has occurred. The first col-
umn is used to identify major measurement
uncertainty and the second column identifies
minor uncertainty. When major uncertainty
occurs, the QA manager informs the analytical
laboratory to check for errors in their data for
the parameter and batch in question prior to
initiating reanalysis. If the laboratory confirms
the values, then reanalysis of the parameter in
question is requested for all samples in the
batch. The following types of measurement
uncertainty are considered major for all param-
eters and constitute the basis for requesting
confirmation/reanalysis:
• Precision/accuracy - the two pairs of
PD/routine samples, one pair of LAP
samples, and one LAL sample are grouped
to allow four evaluations. The PDs are
evaluated for precision, the LAPs are
evaluated for precision and accuracy, and
the LAL is evaluated for accuracy. Major
uncertainty exists if any two of these four
evaluations fail to satisfy the MQOs.
-------
Section 6
Revision 1
Date: 4/89
Page 10 of 10
• Internal consistency checks - Major
uncertainty exists if more than ten values
from a batch are outlying for a given
parameter.
• Soil chemistry relationships ~ major
uncertainty exists if six or more SCRs fail
within a batch.
• Cumulative minor uncertainty - if three
or more instances of minor uncertainty
occur, this is considered to be comparable
to major uncertainty.
The following types of measurement
uncertainty are considered minor for the
purposes of establishing reanalysis criteria:
• Precision/accuracy - Minor uncertainty
exists if one of the four precision/ accura-
cy evaluations do not satisfy the MQOs, or
if the FDs or FAPs do not satisfy the
MQOs.
• Internal consistency checks - Minor
uncertainty exists if five to ten values in a
batch are considered outlying for a given
parameter.
• Soil chemistry relationships - Minor
uncertainty exists if one to six values fail
within a batch.
-------
Section 7
Data Quality Assessment and Reporting
Section 7
Revision 1
Date: 4/89
Page 1 of 3
This QA plan has defined the MQOs (see
Section 5) and described the implementation of
the QA program for the MASS (see Section 6).
This section describes the statistical assess-
ment procedures that are applied to the data
and the general assessment of the data
quality accomplishments. Reports to EPA
management are also discussed.
7.1 Statistical Design
7.1.1 Assessment of Detectability
The assessment of detection limits is
accomplished on a parameter basis at four
levels: (1) compliance with CRDLs; (2) calcula-
tion of actual IDLs; (3) calculation of esti-
mated SDLs; and (4) identification of routine
samples having concentrations below the SDL.
The results can be grouped in tabular form to
allow comparisons among the values for any
parameter of interest.
7.1.2 Assessment of Precision
A statistical evaluation procedure that
has been developed by the QA staff and data
users is applied to the data in order to assess
possible uncertainty stemming from confoun-
ded data collection imprecision. An additive
model is used, where an observed value of any
soil characteristic is considered as the sum of
the "true" or accepted value and an error term.
This model assumes that data uncertainty is
directly related to the error variance (Miah and
Moore, 1988).
In the DDRP surveys, the error variance
is dependent upon the "true" value of the soil
characteristics of interest. Because of the
wide range of analyte concentrations for the
soils, it is necessary to separate the concen-
tration range into segments of error variance
that serve as estimates of data measurement
uncertainty. The MASS statistical approach,
therefore, requires the entire analytical concen-
tration range to be partitioned into several
intervals not necessarily of equal length. An
assumption is made that the error variance
within each interval is independent of, and
changes with, the "true" analyte concentration.
Within this framework, the error variance
is represented by a step function (Rudin, 1974)
where each step value is the error variance for
a specific, defined interval. A pooled estimate
of the error variance is obtained by taking a
weighted average of the individual step values,
using the corresponding degrees of freedom
as the weighting characteristic.
A fundamental difficulty is assessing the
effect of the error variances on the routine
sample data. A measure of this effect, delta,
is estimated by averaging the step values of
the error standard deviation, using the
proportion of routine samples in each interval
as the weighting characteristic (Van Remortel
et al., 1988). Since data collection is a multi-
phase process and uncertainty accumulates in
the data with each progressing phase, the
cumulative uncertainty is estimated with the
delta values using the QE/QC data.
7.1.3 Assessment of Accuracy
The accuracy windows for the laboratory
audit samples are based on previous interlab-
oratory analyses of the same sample material
by the same protocols. The objective of set-
ting windows is to specify a range of accept-
able single values based on a mean and
standard deviation computed from a number of
previously observed values. The prediction
intervals used for the accuracy windows are
generally determined by robust estimation
using a biweight procedure (Kadafar, 1982). If
the standard biweight approach produces
windows that are too wide to accommodate
the data requirements of the DDRP data users,
-------
Section 7
Revision 1
Date: 4/89
Page 2 of 3
a MQO-based confidence interval around the
biweight-generated mean is used.
The known accuracy windows for these
samples are used in deciding when a particu-
lar sample is outside of control limits. The
mean of a LAP/LAO pair or triplicate is used in
lieu of an individual sample.
7.1.4 Assessment of
Representativeness
The sampling aspect of representative-
ness is assessed by comparing the individual
site and pedon classifications with the compo-
nent sampling classes to which the soils are
assigned. Soils that fall outside the range of
characteristics of their respective sampling
class would be considered non-representative.
Representativeness of the measurement
quality samples is assessed by comparing the
concentration ranges of data from the FD and
PD samples to the overall concentration range
of the routine sample data. This is accom-
plished through the application of critical
values determined by the Kolmogorov-Smirnov
test (Conover, 1980) which assesses the ability
of the duplicate samples to track the distribu-
tion of routine sample concentrations.
Representativeness of the homogeniza-
tion and subsampling procedures at the prepa-
ration and analytical laboratories is assessed
using precision estimates for the PO and AD
samples, respectively.
7.1.5
ness
Assessment of Complete-
Sampling completeness is assessed by
comparing the actual number of pedons and
associated horizons sampled to that number
specified in the MASS design. Completeness
of the sample preparation and analysis is
easily calculated using data from the verified
data base, while completeness of the data
from a data user's perspective is determined
using confidence level 0, 1, and 2 data as a
percentage of the entire validated data base
(see Section 7.2).
7.1.6 Assessment of
Comparability
Comparability is perhaps the most diffi-
cult of the data quality attributes to assess,
primarily because of the many different as-
pects of comparison that are involved. Follow-
ing completion of the MASS, a comparison is
made across the Northeastern, SBRP, and Mid-
Appalachian regions that focuses on method
differences, audit sample results, laboratory
effects, and other QA features of the surveys.
Comparison of the DDRP data bases to other
similar data bases may also be undertaken.
Summary statistics are used to collate individ-
ual values into pooled groups that enable the
data users to discern trends of interest among
the surveys.
7.2 Overview of Validation
Procedures
While the verification procedures evaluate
data at the sample/batch level, validation
procedures examine the plausibility of data in
the context of a subregional set of samples.
The MASS sampling strategy generally groups
those soils having similar physical and chemi-
cal characteristics together. The validation
process identifies unusual data that warrant
special attention when used in statistical
analysis, particularly in regional estimates
concerning a target population of soils.
Observations identified as anomalous during
the review of routine data at the sampling
class/horizon level or subregional level are
considered outliers in relation to the remainder
of the data. Outliers are identified using the
Box and Whisker outlier test (Velleman and
Hoaglin, 1981). The outliers may result from
the natural variability of soils in the set of
watersheds or from anthropogenic disturban-
ces in the natural environment. Other sources
may be derived from sampling, preparation, or
analytical measurement uncertainty.
Although outliers may represent unusual
data in comparison with other data, these
values are not necessarily inaccurate in their
representation of a watershed. The validation
process is not designed as a stringent pass or
-------
Section 7
Revision 1
Date: 4/89
Page 3 of 3
fail test, but rather as a way to identify points
having suspicious origins. All routine sample
data are subjected to a set of rigorous valida-
tion checks by ERL-C and ORNL personnel that
results in a series of flags being appended
on all data not meeting the criteria (see
Appendix K). Each reviewed datum is as-
signed a level of confidence based upon the
number and weight of the laboratory qualifiers
and the verification/validation flags assigned to
that datum. The levels of confidence range
from 0 to 4, where levels 3 and 4 data are
suspected to be invalid for most uses. The
data users generally consider levels 0, 1, and
2 data to be suitable for most modeling appli-
cations.
The verified data are subjected to an
investigation of parameter relationships within
a sampling class/horizon group.
7.3 Quality Assurance Reports
to Management
The QA staff is required to produce a
series of reports, both verbal and written, to
document the MASS QA activities, as des-
cribed below.
7.3.1 Status Reporting
Communication among the various parti-
cipants in the MASS is maintained through
scheduled conference calls, electronic mail, site
visits, and meetings. These activities provide
all participants with the current status of
operations and allow prompt discussion and
resolution of issues related to the research
plan, methodologies, or QA implementation.
Task leaders for the various stages of
the MASS provide a written summary of opera-
tions to ERL-C on a quarterly basis. These
reports describe the kinds of data collected as
well as summarize the QA activities associated
with the data. The summary of QA activities
includes the following:
• overview of QA activities
• list of changes to the QA program
• results of system and performance audits
• assessment of data quality based on the
verified data bases
• documentation of unfavorable incidents
and corrective actions
• distribution of updated control charts
• results of special studies
Reports relating to data quality assess-
ment are also prepared by QA staff. These
reports include interpretation of performance
audits and replicate sample data, estimates of
measurement error, and identification of any
major discrepancies found during the assess-
ment. The reports are submitted to the
technical monitor at EMSL-LV.
7.3.2 Formal Reporting
In addition to this QA Plan, the DDRP
staff at EMSL-LV produces laboratory opera-
tions manuals for use in the preparation and
analysis of soil samples. Upon completion of
the data verification activities, summary QA
reports of the sample preparation and sample
analysis data are produced and distributed to
all cooperating DDRP staff and data users.
-------
-------
Section 8
Revision 1
Date: 4/89
Page 1 of 3
Section 8
Data Management System
8.1 Overview of the Data Bases 8.1.1 Field Sampling Data
The purpose of data base management
for the MASS is to facilitate the collection,
entry, review, modification, and distribution of
all data associated with the survey (see Figure
8-1). Additional tasks include the data analy-
sis for report generation, statistical analysis,
verification, and data file security. The MASS
final data base contains three progressive
versions of the data gathered: (1) the raw data
base, (2) the verified data base, and (3) the
validated data base. A user's guide has been
developed to assist the data users in under-
standing the structure, codes, and flags of the
DDRP data bases (Turner et al., 1989).
The raw data base is the version of data
that is produced from the data entry operation.
This data base is assembled by EMSL-LV and
sent to ORNL for storage and control of the
official raw data base. It is comprised of data
from three sources: field sampling, sample
preparation, and sample analysis. Each of
these data sources contributes to the com-
pleted raw data base.
The verified data base has the same
structure as the raw data base but is modified
to reflect changes resulting from data verifica-
tion activities. All modifications are tracked
with an electronic audit trail system. The
verified data base is generated by EMSL-LV
and sent to ERL-C and ORNL for processing
and validation.
The validated data base consists of a
file listing all verification and validation flags
plus a single level of confidence assigned for
each datum, as well as transposed files in
tabular format containing the higher quality
data and imputed data replacing the lower
quality data in an enhanced data set.
The data collected during the field sam-
pling operation are recorded on SCS-SOI-232
field data forms by the sampling crews. An
initial review of each form is performed by the
crew leader before copies are sent to the
preparation laboratory manager and to the
EMSL-LV QA staff, where the data are double
entered into the NCC IBM with SAS screens.
Entries are compared and reviewed for entry
errors and the corrected data are added to the
raw data base file. This file is archived and
made available to the data base manager.
The verification program is run on a working
copy of the raw data base in order to perform
extensive data checks and reviews. As data
anomalies are resolved with each sampling
crew and field liaison, corrections are entered
into the working copy in order to create the
verified data base. The program is run once
more in order to assign verification flags and
is then submitted to the data base manager.
The data structure for the field data
bases consists of two files: the pedon base
data and the pedon horizon data. The transfer
of field data forms occurs simultaneously for
both types of data, but the computer opera-
tions are performed separately.
8.1.2 Preparation Laboratory Data
The data generated at the preparation
laboratory are entered directly into SAS files
on a personal computer in the laboratory.
Upon receipt of soil samples and field data
forms, the sample receipt data are entered
and checked. As analyses are performed in
the laboratory, the data are also entered
into the computer and verification programs
are run to check the data. All raw and verified
data files are transferred to the NCC IBM for
-------
MASS
DDRP SOIL SURVEY
FLOWCHART
gc/qc
Section 8
Revision 1
Date: 4/89
Page 2 of 3
Figure 8-1. Flowchart of data management activities for the Direct/Delayed Response Prefect.
-------
Section 8
Revision 1
Date: 4/89
Page 3 of 3
storage and eventual integration with other
MASS data and undergo further verification,
e.g., comparison of field-moist pH values with
air-dry pH values from the analytical laborator-
ies. Finally, the preparation laboratory data
are merged with the analytical laboratory data
to form the laboratory data base.
The raw data file for the preparation
laboratory data on the NCC IBM consists of
.only one file containing all of the required data.
Five intermediate files on the personal com-
puter are used to capture the laboratory opera-
tions data and sample tracking information.
8.1.3 Analytical Laboratory Data
Data generated at the analytical laborat-
ory are entered by laboratory personnel into
the Laboratory Entry and Verification Informa-
tion System (LEVIS) on personal computers at
each laboratory. The LEVIS scheme facilitates
the entry, edit, and review of intermediate
data, as well as the calculation of final data
values. The LEVIS program also performs
QE/QC checks and produces summary reports.
Phone modem access to each laboratory
personal computer is available to facilitate the
transfer of preliminary data. Preliminary data
are used only by the QA staff to evaluate the
current status of laboratory operations and
data entry. The laboratories deliver the official
analytical data to EMSL-LV on floppy disks in
the LEVIS data base structure. The QA staff
performs additional QE/QC and verification
checks on the personal computer and the data
are reviewed for confirmation/reanalysis re-
quirements. The entry of data from reanalysis
follows the same procedures identified above.
The LEVIS data entry screens are
designed to prompt an entry field for each
parameter separately and to combine the
parameters into groups for storage. The
intermediate personal computer data file
structure consists of 42 files and can be
grouped into three main data types of 14 files
each: routine data, matrix spike data, and
QCCS data. The raw data files submitted by
the laboratories and the personal computer
verified data files are uploaded to the NCC
IBM to create the EMSL-LV raw data base and
verified data base. Additional verification
programs on the NCC IBM are run to complete
the verification process.
8.2 Data Base Audits
All of the DDRP data bases are subject
to independent audits by a group of reviewers
under the direction of EPA through the techni-
cal director. The purpose of the audits is to
ensure that all aspects of the data tracking
and management system are being conducted
properly. The audits are performed in a man-
ner similar to those in the National Surface
Water Survey conducted by an independent
auditor contracted by the EPA (IS&T, 1986).
-------
-------
References
Revision 1
Date: 4/89
Page 1 of 2
References
Barth, D. S., B. J. Mason, T. H. Starks, and K.
W. Brown. 1989. Soil Sampling Quality
Assurance Guide. Second Edition.
EPA/600/8-89/046. U.S. Environmental
Protection Agency, Las Vegas, Nevada.
Bartling, M. H., M. L Papp, and R. D. Van
Remortel. 1988. Direct/Delayed Response
Project: Preparation Laboratory Standard
Operating Procedures for the Mid-
Appalachian Soil Survey. U.S. Environ-
mental Protection Agency, Las Vegas,
Nevada.
Belsley, D. A., E. Kuh, and R. E. Welsch. 1980.
Regression Diagnostics. J. Wiley and
Sons, New York. 352 pp.
Chen, C. W., S. A. Gherini, J. D. Dean, R. J. M.
Hudson, and R. A. Goldstein. 1984.
Development and Calibration of the Inte-
grated Lake-Watershed Acidification
Study Model. In: Modeling of Total Acid
Precipitation Impacts, J. L. Schnoor (Ed.),
pp. 175-203. Butterworth Publishers,
Boston, Massachusetts.
Conover, W. J. 1980. Practical Nonparametric
Statistics, Second Edition. J. Wiley and
Sons, New York. 493 pp.
Cosby, B. J., R. F. Wright, G. M. Hornberger,
and J. N. Galloway. 1984. Model of
Acidification of Groundwater in Catch-
ments. Internal report to EPA. North
Carolina State University, Raleigh, North
Carolina.
Costle, D. M. 1979a. EPA Quality Assurance
Policy Statement. Administrator's Memo-
randum, 5-30-79. U.S. Environmental
Protection Agency, Washington, D.C.
Costle, D. M. 1979b. Quality Assurance Requi-
rements for all EPA Extramural Projects
Involving Environmental Measurements.
Administrator's Policy Statement, 5-30-
79. U.S. Environmental Protection
Agency, Washington, D.C.
International Science & Technology, Inc. 1986.
Data Base Audit: National Lake Survey,
Phase I - Western Lake Survey. IS&T
Report No. 10067. International Science
& Technology, Inc., Reston, Virginia.
Kadafar, K 1982. A Biweight Approach to the
One Sample Problem. Jour. Amer. Stat.
Assoc. 77(378) :416-424.
Kern, J. S., M. L. Papp, J. J. Lee, D. A. Lam-
mers, and L J. Blume. 1988. Soil Sam-
pling Manual for the Direct/Delayed
Response Project Mid-Appalachian Soil
Survey. U.S. Environmental Protection
Agency, Corvallis, Oregon.
Lammers, D. A., J. J. Lee, and J. A. Ferwerda.
1988. Mapping Protocols for Direct/
Delayed Response Project Soil Mapping
Activities in the Mid-Appalachian Region.
U.S. Environmental Protection Agency,
Corvallis, Oregon.
Lee, J., R. Church, D. Lammers, L. Liegel, M.
Johnson, D. Coffey, R. Holdren, D.
Stevens, R. Turner, and L. Blume. 1989.
Watershed Surveys to Support an
Assessment of the Regional Effects of
Acidic Deposition on Surface Water
Chemistry. Environmental Management
13(1)95-108.
Messer, J. J., K. N. Eshleman, S. M. Stam-
baugh, and P. R. Kaufmann. 1986.
National Surface Water Survey: National
Stream Survey, Phase I - Pilot Survey.
EPA/600/4-86/026. U.S. Environmental
Protection Agency, Washington, D. C.
Miah, M. J., and J. M. Moore. 1988. Parameter
Design in Chemometry. In: Chemo-
metrics and Intelligent Laboratory Sys-
tems, 3(1988)31-37. Elsevier Science
Publishers, Amsterdam, The Netherlands.
Rudin, W. 1974. Real and Complex Analysis.
McGraw-Hill Publishers, New York. 22
pp.
-------
References
Revision 1
Date: 4/89
Page 2 of 2
References (continued)
Science Applications International Corporation.
1988. User's Guide: Laboratory Entry and
Verification Information System. Oak
Ridge National Laboratory, Oak Ridge,
Tennessee.
SAS Institute. 1986. SAS System for Linear
Models. SAS Institute, Inc., Gary, North
Carolina. 210 pp.
Schnoor, J. L, W. D. Palmer, Jr., and G. E.
Glass. 1984. Modeling Impacts of Acid
Precipitation for Northeastern Minnesota.
In: Modeling of Total Acid Precipitation
Impacts, J. L Schnoor (Ed.), pp. 155-173.
Butterworth Publishers, Boston, Massa-
chusetts.
Stanley, T. W., and S. S. Verner. 1985. The
U.S. Environmental Protection Agency's
Quality Assurance Program. In: Quality
Assurance for Environmental Measure-
ments, pp. 12-19. ASTM STP 867. Ameri-
can Society for Testing and Materials,
Philadelphia, Pennsylvania.
Steers, C. A., and B. F. Hajek. 1979. Deter-
mination of Map Unit Composition by a
Random Selection of Transects. Soil Sci.
Soc. Amer. Jour. 43:156-160.
Taylor, J. K. 1987. Quality Assurance of Che-
mical Measurements. Lewis Publishers,
Chelsea, Michigan. 328 pp.
Turner, R. S., C. C. Brandt, D. D. Schmoyer, J.
C. Goyert, K. D. Van Hoesen, L J. Allison.
G. R. Holdren, P. W. Shaffer, M. G.
Johnson, D. A. Lammers, J. J. Lee, M. R.
Church, L J. Blume, and M. L. Papp.
1989. Direct/Delayed Response Project
Data Base User's Guide. Environmental
Sciences Division Publication Number
2871. Oak Ridge National Laboratory,
Oak Ridge, Tennessee.
U.S. Agency for International Development.
1987. Keys to Soil Taxonomy. Technical
Monograph No. 6. Soil Management
Support Services. Third Printing.
Cornell University, Ithaca, New York.
U.S. Department of Agriculture/Soil Conserva-
tion Service. 1981. Soil Survey Manual.
Agriculture Handbook 18. Soil Survey
Staff. U.S. Government Printing Office,
Washington, D.C.
U.S. Environmental Protection Agency. 1985.
Direct/Delayed Response Project Soil
Survey Data Quality Objectives (Review
Draft). U.S. Environmental Protection
Agency, Washington, D.C.
U.S. Environmental Protection Agency. 1988.
Statements of Work for Soil Analysis in
the Direct/Delayed Response Project Mid-
Appalachian Soil Survey. Attachment A
in Invitation for Bid Solicitations
W802498D1 and W802621D1. U.S. Envi-
ronmental Protection Agency, Las Vegas,
Nevada.
U.S. Environmental Protection Agency. 1989.
An Overview of the Direct/Delayed
Response Project. In: AERP Status, April
1989. EPA/600/M-89/005. U.S. Environ-
mental Protection Agency, Washington,
D.C.
Van Remortel, R D., G. E. Byers, J. E. Teberg,
M. J. Miah, C. J. Palmer, M. L. Papp, M.
H. Bartling, A. D. Tansey, D. L Cassell,
and P. W. Shaffer. 1988. Direct/Delayed
Response Project: Quality Assurance
Report for Physical and Chemical Analy-
ses of Soils from the Southern Blue
Ridge Province of the United States.
EPA/600/8-88/100. U.S. Environmental
Protection Agency, Las Vegas, Nevada.
Velleman, P. F., and D. C. Hoaglin. 1981.
Applications, Basics, and Computing of
Exploratory Data Analysis. Duxbury
Press, North Scituate, Massachusetts.
Winefordner, J. D., and G. L Long. 1983. Limit
of Detection: A Closer Look at the IUPAC
Definition. Anal. Chem. 55(7)712-724.
-------
Appendix A
Revision 1
Date: 4/89
Page 1 of 14
Appendix A
SCS-SOI-232 Field Data Form and Codes
Field data describing each sampled pedon are recorded on the SCS-SOI-232 form. This
appendix also includes a list of the specific codes to be used on the form.
-------
Appendix A
Revision 1
Date: 4/89
Page 2 of 14
US DEPARTMENT OF AGRICULTURE SOIL DESCRIPTION SCS SOID
SOIL CONSERVATION SERVICE 1 17
SOIL SERIES REPRESENTED
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
DATE
MO OAV YR
I 1 , 1 ,
SITE ID s
St COUNTY UNIT U
, 1 , , 1 , , l$
MLRA
t t 1
LATITUDE 0
DEC WIN SEC l
,1,1,1
LONGITUDE 0
DEC MIN SEC '
,,1,1,1
PEOON CLASSIFICATION
SG PSC MIN RX IMP O1H
1 1 I 1 L i _ I I 1
PRECIP
' 1
WATERTABUE
DEPTH DAYS
1 1 111
K
n
i
i
s
T
i
H
c
1
D
R
,.1.
ELEVATION
till
PARENT MATERIAL
1 1
W B M ORIG W B 1
O | |
• i
1 ORIG . W a
1 l«l
J
M
ORICi W 8 M ORIG R
1
°l i
1
K
A
ANN
1 i
/ERAGE A
SUM
_J 1_
UMPtRATURlS "C
R A\
WINTER
1 1
ANN
1 1
/ERAUC St
SUM
1 1
IL
WINTER
1 1
MS,
RGE
1
WIAIIKR
SIAIION NO
11,11
CONTRUL
SECTION
1 1
1"
yV»
ON
(W
DIAONOS1IC
DEPTH
I I
1
6
-L-L 111.
2
1 1 1 1 1
7
1 1 1 1 1
1
1 1 1 1 1 1 1 1 1 1 1
8 I
1 1 1 1 , 1 1 1 1 1 I
S
1 1 1 1 1
10
1 1 1 1 1
otscmetRS NAMES
LOCATION DESCRIPTION
NOTES
WATERSHED
CLASS
1 I
ST
Olfl D1ST
1
CHEW
1 1 1
WA1ERSHEDNAME
1 1 1 I i L 1 1 1 i | 1 1 1 1 1 1 I 1 1 1 1 1 1 1 1 1 •
-------
Appendix A
Revision 1
Date: 4/89
Page 3 of 14
1
2
3
4
b
6
/
8
9
10
DEPTH
| |
I I
| ]
I I
i I
I I
I I
I I
I I
I I
I I
i I
I I
! I
I |
I I
I I
I !
I i
I I
HORIZON
DESIGNATION
0
S MASTER
C LETTER SUFFI
I I II
II II
II I I
II II
II II
! I I I
II I I
II II
II II
II I I
MOIST COLOR
I I
I I
(REE FORM NOTES
I I
I I
I I I
I I
I I
I I I
I I I
I I I
I I
I I
I I
I I
I I
I I
1
2
3
4
5
6
7
8
9
10
SAMPLE
NUMBERS
BULK Denary
T
N f
0 0
HORIZON NOTES
-------
Appendix A
Revision 1
Date: 4/89
Page 4 of 14
MOTTLES
c v c
0 AH
SZ N HUE L R
1
1
1
1
|
|
1
|
1
1
1
|
1
|
1
1
|
I
I
I
1
1
]
1
1
1
!
1
!
i
i
i
SURfACEfEATURES
K 0 L « c
N C S 0 AH
0 AS N T C MM L H
I
1
|
1
1
1
|
1
1
|
1
1
|
1
1
|
r 1
1
I
1
1
1
1
1
I
1
I
1
1
,
1 1 1
i r i
l i 1
i i 1
i 1 1
1 i i
i i i
1 l i
1 ! 1
1 1 1
1 1 1
1 1 1
I 1 1
1 1 1
1 1 1
1 1 1
1 1 1
! 1 1
1 1 1
1 1 !
! 1 1
1 1 t
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1 1
1 1 1 1
BOUN
OARV
L
|
|
pMMHHUlM
JL
2
3
J_
5
6
7
8
9
10
-------
Appendix A
Revision 1
Date: 4/89
Page 5 of 14
EFFER-
VES-
CENCE
C A E
L 0 X
| |
I I
I |
I |
! I
I I
I I
I !
I I
[ I
FIELD
MEASURED
PROPERTIES
KND AMOUNT
" 1
C |L
(
" 1
C |L
I
p 1
C [I
1
' 1
C |U
1
p 1
c |L
I
» 1
C |l
1
' \
c |L
1
° \
C li
i
* 1
C |L
1
p 1
' |L
1
1 1
! 1
[ |
1 1
1 1
1 1
1 1
1 1
i 1
1 1
1 1
1 1
1 1
1 1
! 1
1 1
i 1
1 i
1 1
1 1
1 1
1 1
! !
1 1
1 1
1 1
1 1
1 I
1 1
i i
FIELD
MEASURED
PROPERTIES
UNO AMOUNT
I
|
1
1
1
1
1
1
1
1
|
|
1
|
|
|
|
|
|
|
1
|
|
|
|
|
|
|
|
1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 [
1 1
1 1
1 1
1 1
1 1
1 !
1 I
I 1
1 i
1 1
1 1
1 1
1 1
1 1
1 1
! 1
1 1
1 I
1 1
1 1
i !
w
E
T
N
E
S
S
H
Y
0
0
C
0
N
0
HOOTS
L
0
QT SZ C
1
|
1
1
1
|
|
1
1
|
1
1
1
|
1
1
|
1
1
I
1
I
1
1
|
|
|
1
I
1
1
I
|
1
1
1
1
|
1
(
1
1
1
1
1
1
1
1
1
1
1
1
1
t
1
1
1
|
1
1
PORES
SHP OT SZ CN
1
|
|
1
|
1
1
(
|
1
1
|
|
|
|
1
1
1
1
1
|
1
|
1
1
I
1
1
1
1
|
|
|
|
1
1
|
1
|
1
|
|
|
|
|
1
|
1
1
|
1
1
|
1
|
1
1
1
1
1
|
1
I
1
1
1
I
1
1
1
1
|
|
|
I
1
)
1
1
I
1
1
1
1
1
1
1
1
1
i
1
|
1
I
1
|
|
|
|
1
1
|
|
1
1
|
|
!
I
I
|
|
1
1
1
|
1
I
1
1
CONCENTRATIONS
S
H
KNO OT P SZ
|
1
1
1
1
1
1
1
1
1
1
|
1
|
I
1
]
1
I
|
|
1
|
1
1
|
1
I
1
|
I
1
1
|
1
I
1
1
1
1
|
|
1
|
|
|
|
|
|
|
1
I
I
1
1
1
1
1
1
1
1
1
1
flOCK
FRAGMENTS
K R
N N S
D % 0 Z
1
1
1
1
1
1
1
|
1
1
1
1
1
|
1
1
1
1
1
I
1
1
1
1
1
1
1
I
,
1
1
1
1
1
1
|
1
1
1
1
|
|
|
1
|
I
|
1
I
I
1
1
1
1
1
1
1
I
1
1
ROCK
FRAGMENTS
K R
N N S
0 ", O Z
1
|
1
|
1
1
1
|
1
1
|
I
|
|
1
1
1
1
1
1
1
1
1
1
1
1
|
1
1
1
1
1
1
|
1
1
1
|
1
1
1
i
1
1
I
1
1
1
1
1
1
1
i
1
1
|
1
1
1
1
FREE FORM NOTES
LOG
WEATHER
SET 1 0
UNDERSTORY VEG.
SLIDES H PED FACE
UNDERSTORY
OVERSTORY
LANDSCAPE
-------
Appendix A
Revision 1
Date: 4/89
Page 6 of 14
SITE CODES
Slope Shape (U/A)
1
2
3
4
5
convex
plane
concave
undulating
complex
Geomorphic Component (GM)
1 interfluve
2 head slope
3 side slope
4 nose slope
Hillslope Component (HS)
1
2
3
4
5
Slope
45
90
135
180
225
270
315
0
Major
BA
BI
BO
CA
CF
CP
DF
DP
FH
GF
GU
HH
HI
KP
LP
LU
LV
MO
MV
PI
PL
PT
RV
SB
SH
TA
VM
summit
shoulder
back slope
foot slope
toe slope
Aspect (ASP)
northeast
east
southeast
south
southwest
west
northwest
north
Landform (MAJ)
Badlands
Barrier Island
Bolson
Canyon
Coalescent Fan Piedmont
Coastal Plain
Drumlin Field
Deeply Dissected Plateau
Foothills
Glaciofluvial Landform
Glaciated Upland
High Hills
Hills
Karst Plain
Lake Plain
Level or Undulating Upland
Lava Plain
Mountains
Mountain Valley
Piedmonts
Plains
Plateau
River Valley
Semibolson
Sandhills
Tableland
Volcanic Mountains
Local Landform (LOC)
AF Alluvial Fan
AP Alluvial Flat (plain)
BE Beach
BF Barrier Flat
BO Bog
BS Backswamp
BT Beach Terrace
BU Butte
CB Carolina Bay
CO Cove
CR Crater
CU Cuesta
DE Delta
DO Dome
DR Drumlin
DU Dune
EK Esker
EM End Moraine
ES Escarpment
FE Felsenmeer
FP Flood Plain
FT Fluvial Terrace
FJ Fjord
GM Ground Moraine
HO Hogback
HS Hillside
KA Kama
KE Kettle
LS Low Sand Ridge
LT Lake Terrace
MA Marsh
ME Mesa
MO Mountainside
MT Marine Terrace
OP Outwash Plain
OT Outwash Terrace
OX Oxbow
PE Pediment
PL Playa
RI Ridge
SB Structural Bench
SI Sink
SL Slough
SM Salt Marsh
SW Swamp
VC Volcanic Cone
VS Valleyside
Kind of Watertable (KD)
0 none
2 perched
3 apparent
4 ground
Land Use (LU)
A abandonded cropland
C cropland
E forest land grazed
F forest land not grazed
G pasture land and native
pasture
H horticultural land
I cropland irrigated
J hayland
L waste disposal land
N barren land
O other
P rangeland grazed
Q wetlands drained
R wetlands
S rangeland not grazed
T tundra
U urban and built-up land
Stoniness Class (ST)
0 class 0
1 class 1
2 class 2
3 class 3
4 class 4
5 class 5
Hydraulic Conductivity Class
(HC)
1 very slow
2 slow
3 moderately slow
4 moderate
5 moderately rapid
6 rapid
7 very rapid
Drainage Class (DR)
1 very poorly drained
2 poorly drained
3 somewhat poorly drained
4 moderately well drained
5 well drained
6 somewhat excessively
drained
7 excessively drained
Parent Material Weathering (W)
1 slight
2 moderate
3 strong
Bedrock Inclination (B)
1 < 5 degree inclination
2 5-30 degree inclination
3 > 30 degree inclination
-------
Appendix A
Revision 1
Date: 4/89
Page 7 of 14
Parent Material Mode of Deposition
(M)
A alluvium
D glacial drift
E eolian
F mine spoil and earth fill
G glacial outwash
H volcanic ash
I lacustrine sands
J lacustrine silts
K lacustrine clays
L lacustrine
M marine
N marine sands
0 organic
P marine silts
Q marine clays
R solid rock
S eolian-sand
T glacial till
U unconsolidated sediments
V local colluvium
W loess
X residuum
Y solifluctate
Z estuarine
Parent Material Origin (ORIG)
AO sandstone unspecified
A1 sandstone-noncalcareous
A2 arkosic-sandstone
A4 sandstone-calcareous
BO interbedded sedimentary
unspecified
B1 limestone-sandstone-shale
B2 limestone-sandstone
B3 limestone-shale
B4 limestone-siltstone
B5 sandstone-shale
B6 sandstone-siltstone
B7 shale-siltstone
CO conglomerate unspecified
C1 conglomerate-noncalcareous
C2 conglomerate-calcareous
EO ejecta-ash unspecified
E1 acidic-ash
E2 basic-ash
E3 basaltic-ash
E4 andesitic-ash
E5 cinders
E6 pumice
E7 scoria
E8 volcanic bombs
HO shale unspecified
H1 shale-noncalcareous
H2 shale-calcareous
H3 shale-clay
10 igneous unspecified
11 igneous-coarse (or intrusive)
12 igneous-basic (eg., gabbro)
13 igneous-intermediate (eg.,
diorite)
14 igneous-granite
15 igneous-fine (or extrusive)
16 igneous-basalt
17 igneous-andesite
Parent Material Origin (cont.)
18 igneous-acid (eg., rhyolite)
19 igneous-ultrabasic
KO organic unspecified
K1 mossy material
K2 herbaceous material
K3 woody material unspecified
K4 wood fragments
K5 logs and stumps
K6 charcoal
K7 coal
LO limestone unspecified
L1 chalk
L2 marble
L3 dolomite
L4 limestone-phosphatic
L5 limestone-arenaceous
L6 limestone-argillaceous
L7 limestone-cherty
MO metamorphic unspecified
M1 gneiss unspecified
M2 gneiss-acidic
M3 gneiss-basic
M4 serpentine
MS schist unspecified
M6 schist-acidic
M7 schist-basic
M8 slate
M9 quartzite
PO pyroclastic unspecified
P1 tuff unspecified
P2 tuff-acidic
P3 tuff-basic
P4 volcanic breccia unspecified
P5 breccia-acidic
P6 breccia-basic
P7 tuff-breccia
P8 aa
P9 pahoehoe
SO sedimentary unspecified
S1 marl
S2 glauconite
TO siltstone unspecified
T1 siltstone-noncalcarebus
T2 siltstone-calcareous
YO mixed unspecifed
Y1 mixed-noncalcareous
Y2 mixed-calcareous
Y4 mixed-igneous-metamorphic &
sedimentary
Y5 mixed-igneous & metamorphic
Y6 mixed-igneous & sedimentary
Y7 mixed-metamorphic &
sedimentary
MS meta-sedimentary
Bedrock fracture (BDRK)
1 < 10 cm between fractures
2 10 to 45 cm between fractures
3 45 cm to 1.0 m between
fractures
4 1.0 to 2.0 m between fractures
5 > 2.0 m between fractures
Moisture Regime (MST RGE)
AQ aquic
AR aridic
PA peraquic
PU perudic
TO torric
UD udic
US ustic
XE xeric
Erosion Class (ER WA)
0 none
1 slight
2 moderate
3 severe
Runoff Class (RN OF)
1 none
2 ponded
3 very slow
4 slow
5 rapid
6 moderate
7 very rapid
Diagnostic Features (KND)
A anthropic
B cambic
C calcic
D durinodes
E petrocalcic
F fragipan
G gypsic
H histic
I sombric
J petrogypsic
K placic
L lithic contact
M mollic
N natric
O ochric
P plaggen
Q albic
R argic
S spodic
T argillic
U umbric
V sulfuric
W paralithic contact
X oxic
Y salic
Z duripan
1 kandic
Flooding and Ponding, frequency
NO none
RA rare
OC occasional
FR frequent
CO common
-------
Appendix A
Revision 1
Date: 4/89
Page 8 of 14
HORIZON CODES
Color location (LOC)
0 not given
1 interior
2 exterior
3 crushed
4 dithionite-citrate pretreated
5 after exposure to air
6 after ignition
7 pyrophosphate extract
Texture class (CLASS)
C clay
CE coprogenous earth
CIND cinders
CL clay loam
COS coarse sand
COSL coarse sandy loam
CSCL coarse sandy clay loam
DE diatomaceous earth
FB fibric material
FM fragmental material
FS fine sand
FSL fine sandy loam
G gravel
GYP gypsiferous earth
ICE ice or frozen soil
L loam
LCOS loamy coarse sand
LFS loamy fine sand
LS loamy sand
LVFS loamy very fine sand
MARL marl
MPT mucky peat
MUCK muck
OPWD oxide protected weathered
bedrock
PDOMpartially decomposed organics
PEAT peat
S sand
SC sandy clay
SCL sandy clay loam
SG sand and gravel
SI silt
SIC silty clay
SICL silty clay loam
SIL silt loam
SL sandy loam
SP sapric material
U unkown texture
UDOMundecomposed organics
UWB unweathered bedrock
VAR variable
VFS very fine sand
VFSL very fine sandy loam
WB weathered bedrock
Texture modifier (MOD)
BY bouldery
BYV very bouldery
BYX extremely bouldery
CB cobbly
CBA angular cobbly
CBV very cobbly
CBX extremely cobbly
CN channery
CNV very channery
CNX extremely channery
FL flaggy
FLV very flaggy
FLX extremely flaggy
GR gravelly
GRC coarse gravelly
GRF fine gravelly
GRV very gravelly
GRX extremely gravelly
ST stony
STV very stony
STX extremely stony
Structure grade (GRD)
0 structureless
1 weak
2 moderate
3 strong
4 very strong
5 weak and moderate
6 moderate and strong
Structure size (SZ)
CO coarse
CV coarse and very coarse
F fine
FF very fine and fine
FM fine and medium
M medium
MC medium and coarse
TK thick
TN thin
VC very coarse
VF very fine
VK very thick
VN very thin
Structure shape (SHP)
ABK angular blocky
BK blocky
CDY cloddy
COL columnar
CR crumb
GR granular
LP lenticular
MA massive
PL platy
PR prismatic
SBK subangular blocky
SGR single grain
WEG wedge
Dry consistence (DRY)
EH extremely hard
H hard
L loose
S soft
SH slightly hard
VH very hard
Moist consistence (MOIST)
EFI extremely firm
FI firm
FR friable
L loose
VFI very firm
VFR very friable
Other consistence (OTHER)
B brittle
CO uncememented
D deformable
I indurated
MS moderately smeary
R rigid
SC strongly cemented
SD semi deformable
SM smeary
VR very rigid
VWC very weakly cemented
WC weakly cemented
WSM weakly smeary
Stickiness (ST)
S sticky
SO non sticky
SS slightly sticky
VS very sticky
Plasticity (PL)
P plastic
PO non plastic
SP slightly plastic
VP very plastic
Cementing agent (CEM)
H humus
I iron
L lime
S silica
X lime and silica
Mottle abundance (AB)
F few
C common
M many
Mottle size (SZ)
1 fine
12 fine and medium
13 medium and coarse
2 medium
3 coarse
-------
Appendix A
Revision 1
Date: 4/89
Page 9 of 14
Mottle distinctness
D distinct
F faint
P prominent
Surface features kind of coat (KND)
A skeletans over cutans
B black stains
C chalcedony on opal
D clay bridging
G gibbsite coats
I iron stains
K intersecting slickensides
L lime or carbonate coats
M manganese or iron-
manganese stains
0 organic coats
P pressure faces
Q nonintersecting slickensides
S skeletans (sand or silt)
T clay films (cutans)
U coats
X oxide coats
Abundance of coats (AB)
F few
C common
M many
Continuity of coat (CN)
C continuous
D discontinuous
P patchy
Distinctness of coat (DST)
D distinct
F faint
P prominent
Location of coat (LOG)
B between sand grains
C on tops of columns
F on faces of peds and in pores
H on horizontal faces of peds
I in root channels and/or pores
L on lower surfaces of peds or
stones
M on bottoms of plates
N on nodules
P on faces of peds
R on rock fragments
S on sand and gravel
T throughout
U on upper surfaces of peds or
stones
V on vertical faces of peds
Z on vertical and horizontal
faces of peds
Boundary distinctness
A abrupt
C clear
D diffuse
G gradual
Boundary topography
B broken
I irregular
S smooth
W wavy
Effervescence class (CL)
0 very slightly effervescent
1 slightly effervescent
2 strongly effervescent
3 violently effervescent
Effervescence agent (AG)
H HCI, unspecified
I HCI. 1 normal
O Hydrogen peroxide, 3 to 4
percent
P Hydrogen peroxide,
unspecified
Effercescence extension (EX)
C continuous
D discontinuous
Kind of field measurement (KND)
CL clay
PB Bromthymol blue
PC Cresol red
PG Bromcresol green
PH hellige-truog
PL LaMotte-Morgan
PP Phenol red
PR Chlorophenol red
PS soiltest
FT Thymol-blue
PY pH Ydrion
SA sand
SC coarse and very coarse sand
SF fine sand
SI silt
SM medium sand
SV very fine sand
OB fiber unrubbed
OR rubbed fiber
Wetness class (WETNESS)
D dry
M moist
W wet
Hydraulic conductivity class
1 very slow
2 slow
3 moderately slow
4 moderate
5 moderately rapid
6 rapid
7 very rapid
Root quantity (QT)
VF very few
FF very few to few
F few
FC few to common
C common
CM common to many
M many
Root size (SZ)
1 fine 1-2mm
11 very fine and fine
12 fine and medium
13 fine to coarse
2 medium 2-5mm
23 medium and coarse
3 coarse > 5mm
V1 very fine < 1mm
Root location (LOC)
C in cracks
M in mat at top of horizon
P between peds
S matted around stones
T throughout
Shape of pore (SHP)
IE filled with coarse material
IF void between rock
fragments
IR interstitial
IT interstitial and tubular
TC continuous tubular
TD discontinuous tubular
TE dendritic tubular
TS constricted tubular
TU tubular
VS vesicular
VT vesicular and tubular
Quantity of pores (QT)
VF very few
FF very few to few
F few
FC few to common
C common
CM common to many
M many
Size of pore (SZ)
1 fine .05-2mm
11 very fine and fine
12 fine and medium
13 fine to coarse
2 medium 2-5mm
23 medium and coarse
3 coarse > 5mm
V1 very fine < .05mm
Pore Continuity (CN)
L low
M moderate
H high
-------
Appendix A
Revision 1
Date: 4/89
Page 10 of 14
Kind of concentration (KND)
A2 clay bodies
B1 barite crystals
B2 soft masses of barite
C1 calcite crystals
C2 soft masses of lime
C3 lime concretions
C4 lime nodules
01 mica flakes
D2 soft dark masses
D3 dark concretions
D4 dark nodules
E3 gibbsite concretions
E4 gibbsite nodules
F1 plinthite segregations
F2 soft masses of iron
F3 iron concretions
F4 ironstone nodules
G1 gypsum crystals
G2 masses of gypsum
G3 nests of gypsum
G4 gypsum threads
H1 halite crystals
H2 salt masses
K2 soft masses of carbonate
K3 carbonate concretions
K4 carbonate nodules
K5 carbonate threads
M1 nonmagnetic shot
M2 soft masses of iron-
manganese
M3 iron-manganese concretions
M4 magnetic shot
S1 opal crystals
S2 soft masses of silica
S3 silica concretions
S4 durinodes
T2 worm casts
T3 insects casts
T4 worm nodules
Quantity of concentrations (QT)
VF very few
FF very few to few
F few
FC few to common
C common
CM common to many
M many
Shape of concentration (SHP)
C cylindrical
D dendritic
0 rounded
P plate like
T threads
Z irregular
Size of concentration (SZ)
1 fine <2mm
12 fine and medium
2 medium 2-5mm
23 medium and coarse
3 coarse > 5-20mm
34 coarse and very coarse
4 very coarse 20-76mm
45 very coarse and extremely
coarse
5 extremely coarse > 76mm
Roundness of rock fragment (RND)
1
2
3
4
5
angular
subangular
subrounded
rounded
well rounded
Size of rock fragment (SZ)
1 pebbles
2 cobbles
3 stones
4 boulders
5 charmers
6 flagstones
Kind of rock fragment (KND)
A sandstone
B mixed sedimentary
E ejecta
F ironstone
H shale
I igneous rocks
K organic fragments
L limestone
M metamorphic rocks
O oxide-protected rock
P pyroclastic rocks
R saprolite
S sedimentary rocks
Y mixed lithology
-------
Appendix A
Revision 1
Date: 4/89
Page 11 of 14
CLASSIFICATION
Classification, Great Group
Alfisols
AAQAL
AAQDU
AAQFR
AAQGL
MQKA
AAQNA
AAQOC
AAQPN
AAQUM
ABOCR
ABOEU
ABOFR
ABOGL
ABONA
ABOPA
AUDAG
AUDFE
AUDFR
AUDFS
AUDGL
AUDHA
AUDKA
AUDKH
AUDNA
AUDPA
AUDRH
AUSDU
AUSHA
AUSKA
AUSKH
AUSNA
AUSPA
AUSPN
AUSRH
AXEDU
AXEFR
AXEHA
AXENA
AXEPA
AXEPN
AXERH
Aridisols
DARDU
DARHA
DARNO
OARNT
DARPA
DORCL
DORCM
DORDU
DORGY
DORPA
DORSA
Albaqualf
Duraqualf
Fragiaqualf
Glossaqualf
Kandiaqualf
Natraqualf
Ochraqualf
Plinthaqualf
Umbraqualf
Cryoboralf
Eutroboralf
Fragiboralf
Glossoboralf
Natriboralf
Paleboralf
Agrudalf
Ferrudalf
Fragiudalf
Fraglossudalf
Glossudalf
Hapludalf
Kandiudalf
Kandhapludalf
Natrudalf
Paleudalf
Rhodudalf
Ourustalf
Haplustalf
Kandiustalf
Kandhaplustalf
Natrustalf
Paleustalf
Plinthustalf
Rhodustalf
Durixeralf
Fragixeralf
Haploxeralf
Natrixeralf
Palexeralf
Plinthoxeralf
Rhodoxeralf
Durargid
Haplargid
Nadurargid
Natrargid
Paleargid
Calciorthid
Camborthid
Durorthid
Gypsiorthid
Paleorthid
Salorthid
Entisols
EAQCR
EAQFL
EAQHA
EAQHY
EAQPS
EAQSU
EAQTR
EARAR
EFLCR
EFLTO
EFLTR
EFLUD
EFLUS
EFLXE
EORCR
EORTO
EORTR
EORUD
EORUS
EORXE
EPSCR
EPSQU
EPSTO
EPSTR
EPSUD
EPSUS
EPSXE
Inceptisols
Cryaquent
Fluvaquent
Haplaquent
Hydraquent
Psammaquent
Sulfaquent
Tropaquent
Arent
Cryofluvent
Torrifluvent
Tropofluvent
Udifluvent
Ustifluvent
Xerofluvent
Cryorthent
Torriorthent
Troporthent
Udorthent
Ustorthent
Xerorthent
Cryopsamment
Quartzipsamment
Torripsamment
Tropopsamment
Udipsamment
Ustipsamment
Xeropsamment
Histosols
HFIBO
HFICR
HFILU
HFIME
HFISP
HFITR
HFOBO
HFOCR
HFOTR
HHEBO
HHECR
HHELU
HHEME
HHESI
HHESO
HHETR
HSABO
HSACR
HSAME
HSATR
Borofibrist
Cryofibrist
Luvifibrist
Medifibrist
Sphagnofibrist
Tropofibrist
Borofolist
Cryofolist
Tropofolist
Borohemist
Cryohemist
Luvihemist
Medihemist
Sulfihemist
Sulfohemist
Tropohemist
Borosaprist
Cryosaprist
Medisaprist
Troposaprist
IANCR
IANDU
IANDY
IANEU
IANHY
IANPK
IANVI
IAQAN
IAQCR
IAQFR
IAQHL
IAQHP
IAQHU
IAQPK
IAQPN
IAQSU
IAQTR
IOCCR
IOCDU
IOCDY
IOCEU
IOCFR
IOCUS
IOCXE
IPLPL
ITRDY
ITREU
ITRHU
ITRSO
ITRUS
IUMCR
IUMFR
IUMHA
IUMXE
Mollisols
MALAR
MALNA
MAQAR
MAQCA
MAQCR
MAQDU
MAQHA
MAQNA
MBOAR
MBOCA
MBOCR
MBOHA
MBONA
MBOPA
MBOVE
MRERE
MUDAR
MUDHA
MUDPA
MUDVE
MUSAR
MUSCA
MUSDU
MUSHA
MUSNA
MUSPA
Cryandept
Durandept
Dystrandept
Eutrandept
Hydrandept
Placandept
Vitrandept
Andaquept
Cryaquept
Fragiaquept
Halaquept
Haplaquept
Humaquept
Placaquept
Plinthaquept
Sulfaquept
Tropaquept
Cryochrept
Durochrept
Dystrochrept
Eutrochrept
Fragiochrept
Ustochrept
Xerochrept
Plaggept
Dystropept
Eutropept
Humitropept
Sombritropept
Ustropept
Cryumbrept
Fragiumbrept
Haplumbrept
Xerumbrept
Argialboll
Natralboll
Argiaquoll
Calciaquoll
Cryaquoll
Duraquoll
Hapfaquoll
Natraquoll
Argiboroll
Calciboroll
Cryoboroll
Haploboroll
Natriboroll
Paleboroll
Vermiboroll
Rendoll
Argiudoll
Hapludoll
Paleudoll
Vermudoll
Argiustoll
Calciustoll
Durustoll
Haplustoll
Natrustoll
Paleustoll
-------
Appendix A
Revision 1
Date: 4/89
Page 12 of 14
Mollisols (cont)
MUSVE
MXEAR
MXECA
MXEDU
MXEHA
MXENA
MXEPA
Oxisols
OAQAC
OAQEU
OAQHA
OAQPN
OPRAC
OPREU
OPRHA
OPRKA
OPRSO
OTOAC
OTOEU
OTOHA
OUDAC
OUDEU
OUDHA
OUDKA
OUDSO
OUSAC
OUSEU
OUSHA
OUDKA
OUSSO
Vermustoli
Argixeroll
Calcixeroll
Durixeroll
Haploxeroll
Natrixeroll
Palexeroll
Acraquox
Eutraquox
Haplaquox
Plinthaquox
Acroperox
Eutroperox
Haploperox
Kandiperox
Sombriperox
Acritorrox
Eutrotorrox
Haplotorrox
Acrudox
Eutrudox
Hapludox
Kandiudox
Sombriudox
Acrustox
Eutrustox
Haplustox
Kandiustox
Sombriustox
Spodosols
SAQCR
SAQDU
SAQFR
SAQHA
SAQPK
SAQSI
SAQTR
SFEFE
SHUCR
SHUFR
SHUHA
SHUPK
SHUTR
SORCR
SORFR
SORHA
SORPK
SORTR
Cryaquod
Duraquod
Fragiaquod
Haplaquod
Placaquod
Sideraquod
Tropaquod
Ferrod
Cryohumod
Fragihumod
Haplohumod
Placohumod
Tropohumod
Cryorthod
Fragiorthod
Haplorthod
Placorthod
Troporthod
Ultisols
UAQAL
UAQFR
UAQKA
UAQKH
UAQOC
UAQPA
UAQPN
UAQUM
UHUHA
UHUKA
UHUKH
UHUPN
UHUSO
UUDFR
UUDHA
UUDKA
UUDKH
UUDPA
UUDPN
UUDRH
UUSHA
UUSKA
UUSKH
UUSPA
UUSPN
UUSRH
UXEHA
UXEPA
Vertisols
VTOTO
VUDCH
VUDPE
VUSCH
VUSPE
VXECH
VXEPE
Albaquult
Fragiaquult
Kandiaquult
Kandhaplaquult
Ochraquult
Paleaquult
Plinthaquult
Umbraquult
Haplohumult
Kandihumult
Kandhaplohumult
Plinthohumult
Sombrihumult
Fragiudult
Hapludult
Kandiudult
Kandhapludult
Paleudult
Plinthudult
Rhodudult
Haplustult
Kandiustult
Kandhaplustult
Paleustult
Plinthustult
Rhodustult
Haploxerult
Palexerult
Torrert
Chrotnudert
Pelludert
Chromustert
Pellustert
Chromxerert
Pelloxerert
Classification, Subgroup
AA Typic
AB Abruptic
AB04 Abruptic aridic
AB08 Abruptic cryic
AB10 Abruptic haplic
AB14 Abruptic udic
AB16 Abruptic xerollic
AC Acric
AC05 Acric Plinthic
AE Aerie
AE03 Aerie arenic
AE05 Aerie grossarenic
AE06 Aerie humic
AE08 Aerie mollic
AE09 Aerie tropic
AE10 Aerie umbric
AL Albaquic
AL02 Albaquultic
AL04 Albic
AL08 Albic glossic
AL10 Alfic
AL12 Alfic arenic
AL13 Alfic andeptic
AL16 Alfic lithic
AN Andic
AN01 Andeptic
AN03 Andaquic
AN06 Andic dystric
AN08 Andic epiaquic
AN 11 Andeptic glossoboric
AN20 Andic udic
AN22 Andic ustic
AN24 Andaqueptic
AN25 Anionic
AN30 Anthropic
AQ Aqualfic
AQ02 Aquentic
AQ04 Aquaptic
AQ06 Aquic
AQ07 Aquic anionic
AQ08 Aquic arenic
AQ14 Aquic duric
AQ16 Aquic duriorthidic
AQ18 Aquic dystric
AQ24 Aquic haplic
AQ26 Aquic lithic
AQ28 Aquic petroferric
AQ34 Aquollic
AQ36 Aquultic
AR Arenic
AR02 Arenic aridic
AR04 Arenic plinthaquic
AR06 Arenic plinthic
AR08 Arenic rhodic
AR10 Arenic ultic
AR14 Arenic umbric
AR16 Arenic ustalfic
AR18 Arenic ustollic
AR22 Argiaquic
AR24 Argiaquic xeric
AR26 Argic
AR28 Argic lithic
AR30 Argic pachic
AR32 Argic vertic
AR34 Aridic
AR36 Aridic calcic
AR42 Aridic duric
AR52 Aridic petrocalcic
BO Boralfic
BO02 Boralfic lithic
BO04 Boralfic udic
B006 Borollic
BOOS Borollic glossic
BO10 Borollic lithic
B012 Borollic vertic
-------
Appendix A
Revision 1
Date: 4/89
Page 13 of 14
Classification, Subgroup (cont)
CA
CA04
CA06
CA10
CA20
Calcic
Calcic pachic
Calciorthidic
Calcixerollic
Cambic
HA Haplaquodic
HA02 Haplic
HA07 Haploxerollic
HA09 Hapludic
HA12 Hapludollic
HA16 Haplustollic
CH Chromic
HE
HE02
CR
CR10
CR14
CU
CU02
CU04
DU
DU02
DUOS
DU10
DU12
DU14
DY02
DY03
DY04
DY06
EN
EN02
EN06
Cryic
Cryic lithic
Cyric pachic
Cumulic
Cumulic udic
Cumulic ultic
Durargidic
Duric
Durixerollic
Durixerollic lithic
Durorthidic
Durorthidic xeric
Dystric
Dystric entic
Dystric fluventic
Dystric lithic
Entic
Entic lithic
Entic ultic
EP Epiaquic
EU
EU02
EU04
Eutric
Eutrochreptic
Eutropeptic
FE Ferrudalfic
FI Fibric
FI02 Fibric terric
FL02 Fluvaquentic
FL06 Fluventic
FL12 Fluventic umbric
FR10 Fragiaquic
FR18 Fragic
GL02 Glossaquic
GL04 Glossic
GL10 Glossic udic
GL12 Glossic ustollic
GL16 Glossoboric
GR Grossarenic
GR01 Grossarenic entic
GR04 Grossarenic plinthic
GY Gypsic
Hemic
Hemic terric
HI Histic
HI02 Histic lithic
HI06 Histic pergelic
HU
HU02
HU05
HU10
HU15
HU20
Humic
Humic lithic
Humic pergelic
Humaqueptic
Humic rhodic
Humic xanthic
HY Hydric
HY02 Hydric lithic
IN Incept ic
KA Kandic
KA02 Kandiudalfic
KA04 Kandiustalfic
KH Kandhaplic
LE Leptic
LI Limnic
LI02 Lithic
LI04 Lithic mollic
LI05 Lithic pergelic
LI03 Lithic petrocalci
LI06 Lithic ruptic-alfic
LI07 Lithic ruptic-argic
LIOS Lithic ruptic-entic-xerollic
LI09 Lithic ruptic-entic
LI11 Lithic ruptic-xerorthentic
LI12 Lithic ultic
LI13 Lithic ruptic-ultic
LI14 Lithic umbric
LI15 Lithic ruptic-xerochreptic
LI16 Lithic ustic
LI18 Lithic ustollic
LI22 Lithic xeric
LI24 Lithic xerollic
MO Mollic
NA06 Natric
OC Ochreptic
OR Orthidic
OR01 Orthic
OR02 Orthoxic
OX Oxic
PA
PA02
PA04
PA08
PA10
PE
PE01
PE02
PE04
PE06
PE08
PE14
PE16
PE20
PK
PK10
PK12
PL
PL04
PL06
PS
PS02
QU
RE
RH
RU02
RU09
RU11
RU15
RU17
RU19
SA
SA02
SA04
SI
SO
SP
SP02
SP04
SU
TE
TH04
TH06
TO
TO02
TO04
T006
TO10
Pachic
Pachic udic
Pachic ultic
Paleustollic
Palexerollic
Pergelic
Pergelic ruptic-histic
Pergelic sideric
Petrocalcic
Petrocalcic ustalfic
Petrocalcic ustollic
Petrocalcic xerollic
Petroferric
Petrogypsic
Placic
Plaggeptic
Plaggic
Plinthaquic
Plinthic
Plinthudic
Psammaquentic
Psammentic
Quartzipsammentic
Rendollic
Rhodic
Ruptic-alfic
Ruptic-lithic
Ruptic-lithic-entic
Ruptic-lithic-xerochreptic
Ruptic-ultic
Ruptic-vertic
Salorthidic
Sapric
Sapric terric
Sideric
Sombric
Sphagnic
Sphagnic terric
Spodic
Suflic
Terric
Thapto-histic
Thapto-histic tropic
Torre rt ic
Torrifluventic
Torriorthentic
Torripsammentic
Torroxic
-------
Appendix A
Revision 1
Date: 4/89
Page 14 of 14
Classification, Subgroup (cont)
TR Tropaquodic
TR04 Tropic
UD Udertic
UD01 Udalfic
UD02 Udic
UD03 Udollic
UD05 Udorthentic
UL Ultic
UM Umbreptic
UM02 Umbric
US Ustalfic
US02 Ustertic
US04 Ustic
US06 Ustochreptic
USOS Ustollic
US12 Ustoxic
VE Vermic
VE02 Vertic
XA Xanthic
XE Xeralfic
XE02 Xerertic
XE04 Xeric
XE08 Xerollic
Classification, Particle Size Class
003 cindery
004 cindery over sandy or sandy-
skeletal
005 ashy
006 cindery over loamy
007 ashy over cindery
008 ashy over loamy
009 ashy-skeletal
010 medial
011 medial-skeletal
012 medial over cindery
013 ashy over loamy-skeletal
014 medial over clayey
015 cindery over medial-skeletal
016 medial over fragmental
017 cindery over medial
018 medial over loamy
019 ashy over medial
020 medial over loamy-skeletal
022 medial over sandy or sandy-
skeletal
024 medial over thixotropic
026 thixotropic
027 thixotropic-skeletal
028 thixotropic over fragmental
030 thixotropic over sandy or
sandy-skeletal
032 thixotropic over loamy-skeletal
034 thixotropic over loamy
036
044
046
050
051
052
054
055
056
058
062
063
064
066
068
072
080
082
084
086
088
092
094
096
097
098
100
102
106
108
110
112
114
116
118
120
122
124
126
134
fragmental
sandy-skeletal
sandy-skeletal over loamy
loamy- skeletal
loamy-skeletal over
fragmental
loamy-skeletal over sandy
loamy-skeletal over clayey
loamy-skeletal or clayey-
skeletal
clayey-skeletal
clayey-skeletal over sandy
sandy
sandy or sandy-skeletal
sandy over loamy
sandy over clayey
loamy
loamy over sandy or sandy-
skeletal
coarse-loamy
coarse-loamy over fragmental
coarse-loamy over sandy or
sandy-skeletal
coarse-loamy over clayey
coarse-silty
coarse-silty over sandy or
sandy-skeletal
coarse-silty over clayey
fine-loamy
fine-loamy over cindery
fine-loamy over fragmental
fine-loamy over sandy or
sandy-skeletal
fine-loamy over clayey
fine-silty
fine-silty over fragmental
fine-silty over sandy or sandy-
skeletal
fine-silty over clayey
clayey
clayey over fragmental
clayey over sandy or sandy-
skeletal
clayey over loamy-skeletal
clayey over fine-silty
clayey over loamy
fine
very fine
Classification, Mineralogy Class
03
05
08
09
10
» 12
14
16
18
20
22
24
Allitic
carbonatic
coprogenous
chloritic
diatomaceous
ferrihumic
ferritic
ferruginous
gibbsitic
glauconitic
gypsic
halloysitic
26 illitic
27 illitic (calcareous)
28 kaolinitic
30 marly
32 micaceous
34 mixed
35 mixed (calcareous)
37 montmorillonitic
38 montmorillonitic
(calcareous)
40 oxidic
44 serpentinitic
46 siliceous
47 siliceous (calcareous)
48 sesquic
50 vermiculitic
Classification, Reaction Class
04
06
08
10
12
14
acid
allic
dysic
euic
nonacid
noncalcareous
Classification, Temperature
Regime
04 frigid
06 hyperthermic
08 isofrigid
10 isohyperthermic
12 isomesic
14 isothermic
16 mesic
18 thermic
Classification, Other Class
04 coated
05 cracked
06 level
08 micro
12 ortstein
14 shallow
15 shallow & uncoated
16 sloping
17 shallow & coated
20 uncoated
-------
Appendix B
Revision 1
Date: 4/89
Page 1 of 11
Appendix B
Preparation Laboratory Data Forms
Raw and calculated data from samples undergoing analysis at the MASS preparation
laboratory are recorded on the data forms presented in this appendix. Examples of the sample
labels are also provided. The information on the raw data forms is entered into a personal
computer and undergoes data verification.
-------
Appendix B
Revision 1
Date: 4/89
Page 2 of 11
DIRECT/DELAYED RESPONSE PROJECT (DDRP) SOIL SURVEY
FORM 101
Batch ID
Crew ID
Lab Set Sent to
Date Shipped
Set ID
Date Sampled
Date Received
Date Prep Completed
No. of Samples
Air-dry Field OM- Rock Bulk
Samp Set Moisture Moist LO1 Fragments % Dens
No ID Sample Code Site ID % pH % 2-4.7 4.7-20 g/cm3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
-------
Appendix B
Revision 1
Date: 4/89
Page 3 of 11
DIRECT/DELAYED RESPONSE PROJECT (DDRP)
SHIPPING FORM 102
DATE RECEIVED
BY DATA MGT.
Prep Lab ID
Batch ID
D D M M M Y Y
Date Received
Date Shipped
| Analytical
SAMPLE BO.
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
BULK SAMPLES
SHIPPED
1 1
39
40 1
RECEIVED
Signature of Preparation Laboratory 1
Comments :
PH < 6.5
RETURNED TO LEMSCO
(CHECK T IF TBS)
'
lanaqer:
FIGURE FORM
40
102
WHITE - SMO
CANARY - ANALYTICAL
WITH COPY TO SMO
PINK - ANALYTICAL
WITH COPY TO EMSL-LV
GOLD - ANALYTICAL LAB
-------
Appendix B
Revision 1
Date: 4/89
Page 4 of 11
NAOSS Ubel A
Date Sampled:
Crew ID: _
Site ID:
DD MMMY Y
Sample Code:
Horizon: „„,,...
Set ID;
Depth:.
cm
NADSS
Batch ID
Sample
Ub«l B
No:._
-------
SOIL SAMPLE RECEIPT FOR*
SAX CODE
CUM
ID
SITE
ID
SET
ID
DATE
SAMPLED
DATE
SUPPED
DATS
RECEIVED
RECEIVED
BI
SAMPLE CONDITION
WET/DRY (N/D) BAG SPLIT (B/S)
SIEVED/UMSIEVED (E/U)
ODDER VOLUME (DV)
CLOD
NUMBER
KNONN
VOLUME
CAVITT
SAMPLE
NUMBER
COMMENTS
-------
SAMPLE ID:
SITE ID:
SET ID:
BATCH ID:
BULK SAMPLE RAW DATA
PATE SAMPLED:
DATE REC'D:
PROCESS START:
PROCESS COMPLETE:
Appendix B
Revision 1
Date: 4/89
Page 6 of 11
SOIL TYPE: M/0
Initials:
FIELD pH:
Initials:
AIR SAMPLE DRYING:
Date % Moisture Initials
Date:
2 to 4.75 ma:
Data:
ROCK FRAGMENT WT:
_. g 4.75 to 20 mm:
' Initials:
ENTERED IN COMPUTER: Date:
Initials:
COMMENTS:
-------
Appendix B
Revision 1
Date: 4/89
Page 7 of 11
pB RAW DATA
DATE / /
SAMPLE CODE
Init OCCS
D
QCCS
D
SAMP.
*
PH
SAMPLE CODE
occs
D
QCCS
Final QCCS
SAMP.
*
pH
-------
Appendix B
Revision 1
Date: 4/89
Page 8 of 11
SOIL SAfflLX UK BRI DETERJOKATIOJ
ALL WEIGHTS REPORTED TO 0.01 GRAMS
SAX CODE
TIB
DO.
TII
w.
AIR DRI
OVE1 DRT
(ira+son,)
DATE
ID
COKXZMTS
-------
Appendix B
Revision 1
Date: 4/89
Page 9 of 11
SAXFU LOSS-OR-IGXmOl DATA
ALL WIGHTS KBPORIED H 0.01 CXAX8
SAX CODI
CROC.
90.
AIR
Wt.
0. D.
HI.
ASIKD
W.
OATI
ID
couant
-------
CLOD BULK DENSITY RAW DATA
P«9« /
ALL WEIGHTS RECORDED TO 0.01 GRAMS
SAN CODE
REP
FLD DP
•oi«t7dry
LAB DP
LAB KT
CLOD B20
TEKP
CRUC
NO.
R FRAG
FLOAT
PRO!
T/N
ID
COMMENTS
**
-------
Appendix B
Revision 1
Date: 4/89
Page 11 of 11
KMWH VOLOtt IDLX DMIITT DMA
ALL WIGHTS RBCORDCO TO 0.0 GRAMS
SAX CODI
AIR m
00 NT
R FRM
VOL
(cc.)
DAII
ID
conxnr*
PACI
-------
Appendix C
Revision 1
Date: 4/89
Page 1 of 5
Appendix C
Analytical Parameters in the Mid-Appalachian Soil Survey
Following is a list and description of the analyses that are performed at the preparation
laboratory and the analytical laboratories during the MASS. The parameters are described by their
data variable name, which is consistent with their presentation in the main text.
-------
Appendix C
Revision 1
Date: 4/89
Page 2 of 5
Appendix C. Analytical Parameters Measured In the Mid-Appalachian Soil Survey
Parameter
Description of Parameter
PH_MP*
MOIST_P*
OMJ.OI*
RF_FG*
RF_MG*
BD_CLD*
BD_KV*
MOIST_A
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
PHJH20
PH 002M
Field-moist pH determined in a deionized water extract using a 1:1 mineral soil to solution ratio and 1:4
organic soil to solution ratio, measured with a pH meter and combination electrode.
Air-dry soil moisture measured at the preparation laboratory and expressed as a percentage on an oven-
dry weight basis; mineral soils are dried at 105°C, organic soils at 60°C.
The percent organic matter content is measured by loss on ignition of an oven-dry sample; a cutoff value
of 20 percent organic matter is used to separate mineral soils from organic soils.
The 2.00- to 4.75-mm fine gravel rock fragments are sieved from the bulk samples and the weight percent
is determined gravimetrically.
The 4.75- to 20.00-mm medium gravel rock fragments are sieved from the bulk samples and the weight
percent is determined gravimetrically.
The oven-dry soil bulk density, minus rock fragments, of replicate soil clods is determined by a gravimetric
displacement method.
The oven-dry soil bulk density, minus rock fragments, of duplicate known volume samples is determined
by a gravimetric replacement method or a gravimetric filling method.
Air-dry soil moisture measured at the analytical laboratory and expressed as a percentage on an oven-
dry weight basis; mineral soils are dried at 105°C, organic soils at 60°C.
Total sand is the portion of the sample with particle diameter between 0.05 mm and 2.0 mm, and is
calculated as the summation of percentages for individual sand fractions: VCOS + COS + MS + FS + VFS.
Very coarse sand is the sand fraction between 1.0 mm and 2.0 mm, and is determined by sieving the sand
which has been separated from the silt and clay.
Coarse sand is the sand fraction between 0.5 mm and 1.0 mm, and is determined by sieving the sand
which has been separated from the silt and clay.
Medium sand is the sand fraction between 0.25 mm and 0.50 mm, and is determined by sieving the sand
which has been separated from the silt and clay.
Fine sand is the sand fraction between 0.10 mm and 0.25 mm, and is determined by sieving the sand
which has been separated from the silt and clay.
Very fine sand is the sand fraction between 0.05 mm and 0.10 mm, and is determined by sieving the sand
which has been separated from the silt and clay.
Total silt is the portion of the sample with particle diameter between 0.002 mm and 0.05 mm, and is
calculated by subtracting from 100 percent the sum of the total sand and clay.
Coarse silt is the silt fraction between 0.02 mm and 0.05 mm, and is calculated by subtracting the fine
silt fraction from the total silt.
Fine silt is the silt fraction between 0.002 mm and 0.02 mm; it is determined by pipetting and is calculated
by subtracting the clay fraction from the less than 0.02-mm fraction.
Total clay is the portion of the sample with particle diameter of less than 0.002 mm; it is determined by
pipetting.
pH determined in a deionized water extract using a 1:1 mineral soil to solution ratio or 1:5 organic soil to
solution ratio, measured with a pH meter and combination electrode.
pH determined in a 0.002M calcium chloride extract using a 1:2 mineral soil to solution ratio or 1:10 organic
soil to solution ratio, measured with a pH meter and combination electrode.
(continued)
-------
Appendix C
Revision 1
Date: 4/89
Page 3 of 5
Appendix C. Continued
Parameter
Description of Parameter
PH_01M pH determined in a 0.01M calcium chloride extract using a 1:1 mineral soil to solution ratio or 1:5 organic
soil to solution ratio, measured with a pH meter and combination electrode.
CA_CL Exchangeable calcium determined with an unbuffered 1M ammonium chloride solution; approximately 1:26
mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption spectrometry
or inductively coupled plasma atomic emission spectrometry is specified.
MG_CL Exchangeable magnesium determined with an unbuffered 1M ammonium chloride solution; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption
spectrometry or inductively coupled plasma atomic emission spectrometry is specified.
K_CL Exchangeable potassium determined with an unbuffered 1M ammonium chloride solution; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption
spectrometry or flame emission photometry is specified.
NA_CL Exchangeable sodium determined with an unbuffered 1M ammonium chloride solution; approximately 1:26
mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption spectrometry,
inductively coupled plasma atomic emission spectrometry, or flame emission photometry is specified.
AL_CL Exchangeable aluminum determined with an unbuffered 1M ammonium chloride solution; approximately 1:26
mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; inductively coupled plasma
atomic emission spectrometry is specified.
CA_OAC Exchangeable calcium determined with 1M ammonium acetate solution buffered at pH 7.0; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption
spectrometry or inductively coupled plasma atomic emission spectrometry is specified.
MG_OAC Exchangeable magnesium determined with 1M ammonium acetate solution buffered at pH 7.0;
approximately 1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic
absorption spectrometry or inductively coupled plasma atomic emission spectrometry is specified.
K_OAC Exchangeable potassium determined with 1M ammonium acetate solution buffered at pH 7.0; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption
spectrometry or flame emission photometry is specified.
NAJDAC Exchangeable sodium determined with 1M ammonium acetate solution buffered at pH 7.0; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; atomic absorption
spectrometry, inductively coupled plasma atomic emission spectrometry, or flame emission photometry is
specified.
CEC_CL Cation exchange capacity determined with an unbuffered 1M ammonium chloride solution is the effective
CEC which occurs at approximately the field pH when combined with the acidity component; approximately
1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used; samples are analyzed for
ammonium content by one of three methods: automated distillation/titration; manual distillation/automated
titration; or ammonium displacement/flow injection analysis.
CEC_OAC Cation exchange capacity determined with 1M ammonium acetate solution buffered at pH 7.0 is the
theoretical estimate of the maximum potential CEC for a specific soil when combined with the acidity
component; approximately 1:26 mineral soil to solution ratio or 1:52 organic soil to solution ratio are used;
samples are analyzed for ammonium content by one of three methods: automated distillationAitration;
manual distillation/automated titration; or ammonium displacement/flow injection analysis.
AC_BACL Total exchangeable acidity determined by titration in a buffered (pH 8.2) barium chloride triethanolamine
extraction using an approximately 1:30 soil to solution ratio.
CA_CL2 Extractable calcium determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution ratio
or 1:10 organic soil to solution ratio are used; the calcium is used to calculate lime potential; atomic
absorption spectrometry or inductively coupled plasma atomic emission spectrometry is specified.
(continued)
-------
Appendix C
Revision 1
Date: 4/89
Page 4 of 5
Appendix C. Continued
Parameter
Description of Parameter
MG_CL2 Extractable magnesium determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution
ratio or 1:10 organic soil to solution ratio are used; atomic absorption spectrometry or Inductively coupled
plasma atomic emission spectrometry is specified.
K_CL2 Extractable potassium determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution
ratio or 1:10 organic soil to solution ratio are used; atomic absorption spectrometry or flame emission
photometry is specified.
NA CL2 Extractable sodium determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution ratio
or 1:10 organic soil to solution ratio are used; atomic absorption spectrometry, inductively coupled plasma
atomic emission spectrometry, or flame emission photometry is specified.
FE CL2 Extractable iron determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution ratio
or 1:10 organic soil to solution ratio are used; inductively coupled plasma atomic emission spectrometry
is specified.
AL_CL2 Extractable aluminum determined by a 0.002M calcium chloride extraction; a 1:2 mineral soil to solution
ratio or 1:10 organic soil to solution ratio are used; the aluminum concentration obtained from this procedure
is used to calculate aluminum potential; inductively coupled plasma atomic emission spectrometry is
specified.
FE_PYP Extractable iron determined by a 0.1M sodium pyrophosphate extraction using a 1:100 soil to solution ratio;
the pyrophosphate extract estimates organically-bound iron; inductively coupled plasma atomic emission
spectrometry is specified.
AL_PYP Extractable aluminum determined by a 0.1M sodium pyrophosphate extraction using a 1:100 soil to solution
ratio; the pyrophosphate extract estimates organically-bound aluminum; inductively coupled plasma atomic
emission spectrometry is specified.
FE_AO Extractable iron determined by an ammonium oxalate-oxalic acid extraction using a 1:100 soil to solution
ratio; the acid oxalate extract estimates organic and amorphous iron oxides; inductively coupled plasma
atomic emission spectrometry is specified.
AL_AO Extractable aluminum determined by an ammonium oxalate-oxalic acid extraction using a 1:100 soil to
solution ratio; the acid oxalate extract estimates organic and amorphous aluminum oxides; inductively
coupled plasma atomic emission spectrometry is specified.
SI_AO Extractable silicon determined by an ammonium oxalate-oxalic acid extraction using a 1:100 soil to solution
ratio; inductively coupled plasma atomic emission spectrometry is specified.
FE CD Extractable iron determined by a sodium citrate-sodium dithionite extraction using a 1:30 soil to solution
ratio; the citrate dithionite extract estimates non-silicate iron; inductively coupled plasma atomic emission
spectrometry is specified.
AL CD Extractable aluminum determined by a sodium citrate-sodium dithionite extraction using a 1:30 soil to
solution ratio; the citrate dithionite extract estimates non-silicate aluminum; inductively coupled plasma
atomic emission spectrometry is specified.
SO4 H20 Extractable sulfate determined with a double deionized water extract; this extraction approximates the
sulfate which will readily enter the soil solution and uses a 1:40 soil to solution ratio; ion chromatography
is specified.
SO4 PO4 Extractable sulfate determined with a 0.016M sodium phosphate (500 mg P/L) extract; this extraction
approximates the total amount of adsorbed sulfate and uses a 1:40 soil to solution ratio; ion
chromatography is specified.
SO4_0 Sulfate remaining in a 0 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio or
1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography is
specified.
(continued)
-------
Appendix C
Revision 1
Date: 4/89
Page 5 of 5
Appendix C. Continued
Parameter Description of Parameter
804 2 Sulfate remaining in a 2 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio or
1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography is
specified.
SO4 4 Sulfate remaining in a 4 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio or
1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography is
specified.
S04_8 Sulfate remaining in a 8 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio or
1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography is
specified.
S04J6 Sulfate remaining in a 16 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio
or 1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography
is specified.
S04_32 Sulfate remaining in a 32 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio
or 1:20 organic soil to solution ratio; the data are used to develop sulfate isotherms; ion chromatography
is specified.
C_TOT Total carbon determined by rapid oxidation followed by infrared detection or thermal conductivity detection
using an automated CHN analyzer; total carbon can be used to characterize the soil organic matter.
N_TOT Total nitrogen determined by rapid oxidation followed by infrared detection or thermal conductivity detection
using an automated CHN analyzer; total nitrogen can be used to characterize the soil organic matter.
S_TOT Total sulfur determined by automated sample combustion followed by infrared detection of evolved sulfur
dioxide.
* This parameter is measured at the preparation laboratory.
-------
-------
Appendix D
Revision 1
Date: 4/89
Page 1 of 17
Appendix D
Analytical Laboratory Verification Criteria
The following QC criteria and data reporting forms are used by the LEVIS programs to
evaluate the results of laboratory analysis. The results are obtained using the analytical procedures
detailed in the Analytical Laboratory Statements of Work. The raw data are entered into LEVIS,
calculations are performed, and summary reports are generated for review by the analytical
laboratories.
-------
CRITERIA FOR QUALITY CONTROL SUMMARY CHECKS
(FLAG IF THESE CRITERIA ARE NOT MET)
Appendix D
Revision 1
Date: 4/89
Page 2 of 17
Summary Page 1 - NA
Summary Page 2 - NA
summary Page 3
Replicate Evaluation
summary Page 4
Spike Recovery
summary Page 5
Reagent Blank Mean
All pH Parameters and all Particle Size
parameters, calculated SD <= SD from the
RED form,
All other parameters, calculated SD
<= SD from the RED form if the mean
of the reps, is < the KNOT from the RED
form and calculated %RSD <= RSD from
the RED form if the mean is >= the KNOT
from the RED form.
All Sulfate Isotherms, 95% <= x <= 105%
All other parameters, 90% <= x <= 110%
FINE SILT + CLAY parameter - no check,
All pH parameters, 4.5 <= Mean <= 7.5,
AC_BACL, 1.300 <= Mean <= 1.800 meg,
CA_CL2, 76 <= Mean <= 84, (+/- 5%)
SO4_0 <= CRDL,
SO4_2, 1.94 <= Mean <= 2
SO4_4, 3.88 <= Mean <= 4
SO4 8, 7.76 <= Mean <= 8
,06,
.12,
• 24,
(+/- 3%)
(+/- 3%)
(+/- 3%)
Summary Page 6
QCCS - True
QCCS - NO. Run
SO4_16,15.52 <= Mean <= 16.48, (+/- 3%)
S04_32,31.36 <= Mean <= 32.64, (+/- 2%)
All other parameters, <= CRDL from
the CRD Form.
Report True value from Q.C Form
Particle Size (SAND, SILT and CLAY only),
1 or more,
All other parameters,
(Ceiling of (# Samples in Batch/10) )+l.
(continued)
-------
Appendix D
Revision 1
Date: 4/89
Page 3 of 17
QCCS -No. of Out of Range
(Report number of QCCS
not meeting the criteria)
DLQCCS - True
DLQCCS - Meas.
Calib. Blank - No. Run
Calib. Blank - No. of Out of Range
(Report number of Calib.
Blanks not meeting criteria)
QA Check Sample
All Sulfate Isotherms,
QCCS within +/- 5% of the True QCCS
form the QC Form,
All other parameters,
QCCS within +/- 10% of the True QCCS
from the QC Form.
Report DLQCCS value from the QC Form.
Within +/- 20% of the True DLQCCS from
the QC Form.
(Ceiling of (# samples in Batch/10))+l.
AC_BACL, 26.0 <= Calib. Blank <= 36.0 mL
All other parameters,
<= CRDL from the CRD Form.
Sample Result between High/Low range.
(range supplied on the summary page)
summary Page 7 - NA
Summary Page 8
All samples in a batch must meet these Criteria or a flag is set.
Sample Wt. Check Particle Size - Measured Sample Wt.
10 +/- 5% or 20 +/- 5%,
All other parameters - Measured Sample
Wt. between Method Wt. +/- 5%
Sample Vol. check Measured Sample Vol. = Method Vol.
s===sss===ss==:sas==ss=ss::3s^s:^^s^=s^=:^^^^^^^^^^^s:==^==^ssr:=;s^^^^™=™ss^s^a:^^^^^^:^^^ss^^^5=^:s^:s;^^~^~^=
Summary Page 9
spike solution Check Each Inst. Reading between:
CA_CL2, 76 <= X <= 84, (80 + /- 5%)
SO4_0 <= CRDL,
S04_2, 1.94 <= Mean <= 2.06, (+/- 3%)
S04_4, 3.88 <= Mean <= 4.12, (+/- 3%)
S04_8, 7.76 <= Mean <= 8.24, (+/- 3%)
SO4_16,15.52 <= Mean <= 16.48, (+/- 3%)
S04_32,31.36 <= Mean <= 32.64, (+/- 2%)
Summary Page 10
Instrument Detection Limit <= the CRDL from the CRD Form.
-------
Appendix D
Revision 1
Date: 4/89
Page 4 of 17
SOIL CHEMISTRY RELATIONSHIPS
Simple mathematical relationships are used to examine the soil chemistry
of data for each sample. For each SCR, it is expected that approximately 10
percent of the data may not comply with these relationships. These anomalous
data are examined by a soil chemist who qualifies them or assigns appropriate
flags. The following SCRs are examined in qualifying the data:
(1) The analytical laboratory pH values should relate as follows:
PH_01M < PH___002M < PH_H20
if pH dTfferences are > 0.05 units
Gael, solution masks the effect of soluble salts in soils; Ca*4 ions
displace H4 ions from exchange sites; the H4 ion concentration in solution
increases, resulting in the measurement of lower pH.
(2) The cation exchange capacity parameters should relate as follows:
CEC_CL < CEC_OAC
if CEC_CL > Imeq/lOOg and PH_H20 < 7.0
A higher CEC is measured by using a buffered (pH 7) NH4OAc saturating
solution to determine CEC in an acid soil. With an increase in pH,
hydrogen ions are displaced. This creates more exchange sites for
retention of NH44. The NH4cl saturating solution is unbuffered; therefore,
cation exchange takes place nearer the soil pH, resulting in the
measurement of lower CEC.
(3) The exchangeable cations and CEC should relate as follows:
CA_OAC + MG_OAC + K_OAC + NA_OAC < CEC_OAC
if CEC_CL > Imeq/lOOg
CA_CL + MG_CL + K_CL + NA_CL < CEC_CL
if CEC_CL > Imeq/lOOg
The total CEC is made up of all cations adsorbed on the surface of the clay
minerals and organic matter, including Ca, Mg, K, Na, Al, and H. Therefore
the addition of the base cations excluding the acid Al and H should be less
than the total capacity of the soil to hold cations.
(4) The relationship between CEC and the percent clay is as follows:
CEC_OAC/CLAY < 50
if CLAY > 1.0 and CEC_CL > Imeq/lOOg
CEC_CL/CLAY < 50
if CLAY > 1.0 and CEC_CL > Imeq/lOOg
Clay mineralogy is a major factor in predicting CEC in soils. The
theoretical maximum CEC value for vermiculite clay (which has the highest
CEC values) is 180 meq/lOOg and the highest clay content in soil samples
from previous DDRP surveys is 36 percent. Because the percent clay and
vermiculite content of the MASS soils are expected to be lower than the
highest values from previous surveys, this relationship should hold.
-------
Appendix D
Revision 1
Date: 4/89
Page 5 of 17
(5) The extractable sulfates should relate as follows:
SO4_H2O < SO4_SO4
if so4_po4 > Imgs/kg soil
The phosphate extractable solution estimates the total adsorbed sulfate;
water extractable solution estimates the sulfate which readily enters soil
solution.
(6) The relationships between the sulfate isotherms is as follows:
SO4_ON < SO4_2N < SO4_4N < SO4_8N < SO4_16N < SO4_32N
if SO4_32N >= 7.5 mg s/kg soil
The adsorption isotherms for sulfate should be greater as more sulfate is
added to the soils for potential adsorption onto the anion exchange
complexes.
(7) The relationships between the sulfate isotherms is as follows:
SO4_0 < SO4_2 < SO4_4 < SO4_8 < SO4 16 < SO4_32
if so4_32N <= 7.5 mg s/kg soil ~
The adsorption isotherms for sulfate should be greater as more sulfate is
added to the soils for potential adsorption onto the anion exchange
complexes.
(8) The ratio of water-extractable sulfate and the zero sulfate isotherm is
as follows:
4 < SO4 H2O/SO4_0 < 20
if s"o4_H2o >= 2mgs/kg and SO4_0 >= 0.1 mgS/kg
This is based on the theoretical relationship between the two different
extraction methods and provides a check on the extraction procedures.
(9) The relationship between the calcium chloride extracted cations and the
ammonium chloride exchangeable cations is as follows:
NA_CL2 < NA_CL * 1.10
if NA_CL2 > 0.03meq/100g
K_CL2 < K_CL * 1.10
if K_CL2 > 0.003meq/100g
MG_CL2 < MG_CL * 1.10
if MG_CL2 > 0.008meq/100g
The extraction of these three cations by NH,Cl is an exhaustive extraction
while the extraction of the cations by CaCl, is done with a much more
dilute extraction agent and will result in a nearly complete extraction
of the cation. Therefore by increasing the NH,Cl extracted cation by a
factor of 1.10 (chosen to include 10% error during the extraction
procedure) should be greater than the CaCl, extracted cations.
-------
Appendix D
Revision 1
Date: 4/89
Page 6 of 17
(10) The relationships of total carbon, nitrogen, and sulfur should relate as
follows:
7 < C TOT/N_TOT < 50
i7 C_TOT >- 0.1% and NJTOT >- 0.03%
40 < C_TOT/S_TOT < 400
if C_TOT >» 0.1% and SJTOT >» 0.005%
The ratios are based on common C:N and C:S relationships found in previous
surveys and should hold for all except the lowest concentration samples.
-------
Appendix D
Revision 1
Date: 4/89
Page 7 of 17
QC Summary Report
Page 1 of 10
number of Observation*
and Submission Selection
Batch t
Date Run / /_
Number of Sample* in Batch _
Submission 1
Run 1
Form f
01
03
03
03
03
03
03
03
03
03
03
04
OS
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
MOIST
SAND
SILT
CLAY
VCOS
COS
MS
FS
VFS
COSI
FSI
PH_H2O
PH~01M
CEC_OAC
CBC_CL
CA_OAC
1
1
-
-
-
~ \
-
1
: i :
Number of Observation* and Submission Selection
XX *
MG OAC
K_OAC
NAJ5AC
CAjCL
MG CL
K CL
HA_CL
ALjCL
AC BACL
PH_002M
CA_CL2
MG~CL2
K_CL2
NA_CL2
FB_CL2
AL CL2
PE_PYP
ALJ?yP
FE_AO
AL_AO
SI_AO
FE CD
AL_CD
SO4 H2O
S04 P04
S04_0
SO4~2
S04~4
S04~8
S04~1C
S04~32
C TOT
H_TOT
S_TOT
XX *
XX *
1
XX * XX *
f
XX *
XX *
XX *
"*" Denotes the Run I selected for submission.
-------
Appendix D
Revision 1
Date: 4/89
Page 8 of 17
QC Summary Report
Page 2 of 10
Number of Samples in Batch __
Range Checks
Batch * _
Date Run /
Form *
01
03
03
03
03
03
03
03
03
03
03
04
05
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
HOIST
SAND
SILT
CLAY
vcos
COS
MS
FS
VPS
COS I
FSI
PH_H2O
PH_01M
CEC OAC
CECjCL
CA_OAC
MG_OAC
K_OAC
NA_OAC
CA_CL
MG_CL
K_CL
NA_CL
AL_CL
AC_BACL
PHJD02M
CA_CL2
MG_CL2
K_CL2
NA CL2
FE_CL2
AL_CL2
FB_PYP
AL PYP
FE_AO
AL_AO
SI_AO
FE_CD
AL_CD
SO4_H2O
S04 P04
S04~0
S04_2
SO4_4
SO4_8
SO4_16
SO4 32
C_TOT
H TOT
S_TOT
Units
wt.
wt.
wt.
wt.
wt.
wt.
wt.
wt.
wt.
wt.
wt.
pH units
pH units
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
pH units
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
wt.
wt.
wt.
wt.
wt.
wt.
wt.
mg S/kg
mg S/kg
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
wt. %
wt. I
wt. t
Number
of Obs.
XX
Minimum
Value
xxx.xxxx
Maximum
Value
xxx.xxxx
Mean
Value
xxx.xxxx
1
-------
Appendix D
Revision 1
Date: 4/89
Page 9 of 17
QC Summary Report
Page 3 of 10
Replicate Evaluation
Batch t
Date Run /
Form I
01
03
03
03
04
05
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
MOIST
SAND
SILT
CLAY
PH_H2O
PH_01M
CEC_OAC
CEC_CL
CA_OAC
KG OAC
K_OAC
NA_OAC
CA_CL
MG_CL
K_CL
NA_CL
AL_CL
AC BACL
PH_002M
CA_CL2
MG_CL2
K_CL2
NA_CL2
FE_CL2
AL CL2
FE_PYP
AL_PYP
FE_AO
AL_AO
SI_AO
FE CD
AL_CD
SO4_H2O
SO4_P04
S04_0
S04_2
S04_4
S04_8
SO4 16
S04_32
C_TOT
N_TOT
S_TOT
1st
xxx. xxx * (S/R)
5D or % RSD and Fl
2nd
xxx. xxx * (S/R)
ag
3rd
xxx . xxx * (S/R)
"*" In column indicates a problem with that parameter.
"S" In column indicates the reported value is the standard deviation.
•R" In column indicates the reported value is the % RSD.
-------
Appendix D
Revision 1
Date: 4/89
Page 10 of 17
QC Summary Report
Page 4 of 10
Percent Spike Recovery
Batch *
Date Run /
Form
t
01
02
03
04
05
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
\ 23
24
25
26
27
28
29
30
31
32
33
, 34
•35
36
37
38
39
Parameter
% Spike Recovery and Flag
1st | 2nd | 3rd
XXX. XX * j XXX. XX * XXX. XX *
HOIST | XXXXXXXXXXX | XXXXXXXXXX | XXXXXXXXXX
Particle Sice j XXXXXXXXXXX j XXXXXXXXXX | XXXXXXXXXX
Particle Site j XXXXXXXXXXX j XXXXXXXXXX j XXXXXXXXXX
PHJC20 1 XXXXXXXXXXX j XXXXXXXXXX j XXXXXXXXXX
PH_0 1M j XXXXXXXXXXX | XXXXXXXXXX 1 XXXXXXXXXX
CBC OAC
CBC_CL
CA_OAC
MG_OAC
K_OAC
HA_OAC
CA_CL
MG_CL
K_CL
HA_CL
AL CL
AC BACL
PHJ>02M
CA CL2
MG CL2
K_CL2
NA CL2
PB_CL2 *
AL_CL2
FE_PYP
AL_PYP
FB_AO
AL_AO
SI_AO
FB_CD
AL_CD
S04_H2O
S04 PO4
S04 0
S04 2 (1)
(2)
S04 4
S04 8 (1)
(2)
S04 16
S04_32 (1)
/ o %
40 C_TOT
41 H_TOT
42 S_TOT
XXXXXXXXXXX
XXXXXXXXXXX
XXXXXXXXXXX
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx | xxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxx xxxxxxxxxx
In column indicates a problem with that parameter.
-------
Appendix D
Revision 1
Date: 4/89
Page 11 of 17
QC Summary Report
Page 5 of 10
Reagent Blanks
Batch t _
Date Run /
Form
f
01
02
04
05
06A/B
07A/B
08
09
10
11
12
13
14
IS
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
MOIST
FSI + CLAY
PH_H2O
PH_01M
CEC_OAC
CEC_CL
CA_OAC
MG_OAC
K_OAC
HA_OAC
CA_CL
MG_CL
K_CL
NA_CL
AL_CL
AC_BACL
PH_002M
CA_CL2
MG_CL2
K_CL2
NA_CL2
FE_CL2
AL_CL2
FE_PYP
AL_PYP
FE_AO
AL_AO
SI_AO
FE_CD
AL_CD
SO4_H2O
S04 PO4
S04 0
S04_2
SO4 4
SO4_8
S04_16
S04_32
C_TOT
H_TOT
S_TOT
Reagent Blank and Flag
Units
XXXXXXXXXXXXXX
9
pH units
pH units
mg N/L or meq
mg N/L or meq
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
meq
pH 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 S/L
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
wt. %
wt. %
wt. %
1st | 2nd 3rd
XXX. XXX | XXX. XXX XXX. XXX
xxxxxxxxx 1 xxxxxxxxx 1 xxxxxxxxx
1 xxxxxxxxx 1 xxxxxxxxx
j xxxxxxxxx j xxxxxxxxx
j xxxxxxxxx j xxxxxxxxx
xxxxxxxxx
xxxxxxxxx
Mean
XXX . XXX *
xxxxxxxxxxx
In column indicates a problem with that parameter.
-------
Appendix D
Revision 1
Date: 4/89
Page 12 of 17
QC Summary Report
Page 6 of 10
QCCS, DLQCCS, and Calibration Blank
Batch f
Date Run /
Number of Samples in Batch
Form t
01
03
03
03
03
03
03
03
03
03
03
04
05
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
MOIST
SAND
SILT
CLAY
VCOS
COS
MS
FS
VFS
COS I
FSI
PH_H2O
PH 01M
CEC OAC
CEC_CL
CA_OAC
MG_OAC
K_OAC
NA_OAC
CA CL
MG_CL
K_CL
NA_CL
AL_CL
AC_BACL
PH_002M
CA_CL2
MG_CL2
K CL2
NA_CL2
FE CL2
AL_CL2
FE~PYP
AL_PYP
FE_AO
AL_AO
SI AO
FE_CD
AL_CD
SO4 H2O
SO4 PO4
SO4_0
SO4_2
S04 4
S04_8
SO4_16
SO4 32
C_TOT
N TOT
S_TOT
QCCS
True
Value
XXX . XXX
XXXXXXXXX
No. | No. Out
Run
x *
XXXXX
Range
X
XXXXXX
DLQCCS Calib. Blank |
1
True | No. |No.Out|
Value | Meas. Run Range]
XXX. XXX 1 * X * | X
| XXXXXXXXX | XXXXXXX | | XXXXX | XXXXXX |
j xxxxxxxxx 1 xxxxxxx j j xxxxx j xxxxxx
j xxxxxxxxx j xxxxxxx j 1 xxxxx 1 xxxxxx
| xxxxxxxxx j xxxxxxx 1 xxxxx xxxxxx
1 xxxxxxxxx 1 xxxxxxx 1 1 xxxxx | xxxxxx j
j xxxxxxxxx 1 xxxxxxx j | xxxxx | xxxxxx j
1 xxxxxxxxx 1 xxxxxxx 1 xxxxx j xxxxxx 1
1 xxxxxxxxx j xxxxxxx j xxxxx | xxxxxx 1
1 xxxxxxxxx 1 xxxxxxx | xxxxx | xxxxxx j
1 xxxxxxxxx | xxxxxxx j j xxxxx 1 xxxxxx |
xxxxxxxxx 1 xxxxxxx 1 j xxxxx j xxxxxx |
xxxxxxxxx 1 xxxxxxx j
xxxxxxxxx 1 xxxxxxx 1
xxxxxxxxx
xxxxxxx
xxxxx
xxxxx
xxxxx
xxxxxx
xxxxxx
xxxxxx
1 xxxxx | xxxxxx
xxxxx xxxxxx
| xxxxx 1 xxxxxx
1 xxxxx 1 xxxxxx
1 xxxxx j xxxxxx
In column indicates a problem with that parameter.
-------
Appendix D
Revision 1
Date: 4/89
Page 13 of 17
QC Summary Report
Page 7 of 10
O.A Check Sample Results
Batch t _
Date Run /
Form t
01
03
03
03
03
03
03
03
03
03
03
04
05
06A/B
07A/B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Parameter
MOIST
SAND
SILT
CLAY
VCOS
COS
MS
FS
VFS
COSI
FSI
PH_H20
PH_01M
CEC OAC
CEC CL
CA OAC
MG OAC
K_OAC
NA_OAC
CA_CL
MG_CL
K_CL
NA_CL
AL_CL
AC_BACL
PH_002M
CA CL2
MG_CL2
K CL2
NA_CL2
FE_CL2
AL_CL2
FE_PYP
AL_PYP
FE_AO
AL_AO
SI_AO
FE_CD
AL_CD
S04_H20
SO4_P04
SO4_0
S04_2
S04_4
S04_8
SO4 16
S04_32
C TOT
N TOT
SJTOT
LOW
Range Value
XXX . XXX
XXXXXXXXXXXXXXX
51.159
19.059
16.567
0.426
2.602
7.650
19.831
17.460
8.232
11.349
4.440
3.890
15.107
6.645
0.222
0.187
0.224
0.014
0.239
0.181
0.208
0.0002
XXXXXXXXXXXXXXX
15.119
4.140
0.336
0.087
0.038
0.022
0.011
0.027
0.543
0.518
0.350
0.341
XXXXXXXXXXXXXXX
1.456
0.423
25.143
64.396
3.884
5.183
6.505
9.310
15.221
27.567
4.261
0.122
0.0164
Sample
Result Value
XXX . XXX *
xxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxx
High
Range Value
XXX • XXX
XXXXXXXXXXXXXXX
62.397
31.068
19.999
1.346
3.631
8.904
27.371
24.373
13.639
17.250
4.606
4.133
20.440
8.991
0.306
0.277
0.280
0.058
0.323
0.247
0.289
0.074
XXXXXXXXXXXXXXX
22.676
4.310
0.486
0.171
0.089
0.039
0.028
0.141
0.735
0.701
0.474
0.511
XXXXXXXXXXXXXXX
2.184
0.572
35.548
87.778
4.514
6.024
7.560
10.820
17.689
32.038
5.041
0.191
0.0194
In column indicates a problem with that parameter.
-------
l\.
). UOJSIA8U
Q xipuaddv
xxxxxxxxxx | xxxxxxxx | |E*O '8*o| JXXL s
xxxxxxxxxx | xxxxxxxx I jzo'o't'ol xoi N
xxxxxxxxxx j xxxxxxxx j
JO'SZ'OS
[O'SZ'OS
JO'SZ'OS
JO'SZ'OS
JO'SZ'OS
JO'SZ'OS
JO'OS'OOI
\0'Ot'OB
0'6ZI
0'6ZT
O'fOZ
0'»OZ
O'JOZ
O'tOZ
O'tOZ
O'O*
O'Ot
O'Ofr
O'Ot
0'0»
O'Ot
O'Ofr
j xxxxxxxxxx | xxxxxxxx |
j xxxxxxxxxx 1 xxxxxxxx j
j xxxxxxxxxx j xxxxxxxx 1
j xxxxxxxxxx j xxxxxxxx j
j xxxxxxxxxx 1 xxxxxxxx j
xxxxxxxxxx j xxxxxxxx j
xxxxxxxxxx j xxxxxxxx j
j xxxxxxxxxx 1 xxxxxxxx |
j xxxxxxxxxx j xxxxxxxx 1
1 xxxxxxxxxx 1 xxxxxxxx
j xxxxxxxxxx 1 xxxxxxxx
1 xxxxxxxxxx 1 xxxxxxxx 1
O'OZ I
O'OZ I
j xxxxxxxxxx j xxxxxxxx 1 xxxxxxxxxx
1 xxxxxxxxxx 1 xxxxxxxx 1
1 xxxxxxxxxx 1 xxxxxxxx |
" TOA
' T°A ' }M
ZO'O'l'Ol XOX D
O'S'OI
O'S'OI
O'S'OI
O'S'OI
O'S'Ot
O'S'OI
S'Z'S
O'Z't
O't
O't
O'Z
O'Z
O'Z
O'Z
O'Z
O'OZ
O'OZ
O'OZ
O'OZ
O'OZ
O'OZ
O'OZ
O'Z
O'S
O'S
O'S
O'S
O'S
O'S
O'S
O'S
O'S
O'S
O'S
O'OZ
O'OZ
xxxxxxxx
o-oz'ot
O'OI
(6)
po^aH
ZE tOS
91 tOS
8 tOS
t tos
z tos
o tos
tOd tOS
OZH fOS
QD TV
aD~aj
OY~IS
OV TY
ov ad
did TY
did 3d
Z1D~*IY
zio~ad
Z1D YN
Z1D 51
Z1D OH
Z1O~YD
HZOO~Hd
1DYH OV
1O TV
ID YN
ID X
13 SM
1D~YD
DYO YN
DYO S
DYO OK
3VO YD
13 D3D
DYO D33
HtO Hd
OZH Hd
azfs ataf^fed
azts aiDT^tBd
XSIOH
^annijvd
zt
It
ot
6E
8E
/.E
9E
SE
»E
£E
ZE
IE
OE
6Z
8Z
LZ
9Z
sz
>z
EZ
zz
IZ
oz
61
81
it
91
SI
n
El
ZI
II
01
60
80
S/ViO
S/V90
so
to
EO
zo
to
t uuoj
"[OA
Ot jo 8
-------
QC Summary Report
Page 9 of 10
Spike Solution Check
Batch t
Date Run / /
0 mg S/l
Theor.
Value
(mg S/L)
0.00
0.00
0.00
j Isotherm
Inst.
Reading
(mg S/L) *
2 mg S/l
Theor .
Value
(mg S/L)
2.00
2.00
2.00
j Isotherm
Inst.
Reading
(mg S/L) *
8 mg S/I
Theor.
Value
(mg S/L)
8.00
8.00
8.00
^ Isotherm
Inst.
Reading
(mg S/L) *
16 mg S/l
Theor.
Value
(mg S/L)
16.00
16.00
16.00
i Isotherm
Inst.
Reading
(mg S/L) *
4 mg S/l
Theor.
Value
(mg S/L)
4.00
4.00
4.00
J Isotherm
Inst.
Reading
(mg S/L) *
32 mg S/l
Theor.
Value
(mg S/L)
32.00
32.00
32.00
J Isotherm
Inst.
Reading
(mg S/L) *
CA_
Theor.
Value
(mg/L)
80.00
80.00
80.00
_CL2
Inst.
Reading
(mg/L) *
•*• In column indicates a problem with the parameter.
Appendix D
Revision 1
Date: 4/89
Page 15 of 17
-------
Appendix D
Revision 1
Date: 4/89
Page 16 of 17
QC Summary Report
Page 10 of 10
Detection Limits
Batch t _
Date Run /
Form
*
01
02
03
04
05
06A
06B
07A
07B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
| Calculated | Instrument Date
Reporting Contract Required | Detection Determined
Parameter Units Detection Limit j Limit * (MM/DD/YY)
HOIST | XXXXXXXXXXXX | XXXXXXXXXXXXXXXXXX | XXXXXXXXXXX | XXXXXXXXXXX
Particle Size | XXXXXXXXXXXX | XXXXXXXXXXXXXXXXXX j XXXXXXXXXXX | XXXXXXXXXXX
Particle Size j XXXXXXXXXXXX | XXXXXXXXXXXXXXXXXX | XXXXXXXXXXX 1 XXXXXXXXXXX
PH_H20 | XXXXXXXXXXXX j XXXXXXXXXXXXXXXXXX 1 XXXXXXXXXXX 1 XXXXXXXXXXX
PH_0 1M j XXXXXXXXXXXX j XXXXXXXXXXXXXXXXXX 1 XXXXXXXXXXX I XXXXXXXXXXX
CEC_OAC (FIA)
CEC_OAC (Tit.)
CEC_CL (FIA)
CEC_CL (Tit.)
CA_OAC
MG_OAC
K_OAC
NA_OAC
CA_CL
MG_CL
K_CL
NA_CL
AL_CL
AC_BACL
PH_002M
CA_CL2
MG_CL2
K_CL2
NA_CL2
FE CL2
AL_CL2
FE_PYP
AL_PYP
FE AO
AL_AO
SI AO
FE_CD
AL_CD
S04_H2O
S04_P04
S04_0
SO4 2
SO4 4
S04_8
SO4_16
S04_32
C_TOT
N_TOT
S TOT
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
XXXXXXXXXXXX
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
wt.
wt.
wt.
wt.
wt.
wt. %
wt. %
mg S/kg
mg S/kg
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
mg S/L
wt. t
wt. %
wt. t
1.050 mg N/L
0.0075 meq **
1.050 mg N/L
0.0075 meq **
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.100 mg/L
0.005 meq
XXXXXXXXXXXXXXXXXX
0.500 mg/L
0.050 mg/L
0.050 mg/L
0.050 mg/L
0.100 mg/L
0.100 mg/L
0.500 mg/L
0.500 mg/L
0.500 mg/L
0.500 mg/L
0.500 mg/L
0.500 mg/L
0.500 rog/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.025 mg S/L
0.010 wt. t
0.005 wt. t
0.001 wt. %
XXXXXXXXXXX
xxxxxxxxxxx
•*" In column indicates a problem with that parameter.
** For titrations, the instrumental detection limit is a calculated
value based upon a minimum titration.
-------
Appendix D
Revision 1
Date: 4/89
Page 17 of 17
Foxn Descriptions Sorted by Form t
Form
t
01
02
03
04
05
06A
06B
07A
07B
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Form
Type
0
10
11
8
8
2
6
2
6
2
2
2
2
2
2
2
2
2
7
8
1
1
1
1
1
1
3
3
3
3
3
3
3
4
4
5
5
5
5
5
5
9
9
9
Parameter
HOIST
Particle Size Raw
Particle Size Calc.
PH_H2O
PHJJ1M
CEC_OAC (FIA)
CEC_OAC (Titr.)
CEC_CL (FIA)
CEC CL (Titr.)
CA_OAC
MG OAC
K OAC
NA OAC
CA_CL
MG_CL
K_CL
HA_CL
AL_CL
AC_BACL
PH_002M
CA_CL2
MG_CL2
K_CL2
NA_CL2
FE CL2
AL_CL2
FE_PYP
AL_PYP
FE_AO
AL_AO
SI_AO
FE_CD
A1,_CD
SO4 H2O
SO4 PO4
S04_0
SO4 2
SO4_4
SO4 8
S04_16
SO4_32
CJTOT
H_TOT
S TOT
Description
Air Dry Moisture Percent
Particle Size (Raw Data)
Particle Size (Calculated Data)
pH in Hater
pH in 0.0 1M Calcium Chloride
Cation Exchange Capacity - Ammonium Acetate
Cation Exchange Capacity - Ammonium Acetate
Cation Exchange Capacity - Ammonium Chloride
Cation Exchange Capacity - Ammonium Chloride
Calcium in Ammonium Acetate
Magnesium in Ammonium Acetate
Potassium in Ammonium Acetate
Sodium in Ammonium Acetate
Calcium in Ammonium Chloride
Magnesium in Ammonium Chloride
Potassium in Ammonium Chloride
Sodium in Ammonium Chloride
Aluminum in Ammonium Chloride
Barium Chloride - TEA Acidity
pH in 0.002M Calcium Chloride
Calcium in Calcium Chloride
Magnesium in Calcium Chloride
Potassium in Calcium Chloride
Sodium in Calcium Chloride
Iron in Calcium Chloride
Aluminum in Calcium Chloride
Pyrophosphate Extractable Iron
Pyrophosphate Extractable Aluminum
Acid Oxalate Extractable Iron
Acid Oxalate Extractable Aluminum
Acid Oxalate Extractable Silicon
Citrate Dithionite Extractable Iron
Citrate Dithionite Extractable Aluminum
Water Extractable Sulfate
Phosphate Extractable Sulfate
Zero mg S/L Isotherm Parameter
Two mg S/L Isotherm Parameter
Four mg S/L Isotherm Parameter
Eight mg S/L Isotherm Parameter
Sixteen mg S/L Isotherm Parameter
Thirty-two mg S/L Isotherm Parameter
Total Carbon
Total Nitrogen
Total Sulfur
-------
Appendix E
Revision 1
Date: 4/89
Page 1 of 7
Appendix E
Analytical Laboratory Pre-Award Evaluation Scoring Sheet
The following format is used to grade each contract laboratory that analyzes ore-award audit
samples provided by the QA staff. The scores from this evaluation are used in considering the
award of contracts for DDRP analysis.
-------
Appendix E
Revision 1
Date: 4/89
Page 2 of 7
APPENDIX E
PRE-AWARD PERFORMANCE EVALUATION SCORING SHEET
LABORATORY:
DATE:
Quantitation:
Relationships:
Quality Control:
Deliverables:
TOTAL SCORE:
PERCENTAGE:
Sample 1:
Sample 2:
Sample 3:
Sample 4:
Sample 5:
(Maximum score per sample
250 points)
r^*u\.j. x* v**"*1* A J- A**!, a. vw
Parameter Possible Point a
PH_H2O 3
PH_002M 3
PH_0 1M 3
S04_0 3
S04_2 3
S04_4 3
S04_8 3
S04_16 3
S04_32 3
SO4_H2O 3
SO4_PO4 3
CA_CL 3
MG_CL 3
K_CL 3
NA_CL 3
AL_CL 3
CEC_CL 3
Points Awarded
( Samoles )
1
2
3
4
5
Total
Score
-------
Appendix E
Revision 1
Date: 4/89
Page 3 of 7
Parameter Possible Points
CA_OAC 3
MG_OAC 3
K_OAC 3
NA_OAC 3
CEC_OAC 3
CA_CL2 3
MG_CL2 3
K_CL2 3
NA_CL2 3
FE_CL2 3
AL_CL2 3
AC_BACL 3
FE_PYP 3
FE_AO 3
FE_CD 3
ALJ?YP 3
AL_AO 3
AL_CD 3
SI_AO 3
SAND 3
M_SAND 3
SILT 3
CLAY 3
Subtotals
Points Awarded
(
1
2
Samol
3
33)
4
5
Total
Score
- Points are awarded per sample for being within accuracy windows
or for meeting %RSD precision limits.
-------
Appendix E
Revision 1
Date: 4/89
Page 4 of 7
PART II. STANDARD RELATIONSFIPS
Possible
Parameter Grouo Points
Soil pH 5
Sulfate isotherms 10
Extractable Sulfate 5
CEC 5
article Size 5
Subtotals
Points Awarded
( Samples }
1
2
3
4
5
Total
Score
- Points are awarded per sample.
PART III. QUALITY CONTROL
Sample Type Possible Points'
Reagent Blanks
All < IDL 6
One or more > IDL 0
QC check Samples
All QCCS within 6
One or more QCCS out 0
Matrix Spikes
All within 100 + 10%
recovery 8
One or more out of
criteria 0
Detection Limit QCCS
All within 20% 30
One or two outside 20% 15
Three or more outside 0
20%
Instrument Detection Limits
All IDLs < CRDL 30
One IDL > CRDL 15
Two or more IDLs > CRDL 0
subtotals
Points Awarded
(Samples)
1
2
3
4
5
Total
Score
- Points are awarded per sample.
-------
Appendix E
Revision 1
Date: 4/89
Page 5 of 7
PART IV. REPORTING AND DELIVERABLES
Possible
Criteria Points
Analytical data submitted in
specified format 5
QC data submitted in
specified format 5
Tabulated IDLs and
associated blank data 5
supplied
Confirmation of summary
results by signature of
lab manager 5
Subtotals
Points
(Si
1
2
i Awai
uncles
3
•ded
i)
4
5
Total
Score
- Points are awarded per sample.
-------
ELEMENTAL ANALYSIS
PRE-AWARD PERFORMANCE EVALUATION SCORING SHEET
Appendix E
Revision 1
Date: 4/89
Page 6 of 7
LABORATORY:
DATE:
Quantitation:
Relationships:
Quality Control:
Deliverables:
TOTAL SCORE:
Sample 1:
Sample 2:
Sample 3:
Sample 4:
Sample 5:
PERCENTAGE:
(200 points possible per sample)
r-Anx -L . uuATiXJ.xnxj.un
Parameter* Possible Points"
C_TOT 20
N_TOT 2 0
S_TOT 20
Subtotals
Points Awarded
(Samples)
1
2
3
4
5
Total
Score
* Refer to glossary for descriptions of parameters.
b Points are awarded per sample: two for being within accuracy windows and
one for meeting %RSD precision limits; for certain summary parameters the
values are three and two, respectively.
PART II. STANDARD RELATIONSHIPS
Parameter* Possible Points"
C_TOT/N_TOT 10
C TOT/S TOT 10
Subtotals
Points Awarded
(Samples)
1
2
3
4
5
Total
Score
Points are awarded per sample
-------
Appendix E
Revision 1
Date: 4/89
Page 7 of 7
PART III. QUALITY CONTROL
Parameter* Possible Points"
Reagent Blanks
All < IDL 10
One or more > IDL 0
QC check samples
All QCCS within 10
One or more QCCS out 0
Points Awarded
(SamDles)
1
2
3
4
5
Total
Score
Parameter* Possible Points"
Matrix Spikes
All within 100 + 10%
recovery 2 0
One or more out of
criteria 0
Detection Limit QCCS
All within + 20% 30
Two or less outside + 20% 15
Three or more outside +20% 0
Instrument Detection Limits
All IDLs within 30
One IDL out 15
Two or more IDLs out 0
Subtotals
Points Awarded
< Samples )
1
2
3
4
5
Total
Score
Points are awarded per sample.
PART IV. REPORTING AND DELIVERABLES
Criteria Possible Points*
Analytical data submitted in
specified format 5
QC data submitted in specified
format 5
Tabulated IDLs and associated
blank data supplied 5
confirmation of summary results
by signature of lab manager 5
Subtotals
Points Awarded
f Samples)
1
2
3
4
5
Total
Score
* Points are awarded per sample.
-------
-------
Appendix F
Revision 1
Date: 4/89
Page 1 of 54
Appendix F
Analytical Laboratory On-Site Evaluation Questionnaire
The following questionnaire is used by QA staff to provide documentation of on-site
laboratory audits. An analytical laboratory is evaluated prior to the award of a contract to assess
the ability of the laboratory, in terms of personnel, facilities, and equipment, to analyze soil samples
successfully. A second evaluation is made after sample analysis is underway. At the time of the
second evaluation, adherence to the Analytical Laboratory Statements of Work is evaluated and
specific discrepancies are addressed.
-------
Appendix F
Revision 1
Date: 4/89
Page 2 of 54
ANALYTICAL LABORATORY ON-SITE EVALUATION QUESTIONNAIRE
DDRP SOIL SURVEY
GENERAL (Page 1 of 2)
Date:
Laboratory:
Street Address:
Mailing Address (if different from above)
City:
State: Zip:
Laboratory Telephone Number: ( )
Laboratory Director: .
Laboratory Quality Assurance Officer:
(Quality Control chemist)
Type of Evaluation:
Contract Number:
Contract Title:
-------
Appendix F
Revision 1
Date: 4/89
Page 3 of 54
GENERAL (Page 2 of 2)
Personnel Contacted:
Name Title
Laboratory Evaluation Team:
Name Title
-------
ORGANIZATION AND PERSONNEL (Page 1 of 3)
LABORATORY ORGANIZATIONAL CHART
Appendix F
Revision 1
Date: 4/89
Page 4 of 54
-------
Appendix F
Revision 1
Date: 4/89
Page 5 of 54
ORGANIZATION AND PERSONNEL (Page 2 of 3)
Laboratory Personnel
Academic Special Years
Position Name Training* Training Experience**
* List highest degree obtained and specialty. Also list years toward a
degree.
**List only experience directly relevant to task to be performed.
-------
Appendix F
Revision 1
Date: 4/89
Page 6 of 54
ORGANIZATION AND PERSONNEL (Page 3 of 3)
Item
Do personnel assigned to this project have the
appropriate educational background to success-
fully accomplish the objectives of the program?
Do personnel assigned to this project have the
appropriate level and type of experience to
successfully accomplish the objectives of this
program?
Is the organization adequately staffed to meet
project commitments in a timely manner?
Does the laboratory Quality Assurance
Supervisor report to senior management levels?
Was the Project Manager available during the
evaluation?
Were chemists and technicians available during
the evaluation?
Was the Quality Assurance Supervisor available
during the evaluation?
Yes
No
Comment
Item
LABORATORY MANAGER (Page 1 of 1)
NO
Yes
Comment
Does the laboratory manager have his/her own
copy of the standard operating procedures?
Does the laboratory manager have his/her own
copy of the instrument performance data?
Does the laboratory manager have his/her own
copy of the latest monthly QC plots?
Is the laboratory manager aware of the most
recent control limits?
Does the laboratory manager review the
following before reporting data:
a. The data itself?
b. The quality control data sheet
with analyst notes?
c. The general instrument performance
and routine maintenance reports?
-------
Appendix F
Revision 1
Date: 4/89
Page 7 of 54
STANDARD OPERATING PROCEDURES (SOP) (Page 1 of 1)
Item
Yes
No
Comment
Does the laboratory have a standard
operating procedure (SOP) manual?
Is the SOP manual followed in detail?
Does the SOP manual contain quality control
practices?
Does each analyst/technician have a copy
of the SOP manual?
Does the SOP manual deviate from the
procedures required by the project?
If the SOP manual does deviate, are the
deviations documented in written form?
Does each analyst/technician have a copy of
all methods and procedures required by this
project?
Are plots of instrumental accuracy and
precision available for every analysis?
Are detection limit data tabulated for
each analysis?
-------
Appendix F
Revision 1
Date: 4/89
Page 8 of 54
LABORATORY FACILITIES (Page 1 of 3)
When touring the facilities, give special attention to:
(1) the overall appearance of organization and neatness,
(2) the proper maintenance of facilities and instrumentation,
(3) the general adequacy of the facilities to accomplish
the required work.
Item
Yes No
Comment
Does the laboratory appear to have adequate
workspace (6 linear meters of unencumbered
bench space per analyst)?
Does the laboratory have a source of
distilled/demineralized water?
Is the specific conductance of distilled/
demineralized water routinely checked and
recorded?
Are the analytical balances located away
from draft and areas subject to rapid
temperature changes?
Has the balance been calibrated within one
year by a certified technician?
Is the balance checked with a class S
standard before each use and recorded in a
logbook? Have technician demonstrate
how this is done.
Are exhaust hoods provided to allow effi-
cient work with volatile materials?
Have the hoods been checked for operating
efficiency? How often is this done?
Is the laboratory maintained in a clean and
organized manner?
Are contamination-free work areas provided
for the handling of toxic materials?
Are adequate facilities provided for
separate storage of samples, extracts, and
standards, including cold storage?
Is the temperature of the cold storage units
recorded daily in logbooks?
Are chemical waste disposal policies/
procedures adequate?
Are contamination-free areas provided for
trace level analytical work?
Can the laboratory supervisor document that
trace-free water is available for prepara-
tion of standards and blanks?
-------
Appendix F
Revision 1
Date: 4/89
Page 9 of 54
LABORATORY FACILITIES (Page 2 of 3)
item
Yes
No
Comment
Do adequate procedures exist for disposal
of waste liquids from the ICP and AA
spectrometers ?
Do adequate procedures exist for disposing
of liquid and solid wastes?
Is the laboratory secure?
Are all chemicals dated on receipt and
thrown away when shelf life is exceeded?
Are all samples stored in the refrigerator
between analyses?
Are acids and bases stored in separate
areas?
Are hazardous, combustible, and toxic
materials stored safely?
LABORATORY FACILITIES
Item
Gas
Lighting
Compressed air
Vacuum system
Electrical services
Hot and cold water
Distilled water
Laboratory sink
Ventilation system
Hood space
Cabinet space
Storage space (m*)
Refrigerated storage (4°C)
Avai;
Yes
[able
No
Comments
(where applicable, cite system,
QC check, adequacy of space)
-------
LABORATORY FACILITIES (page 3 of 3)
COMMENTS ON LABORATORY FACILITIES
Appendix F
Revision 1
Date: 4/89
Page 10 of 54
-------
Appendix F
Revision 1
Date: 4/89
Page 11 of 54
EQUIPMENT GENERAL (Page 1 of 2)
item
Balance, analytical
(1)
(2)
(3)
Balance, top
loader
class "S"
weights
Balance table
NBS-calibrated
thermometer
Desiccator
Distilled water
Double deionized,
distilled/deion-
ized, or double
distilled water
Glassware
(1) Beakers
(2) Erlenmeyer
flasks
( 3 ) Sedimentation
cylinders
( 4 ) Graduated
cylinders
(5) Fleakers
(6) Other
Drying ovens
Hot plates
Water bath
Centrifuge
Vortex mixer
Equipment
# of
Units
Make
Model
Condition/aae
Good
Fair
Poor
Comments
-------
Appendix F
Revision 1
Date: 4/89
Page 12 of 54
EQUIPMENT GENERAL (Page 2 of 2)
item
Eppendorf pipets
(or equivalent)
Reciprocating
shaker
Eauioment
f of
Units
Make
Model
Cond:
Good
ition/aae
Fair
Poor
Comments
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 13 of 54
MOISTURE CONTENT
Item
Manufacturer
Model
Installation
Date
Comments
Balance, +0.01 g
Convection ovens
Item
Available
Quantity | Type
Comments
Thermometers
0 to 200°C
Weighing
containers
Desiccator
Desiccant
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 14 of 54
Question
MOISTURE CONTENT (Page 2 of 2)
Yes
No
NA
Comments
Is the balance calibrated weekly?
Do thermometers have a range of -20 to
200°C?
Are thermometers calibrated (with barometric
correction) at the boiling and freezing
points at least once every 3 months?
Is the oven temperature checked and
recorded daily?
Is the oven temperature calibrated at
least monthly?
Are organic soil samples dried at the
specified temperature?
Are replicates of each sample prepared
and run?
Are mineral soil samples dried at the
specified temperature?
Are two separately calibrated ovens used,
one for organic and one for mineral soils?
If only one oven is used, is at least 24
hours allowed for the oven to stabilize
at the new temperature?
Is sample-drying time extended as
specified in the procedure?
Are calculations correctly performed, and
are at least 5% (or 2 per batch) checked
by hand?
-------
Appendix F
Revision 1
Date: 4/89
Page 15 of 54
PARTICLE SIZE ANALYSIS
Item
Manufacturer
Model
Installation Date
Comments
Hot plate or
block digester
Analytical
balance,
0. Img
Shaker hori-
zontal recip-
rocating (120
oscillations/
min)
Sieve shaker
(1.25 cm
vertical and
lateral
movement)
Complete
sieve set with
receiving pan
Automatic
pipets
Shaw pipet
rack
Motor-driven
stirrer
PARTICLE SIZE ANALYSIS
Item
Available
Quantity
Type
Comments
Thermometer
10 to 50°c
Erlenmeyer flask
or Fleaker 300
mL
Pasteur-
chamberlain
filter candles
(fineness"F")
1-L Sedimenta-
tion cylinders
Insulation
covering
Hand-driven
stirrer
-------
Appendix F
Revision 1
Date: 4/89
Page 16 of 54
PARTICLE SIZE ANALYSIS (Page 2 of 4)
Item
Available
Quantity
Type
Comments
Shaw pipet rack
equivalent
Ring stand
Clamp
Volumetric
pipet, 25 mL
Evaporating
dishes
Waterproof
markers
or paint pens
Weighing bottles
90-mL wide-mouth
Desiccator
Set of sieves, square-mesh, woven phosphor-bronze or stainless steel wire
cloth; U.S. Series and Tyler Screen Scale equivalent designations as follows;
Nominal
ODeninq(mm)
1.0
0.5
0.25
0.105
0.046
U.S.
No.
18
35
60
140
300
Tyler
Mesh Size
16
32
60
150
300
Chemical
PARTICLE SIZE ANALYSIS
Quantity
Grade
Expiration Date
Comments
Hydrogen
peroxide (HO)
30 to 35% 2 2
Dessicant:
Phosphorus
pentoxide
(P O )
1 2 5'
Sodium carbon-
ate (Na CO )
2 3
Sodium Hexameta
phosphate
(NaPO )6
-------
Appendix F
Revision 1
Date: 4/89
Page 17 of 54
PARTICLE SIZE ANALYSIS (Page 3 of 4)
comments:
PARTICLE SIZE ANALYSIS
Question
Yes
No
NA
Comments
Is analysis performed on mineral horizons
only?
Is the organic matter removed as specified
before proceeding?
Are chemicals reagent grade or better?
Is heat applied after organic matter is
visibly destroyed to remove excess HO?
Is reciprocating shaker calibrated once
every 6 months if no gauge is included
(every year with gauge)?
Is the 500 stroke per minute (1.25 cm
vertical and lateral oscillator) shaker
calibrated once every 6 months?
Are pipets calibrated monthly, gravimetri-
cally on a calibrated balance?
-------
Appendix F
Revision 1
Date: 4/89
Page 18 of 54
Question
PARTICLE SIZE ANALYSIS (Page 4 of
Yes No NA
4)
Comments
Are the specified methods used for
separating sand, silt, and clay?
Is a standard sand, silt, clay "soil"
used as a control?
Is the water temperature checked during
sedimentation to determine when to take
a sample?
Are the specified procedures followed
during sedimentation?
Is note made of which sedimentation table
is used to determine sampling depth and
time?
Are weights for each mineral fraction
correctly recorded and calculated?
Are calculations correctly performed, and
are at least 5% (or 2 per batch) checked
by hand?
-------
Appendix F
Revision 1
Date: 4/89
Page 19 of 54
Item
Manufacturer
pH DETERMINATION
Model Installation Date
Comments
Digital
pH meter
Combination
electrodes,
non-gel type
Item
Available
Quantity
Type
comments
Thermometer
Beakers, 50 mL
stirrers
QCCS standard
Chemical
Quantity
Grade
Expiration Date
Comments
Calcium
Chloride
Calcium
hydroxide
(Ca(OH)2)
Chloroform
(CHClo) or
Thymol
(C10H1 O)
4
Hydrochloric
acid (HCl)
National
Bureau of
Standards
(NBS) buffers
Potassium
Biphthalate
(KHC H O )
V 844'
Potassium
chloride (KCl)
-------
Appendix F
Revision 1
Date: 4/89
Page 20 of 54
Question
pH DETERMINATION (Page
Yes
2 of 3)
No
NA
Comments
Are chemicals reagent grade or better?
Is the air-dried soil stored in sealed
containers?
Is the pH meter digital to +0.01
(and +1 mv)?
Does the pH meter have internal
temperature compensation to +0.5°c?
Is the combination electrode a non-gel
type?
Is the combination electrode of the
recommended style with retractable
sleeve junction?
Are the buffers calibrated daily to +.01
pH units?
Is the pH meter:
- calibrated before samples are
analyzed
-checked every batch as stated
in methods
Is temperature compensation manual or
internal?
Are equilibrium times required for
standards checked, to see if electrode
response is slowing?
Is a spare combination electrode available
and properly stored?
Is manufacturer recommended warm-up time
allowed before samples are run?
Are pH meters placed away from drafts and
areas of rapid temperature change?
Are the specified between-sample
procedures followed?
Are pH units equipped with programmable
sampling times?
If yes above, are they used in this
analysis?
Are electrodes properly stored and
maintained?
Are the QC results plotted in real time?
-------
Appendix F
Revision 1
Date: 4/89
Page 21 of 54
Question
pH DETERMINATION (Page 3 of 3)
Yes No NA
Comments
what is the QCCS sample?
Is the QCCS solution analyzed first and
thereafter as called for in the methods?
Are a QCCS and duplicate sample included
in each run?
Is the quality control data reviewed by
the analyst before deciding whether to
release the data for reporting?
-------
Appendix F
Revision 1
Date: 4/89
Page 22 of 54
Item
CATION EXCHANGE CAPACITY
Manufacturer
Model
Installation Date
Comments
Mechanical
extractor
Flow injection
analyzer
Titration
apparatus
Reciprocating
shaker
Item
Available
Quantity
Type
Comments
Steam
distillation
unit
Digestion
tubes 250 mL
Kjeldahl
flasks, 800 mL
Analytical
filter pulp
Disposable
syringes 60 mL
Rubber tubing
connectors
Linear
polyethylene
bottles, 25 mL
Chemical
CATION EXCHANGE CAPACITY
Quantity
Grade
Expiration Date
Comments
Glacial acid
(HC2H302)
Ammonium
hydroxide
(NH4OH)
Ammonium
acetate
(NH40AC)
Ammonium
chloride
-------
Appendix F
Revision 1
Date: 4/89
Page 23 of 54
CATION EXCHANGE CAPACITY (Page 2 of 5)
Chemical
Quantity
Grade
Expiration Date
Comments
(NH4C1)
Ethanol
(CH3CH20), 95%
Nessler's
reagent
Potassium
iodide (KI)
Mercuric
iodide (Hgl2)
Sodium
hydroxide
(NaOH)
Sodium
chloride
(Nad)
Antifoam
Hydrochloric
acid (HCl)
Sodium
carbonate
(Na2CO3)
Methyl orange
indicator
Boric Acid
Zinc, granular
Phenol
(C6H6O)
Potassium
sodium
tartrate
(KNaC4H406).
4H20
Sodium
citrate
(Na3C6H5o7).
2H2O)
Sodium nitro-
ferricyanide
(Na3Fe(CN)5
N03.2H2O)
-------
Appendix F
Revision 1
Date: 4/89
Page 24 of 54
chemical
CATION EXCHANGE CAPACITY (Page 3 of 5)
Quantity Grade Expiration Date
Comments
Sodium hypo-
chlorite
(NaOCl)
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 25 of 54
Question
CATION EXCHANGE CAPACITY (Page 4 of 5)
Yes No
NA
Comments
Are the chemicals reagent grade or better?
Are dilute standards prepared and cali-
brated daily?
Are working standards prepared and
calibrated at least weekly?
Are reagents stored properly to prevent
premature decomposition?
Are hazardous chemicals used strictly
under the hood?
Is an antifoam agent available for use?
Is all glassware cleaned and stored as
specified?
Does the flow injection analyzer (FIA)
have the correct interference filter?
Are the pump lines inspected for wear
before each run?
Is the heat bath of the FIA calibrated
monthly and checked before each run?
Are the pump tubes all of the correct
type for the agents and method in use?
Are all peripherals such as printer,
plotter, and disk drives functional and,
in the case of recorders and plotters,
calibrated before each run?
Is the shaker used for organic samples
calibrated every six months or less along
with general maintenance?
Is the auto analyzer (distillation/
titration) calibrated for titration before
each run?
Are the condensation facilities of the
distillation apparatus inspected before
each run?
Are all calculations performed correctly,
and are at least 5% being checked by hand?
Is the mechanical extractor calibrated
for extraction time?
Is the calibration of the mechanical
extractor checked at least monthly?
-------
Appendix F
Revision 1
Date: 4/89
Page 26 of 54
Question
CATION EXCHANGE CAPACITY (Page
Yes No
5 of 5)
NA
Comments
Are the specified size, type, and grade of
disposable syringes used with the
extractor?
Is the tubing checked frequently and
replaced when needed?
Is the filter pulp washed before the
extraction is performed?
Are all procedures involving the
extraction followed precisely according to
the statement of work?
Are three blanks carried through to record
mean and standard deviation?
-------
Appendix F
Revision 1
Date: 4/89
Page 27 of 54
Item
EXCHANGEABLE CATIONS
Manufacturer
Model/Grade
Installation Date
Comments
Flame atomic
absorption
spectrometer
Inductively
coupled plasma
emission
spectrometer
Atomic flame
emission
spectrometer
chemical
Quantity
Grade
Expiration Date | Comments
Argon
Acetylene gas
(C H )
v 2 4;
Natural gas
Hydrochloric
acid (HCl)
Nitric acid
Calcium
carbonate
Magnesium
oxide (MgO)
Potassium
chloride (KCl)
Sodium
chloride (Nad)
Lanthanum
oxide (La O )
v 2 3;
Cesium
chloride (CsCl)
-------
Appendix F
Revision 1
Date: 4/89
Page 28 of 54
Chemical
EXCHANGEABLE CATIONS (Page 2 of 3)
Quantity
Grade
Expiration Date
Comments
Lithium
chloride
(LiCl)
Lithium
nitrate
QCCS
Calcium (Ca)
Magnesium (Mg)
Potassium (K)
Sodium ( Na )
Aluminum ( Al )
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 29 of 54
EXCHANGEABLE CATIONS (Page 3 of 3)
Question
Yes
No
NA
Comments
Is the analytical instrument cleaned and
adjusted before and after each run?
Is the power source secure, that is,
protected against line fluctuation?
Are standards made in a matrix as close as
possible to that of the extract?
If the lanthanum oxide method is used, is
the La2O3 added to the samples and
standards?
Are the pHs of the samples and standards
approximately identical?
Are all chemicals of analytical reagent
grade or better?
Are chemicals used for standards traceable
to NBS standards?
Does the laboratory have copies of Methods
for Chemical Analysis of Water and Wastes,
and Standard Methods 14th edition or
access to them?
Are all calculations performed correctly,
and are at least 5% (2 per batch) checked
manually?
-------
Appendix F
Revision 1
Date: 4/89
Page 30 of 54
LIME AND ALUMINUM POTENTIAL
Item
Manufacturer
Model/Grade
Installation Date
Comments
Flame atomic
absorption
spectrometer
Inductively
coupled plasma
spectrometer
Flame atomic
emission
spectrometer
Item
Available
Quantity
Type
Comments
Reciprocating
shaker
Linear poly-
ethylene
bottles
(25 and 50mL)
250 mL
polypropylene
centrifuge
bottles
Chemical
Quantity
Grade
Expiration Date
Comments
Calcium
chloride
Hydrochloric
acid(HCl)
NBS traceable
standards :
Calcium (Ca)
Magnesium (Mg)
Potassium (K)
Sodium (Na)
Iron ( Fe )
Aluminum ( Al )
-------
Appendix F
Revision 1
Date: 4/89
Page 31 of 54
LIME AND ALUMINUM POTENTIAL (Page 2 of 2)
Comments:
LIME AND ALUMINUM POTENTIAL
Question
Is the reciprocating shaker calibrated
every six months or less in addition to
general maintenance?
What is the QC sample?
Is the QC solution analyzed initially
as called for in the methods?
Are the QC results plotted on QC charts
in real time?
Is the quality control data reviewed by
the analyst before deciding whether to
release the data for reporting?
Are the results for Ca reported after
adjusting for the CaCl extraction
solution? ^
Are results reported based on oven-dry
soil weight?
Are all calculations correctly performed,
and are 5% (2 per batch) checked manually?
Yes
No
NA
Comments
-------
Appendix F
Revision 1
Date: 4/89
Page 32 of 54
EXTRACTABLE IRON AND ALUMINUM
Sodium Pvrophosphate. citrate Dithionite and Acid Qxalate Methods
Item
Manufacturer
Model
Installation Date
Comments
Centrifuge
Inductively
coupled plasma
spectrometer
Item
Available
Quantity
Type
Comments
Reciprocating
shaker
Repipet
Automatic
pipet
Buret
250 mL
polypropylene
centrifuge
bottles
Fleakers
Volumetric
pipets
Volumetric
flasks
Chemicals
EXTRACTABLE IRON AND ALUMINUM
Quantity Grade Expiration Date
Comments
Sodium pyro-
phosphate
Sodium hyrox-
ide (Na OH)
pH buffers,
pH=7 and 10
Phosphoric
acid(H3PO4)
Superfloc 16
Sodium
dithionite
(Na2S204)
-------
Appendix F
Revision 1
Date: 4/89
Page 33 of 54
EXTRACTABLE IRON AND ALUMINUM (Page 2 of 3)
Chemicals
Quantity
Grade
Expiration Date
Comments
Sodium citrate
(Na3C6H5O7 H O)
Ammonium
oxalate
((NH4)2C2O4.
H20)
Oxalic acid
(H2C2O4.H O)
pH buffers,
pH = 4 and 2
Nitric acid
(HNO3)
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 34 of 54
Question
EXTRACTABLE IRON AND ALUMINUM (Page 3 of 3)
Yes
No
NA
Comments
Are the proper type of polypropylene 250
mL centrifuge tubes used?
Is the reciprocating shaker calibrated
yearly if it possesses a speed gauge,
every 6 months if not?
Is the centrifuge calibrated yearly if
it possesses a speed gauge, every 6 months
if not?
Are standards made up in the same expected
matrix as are the extracts?
Are the chemicals reagent grade or better?
Is the extract promptly stored at 4°C?
Is analysis performed for Fe and Al within
24 hours of extraction?
Are the calculations carried out correctly
and are at least 5% (2/batch) checked
by hand?
-------
Appendix F
Revision 1
Date: 4/89
Page 35 of 54
EXTRACTABLE SULFATE
item
Manufacturer
Model
Installation Date
Comments
Balance,
+0.01 g
ion chromato-
graph
Automated
injection
system
Filtration
apparatus
Centrifuge
Vortex mixture
Rec iproc at ing
shaker
Item
Available
Quantity
Type
Comments
100-raL centri-
fuge tubes
with screw
caps
Volumetric
flasks
0.20m pore
size membrane
filters
volumetric
pipets
Chemical
Quantity
Grade
Expiration Date
Comments
Monobasic
sodium
phosphate
(N3H2P04.H20)
Sodium
carbonate
(Na2CO3)
Sodium
hydroxide
(NaOH)
-------
Appendix F
Revision 1
Date: 4/89
Page 36 of 54
EXTRACTABLE SULFATE (Page 2 of 3)
Chemical
Quantity
Grade
Expiration Date
Comments
Sulfuric acid
(H2SO4)
Magnesium
sulfate
(Mg2so4)
Sodium nitrate
(NaNO3)
Question
Yes
No
Comments
Is the ion chromatograph maintained
according to manufacturer's specifi-
cations?
Are manufacturer recommendations for
optimum 1C sensitivity used?
Are chemicals reagent grade or better?
Are the phosphate and sulfate concentra-
tions low enough so they elute separately?
Are dilutions made if not?
Are all the proper accessories maintained
on the 1C?
-anion separation column?
-micro-membrane suppressor (anion
separation) column?
Is the optional automatic injection
system used?
Are all solutions made fresh when needed?
-0.40 M NaCO3
-0.0020 M Na2C03 / 0.0020 M NaOH
-other
-stock resolution standard
-working resolution standard
-sulfate and nitrate calibration
Are conversion operations performed
correctly for:
-meq/L to mg/L SO4?
(1 meq = 48.0288 mg)
-mg/L to meq/L SO4?
(1 meq = 0.02082 meq)
-SO4 to S?
(1 mg SO4 = 0.3338 mg S)
-S to S04?
(1 mg S = 2.9962 mg SO4)
Are recording instruments calibrated
before each use?
-------
Appendix F
Revision 1
Date: 4/89
Page 37 of 54
EXTRACTABLE SULFATE (Page 3 of 3)
Question
Yes
No
NA
Comments
Is a pump stroke noise suppressor or
pressure gauge used to stabilize pressure?
Is resolution high enough so no startover
is required?
Are peak heights/areas recorded in a
logbook?
If peaks are not sharp and symmetric, is
an approved method for peak area
determination used?
Is peak area determined by microprocessor?
If yes, which methods and formulas does
it use?
Are calibration curves constructed accord-
ing to manufacturer's recommendations?
Is the flow rate checked gravimetrically
with time for consistency of flow?
Is the rate of flow calibrated before each
batch is run?
-------
Appendix F
Revision 1
Date: 4/89
Page 38 of 54
Item
SULFATE ADSORPTION ISOTHERMS
Manufacturer Model Installation Date
Comments
Balance,
+0.01g
Ion chromato-
graph
Centrifuge
Filtration
apparatus
Reciprocating
shaker
Item
Available
Quantity
Type
Comments
Centrifuge
tubes with
screw caps,
100 or 50mL
0.45 pm pore
size membrane
filters
volumetric
pipet, 50 mL
chemical
Quantity
Grade
Expiration Date
Comments
Magnesium
sulfate
Comments
-------
Appendix F
Revision 1
Date: 4/89
Page 39 of 54
Question
SULFATE ADSORPTION ISOTHERMS (Page 2 of 2)
Yes No NA Comments
Are MgSO4 adsorption solutions correctly
prepared?
Are the adsorption solutions calibrated
for accuracy before being used in the run?
Are the working standards made fresh
daily?
Is the deionized water sent through the
0.20 pm membrane filter?
Is the supernatant sent through the
0.45 pro membrane filter?
Is the correct amount of soil (oven-dried
weight) used?
Are methods of analysis by ion chroma-
tography the same as used in the extract-
able sulfate procedure?
Are the correct conversion factors used as
in the extractable sulfate procedure?
Are all calculations performed correctly,
and are at least 5% (2 per batch) checked?
-------
Appendix F
Revision 1
Date: 4/89
Page 40 of 54
Item
EXCHANGEABLE ACIDITY
Manufacturer Model Installation Date comments
Mechanical
extractor
Rec iproc ating
shaker
Automatic
titrator
pH meter
pH electrode
Item
Available
Quantity
Type
Comments
Syringes 60mL
Filter pulp
Pipettors,
adjustable to
25 mL
Stirring rods
Titration
(Erlenmeyer)
250 and 125 mL
Tubes, 25 roL
glass
Eppendorf
pipets, 5 mL
and 5L
Diluter
Volumetric
flasks
Linear poly-
ethylene
bottles 25 mL
Drying tube
-------
Appendix F
Revision 1
Date: 4/89
Page 41 of 54
Chemical
EXCHANGEABLE ACIDITY (Page 2 of 4)
Quantity
Grade
Expiration Date
Comments
Ascarite
Barium
chloride
(BaCl2.2H2O)
Triethanola-
mine
(N(CH2CH2OH)3)
Hydrochloric
acid (HCl)
Sodium hydrox-
ide (NaOH)
Methyl orange
indicator
NBS-traceable
buffers, pH=4,
7, and 10
Comments:
-------
Appendix F
Revision 1
Date: 4/89
Page 42 of 54
EXCHANGEABLE ACIDITY (Page 3 of 4)
Question
Yes
No
NA
Comments
Is the buffer solution protected from CO ?
Are the syringes prepared according to
protocol?
Is the pH calibration the same as for the
pH procedure (comment on any exceptions)?
Is the automatic titrator calibrated
gravimetrically before each batch?
Is the pH endpoint of the automatic
titrator calibrated to 4.60?
Are at least 5% of the calculations
checked manually?
Are calculations performed correctly?
Is the reciprocating shaker calibrated
every six months or less in addition to
general maintenance?
Is the auto analyzer (distillation/
titration) calibrated for titration before
each run?
Are titration results calculated, and
are 5% hand checked?
Is the 25-mL pipetter calibrated gravi-
metrically daily (if the adjustable type)
and at least weekly if a fixed volume?
Is the diluter calibrated and checked
gravimetrically before each run?
Is the same amount of filter pulp used
with each sample?
Is the filter pulp washed before use?
Is the specified number of blanks run for
each batch?
Are chemicals of reagent grade or better?
Is the mechanical extractor calibrated
for extraction time?
Is the calibration of the mechanical
extractor checked at least monthly?
Are the specified size, type, and grade of
disposable syringes used with the
extractor?
-------
Appendix F
Revision 1
Date: 4/89
Page 43 of 54
Question
EXCHANGEABLE ACIDITY (Page 4 of 4)
Yes No NA I Comments
Is the tubing checked frequently and
replaced when needed?
Are all procedures involving the extr-
action followed precisely according to the
statement of work?
Are three blanks carried through to record
mean and standard deviation?
-------
Appendix F
Revision 1
Date: 4/89
Page 44 of 54
Question
FLAME ATOMIC ABSORPTION SPECTROSCOPY
Yes No NA Comments
For which methods is this instrument used?
Is the burner head cleaned and adjusted
for each run?
Is the burner head cleaned frequently when
solutions of high ionic strength are
analyzed?
Is DI water or cleaning solution aspirated
both before and after a run?
Is the nebulizer cleaned at least weekly?
Is the correct flame type used for deter-
mination of each element?
Is the acetylene of specified purity?
Is gas pressure monitored during a run?
Are filters used to remove water and oil
from the compressed air?
Is constant air pressure maintained? How?
Is the wavelength optimized before a run?
Is the slit width correctly set for the
desired element?
Is the optical system aligned at least
every 6 months? With a major realignment
every 12 months?
Are the lamp and instrument allowed
adequate time to warm up before use
-Lamp time (30-60+ minutes)?
-Instrument time
(constant if possible)?
-Flame time (5+ minutes)?
Is the unit adequately vented?
Is tubing inspected before each run?
-------
Appendix F
Revision 1
Date: 4/89
Page 45 of 54
Question
INDUCTIVELY COUPLED PLASMA EMISSION
Yes No
SPECTROSCOPY
NA
Comments
For which methods is this instrument used?
Is the tubing inspected before each run?
Are the electrodes replaced as instructed
by the manufacturer or more frequently?
Is the instrument adequately vented?
Is the instrument in a temperature
controlled room?
Is ample time allowed for the instrument
to warm up?
Are standards calibrated both alone and
as part of a multi-element matrix?
Is the UV-IR shielding in place?
Is an adequate supply of the carrier gas
present?
Are manufacturer operating procedures
followed?
On multi-element units, are alternate
wavelengths used when necessary to
avoid interference?
-------
Appendix F
Revision 1
Date: 4/89
Page 46 of 54
FLAME PHOTOMETRY (FLAME ATOMIC EMISSION)
Question
Yes
No
NA
Comments
For which methods is this instrument used?
Are the correct filters used for each
element?
Is the pressure of the gases monitored
during a run?
Is the oxygen supply of 99.95% purity or
higher?
Is the fuel supply of sufficient purity
and of constant pressure?
Is the aspirator cleaned before and after
each run?
is a rinse solution of Dl water (or
wash solution) used between samples to
prevent salting-up of the aspirator?
Is the unit given adequate time to warm up
before use?
Is the unit calibrated before use?
Is the aspirator/nebulizer unit inspected
daily for proper seating and function?
Is the unit placed away from areas of
drafts and sudden temperature changes?
-------
Appendix F
Revision 1
Date: 4/89
Page 47 of 54
DOCUMENTATION/TRACKING
Item
Yes
No
Comment
Is a sample custodian designated?
of sample custodian
If yes, name
Are the sample custodian's procedures and
responsibilities documented? If yes, where
are these documented?
Is sample tracking performed via paper or
computer?
Are written standard operating procedures (SOPs)
developed for receipt of samples? if yes,
where are they documented?
Are written standard operating procedures (SOPs)
developed for compiling and maintaining sample
document files? If yes, where are they document-
ed?
Are samples stored under refrigeration?
temperature?
At what
After completion of the analysis are the
samples properly stored for six months or
until laboratory personnel are told otherwise?
-------
Appendix F
Revision 1
Date: 4/89
Page 48 of 54
ANALYTICAL METHODOLOGY
Item
Yes
No
Comment
Are the required methods used?
is there any unauthorized deviation from contract
methodology?
Are written analytical procedures provided to
the analyst?
Are reagent grade or higher purity chemicals
used to prepare standards?
Are fresh analytical standards prepared at a
frequency consistent with good Q.A?
Are reference materials properly labeled with
concentrations, date of preparations, and the
identity of the person preparing the sample?
Is a standard preparation and tracking logbook
maintained?
Do the analysts record bench data in a neat and
accurate manner?
Is the appropriate instrumentation used in
accordance with the required protocol(s)?
-------
ANALYTICAL METHODOLOGY (Page 2 of 2)
COMMENTS ON ANALYTICAL METHODS AND PRACTICES
Appendix F
Revision 1
Date: 4/89
Page 49 of 54
-------
Appendix F
Revision 1
Date: 4/89
Page 50 of 54
Item
QUALITY CONTROL
Yes
No
Comment
Does the laboratory maintain a quality
control manual?
Does the manual address the important
elements of a QC program, including the
following:
a. Personnel?
b. Facilities and equipment?
c. Operation of instruments?
d. Documentation of procedures?
e. Procurement and inventory practices?
f. Preventive maintenance?
g. Reliability of data?
h. Data validation?
i. Feedback and corrective action?
j. Instrument calibration?
k. Record keeping?
1. Internal audits?
Are QC responsibilities and reporting
relationships clearly defined?
Have standard curves been adequately
documented?
Are laboratory standards traceable?
Are quality control charts maintained for
each routine analysis?
Do QC records show corrective action
when analytical results fail to meet
QC criteria?
Do supervisory personnel review the data and
QC results?
Does the QC chemist have a copy of the
standard operating procedures?
Does the QC chemist have a copy of the
instrument performance data?
Does the QC chemist have a copy of the
latest QC plots?
-------
Appendix F
Revision 1
Date: 4/89
Page 51 of 54
Item
QUALITY CONTROL (Page 2 of 2)
Yes No
Comment
Is the QC chemist aware of the most recent
control limits?
Does the QC chemist prepare a blind audit
sample once per week?
Does the QC chemist routinely review and
report blank audit data to the laboratory
manager?
Does the QC chemist update control limits
and obtain new control charts once
per batch?
Are all QC data (e.g., control charts,
regression charts, QC data bases) up to
date and accessible?
Are minimum detection limits calculated
as specified?
Is QC data sheet information reported to
the analyst?
-------
Appendix F
Revision 1
Date: 4/89
Page 52 of 54
DATA HANDLING (Page 1 of 2)
Item
Yes
No
Comment
Does data clerk check all input to the
computer for accuracy?
Are calculations checked by another person?
Are calculations documented?
Does strip chart reduction by on-line
electronic digitization receive at least
5% manual spot checking?
Are data from manually interpreted strip
charts spot-checked after initial entry?
Do laboratory records include the following:
-Sample identification number
-Sample type
-Date sample received in laboratory
-Date of analysis
-Analyst
-Result of analysis (including raw
analytical data)
-Recipient of the analytical data
Does laboratory follow required sample
tracking procedures from sample receipt
to discard?
Does the data clerk routinely report
quality control data sheet information
to the analyst?
Does the data clerk submit quality
control data sheet information to the
laboratory manager, along with the
analytical data to be reported?
Do records indicate corrective action
taken?
Are provisions made for data storage for
all raw data, calculations, quality
control data, and reports?
Are all data and records retained the
required amount of time?
Are computer printouts and reports
routinely spot-checked against laboratory
records before data are released?
-------
Appendix F
Revision 1
Date: 4/89
Page 53 of 54
SUMMARY (Page 1 of 2)
item
Yes
No
Comment
Do responses to the evaluation indicate that
project and supervisory personnel are aware c
QA and its application to the project?
Do project and supervisory personnel place
positive emphasis on QA/QC?
Have responses with respect to QA/QC aspects of
the project been open and direct?
Has a cooperative attitude been displayed by all
project and supervisory personnel?
Does the organization place the proper emphasis
on quality assurance?
Have any QA/QC deficiencies been discussed before
leaving?
is the overall quality assurance adequate to
accomplish the objectives of the project?
Have corrective actions recommended during
previous evaluations been implemented?
Are any corrective actions required? If so,
list the necessary actions below.
-------
Appendix F
Revision 1
Date: 4/89
Page 54 of 54
SUMMARY (Page 2 of 2)
Summary Comments and Corrective Actions
-------
Appendix G
Revision 1
Date: 4/89
Page 1 of 18
Appendix G
Field Data Verification Procedures
The following verification scheme represents the checks and flags that are applied to the
pedon characterization data. After a pedon description undergoes verification, flagged data are
identified on forms and are sent to the appropriate sampling crews for confirmation or
correction. The corrections are edited into the data base and the verification program is applied
to the data base to assign the final verification flags.
-------
Appendix G
Revision 1
Date: 4/89
Page 2 of 18
SCS-SOI-232 FIELD DATA VERIFICATION PROCEDURES (10/19/88)
MID-APPALACHIAN SOIL SURVEY
These procedures are strictly related to computer verification programs.
other verification procedures are discussed in the QA Plan. Parameters that
may be left blank will be denoted by a '*' symbol. All other parameters
should be filled in; if not, they are flagged.
The first test of each parameter is usually to determine whether the value in
that parameter is missing. In SAS, a non-numeric character can not be placed
in a parameter that has been identified for numeric values and will therefore
be considered a missing . If a value for a parameter is considered missing
and flagged AO, the other tests on the particular parameter will not be run.
The field data flag codes are as follows:
FLAG FLAG DESCRIPTION
AO value missing
BO invalid code
CO non-numeric character in numeric field
DO non-alphabetic character in alpha field
EO correlation outlier
FO value inappropriate
NAME - can be left alone
SITE ID
ERL-C will provide a list of the valid STATE/COUNTY/UNIT combinations for each
pedon. This combination will be called SITE_ID. If the combination on the 232
does not match an ERL-C combination or the combination has already been used
flag, flag SITE_ID BO
STATE (SA_ST) alpha
If SA_ST code is missing, flag AO
If SA_ST not equal to PA, VA, WV, flag BO
If SA_ST code contains a numeral, flag DO
COUNTY (SA_CO) - numeric
If SA_CO code missing, flag AO
If SA_CO code contains alpha character, flag CO
UNIT (SA_UN) - numeric
If SA_UN code missing, flag AO
if SA_UN code contains alpha character, flag CO
* sub (SUB__UNS) - Leave alone
-------
Appendix G
Revision 1
Date: 4/89
Page 3 of 18
MLRA
MLRA and SUBMLRA must be linked. MLRA may or may not contain SUBMLRA. MLRA
and SUBMLRA must also be correlated with the STATE codes
MLRA - numeric
If MLRA code missing, flag AO
* SUB (MLRA_S) - alpha (see MLRA)
If MLRA_S contains numeric characters, flag DO
The following are MLRA & MLRA_S codes that apply to each STATE:
PA VA WV
127 128 127
140 130 128
147 147
148
If MLRA is not a valid code for the STATE, flag BO
LATITUDE
LAT - numeric
If LAT code is missing, flag AO
LAT_DR - alpha
if LAT_DR is missing, flag AO
If LAT_DR does not equal to "N", flag BO
If LAT_DR contains numeric characters, flag DO
LONGITUDE
LNG - numeric - Same flags as LAT
LNG_DR - alpha
if LNG_DR code missing, flag AO
If LNG_DR code does not equal to "W", flag BO
If LNG_DR contains numeric characters, flag DO
SLOPE
SL_PCT - numeric
If SL_PCT code is missing, flag AO
If SL_PCT contains alpha characters, flag CO
SL_SHPU/SL_SHPA - numeric
If SL_SHP code is missing, flag AO
If SL_SHP does not equal to a number from 1-5, flag BO
-------
Appendix G
Revision 1
Date: 4/89
Page 4 of 18
SL_GMP - numeric (see gm codes for values)
If SL_GMP code is missing, flag AO
If SL_GMP does not equal to 1,2,3,4, flag BO
SL_HILL - numeric
If SL_HILL code is missing, flag AO
If SL-HILL does not equal 1,2,3,4,5 flag BO
SL_ASP - numeric
If SL_ASP code is missing and PED_O does not equal H, flag AO
If PED_0 does not equal H, then SL_ASP must equal 0, 45, 90, 135, 180, 225,
270, 315 if not flag BO
If PED_O equals H, then SL_ASP can be blank
SL_LEA - numeric
If SL_LEA code missing, flag AO
If SL_LEA contains alpha characters, flag CO
SL_LET - numeric
If SL_LET code missing, flag AO
If SL_LET < SL_LEA, flag EO
IF SL_LET contains alpha characters, flag CO
SL_KND - alpha
If SL_KND code is missing, flag AO
If SL_KND does not equal B,C,F,G,L,M,T,Z, flag BO
If SL_KND contains numeric characters, flag DO
SL_VAR - numeric
If SL_VAR code is missing, flag AO
If SL_VAR does not equal a number from 0,1,2,4, flag BO
SL_PAT - numeric
If SL_PAT code is missing, flag AO
If SL_PAT does not equal a number from 0-3, flag BO
SL_POS - numeric
If SL_POS code is missing, flag AO
If SL_POS does not equal a number from 2,3,4,5,7,8,9, flag BO
if SL_POS contains an alpha character, flag CO
PG_REG - alpha (major landform for codes)
If PG_REG code is missing, flag AO
If PG_REG does not equal a specified code, flag BO
PG_LOC - alpha (see local landform for codes)
If PG_LOC code is missing, flag AO
If PG_LOC does not equal a specified code, flag BO
If PG LOG contains a numeric character, flag DO
-------
Appendix G
Revision 1
Date: 4/89
Page 5 of 18
PEDON CLASSIFICATION
This values will be hand checked against soil series name
* Precip (PREC) - leave alone
WATER TABLE
*WT_DPT - numeric.
If WT_KND is zero, WT_DPT should be blank; if not, flag BO
If WT_KND is not zero and WT_DPT is missing, flag AO
WT_KND - numeric
If WT_KND does not equal a number 0,2,3,4, flag BO
If WT_KND is missing, flag AO
If WT_KND contains alpha characters, flag CO
WT_DAY - numeric
IF WT_KND filled in, WT_DAY should have code; if not, flag AO
IF WT_KND not equal to a number between 1-365, flag FO
LU_CLS - alpha
If LU_CLS code is missing, flag AO
If LU_CLS does not equal A,C,E,F,G,H,I,J,L,N,O,P,R,S,T,U,Q,
flag BO
If LU_CLS contains a numeric character, flag DO
SN_CLS - numeric
If SN_CLS code is missing, flag AO
If SN_CLS does not equal a number from 0-5, flag BO
If SN_CLS contains an alpha character, flag CO
BA_PRM - numeric
If BA_PRM code is missing, flag AO
If BA_PRM does not equal a number from 1-7, flag BO
If BA_PRM contains an alpha character, flag CO
Texture classes from coarse to fine are listed below. Scan TT_CLS1 and
TT_CLS2, find which code on the 232 is che lowest on the listT The numbers to
the right should be the corresponding PERM code, if not, flag PERM with EO
TT_CLS PERM
COS
S
FS
VFS 6-7
LCOS
LS
-------
Appendix G
Revision 1
Date: 4/89
Page 6 of 18
LFS
LVFS
COSL
SL
FSL
VFSL
L
SIL 3-5
SI
CSCL
SCL
CL
SICL
SC
SIC
C 1-2
DR_CLS (numeric)
If DR_CLS code is missing, flag AO
If DR_CLS does not equal a number from 1-7, flag BO
If DR_CLS contains an alpha character, flag CO
If SL_PCT > 15 and PERM = 1-4; DR_CLS should = 1-5; else flag EO
If SL_PCT > 15 and PERM = 5-7; DR_CLS should - 4-7; else flag EO
Elevation (ELEV) - numeric information should be right justified
with leading zeros on the left.
If ELEV code is missing, flag AO
PARENT MATERIAL
For this category only 1 entry for the parameters PM_WTHl, PM_DEPl, PM_Bl,
PM_ORGl is needed.
PM_B1 (2,3,4) - numeric
If PM_Bl code is missing, flag AO
If PM_B1 does not equal 1,2,3, flag BO
PM_DEP1 (2,3,4) - alpha
If PM_DEPl (2,3,4) code is missing, flag AO
If PM_DEP1 does not equal A,S,L,V,R,E,D,M,U,H,G,O,X,W,T,Y,F,
flag BO
If PM_DEPl contains a numeric character, flag DO
PM_ORG1 (2,3,4) alpha in first column, numeric in second
If PM_ORGl code is missing, flag AO
PM_ORG1 If 1st column is not alpha, flag DO
If 2nd column is not numeric, flag CO
* PM BRK - numeric
-------
Appendix G
Revision 1
Date: 4/89
Page 7 of 18
If PM_BRK does not equal a number from 1-5, flag BO
if PM_BRK contains an alpha character, flag CO
•
TEMPERATURES
All parameters can be left alone
MOISTURE REGIME
MREG - alpha
If MREG code ia missing, flag AO
If MREG does not equal UD or AQ, flag BO
If MREG contains numeric values, flag DO
WEATHER STATION NUMBER
Can be left alone
CONTROL SECTION
CS_DPU - numeric
If CS_DPU value is missing, flag AO
If CS_DPU not less than CS_DPL, flag EO
CS_DPL - numeric
If CS_DPL value is missing, flag AO
If control section data is missing the following correlation will not be
executed
CS_DPU & CS_DPL are checked together for the following conditions:
Control section rules for MASS when TT_ORD other than H, or
when HZ_MST not « B and H2_SFX not = l7 2£ when HZJSFX, HZ_MST not = R
1) if depth (HZ_DPL from page 2) of last horizon above any horizon where
HZ_MST="R" is greater than 100; CS_DPU = 025 and CS_DPL = 100
2) if HZ_DPL of last above any horizon where HZ_MST-"R" is greater than 36 and
less than 100; CS_DPU « 025 and CS_DPL » HZ_DPL of last horizon above
horizon where HZ_MST = -R"
3) If HZ_DPL of last horizon above any horizon where HZ_MST="R" is less than
36; CS_DPU = 000 and CS_DPL « HZ_DPL of last horizon~"horizon where HZ_MST =
"R-
If rule is not followed, flag EO where problem occurs
-------
Appendix G
Revision 1
Date: 4/89
Page 8 of 18
If HZ MST or TX ORD is H then:
1) If HZ_DPL of last horizon with a HZ_MST of O is greater than 130 CS_DPU -
000 and CS_DPL = 130
2) If HZ_DPL of last horizon with a HZ_MST of O is less than 130 CS_DPU - 000
and CS_DPL = HZ_DPL of last horizon with HZ_MST of O
If rule is not followed, flag EO where problem occurs
If HZ_MST = B and HZ_SFX = T then:
CS_DPU = HZ_DPU of 1st Bt horizon and CS_DPL = CS_DPU + 50 if, when the 50 is
added it is still in a Bt horizon; if not then CS_DPL = HZ_DPL of last Bt
horizon
If rule is not followed, flag EO where problem occurs
If HZ_MST or HZ_SFX = R then:
1) If HZ_DPL above any horizon where HZ_SFX of "R" occurs is greater than 100,
CS_DPU =025 and CS_DPL = 100; or CS_DPU =025 and CS_DPL = HZ_DPL of
horizon above where HZ_SFX = "R"
2) If HZ_DPL above any horizon where HZ_SFX="R- occurs is greater than 36 and
less than 100, CS_DPU = 025 and CS_DPL = HZ_DPL of horizon where HZ_SFX =
"R"; or CS_DPU =025 and CS_DPL = HZ_DPL of horizon above where HZ_SFX = "R
3) If HZ_DPL above any horizon where HZ_SFX of "R" occurs is less than 36,
CS_DPU = 000 and CS_DPL = HZ_DPL of horizon where HZ_SFX = "RM; or CS_DPU
= 000 and CS_DPL = HZ_DPL of horizon above HZ_SFX of -R-
If rule is not followed, flag EO where problem occurs
* ER_CLS - leave alone
RNOFC - numeric 1-7
If RNOFC code is missing, flag AO
If RNOFC does not equal a number from 1-7, flag BO
If RNOFC contains alpha characters, flag CO
DIAGNOSTIC FEATURES
If TX_ORD is equal to "E" code, a diagnostic feature is not needed
If TX_ORD is not equal to "E", one set of diagnostic features (DF_DPU1,
DF_DPL1, DF_KND1) should be entered, if not flag DF_DPUl, DF_DPLl, DF_KNDl AO
DF_DPU1 (2,3,4,5) - numeric
If DF_DPU1 etc.is not less than DF_DPLl etc., flag BO
-------
Appendix G
Revision 1
Date: 4/89
Page 9 of 18
DF_DPL1 (2,3,4,5) - numeric
If DF_DPL1 etc. > DF_DPU2, flag EO
DF_KND1 (2-5) - alpa (see KND codes)
If DF_KNDl etc. does not contain appropriate codes, flag BO
If DF_KND1 etc. contains a numeric character, flag DO
FLOODING
FL_FRQ - alpha
If FL_FRQ is missing, flag AO
If FL_FRQ does not contain codes NO,RA,OC,FR,CO, flag BO
if FL_FRQ contains numeric characters, flag DO
PONDING
PO_FRQ - alpha
IF PO_FRQ is missing, flag AO
IF PO_FRQ does not contain codes NO,RA,OC,FR,CO, flag BO
IF PO_FRQ contains numeric characters, flag DO
VEGETATION
Leave alone - manually checked
DEPTH
HZ_DPU - numeric
If HZ_DPU is missing, flag AO
HZ DPL — numeric
IF HZ_DPL is missing from any horizon but the last, flag AO
If HZ_MST = "R", HZ_DPL should be blank; if not, flag BO
AS you proceed from one horizon to the next, HZ_DPL must be equal to HZ_DPU of
the horizon below.
example
horz depth
000 HZ_DPU
01
005 HZ DPL
005 HZ_DPU
02
025 HZ DPL
-------
Appendix G
Revision 1
Date: 4/89
Page 10 of 18
If this rule is not followed, flag HZ_DPU EO
HORIZON DESIGNATION
* HZ_DSC - numeric
If HZ_DSC is blank, it is assumed to be "1". Numbering starts at "2- with the
second layer of contrasting material.
If HZ_DSC equals "1", flag BO
The first non-blank HZ_DSC must be a "2", if not, flag BO
Each proceeding HZ_DSC must be >= the previous HZ_DSC, if not flag the first
HZ_DSC where anomoly occurs EO
HZ_MST - alpha
If HZ_MST code is missing, flag AO
If HZ_MST does not contain a letter O,A,E,B C,R, or a combination of these
letters, flag BO
If HZ_MST contains numeric values, flag DO
* HZ_SFX - alpha-numeric
If alpha characters not = to A,B,C,E,F,G,I,K,M,N,O,P,R,S,T,V,W,H,X,Y,Z, Flag
BO
THICKNESS
HZ_AVG - numeric
If HZ_AVG value is missing, flag AO
IF HZ_AVG is not > HZ_MIN and <= HZ_MAX, flag EO
HZ_MAX - numeric
If HZ_MAX value is missing, flag AO
If HZ_MAX is not > HZ_MIN & HZ_AVG, flag EO
HZ_MIN - numeric
If HZ_MIN value is missing, flag AO
If HZ_MIN not < HZ_MAX & HZ_AVG, flag EO
If HZ MIN > difference of HZ DPU and HZ DPL of same horizon, flag EO
DRY COLOR
If one parameter is filled in, all others on that line of the horizon under
this heading, with the possible exception of DC_PCT should be filled in for
that horizon; if not flag AO for that parameter
* DC_LOC1 (2,3) - numeric 0-3
If DC_LOCl etc. not = to numbers from 0-3, flag BO
If DC_LOC1 etc. contains an alpha character, flag CO
* DC PCTl (2,3) - numeric
-------
Appendix G
Revision 1
Date: 4/89
Page 11 of 18
There are three lines for each horizon. If the same DC_LOC code is used on
more than one line for a horizon then a percent must be placed in DC_PCT, if
not flag DC_PCT where problem occurs AO
The DC_PCT for a horizon containing the same DC_LOC must add
to 90% - 100% if not, flag DC_PCT EO
example
DC_LOC DC_PCT
1 25
1 75
2 *
* this parameter can be left blank because the DC_LOC value is used only once
for this horizon.
* DC_HUE1 (2,3) - alpha-numeric
If numeric part of hue > 10, flag AO
If numeric part not equal to 0, 2.5, 5, 7, 7.5, 10, flag BO
* DC_VAL1 (2,3) - numeric
* DC_CHR1 (2,3) - numeric
MOIST COLOR
If H2_MST = "O" or "R" or HZ_SFX = "R" ; MC_LOC, MC_HUE, MC_VAL,
MC_CHR does not need to be filled in. All other horizons must have at least
one MC_LOC, MC_HUE, MC_VAL,MC_CHR else flag AO.
* MC_LOC1 (2,3)
* MC_PCT1 (2,3)
* WC_HUE1 (2,3)
* MC_VAL1 (2,3)
* MC_CHR1 (2,3)
There are three lines for each horizon. If the same MC_LOC code is used on
more than one line for a horizon then a percent must be placed in MC_PCT, if
not flag MC_PCT where problem occurs AO
The MC_PCT for a horizon containing the same MC_LOC must add
to 90% - 100% if not, flag MC_PCT EO
TEXTURE
class (TT_CLS1 & TT_CLS2) - alpha (see texture codes)
If HZ_MST does not equal "O" or "R" or HZ_SFX does not equal "R" TT_CLS1 or
TT_CLS2 must be filled in; if not, flag AO
-------
Appendix G
Revision 1
Date: 4/89
Page 12 of 18
If TT_CLSl or TT_CLS2 does not contain the proper code, flag BO
If TT_CLSl or TT_CLS2 contains numeric characters, flag DO
If HZ_MST equals "O" or "R" or HZ_SFX equals "R"; TT_CLS1 and TT_CLS2 does not
need to be entered.
This next program will have to wait until analytical
data is available
There will be a correlation between the texture class codes and the particle
size measurements from the analytical laboratories. The following texture
classes are defined by this information which should correspond to the
analytical particle size information.
Texture Codes
Sands - 85 percent or more sand and a percentage of silt plus 1.5 times the
percentage of clay is 15 or less. The following codes are classified under
this category:
1) COS 2) S 3) FS 4) VFS
Loamy sands - At the upper limit 85 to 90 percent sand and the percentage of
silt plus twice the percentage of clay exceeds 30; or less than 7 percent
clay, less than 50 percent silt, and between 43 and 52 percent sand. The
following codes are classified under this category:
1) LCOS 2) LS 3) LFS 4) LVFS
Sandy Loams - 20 percent or less clay and 52 percent or more sand and the
percentage of silt plus twice the percentage of clay exceeds 30; or less than
7 percent clay, less than 50 percent silt, and between 43 and 52 percent sand.
The following codes are classified under this category:
1) COSL 2) SL 3) FSL 4) VFSL
Loam (L)- 7 to 27 % clay, 28 to 50 % silt, and less than 52 % sand.
silt Loam (SIL) - 50 % or more silt and 12 to 27 % clay; or 50 to 80 % silt
and less than 12 % clay.
Sandy Clay Loam (SCL) - 20 to 35% clay, less than 28% silt, and 45% or more
sand.
Silt (SI) - 80 % or more silt and less than 12 % clay.
clay Loam (CL) - 27 to 40 % clay and 20 to 45 % sand.
silty clay Loam (SICL) - 27 to 40 % clay and less than 20 % sand.
Sandy Clay (SC) - 35 % or more clay an 45 % or more sand.
-------
Appendix G
Revision 1
Date: 4/89
Page 13 of 18
Silty Clay (SIC) - 40 % or more clay and 40 % or more silt.
Clay (C) - 40 % or more clay, less than 45 % sand, and less than 40 % silt.
* MOD (TT_MOD1 & TT_MOD2) - alpha (see modifier codes)
If TT_MODl or TT_MOD2 does not contain a correct code, flag BO
If TT_MODl or TT_MOD2 contain numeric characters, flag DO
The modifiers are based on % and size of rock fragments (page 4)
RF_PCT
15-35
RF_PCT
36-60
RF_PCT
>60
modifier
BY
ST
CB
CN
CR
FL
GR
SH
SY
GY
CBA
CRC
GRF
GRC
misc.
SR CY
AY MK
RB PT
size
4
3
2
5
1-3
2-3
1
1-3
1-3
1
2
1-3
1
1
modifier
BYV
STV
CBV
CNV
CRV
FLV
GRV
SHV
SYV
GYV
size
4
3
2
5
1-3
2-3
1
1-3
1-3
1
modifier
BYX
STX
CBX
CNX
CRX
FLX
GRX
SHX
SYX
GYX
size
4
3
2
5
1-3
2-3
1
1-3
1-3
1
For example, if a texture modifier of "BYV" is used in the third horizon,
there must be rock fragments of size class three and a percentage (RF_PCT)
between 35-60 in the same horizon; if not, flag TT MODI or TT MOD2 with EO,
STRUCTURE
SR_GRD1 (2,3), SR_SHP1 (2,3), SR_SIZl (2,3) - only one matrix needs to be
entered; the others may be left blank.
SR_GRD1 (2,3), SR_SHP1 (2,3), SR_SIZl (2,3) CS_DRY, CS_MST, CS_OTH, CS_STK,
CS_PLC - IF the horizon sequence number equals "1" and the HZ_MST equals "O"
or HZ_MST equals "R", none of the parameters indicated need values entered in
any of these fields.
SR_GRD1 (2,3) - numeric
if SR_GRDI etc. code is missing, flag AO
If SR_GRD1 etc. not equal to numbers from 0-6, flag BO
If SR_GRD1 etc. contains alpha characters, flag CO
-------
Appendix G
Revision 1
Date: 4/89
Page 14 of 18
* SR_SIZ1 (2,3) - alpha
If SR_GRD1 etc. equals "0", SR_SIZ must be blank; if not, flag BO
If SR_GRD1 etc. does not equal "0", SR_SIZ must not be blank; else flag AO
If SR_SIZ1 etc. not equal to EF,VF,FF,F,FM,M,MC,CO,VC,CV,TK,TN, VK,VN, flag
BO
If SR_SIZ1 etc. contains numeric characters, flag DO
SR_SHP1 (2,3) - alpha
If SR_SHP1 etc. is code missing, flag AO
If SR_GRD1 etc. equals "0", then SR_SHP must equal "MA" or "SGR"; if not, flag
BO
If SR_SHP1 etc. does not equal PL,COL,SBK,CR,WEG,LP,BK,
GR,MA,PR,ABK,CDY,SGR, flag BO
If SR_SHPl etc. contains numeric characters, flag DO
CONSISTENCE
One of these parameters must be filled in for each horizon. The others may be
blank; if not, flag all parameters with AO
* CS_DRY - alpha
If CS_DRY does not equal L,S,SH,H,VH,EH, flag BO
If CS_DRY contains numeric character, flag DO
* CS_MST - alpha
If CS_MST does not equal L,VFR,FR,FI,VFI,EFI, flag BO
If CS_MST contains numeric characters, flag DO
* CS_OTH - alpha
If CS_OTH does not equal WSM,SM,MS,B,R,VR,CO,VWC,WC,SC,I,SF,VF,SD flag BO
If CS_OTH contains numeric characters, flag DO
* CS_STK - alpha
If CS_STK does not equal SO,SS,S,VS, flag BO
If cs_STK contains numeric characters flag DO
* CS_PLC - alpha
If CS_PLC does not equal PO,SP,P,VP, flag BO
If CS_PLC contains numeric characters, flag DO
MOTTLES
If one parameter is filled in, all others in that line of the horizon under
this heading should be filled in; if not, flag AO for the missing parameter
* MO_AMT1 (2,3) - alpha
If MO_AMT1 etc. does not equal F,C,M, flag BO
If MO AMTl etc. contains numeric characters flag DO
-------
Appendix G
Revision 1
Date: 4/89
Page 15 of 18
* MO_SIZ1 (2,3) - numeric
If MO_SIZ1 etc. does not equal 1,2,3,12,13, flag BO
If MO_SIZl etc. contains alpha characters, flag CO
* MO_CON1 (2,3) - alpha
If MO_CON1 etc. does not equal F,D,P, flag BO
If MO_CONl etc. contains a numeric character, flag DO
* MO_HUE1 (2,3) - alpha-numeric
if numeric portion of MO_HUE > 10, flag BO
* MO_VAL1 (2,3) - numeric
If MO_VALl contains alpha characters, flag CO
* MO_CHR1 (2,3) - numeric
If MO_CHRl contains alpha characters, flag CO
SURFACE FEATURES
If one parameter is filled in, all others on that line of the horizon, with
the exception of SF_HUE, SF_VAL and SF_CHR, should be filled in; if not, flag
AO for the missing parameter.
* SF_KND1 (2,3) - alpha
If SF_KND1 etc. does not = U,B,D,I,Q,L,M,S,X,A,C,G,K,P,O,T,
flag BO
If SF_KNDl etc. contains a numeric character, flag DO
* SF_AMTl (2,3) - alpha
If SF_AMT1 etc. does not equal V,F,C,M, flag BO
If SF_AMTl etc. contains a numeric character, flag DO
* SF_DCN1 (2,3) - alpha
If SF_DCN1 etc. does not equal P,D,C, flag BO
If SF_DCNl etc. contains a numeric character flag DO
* SF_DST1 (2,3) - alpha
If SF_DST1 etc. does not equal F,D,P, flag BO
If SF_DSTl etc. contains a numeric character, flag DO
* SF_LOC1 (2,3) - alpha
If SF_LOC1 etc. does not equal P,H,V,Z,U,L,C,M,S,B,R,I,F,T,N, flag BO
If SF_LOCl etc. contains a numeric character, flag DO
* SF_HUE1 (2,3) - alpha-numeric
If numeric portion of SF_HUE >10, flag BO
* SF_VAL1 (2,3) - numeric
If SF_VALl contains alpha characters, flag CO
-------
Appendix G
Revision 1
Date: 4/89
Page 16 of 18
* SF_CHR1 (2,3) - numeric
If SF_CHRl contains alpha characters, flag CO
BOUNDARY
if BD_DST and BD_TPG filled in for last horizon, flag FO
BD_DST - alpha
if BD_DST not filled; except for last horizon, flag AO
if BD_DST does not contain A,C,G,D, flag BO
If BO_DST contains a numeric character, flag DO
BD_TPG - alpha
If BDJTPG not filled; except for last horizon, flag AO
If BD_TPG does not equal S,W,I,B, flag BO
If BD_TPG contains a numeric character, flag DO
EFFERVESCENCE
Leave alone
FIELD MEASURED PROPERTIES
FP_KND1 (2-6) - alpha-numeric
If HZ_MST not equal to -O" or "R" or HZ_SFX not equal to "R" and FP_KNDl (2-3)
code missing, flag AO
If FP_KNDl does not equal CL,PB,PC,PG,PH,PL,PP,PR,PS,PT,PY,SA,SC,
SF,SI,SM,SV,OB,OR, flag BO
if FP_KNDl etc. contains numeric characters flag DO
FP_AMTl (2-6) - numeric -(no decimals).
If HZ_MST not equal to "O" or "R- or HZ_SFX not equal to -R" and FP_AMTl (2,3)
code missing, flag AO
if FP_AMT contains decimals, flag BO
If FP_AHT contains alpha characters, flag CO
A correlation can be run between field pH for a horizon and the corresponding
analytical laboratory pH.
WETNESS
SM_AMT1 - alpha
If HZ_MST not equal to "O" or -R"and SM_AMTl missing, flag AO
If SM_AMT1 etc. does not equal D,M,W, flag BO
If SM_AMTl etc. contains numeric values, flag DO
-------
Appendix G
Revision 1
Date: 4/89
Page 17 of 18
HYDRAULIC CONDUCTIVITY
SM_AMT2 - numeric
if SM_AMT2 code is missing, flag AO
If SM_AMT2 does not equal 1-7, flag BO
If SM_AMT2 contians alpha values, flag
ROOTS
If one parameter is filled in, all others on that line should also be filled
in; if not, flag the unfilled areas AO
* RO_AMT1 (2,3) - alpha
If RO_AMT1 (2,3) does not equal VF,FF,F,FC,CM,C,M, flag BO
If RO_AMT1 (2,3) contains numeric values, flag DO
* RO_SIZ1 (2,3) - alpha-numeric
If RO_SIZ1 (2,3) does not = VI,11,1,12,2,23,3,13, flag BO
* RO_LOC1 (2,3) - alpha
If RO_LOC1 (2,3) does not equal C,P,T,M,S, flag BO
If RO_LOC1 (2,3) contains a numeric character, flag DO
Roots should not skip horizons i.e. if there are roots in horizon 2 and 4,
there must be roots in horizon 3; if not, flag the three root categories of
the suspect horizon EO.
PORES
If one parameter is filled in, all others on that line should also be filled
in; if not, flag the unfilled areas AO
* PO_SHP1 (2,3) - alpha
If PO_SHP1 (2,3) does not = IR,IT,TU,TD,TS,VT,IE,IF,TC,TE,
VS,TP, flag BO
If PO_SHP1 (2,3) contains numeric characters, flag DO
* PO_AMT1 (2,3) - alpha
If PO_AMT1 (2,3) does not equal VF,FF,F,FC,CM,C,M, flag BO
If PO_AMT1 (2,3) contains numeric characters, flag DO
* PO_SIZ1 (2,3) - alpha-numeric
If PO_SIZ1 (2,3) does not equal VI,11,1,12,2,23,3,13, flag BO
* PO_CN1 (2,3) - alpha
If PO_CN does not equal L,M,H, flag BO
If PO CN contains numeric characters, flag DO
-------
Appendix G
Revision 1
Date: 4/89
Page 18 of 18
CONCENTRATIONS
If one parameter is filled in, all others on that line should also be filled
in; if not, flag the unfilled areas AO
* cc_KNDl (2,3) - alpha-numeric (see table kinds of concentrations for values)
If 1st column is not alpha, flag DO
If 2nd column is not numeric, flag CO
if CC_KND1 (2,3) is not a correct value, flag BO
* CC_AMT1 (2,3) - alpha
If CC_AMT1 (2,3) does not equal VF,FF,F,FC,CM,C,M, flag BO
If cc_AMTl (2,3) contains numeric values, flag DO
* CC_SHPl (2,3) - alpha
If CC_SHP1 (2,3) does not equal C,D,0,P,T,Z, flag BO
if CC_SHP1 (2,3) contains a numeric character, flag DO
* CC_SIZl (2,3) - numeric
If CC_SIZ1 (2,3) does not equal 1,12,2,23,3,4,5,34,45, flag BO
if CC_SIZ1 (2,3) contains alpha characters, flag CO
ROCK FRAGMENTS
if one parameter is filled in, all others on that line of the horizon should
be filled in, if not, flag the missing parameters AO.
* RF_KNDl (2-6) - alpha
If RF_KND does not equal Y,S,A,H,K,O,I,B,T,P,F,M,L,E,R, flag BO
if RF_KND contains numeric characters, flag DO
* RF_PCT1 (2-6) - numeric
* RF_SIZl (2-6) - numeric
if RF_SIZ1 does not equal a number from 1-6, flag BO
if RF_SIZ1 contains alpha characters, flag CO
-------
Appendix H
Revision 1
Date: 4/89
Page: 1 of 12
Appendix H
Preparation Laboratory Verification Procedures
The following verification procedures are applied to the soil samples received, processed, and
analyzed at the preparation laboratory. The computer program is used to track the progress of
sample preparation, calculate final values from the raw data, and identify erroneous data. The
flags are applied only to the preparation laboratory data and are unrelated to similarly-defined flags
in the analytical laboratory data.
-------
Appendix H
Revision 1
Date: 4/89
Page: 2 of 12
PREPARATION LABORATORY COMPUTER ENTRY/TRACKING/VERIFICATION PROCEDURES
The following text provides an overview of the computer tracking and
verification program designed for use at the preparation laboratory during the
DDRP Mid-Appalachian soil Survey. The system is designed to track the progress
of samples, calculate final data values, perform verification checks, and assign
flags to any detected aberrant values.
The program creates the Form 101 that summarizes final data reported by the
preparation laboratory. Hard copies of this form are sent to all DDRP
cooperating agencies and groups. In addition, the Form 101 is available on floppy
disk. .
SAMPLE TRACKING
The preparation laboratory manager must track samples as they are received
from the sampling crews, during cold storage, through processing and analysis,
and during batch formation and shipment to the analytical laboratories. The
computer data are used to track the location and progress of each sample while
it is physically at the laboratory.
in order to track the sampling activities and shipment/receipt of bulk
samples, the following information is to be reported to the QA manager weekly:
number of samples received
number of pedons sampled and percentage of total expected
number of sets received
In order to track the progress of the preparation activities, the following
information is to be reported to the QA manager weekly:
number and percent of samples that have completed specific analyses
number of bulk samples which are completely processed (excluding bulk
density) and are ready to be batched
— number of sets that have been completed
number of batches sent to analytical laboratories
DATA VERIFICATION
The system allows the entry and editing of data by preparation laboratory
personnel and the review of progress and data analysis by the QA staff. After
a run of data is entered into a "temporary" data file, the final values are
calculated and verified. A printout of this information is checked by the
preparation laboratory manager and by a representative from the QA staff. The
data that have satisfied the verification criteria are then moved into a "master"
data file which is used to track progress and percentages of the analyses that
have been completed. The master file cannot be edited by preparation laboratory
personnel.
-------
Appendix H
Revision 1
Date: 4/89
Page: 3 of 12
The five raw data forms that are entered into the computer are: (1) sample
receipt raw data form, (2) clod bulk density raw data form, (3) known volume bulk
density raw data form, (4) loss on ignition raw data form, and (5) bulk sample
processing raw data form. In the descriptions of the forms below, a list of the
data calculations, verification checks, and pertinent verification flags are
included. At the beginning of each description is a list of the data from each
particular form which must be transferred to the Form 101. The flags consist
of two characters, an alpha character and a numeric character. The numeric
character designates which data form the flag pertains to, e.g., a flag with the
numeric character "3" pertains to the known volume raw data form.
SUMMARY OF VERIFICATION FLAGS
Sample Receipt Raw Data Form
Al Missing data on form
Bl Sample code mislabeled
Cl site ID mislabeled
Dl More than 2 known volume samples
El More than 3 clods
Clod Bulk Density Raw Data Form
A2 Missing data on form
B2 Submerged clod weight > Lab clod weight, and clod did not float
C2 clod floated
D2 Problem clod (deletes value from average)
E2 Oven-dry clod weight > Lab clod weight
F2 Oven-dry clod weight > Field-moist clod weight
G2 Coefficient of variation > 20 among replicates
H2 Bulk density > 2.0
12 Bulk density < 1.0
J2 Sample code not recorded same as on sample receipt form
K2 Number of replicates not recorded same as on sample receipt form
L2 Replicates > 3
Known Volume Bulk Density Raw Data Form
A3 Missing data on form
B3 Oven-dry weight > Air-dry weight
C3 Weight of fines + Rock fragments > Air-dry weight
D3 Sample code not recorded same as on sample receipt form
E3 Number of replicates not recorded same as on sample receipt form
F3 Bulk density > 2.0
G3 Bulk density < 1.0
H3 Coefficient of variation > 20 among replicates
13 Impossible value because of error in raw data
-------
Appendix H
Revision 1
Date: 4/89
Page: 4 of 12
Loss on ignition Raw Data Form
A4 Missing data on form
B4 Organic matter > 100%
C4 Oven-dry weight > Air-dry weight
D4 Ashed weight > Oven-dry weight
E4 Sample code not recorded same as on sample receipt form
F4 Sample analysis duplicated
G4 Organic soil type not correlating
H4 Mineral soil type not correlating
14 Internal duplicate/routine pair > 15% RSD
J4 FAP samples > 15% RSD
K4 Field duplicate/routine sample > 20% RSD
L4 FAL sample outside accuracy window
M4 FAP sample outside accuracy window
N4 Manager's sample outside accuracy window
nple Processing Raw Data Form
A5 Missing data on form
B5 Mineral soil air-dry moisture > 2.5%
C5 Organic soil air-dry moisture > 6.0%
D5 Field-moist pH > 6.0
E5 Sample code not recorded same as on sample receipt form
F5 Internal duplicate/routine sample > 15% RSD
G5 Field duplicate/routine sample > 20% RSD
H5 FAP samples > 15% RSD
15 pH QCCS buffer < 3.95 or > 4.05
J5 Manager's sample outside accuracy window
K5 FAP sample outside accuracy window
SAMPLE RECEIPT RAW DATA FORM
Form 101 Information;
SAM_CODE
SITE_ID
SET_ID
General;
ERL-C provides EMSL-LV with the pedon number (state, county, unit) and site
ID for each pedon to be sampled. This information is placed in the data base
before preparation activities begin.
All samples received from the field and their condition are recorded on the
sample receipt raw data form and immediately entered into the data base. A file
comparison procedure is initiated, comparing the pedon number and site ID with
the raw data from the sample receipt form. if any discrepancies are found,
processing of the affected samples ceases until the discrepancies are rectified.
-------
Appendix H
Revision 1
Date: 4/89
Page:5 of 12
All flags for the receipt data are unique and identified by the numeric character
H 1 H
X •
After all verification checks for the sample receipt data have been
satisfied, the data are sorted by set ID. All samples with the same set ID will
undergo processing simultaneously.
calculations;
No calculations are to be performed on the sample receipt form.
Verification Flags
If any data are missing on the form; then flag Al
If sample code is mislabeled; then flag Bl
If site ID is mislabeled; then flag Cl
If more than 2 known volume samples are recorded; then flag Dl
If more than 3 clods are recorded; then flag El
When flags other than Dl and El are generated, processing of the affected
samples must cease until the problem is rectified.
Reporting!
The computer produces a printout, sorted by SET_ID and SAM_CODE, that lists
all flags assigned.
CLOD BULK DENSITY RAW DATA FORM
Form 101 Information;
BD_CLD
General;
The clod data are entered into a temporary working file. The verification
program should be run on a regular basis and the discrepancy printout generated.
All flags for the clod data are unique and identified by the numeric character
"2".
After all verification checks for the clod data have been satisfied, the
data are sorted by set ID and sample code. All samples with the same set ID will
undergo processing simultaneously.
Calculations;
SARAN_WT (g) = (FIELD_DP + LAB_DP) * (LAB_WT - FLD_WT) / LAB_DP
OD_SAR (g) = SARAN_WT * 0.85
SARAN VOL (cm3) - SARAN_WT / 1.30
-------
Appendix H
Revision 1
Date: 4/89
Page:6 of 12
R_FRAG VOL (cm1) - R_FRAG / 2.47
WATER VOL (cm') - CLOD_H2O / WT_DEN
SOIL/PORES VOL (cm3) - WATER VOL - (R_FRAG VOL + SARAN VOL)
BD_CLD (g/cm1) - [LAB_WT - (R_FRAG + OD SARANJHT + 1.88)] / SOIL/PORES VOL - 0.20
Verification Flags;
If any parameter field is blank, except for 'COMMENTS'; then flag A2
If CLOD_H20 > LAB_WT and FLOAT not marked 'Y'; then flag B2
If FLOAT is marked "Y"; then flag C2
If PROB is marked "Y"; then flag D2 (deletes value from average)
If FIELD_WT > LAB_WT; then flag E2
If CLOD_OD > FIELD_WT; then flag F2
If CV > 20; then flag G2
If BD_CLD > 2.0; then flag H2
If BD_CLD < 1.0; then flag 12
If a SAM_CODE does not match the receipt file; then flag J2
if REP does not match the receipt file; then flag K2
If REP > 3; then flag L2
Reporting;
For each SAM_CODE there are usually 3 replicates, although this number
varies. Therefore, a check for replicate consistency is necessary, if the PROB
column is marked "Y" for any replicate in a SAM_CODE, this replicate should not
be included in the following calculation.
The computer produces a printout, sorted by SET_ID, SAM_CODE, and REP, that
lists all flags assigned. For each SAM_CODE, the mean, standard deviation, and
coefficient of variation of all replicates are listed.
The number of horizons and the percentage of the total number of samples
having clods are reported.
KNOWN VOLUME BULK DENSITY RAW DATA FORM
Form 101 Information;
BD_KV
General;
Known volume samples are taken from those horizons where clods cannot be
extracted.
-------
Appendix H
Revision 1
Date: 4/89
Page: 7 of 12
Calculations;
FINES_WT (g) = (OD_WT - R_FRAG - BAG_WT)
BD_KV (g/cm3) = FINES_WT / (VOL - (R_FRAG / particle density)
Verification Flags;
If any parameter field is blank, with the exception of 'COMMENTS'; then flag A3
If OD_WT > AIR_WT; then flag B3
If FINES_WT + R_FRAG > AIR_WT; then flag C3
If a SAM_CODE does not match the receipt form; then flag D3
If REP does not match the receipt form; then flag E3
If BD_KV > 2.0; then flag F3
If BD_KV < 1.0; then flag G3
If CV > 20; then flag H3
Reporting;
The computer produces a printout, sorted by SET_ID, SAM_CODE, and REP, that
lists all flags assigned. For each SAM_CODE, the mean, standard deviation, and
coefficient of variation of all replicates are listed.
The number of horizons and the percentage of the total number of samples
having known volume samples are reported.
LOSS ON IGNITION RAW DATA FORM
Form 101 Information;
OM_LOI
Calculations;
Each CRUC_NO will be associated with a pre-determined CRUC_WT.
OM_LOI = [(OD_WT - ASHED_WT) / (OD_WT - CRUC_WT)] * 100
Verification Flags;
If any parameter field is blank, with the exception of 'COMMENTS'; then flag A4
If OM_LOI > 100; then flag B4
If OD_WT > AIR_WT; then flag C4
If ASHED_WT > OD_WT; then flag D4
If a SAM_CODE does not match the receipt file; then flag E4
If the SAM_CODE is duplicated on the LOI form; then flag F4
If OM_LOI > 20 and SOILTYP on the bulk sample form is not "O"; then flag G4
If OM_LOI < 20 and SOILTYP on the bulk sample form is not "M"; then flag H4
If the internal duplicate/routine pair is > 15% RSD; then flag 14
-------
Appendix H
Revision 1
Date: 4/89
Page: 8 of 12
If the FAP samples > 15% RSD; then flag J4
If the field duplicate/routine pair > 20% RSD; then flag K4
If a FAL sample is outside accuracy window; then flag L4
If a FAP sample is outside accuracy window; then flag M4
If a MS is outside accuracy window; then flag N4
Reporting;
The computer produces a printout, sorted by SET_ID and SAM_CODE, that lists
all flags assigned.
The number of horizons and the percentage of the total number of samples
that have been analyzed are reported.
BULK SAMPLE PROCESSING RAW DATA FORM
Form 101 Information;
PH_MP
MOIST_P (1,2,3)
RF_FG
RF_MG
General;
General information concerning the bulk sample is recorded on this form.
when the form is complete, the sample is considered ready to be batched and
shipped to an analytical laboratory.
Calculations;
PH_MP (no calculation required)
MOIST_P (1, 2, 3) = [(INIT_WT1 - OD_WT / (OD_WT - TIN_WT)] * 100
Each MOIST_P calculation will be conducted only if the previous MOISTJP is
> 2.5 percent for mineral samples or > 6.0 percent for organic samples.
RF_FG = (RF_FGRAW / A_BULK) * 100
RF_MG = (RF_MGRAW / A_BULK) * 100
Verification Flags;
If any parameter field is blank, with the exception of 'COMMENTS'; then flag A5
If SOILTYP = 'M' and MOIST_P (1,2,3) > 2.5; then flag B5
If SOILTYP = 'O' and MOIST_P (1,2,3) > 6.0; then flag C5
If PH_MP > 6.0; then flag D5
if a SAM_CODE does not match the receipt file; then flag E5
If the internal duplicate/routine pair > 15% RSD; then flag F5
-------
Appendix H
Revision 1
Date: 4/89
Page:9 of 12
If the field duplicate/routine pair for MOIST_P, RF_FG, or RF_MG > 20% RSD; then
flag G5 (or >0.20 SD for PH_MP)
If the FAP samples for MOIST_P, RF_FG, or RF_MG > 15% RSD; then flag H5 (or
0.15 SD for PH_MP)
If pH QCCS out of window; then flag 15
If MOIST_P is outside accuracy window; then flag J5
If PH_MP is outside accuracy window; then flag K5
Reporting;
The computer produces a printout, sorted by SET_ID and SAM_CODE, that lists
all flags assigned.
The number of horizons and the percentage of the total number of samples
that have been analyzed are reported.
-------
Appendix H
Revision 1
Date: 4/89
Page: 10 of 12
PH
MASS PREP LAB DATA REVIEW
Sample
Date
FAP
acc/prec
FAL
ace
Buffer
Int Dup
(0.15 SD)
Field Dup
(0.20 SD)
Manager
•ample
ace.
PASS
-------
Appendix H
Revision 1
Date: 4/89
Page: 11 of 12
AIR-DRI MOISTURE
MASS PREP LAB DATA REVIEW
Sample
Date
FAP
acc/prec
(15»RSD)
FAL
acc
Int Dup
(15% RSD)
Field Dup
(20% RSD)
Manager
sample
(control)
PASS
-------
Appendix H
Revision 1
Date: 4/89
Page: 12 of 12
WSS-ON-IGNITION
MASS PREP LAB DATA REVIEW
Sample
Date
PAP
acc/prec
(IStRSD)
FAL
ace
Int Dup
(15» RSD)
Field Dup
(20» RSD)
Manager
•anpl*
(control)
PASS
-------
Appendix I
Revision 1
Date: 4/89
Page: 1 of 9
Appendix I
Preparation Laboratory On-Site Evaluation Questionnaire
The following questionnaire is used to provide documentation of on-site audits of the
preparation laboratory by QA staff. Generally, the preparation laboratory is evaluated at least
twice. The first audit is conducted before samples are received in order to assess the ability of
the laboratory, in terms of personnel, facilities, and equipment, to process soil samples
successfully. At the time of the second evaluation, adherence to the Preparation Laboratory
Standard Operating Procedures is evaluated and specific discrepancies are addressed.
-------
Lockheed-ESC PREPARATION LABORATORY ON-SITE
BIWEEKLY EVALUATION QUESTIONNAIRE
Date:
Appendix I
Revision 1
Date: 4/89
Page: 2 of 9
Laboratory Telephone Number:
Laboratory Manager:
Laboratory Quality Assurance Officer:
Technicians Present During Evaluation:
-------
Appendix I
Revision 1
Date: 4/89
Page: 3 of 9
Questions
General
Yes
No
Comments
Is the overall appearance
of the laboratory clean?
Is there proper storage/
disposal of chemicals and
hazardous materials?
Are safety stickers properly
displayed?
Are the fire extinguishers
in place?
Date of last service call for
fire extinguishers?
Is there a disposal system
for paper, etc.?
Is the air compressor
accessible to all areas
of the laboratory?
Is there an adequate supply
of gloves, goggles, DDI HO,
paper towels, etc.? ^
Is there a copy of the SOP
available to technicians?
is there an updated record
of instrument calibrations?
Are the computer records kept
up-to-date?
Is the eye wash station easily
accessible in case of emergency?
Is the power box easily accessible
in case of emergency shutoff?
Comments;
-------
Appendix I
Revision 1
Date: 4/89
Page:4 of 9
Questions
cold Storage Room
Yes
Comments
Is there adequate space for
sample storage?
Is there easy access to
samples?
Is the temperature of the
cold storage kept at
4°C
- 2°C?
Is a systematic way
of shelving and locating
samples being maintained?
Is there clear and precise
labeling of samples?
Are samples being kept in an
area free from moisture
and contamination?
Are samples tightly sealed?
Is there clear separation
of organic and mineral
samples?
Is the cold storage room clean?
Is the floor area clean
of samples?
Comments;
-------
Appendix I
Revision 1
Date: 4/89
Page: 5 of 9
Sample Drying
Questions Yes No comments
Are all samples covered and
free from contamination while
drying?
Are samples that are spread
to dry properly labeled?
Are gloves being used
to mix the samples?
Is the bulk sample form
filled out for each sample
drying?
Is the paper being changed
as needed during drying?
Is the air flow being
maintained?
Are the doors closed?
Is the drying area clean?
Are cleaning protocols being
followed?
Comments;
-------
Appendix I
Revision 1
Date: 4/89
Page: 6 of 9
Sample Preparation
Questions Yes No Comments
Are proper sample grinding
procedures being followed?
Are proper cleaning procedures
between samples being followed?
Is the preparation room clean?
Are the fans being used during
sample preparation?
Comments;
EH
Questions Yes No Comments
Is there an up-to-date calibration
curve for pH determinations?
Is there an up-to-date QCCS log
for pH determinations?
Are pH electrodes stored
properly?
Is there enough filling solution
in the electrode?
Is the procedure for pH
determination being followed
according to the SOP?
Comments;
-------
Appendix I
Revision 1
Date: 4/89
Page: 7 of 9
Moisture and Loss On Ignition
Questions Yes No Comments
Is the sample used to
determine field moisture
properly secured from
contamination prior to the loss-
on-ignition determination?
Are the oven and crucibles
cleaned between each set of
samples?
Are crucibles clearly
numbered?
Are the muffle furnace and
desiccators clean?
Are oven and furnace hazards
clearly posted?
Are oven gloves in good
condition?
Are the platinum-tipped
tongs being used consistently
when moving the crucibles?
comments;
-------
Appendix I
Revision 1
Date: 4/89
Page: 8 of 9
Questions
Procedures Purina Sample Riffle Splitting
Yes No
Comments
Is there proper cleaning of
riffle-splitter and sieves?
Is the door closed?
Is the fan operating?
Is the trailer used only by
personnel performing the riffle-
splitting?
General
IP at ion a
Yes
No
Comments
Is the trailer clean?
Is the balance clean?
Are record books updated?
Are rock fragments labeled
and stored properly?
Are the three riffle-split
subsamples clearly labeled?
Do the technicians understand
and practice the procedures
properly?
Comments;
-------
Appendix I
Revision 1
Date: 4/89
Page: 9 of 9
Questions
Bulk Density
Yes
No
Comments
Is there proper mixing of the
saran and acetone?
Is there proper sealing of the
saran/acetone to prevent
evaporation?
Are the clods labeled
properly?
Are the procedures for both
bulk densities being followed?
Are all weights recorded to the
nearest .Olg?
Do technicians know the proper
procedure for a clod that floats?
Are evaporating dishes clearly
labeled?
Comments;
-------
-------
Appendix J
Revision 1
Date: 4/89
Page: 1 of 21
Appendix J
Quality Assurance Reanalysis Templates
The following QARTs are used in the evaluation of QE/QC and routine sample data. As a
batch of soils data is received, it is reviewed according to the criteria set forth in the template.
If major uncertainty or a large occurrence of minor uncertainty is identified, confirmation or
reanalysis is requested for the affected parameter and batch.
-------
PARAMETER: PH HP
Field Audit
Low & Pair
QCCS
Internal
duplicate
Field
duplicate
Manager
sample
MAJOR
If any 2 of 3 relationships occur ana 2 minor
flag occurs:
1) FAL beyond control limits
2) Mean of FAP or FAO beyond control limits
3) FAP or FAO SD > 0.15
Out of expected range
none
none
none
MINOR
If 1 of 3 of the relationships
listed in the major column occurs
none
Routine/duplicate SD > 0.15
Routine/duplicate SD > 0.20
Sample beyond control limits
NON
If 0 of 3 of the relationships
listed in the major column occur
within expected range
Routine/duplicate SD < 0.15
Routine/duplicate SD < 0.20
Sample within control limits
Reanalyeie is requested with occurrence of 1 major or 3 minor & uncertainty
Quality Assurance Reanalysis Template for the Determination of Field-Moist pH.
0) Q) 0> TJ
(Q •* < -Q
CD ® ft
-------
PARAMETER: OH LOT
Field Audit
Low d Pair
Internal
duplicate
Field
duplicate
Manager
sample
MAJOR
If any 2 of 3 relationships occur and 1 minor
flag occurs:
1) FAL beyond control limits
2) Mean of FAP or FAO beyond control limits
3) FAP or FAO RSD > 15%
none
none
none
MINOR
If 1 of 3 of the relationships
listed in the major column occurs
Routine/duplicate RSD > 151
Routine/duplicate RSD > 20%
Sample beyond control limits
NON
If 0 of 3 of the relationships
listed in the major column occur
Routine/duplicate RSD < 15%
Routine/duplicate RSD < 20%
Sample within control limits
Reanalysis is requested with occurrence of 1 major or 3 minor 4 uncertainty
Quality Assurance Reanalysle Template for the Determination of Organic Matter by Loss on Ignition.
-OO3J
-------
PARAMETER: HOIST P
Field Audit
Low & Pair
Internal
duplicate
Field
duplicate
Manager
sample
MAJOR
If any 2 of three of relationships occur and
one minor flag occurs:
1) FAL beyond control limits
2 ) Mean of FAP or FAO beyond control limits
3) FAP or FAO RSD > 20%
none
none
none
MINOR
If 1 of 3 of the relationships occur
listed in major column occurs
Routine/duplicate RSD > 15%
Routine/duplicate RSD > 20%
Sample beyond control limits
NON
If 0 of 3 of the relationships
listed in the major column occur
Routine/duplicate RSD < 15%
Routine/duplicate RSD < 20%
Sample within control limits
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Assurance Reanalysis Template for the Determination of Air-Dry Moisture.
0> j? §-"8
as-
N>
X
c_
-------
PARAMETER: SAND, SILT
Lab Audits
t
Prep Dupa
Field Dupa
4
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > 3.0 if value at lower limit
2) prep dup/field dup pair:
SD > 3.0 if value at lower limit
3) mean value lab audits out of accuracy window, or
SD > 3.0 if value at lower limit
4) low level lab audit out of accuracy window
NOME
NOME
> 10 outliers
MINOR
If 1 out of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 6.0 if value at lower limit
NONE
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 6.0 if valur at lower limit
NONE
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor 4 uncertainty
Quality Assurance Reanalysis Template for the Determination of Sand and Silt.
-------
PARAMETER: CLAY
Lab Audits
&
Prep Dups
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of the 4 relationships occur:
1) prep dup/routine pair:
SD > 2.0 if value at lower limit
2) prep dup/field dup pair:
SD > 2.0 if value at lower limit
3) mean value lab audits out of accuracy window, or
SD > 2.0 if value at lower limit
4) low level lab audit out of accuracy window
NONE
If any relationship occurs > 6 times:
CEC OAC/CLAY > 50
if clay > 1.0 and CEC_CL > 1 meq/100 g
CEC-CL/CLAY > 50
if clay > 1.0 and CEC-CL > 1 meq/100 g
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 4.0 if value at lower limit
If relationships occur < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 4.0 if value at lower limit
If relationships do not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Assurance Reanalysis Template for the Determination of Clay.
Q> Q) (D "O
0) ® M »
-------
PARAMETER: PH H20, PH OO2M, PH 01M
Lab Audits
I
Prep Dups
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pair:
SD > 0.1 if value at lower limit
2) prep dup/field dup pair:
SD > 0.1 if value at lower limit
3) mean value lab audits out of accuracy window, or
SD > 0.1 if value at lower limit
4) low level lab audit out of accuracy window
NONE
If relationship occurs > 6 times
PH H20 < PH 002M < PH DIM if difference > .05
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 0.2 if value at lower limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 0.2 if value at lower limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Assurance Reanalysis Template for the Determination of Soil pH.
-------
PARAMETER: CA, MG, NA, K_CL
CA, MG, NA, K OAC
Lab Audits
I
Prep Dups
Field Dups
.02 if value at lower limit
%RSD > 15* if value at upper limit
2) prep dup/field dup pair:
SD > .02 if value at lower limit
IRSD > 151 if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .02 if value at lower limit
IRSD > lot if value at upper limit
4) low level lab audit out of accuracy window
none
If relationships occur > 6 times:
CA OAC + MG OAC + NA OAC + K OAC > CEC OAC
if" CEC_CL > 1 meq/100 g
CA CL -4- MG CL + NA CL + K CL > CEC CL
il CEC_CL > 1 meq/lOO g
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .04 if value at lower limit
IRSD > 301 if value at upper limit
If relationship occurs < 6 times
5 - 10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .04 if value at lower limit
IRSD < 301 if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor & uncertainty
Quality Assurance Reanalysis Template for the Determination of the Exchangeable Cation* In Ammonium Chloride and Ammonium Acetate.
-------
PARAMETER: AL CL
Lab Audits
t
Prep Dups
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur!
1) prep dup/ routine pair:
SD > .20 if value at lower limit
%RSD > 22% if value at upper limit
2) prep dup/field dup pair:
SD > .20 if value at lower limit
tRSD > 22% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .20 if value at lower limit
%RSD > 15% if value at upper limit
4) low level lab audit out of accuracy window
HONE
If relationship occurs > 6 times:
AL_CL > 0.1 meq/100 g if pH_H2O > £.0
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 5.0 if value at lower limit
%RSD > 30% if value at upper limit
If relationship occurs < £ times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 5.0 if value at lower limit
%RSD < 30% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor 4 uncertainty
Quality Aeeurance Reanalysis Template for the Determination of Aluminum In Ammonium Chloride.
-------
PARAMETER: CEC OAC, CEC CL
Lab Audits
t
Prep Dups
Field Dups
t
Field Audits
Soil
Chenistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > .25 if value at lower limit
tRSD > 15t if value at upper limit
2) prep dup/field dup pair:
SD > .25 if value at lower limit
tRSD > 151 if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .25 if value at lower limit
tRSD > 101 if value at upper limit
4) low level lab audit out of accuracy window
NONE
If relationship occurs > 6 times
CEC_OAC < CEC_CL
if CEC_CL > 1 meq/100 g and PH_H2O < 7.0
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .50 if value at lower limit
tRSD > 30% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .50 if value at lower limit
tRSD < 30t if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Aaaurance Reanalysis Template lor the Determination ol Cation Exchange Capacity In Ammonium Chloride and Ammonium Acetate.
(D
to
-------
PARAMETER: AC BACL
Lab Audits
(,
Prep Dupa
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pair:
SD > 1.0 if value at lower limit
%RSD > 22% if value at upper limit
2) prep dup/field dup pair:
SD > 1.0 if value at lower limit
%RSD > 22% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > 1.0 if value at lower limit
%RSD > 15% if value at upper limit
4) low level lab audit out of accuracy window
NONE
NONE
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 2.0 if value at lower limit
%RSD > 45% when using upper limit
NONE
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 2.0 when using lower limit
%SDR < 45% when using upper limit
NONE
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Aaaurance Reanalyze Template for the Determination of Barium Chloride Exchangeable Acidity.
-------
PARAMETER: CA CL2
Lab Audits
i.
Prep Dupa
Field Dups
I
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pair:
SD > .05 if value at lower limit
%RSD > 8% if value at upper limit
2) prep dup/field dup pair:
SD > .05 if value at lower limit
%RSD > 84 if value at upper limit
3 ) mean value lab audits out of accuracy window, or
SD > .05 if value at lower limit
%RSD > 5% if value at upper limit
NONE
NONE
> 10 outliers
MINOR
If 1 of 4 or the relationships
listed in the major column occurs
Within batch precision:
SD > .10 if value at lower limit
IRSD > 15% if value at upper limit
NONE
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .10 if value at lower limit
%RSD < 15% if value at upper limit
NONE
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor * uncertainty
Quality Assurance Reanalysis Template for the Determination of Extraetable Calcium In Calcium Chloride.
-------
PARAMETER: MG CL2, NA CL2, K CL2
Lab Audits
S.
Prep Dups
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pair:
SD >.005 if value at lower limit
%RSD > 15% if value at upper limit
2) prep dup/field dup pair:
SD >.005 if value at lower limit
%RSD > 15% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD >.005 if value at lower limit
IRSD > 10% if value at upper limit
4) low level lab audit out of accuracy window
HONE
If relationship occurs > 6 times
MG CL2 > MG CL * 1.10 if MG CL2 > 10 * CRDL
HA CL2 > NA CL * 1.10 if NA CL2 > 10 « CRDL
K_CL2 > K_CL * 1.10 if K_CL2 > 10 * CRDL
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .010 if value at lower limit
%RSD > 30% if value at upper limit
If relationships occur < 6 times
5 - 10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .010 if value at lower limit
%RSD < 30% if value at upper limit
If relationships do not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor * uncertainty
Quality Assurance Reanlyals Template for the Determination of Extractable Magnesium, Potassium, and Sodium In Calcium Chloride.
-------
PARAMETER: FE CL2
Lab Audits
&
Peep Dups
Field Dups
I
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > .01 if value at lower limit
%RSD > 22% if value at upper limit
2) prep dup/field dup pair:
SD > .01 if value at lower limit
%RSD > 22% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .01 if value at lower limit
%RSD > 15% if value at upper limit
4) low level lab audit out of accuracy window
NONE
NONE
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .02 if value at lower limit
%RSD > 45% if value at upper limit
NONE
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .02 if value at lower limit
%RSD < 45% if value at upper limit
NONE
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor t uncertainty
Quality Assurance Reanlysis Template for the Determination of Extractable Iron In Calcium Chloride.
-------
PARAMETER: AL CL2
Lab Audit*
t
Prep Dupe
Field Dups
I
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > .05 if value at lower limit
%RSD > 22% if value at upper limit
2) prep dup/field dup pair:
SD > .05 if value at lower limit
%RSD > 22% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .05 if value at lower limit
%RSD > 15% if value at upper limit
4) low level lab audit out of accuracy window
HONE
NONE
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .10 if value at lower limit
%RSD > 45% if value at upper limit
NONE
5-10 outliers
NOU
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .10 if value at lower limit
%RSD < 45% if value at upper limit
NONE
> 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor i uncertainty
Quality Assurance Reanlysls Template for the Determination of Extractable Aluminum In Calcium Chloride.
0) CB _
?> ?. W ®
11*. 2 a
01 3 '
-------
PARAMETER: FE_PYP, AL_PYP, FE_AO, AL_AO, SI_AO, FE_CD, AL_CD
Lab Audits
I
Prep Dups
Field Dups
d
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pairt
SD > .03 if value at lower limit
%RSD > 15% if value at upper limit
2) prep dup/field dup pair:
SD > .03 if value at lower limit
tRSD > 15% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > .03 if value at lower limit
tRSD > 10% if value at upper limit
4) low level lab audit out of accuracy window
NONE
NONE
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .06 if value at lower limit
%RSD > 30% if value at upper limit
NONE
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .06 if value at lower limit
%RSD < 30% if value at upper limit
NONE
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor I uncertainty
Quality Assurance Reanlysis Template for the Determination of Extractable Iron, Aluminum, and Silicon In Pyrophosphate, Acid Oxalate, and
Citrate DIthlonlte.
o> o>
®
X
o
-------
PARAMETER: SO4 PO4, SO4 K2O
Lab Audits
c,
Prep Dups
Field Dups
&
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > 1.5 if value at lower limit
%RSD > 15t if value at upper limit
2) prep dup/field dup pair:
SD > 1.5 if value at lower limit
IRSD > 15% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD > 1.5 if value at lower limit
IRSD > 5% if value at upper limit
4) low level lab audit out of accuracy window
NONE
If relationship occurs > 6 times
SO4 PO4 < SO4 H2O
if SO4_H2O > 1 mg S/kg soil
S04 H2O/SO4 0 > 4 or SO4 H2O/SO4 0 > 20
if SO4_H2O > 2 mg S/kg and SO4_0 > . 1 mg S/kg
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > 3.0 if value at lower limit
IRSD > 30% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < 3.0 if value at lower limit
%RSD < 30% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor 4 uncertainty
Quality Assurance Reanlyals Template for the Determination of Extractable Sulfate In Water and Phosphate.
•oo:
0) fi> I _ _
® S W <&
'-1 * ° a
to
-------
PARAMETER: SO4 0-32
Lab Audits
&
Prep Dupe
Field Dupe
&
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD > .10 if value at lower limit
%RSD > 8% if value at upper limit
2) prep dup/field dup pair:
SD > .10 if value at lower limit
%RSD > 8% if value at lower limit
3) mean value lab audits out of accuracy window, or
SD > .10 if value at lower limit
%RSD > 5% if value at upper limit
4) low level lab audit out of accuracy window
NONE
If relationship occurs > 6 times
SO4 32 < S04 16 < S04 8 < SO4 4 < SO4 2 < SO4 0
If S04_32N >= 7.5 mg S/Kg soil
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD > .20 if value at lower limit
%RSD > 15% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .20 if value at lower limit
%RSD < 15% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor & uncertainty
Quality Assurance Reanlysis Template for the Determination of Sulfate Adsorption Isotherms.
-------
PARAMETER: C TOT
Lab Audits
«
Prep Dupa
Field Dupa
I
Field Audita
Soil
Chemistry
Relationships
Internal
Re 1 at ions h ips
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/ routine pair:
SD > .05 if value at lower limit
%RSD > 15% if value at upper limit
2) prep dup/field dup pair:
SD > .05 if value at lower limit
%RSD > 15% if value at upper limit
SD > .05 if value at lower limit
IRSD > 10% if value at upper limit
4) low level lab audit out of accuracy window
NONE
If any relationship occurs > 6 times
C TOT/N TOT < 7 or C TOT/N TOT > 50
if" N_TOT > .03 wt% and C_TOT > .1 wt%
C TOT/S TOT < 40 or C TOT/S TOT > 400
if SJTOT > .005 wt% and C TOT > .1 wt%
> 10 outliers
MINOR
If 1 of 4 of the relationships
liated in the major column occurs
Within batch precision:
SD > .10 if value at lower limit
%RSD > 30% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
liated in the major column occur
Within batch precision:
SD < .10 if value at lower limit
IRSD < 30% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysia is requested with occurrence of 1 major or 3 minor I uncertainty
Quality Assurance Reanlysls Template for the Determination of Total Carbon.
-------
PARAMETER: N TOT
Lab Audits
&
Prep Dups
Field Dups
I
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur:
1) prep dup/routine pair:
SD >.015 if value at lower limit
%RSD > 15% if value at upper limit
2) prep dup/ field dup pair:
SD >.015 if value at lower limit
%RSD > 15% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD >.015 if value at lower limit
%RSD > 101 if value at upper limit
4) low level lab audit out of accuracy window
NOKE
If relationship occurs > 6 times
C TOT/N TOT < 7 or C TOT/N TOT > 50
if N TOT > .03 wt« and C TOT > .1 wt«
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .03 if value at lower limit
%RSD > 30% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD < .03 if value at lower limit
%RSD < 30% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor & uncertainty
Quality Assurance Reanlysls Template for the Determination of Total Nitrogen.
-------
PARAMETER: S TOT
Lab Audits
t
Prep Dupe
Field Dups
t
Field Audits
Soil
Chemistry
Relationships
Internal
Relationships
MAJOR
If any 2 of 4 relationships occur!
1) prep dup/routine pair:
SD >.002 if value at lower limit
IRSD > 15% if value at upper limit
2) prep dup/field dup pair:
SD >.002 if value at lower limit
%RSD > 15% if value at upper limit
3) mean value lab audits out of accuracy window, or
SD >.002 if value at lower limit
%RSD > 10% if value at upper limit
4) low level lab audit out of accuracy window
NONE
If relationship occur > 6 times
C_TOT/S_TOT < 40 or C_TOT/S_TOT > 400
if SJTOT > .005 wtl and CJEOT > .1 wt%
> 10 outliers
MINOR
If 1 of 4 of the relationships
listed in the major column occurs
Within batch precision:
SD > .004 if value at lower limit
%RSD > 30% if value at upper limit
If relationship occurs < 6 times
5-10 outliers
NON
If 0 of 4 of the relationships
listed in the major column occur
Within batch precision:
SD <.004 if value at lower limit
%RSD < 30% if value at upper limit
If relationship does not occur
< 5 outliers
Reanalysis is requested with occurrence of 1 major or 3 minor 4 uncertainty
Quality Assurance Reanlyele Template for the Determination of Total Sulfur.
0)
-------
Appendix K
Revision 1
Date: 4/89
Page 1 of 5
Appendix K
Analytical Data Verification and Validation Flags
This appendix lists the data qualifiers, or flags, that are assigned to the verified and
validated data bases. The verification flags specify the nature of discrepancies on a batch and
sample basis, while the validation flags are used to characterize more general types of
discrepancies that may affect the level of confidence applied to each datum in the validated data
base. The verification flags are applied only to the analytical laboratory data and are unrelated to
similarly-defined flags in the preparation laboratory data.
-------
Appendix K
Revision 1
Date: 4/89
Page 2 of 5
ANALYTICAL DATA VERIFICATION FLAGS
Blank Exception Program
B4" Potential negative sample bias based on laboratory blank data.
B5" Reagent blank mean is > CRDL.
B6* Laboratory calibration blanks or reagent blanks are > CRDL.
B9** Insufficient number of calibration blanks were run.
Duplicate Precision Exception Program
Dl" Analytical duplicate (AD/routine) precision exceeds the maximum expected
SD or %RSD.
D5" Preparation duplicate (PDF/routine) precision exceeds the maximum expected
so or %RSD.
D6" Preparation duplicate (PDR/routine) precision exceeds the maximum expected
SD or %RSD.
D7" Field audit pair/triplicate (FAP/FAO) precision exceeds the maximum
expected SD or %RSD.
D8" Laboratory audit pair/triplicate (LAP/LAO) precision exceeds the maximum
expected SD or %RSD.
D9" Field duplicate (FD/routine) precision exceeds the maximum expected SD or
%RSD.
Detection Limit Exception Program
Ll" Instrument detection limit (IDL) exceeds contract-required detection limit
(CRDL).
Audit Accuracy Program
NO" Low-range laboratory audit (LAL) sample value exceeds upper accuracy limit.
Nl" LAL sample is below lower accuracy limit.
N2** Mean of LAP or LAO exceeds upper accuracy limit.
N3** Mean of LAP or LAO is below lower accuracy limit.
N4" Quality control audit sample (QCAS) exceeds upper accuracy limit.
N5" QCAS is below lower accuracy limit.
(Continued)
-------
Appendix K
Revision 1
Date: 4/89
Page 3 of 5
QCCS Exception Program
Ql" Quality control check sample (QCCS) exceeds upper control limit.
Q2" QCCS is below lower control limit.
Q3" insufficient quantity of QCCS were measured.
Q4" Detection limit QCCS is not within 20% of the theoretical concentration.
Matrix Spike Program
Sl" Percent recovery of matrix spike is above contractual criteria (>105% for
sulfur parameters, >110% for all others).
S2" Percent recovery of matrix spike is below contractual criteria (<95% for
sulfur parameters, <90% for all others).
S3" Spike solution readings are outside of contractual criteria (sulfate
isotherm parameters only).
Miscellaneous
AO* Value missing
MO* Value was obtained by using a method that is unacceptable according to the
contract.
WO* Air-dry sample weight is not within contractual requirement.
W2* Sample extract volume is not within contractual requirement.
xo* Invalid but confirmed data based on review of QE/QC data.
Xl* Invalid but confirmed data - potential gross contamination of sample or
parameter.
X2* Invalid but confirmed data - potential gross contamination of pedon.
X3* Possible contamination due to either sampling technique, e.g., bucket
augering, or soil amendments, e.g. herbicides, liming, manure, etc.
X4* Outliers from statistical internal consistency check; raw data and reported
data checked only for transcription errors.
X5* Outliers from manual pedon/horizon internal consistency check; raw data
and reported data checked only for transcription errors.
(Continued)
-------
Appendix K
Revision 1
Date: 4/89
Page 4 of 5
Soil chemistry Relationships
NC* Hot checked because basic initial criteria were not met.
Cl* PH_H20 < PH_002M if pH differences > 0.05 units.
C2* PH_002M < PH_01M if pH differences > 0.05 units.
C3* PH_H20 < PH_01M if pH differences > 0.05 units.
C4* CEC_OAC < CEC_CL if CEC_CL > 1 meq/lOOg and PH_H2O < 7.0
C5* CA_OAC + MG_OAC + K_OAC + NA_OAC > CEC_OAC if CEC_CL > 1 meq/100g
C6* CA_CL + MG_CL + K_CL + NA_CL > CEC_CL if CEC_CL > 1 meq/lOOg
C7* CEC_OAC/CLAY > 50 if CLAY > 1.0% and CEC_CL > 1 meq/100g
C8* CEC_CL/CLAY > 50 if CLAY > 1.0% and CEC_CL > 1 meq/lOOg
C9* AL_CL > 0.01 meq/lOOg if PH_H2O > 6.0
Tl* C_TOT/S_TOT < 7 or > 50 if CJTOT i 0.1% and N_TOT £ 0.03%.
T2* C_TOT/N_TOT < 40 or > 400 if C_TOT £ 0.1% and N_TOT & 0.03%.
II* SO4_32 < SO4_16 if SO4_32N £ 7.5 mg S/kg soil.
12* S04_16 < S04_8 if SO4_32N S 7.5 mg S/kg soil.
13* SO4_8 < SO4_4 if SO4_32N SO4_PO4 if SO4_PO4 > 1 mg S/kg soil (mineral samples); SO4_H2O
> SO4_PO4 * 1.10 (organic samples).
(Continued)
-------
Appendix K
Revision 1
Date: 4/89
Page 5 of 5
R2* SO4_H2O/SO4_0 < 4 or > 20 if SO4_H2O £ 2 rag S/kg and SO4_0 i 0.1 mg S/kg.
HI* NA_CL2 > NA_CL * 1.10 if NA_CL2 > 0.03 meg/100g.
H2* K_CL2 > K_CL * 1.10 if K_CL2 > 0.003 meq/lOOg.
H3* MG_CL2 > MG_CL * 1.10 if MG_CL2 > 0.008 meq/100g.
* Sample Flag: flags the affected parameter for specified samples only.
** Batch-wide Flag: flags the affected parameter for all samples in the batch
(assumes that measurement quality samples are representative for the batch).
ANALYTICAL DATA VALIDATION FLAGS
VI Datum > ±3x interquartile range limits or > ±1.5x interquartile range
limits; has verification flag type K, w, M, or X
V2 QE/QC data outside expected ranges
V3* Horizon aggregation problem
V4 Misclassification in sampling class
V5* Field data notes indicate a problem
V6 Sample contaminated but internally consistent pedon chemistry; verification
flag for individual outlier
V7** Double check for use in aggregating
V8** Internally inconsistent chemistry
V9** Indeterminate problem or negative value '
* Informational flag only
** Data sent to EMSL-LV for confirmation
U.S. GOVEBMOIT rainiXG OFFICE 1990/748-159/00448
-------
|